Evolution of Bionic Advisor from Collaboration of Human & Artificial Intelligence in Finance

Evolution of Bionic Advisor from Collaboration of Human & Artificial Intelligence in Finance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evolution of Bionic Advisor from Collaboration of Human & Artificial Intelligence in Finance Mahesh Sulakhe, Mugdha Shailendra Kulkarni This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8272781/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose – Artificial Intelligence (AI) is an automation and analytical tool that lacks human intuition and specific contextual sensitivity. The adoption of AI for business decision-making in Finance is crucial, necessitating human-AI collaboration. The research aims to understand the factors behind the adoption and the resistance towards AI and explore the evolving role of Finance Manager in the era of AI. This research proposes a hybrid intelligence conceptual model of Bionic Advisor (BA) wherein human intelligence coalesces with AI. Methodology – The research adopts a systematic literature review (SLR) combined with cause-and-effect analysis. Qualitative factors from research articles have been derived through manual content analysis. Fishbone analytical framework has been used to derive relationships among the factors influencing the model. The model is grounded in multidisciplinary theories. Findings – The insight from this research conceptualises a unique hybrid model of BA. This research and analysis reveal that the BA model will develop synergistic intelligence. This synergetic intelligence will influence business decisions to elevate business results efficiently. Finance Manager in this role of BA, will integrate his domain expertise and cognitive ability with AI, enhancing efficacy in business decisions. Originality - Conceptualisation of BA as a role changer to strategic finance business partner contributes to the emerging literature on hybrid intelligence. In this model, the Finance Manager acts as an interpreter and translator for AI. BA model is an emerging paradigm to equip businesses with AI. This research opens avenues for future empirical study on the influence of BA on business decisions. Bionic Advisor Hybrid Intelligence Strategic business partner Finance business partner Artificial Intelligence Finance Management Decision-Making Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Bionic advisor (BA) is a human being with domain expertise, who will interact, coordinate, and communicate with AI. In this model, Human Intelligence and AI offset their individual weaknesses. Bionic models are hybrid systems that combine human intelligence with machine. It is a combination of biological intelligence with AI. The term bionics was proposed by US Air Force Col Jack E. Steele in 1960 in a conference at Wright-Patterson Air Force base in Dayton, Ohio. The word originates from the Greek word “bion” for “life” and “ic” for “having the nature of”. Though it is claimed that Bionics as a Science was launched in 1960, the basic principles were known and applied since the beginning of human civilisation. (Roth, 1983). Bionics is a science for the application of biological mechanisms and processes to Human technologies. Human technologies not only include Machines but also include technological solutions developed to support and replicate human biology and cognition. This core basis of the science of bionics drives the development of multiple models and products. Models such as bionic limbs, eyes, and cochlear implants are used to restore functions of human organs, leveraging technology and biological mechanisms. Such models are categorised as products with living parts. Technological system products, such as AI, are categorised as non-living products devised to simulate cognitive or behavioural processes. Bionics is a science that uses biological principles to construct artificial technology having characteristics of biology (Ren & Liang, 2014). Bionic advisor, using his domain knowledge, evaluates the recommendation made by AI contextually before putting it into action. The results, recommendations, and options of the AI will be analysed and weighted using its cognitive abilities. AI operates on a cyclic repetition of processes over the perception of data from the environment. It lacks the cognitive abilities of human beings. AI lacks intuitive and context sensitivity (Dushkin & Stepankov, 2021). AI does not possess the characteristics of consciousness and is a technological tool inspired by living intelligence. Empirical evidence reveals enhanced bias in decisions driven by the use of AI (Trunk et al., 2020). With cognition at the core, perception and intelligence work at logical levels in humans (Dushkin & Stepankov, 2021). With this cognitive ability, humans have evolved. It is evident in history that humans imitated nature and invented tools and machines for survival and quality of life. AI is a tool developed by humans to ease complex functions and improve efficiency at work in various fields such as Healthcare, E-Commerce, Finance, Education, Manufacturing, Entertainment, and Natural language processing, and many others. AI is expanding in the automation of repetitive and routine tasks in finance. Optimal utilisation of resources and monitoring of risk has been the core focus of finance. The Finance Manager has been the most trusted partner in an organisation (Jarvenpaa et al., 2023). With these characteristics, the role of Finance Manager has evolved from bean counter to Strategic business partner. With the advent of AI in finance and its associated risks (Roy et al., 2025), the role of Finance Manager is driving towards BA. The benefit of compensatory intelligence using a combination of human and AI to the organisation is the foundation of this role. The objective of this research is to develop a conceptual framework revealing the benefits of human and AI collaboration in the role of Finance Manager and to propose a conceptual model of BA that will enhance the efficacy in business decisions and performance. Comprehending the factors driving the adoption and the resistance towards AI in Finance, this research conceptualises a unique hybrid model of BA for the role of Finance Manager. This research, with theoretical support, highlights the upscaling of efficiency due to the adoption of the hybrid model in influencing business decisions. This research features the evolving role of Finance Manager in the era of AI and addresses following research questions. Q1. What are the factors that are adversely influencing adoption of AI in business decisions? Q2. What are the benefits of using hybrid technology in business decisions? Q3. Conceptualise the BA role model for Finance Manager to influence AI adoption in business decisions, enhancing efficacy and performance in business? This research is organised as follows. Section 2 briefs on literature reviewed, Section 3 denotes the methodology used in the research, Section 4 will highlight the Content analysis drawn from the literature reviewed, Section 5 will comprise of the Conceptual and Theoretical underpinning, with Section 6 discussing the findings and implication, and Section 7 drawing a conclusion. 2. Literature Review Use of technology is not new in business. The initial stage of technology driving business was machines replacing humans. The current technological environment is witnessing continuously evolving machines towards intelligence driven by AI and Machine learning. In this epoch of Automation and AI, AI assisted decision-making is widely in use. Application of AI in decision-making was predicted in past research indicating that technology will drive automation in decision-making (Raisch & Fomina, 2025). Digitalisation and AI technologies like advanced analytics and process automation are currently transforming the role of Finance Managers and decision-making (Yigitbasioglu et al., 2023). The role of Finance Managers is changing from controllers to advisors. AI technology is fast expanding its prominence in the field of financial planning (Sorg, 2025). Automation has moved routine and mundane tasks from humans to machines (Raisch & Fomina, 2025). This has resulted in promoting and generating opportunities for quality and creativity in human action. It is empirically evident that the unique intelligence of humans and AI complements each other (Lu et al., 2024). This is driving human AI collaboration towards hybrid technology. The rise of robo-advisors was due to the adoption of AI, and the decline is due to non-adoption of hybrid technology (Cardillo & Chiappini, 2024; D’Acunto et al., 2019). In finance, as AI is spearheading the optimization of efficiency, hybrid technology represents the future. The profession of Accountant has evolved over time from bean counter to information analyst to strategic business partner. Finance Manager as a strategic business partner is an integral part of business decision-making (Byrne & Pierce, 2007). With the adoption of Automation and artificial intelligence in business management, role of the Finance Manager is further evolving from a strategic business partner to an advisor (Yigitbasioglu et al., 2023). As a custodian of important business data and information Finance manager has also developed the skills of business analytics (Boerner et al., 2025). Critical analytical skill is most needed to drive decisions in an ambiguous context. Efficient use of technology fundamentally relies on human cognitive ability, which machines and AI inherently lack (Poláková et al., 2023). Critical Analytical skills are crucial in Business Finance or any finance management. AI can complement by assisting in processing a large volume of data, identifying trends, patterns, and relationships. With the core responsibility of finance management towards financial risk management, he needs to ascertain whether the patterns generated by AI are relevant or misleading. Finance Manager also needs to evaluate the AI output with the objective context in terms of economic and organisational strategy. AI can assist in making predictions based on past data. However, Finance Manager needs to evaluate the AI output in the context of long-term financial and non-financial consequences. AI has changed business operations by automating most of the repetitive and monotonous tasks. This automation is transforming business operations and is necessitating a hybrid approach in complex tasks involving analysis and investigation for new insights (Raisch & Fomina, 2025). The technology-driven business is also driving the need for business decisions to be based on human intervention in deciphering the AI assisted results (Trunk et al., 2020). The adoption of BA model, having a combination of humans and machines, by the Finance Manager, imbued with his tacit knowledge, will be a perfect fit for this demand in business operations. BA epitomises a hybrid intelligence model where human tacit knowledge is merged with AI driven explicit knowledge. This integration of human Tacit knowledge with AI explicit knowledge aligns with the SECI (Socialisation – Externalisation – Combination - Internalisation) process of the theory of organisational knowledge creation. Tacit knowledge is the knowledge that is acquired by practice. It is a combination of cognitive and technical attributes of a person. Tacit knowledge is an unceasing activity of learning. Explicit knowledge refers to knowledge that is integrated, formalised, data driven, and standardised (Nonaka, 1994). The analytical skill and context specific knowledge of Finance Manager acquired by experience in the volatile and uncertain business environment is the tacit knowledge inculcated and deeply embedded in his character. This has driven him from a bean counter to strategic business partner and will further position him strongly in the BA model. Bionic advisor, as a human being, will have the characteristics and skills such as empathy, collaboration, creativity, intricate problem solving, and critical thinking, which make him quintessential. Such qualities cannot be easily replicated by machines (Dumitru & Halpern, 2023). The idiom “Garbage in, Garbage out” was the basis of computers. AI, which is a tool that runs on computers including, embedded systems, servers, cloud platforms, and the algorithms, models, and data processing are powered by computing hardware. The core operation of AI tools is that it is influenced by patterns through repeated acquaintance with data. As the data size involved in such iterative learning is massive and complex, the quality of the data becomes crucial. With business and technology expanding exponentially, the quantum of data is not expected to abate. The environment in which AI operates is also vast, which has the risk of data poisoning through unsocial elements like hackers. Intentionally introducing biased, incorrect, and misleading data in the AI operating environment can lead to erroneous predictions and recommendations (Tian et al., 2023). These characteristics of AI tool make it highly vulnerable, inheriting the basics of “Garbage in, Garbage out”. False Data Injection Attacks (FIDA) embody a critical threat where adversaries manipulate data to disrupt estimation processes and can lead to severe economic disruption. Despite extensive research and advancements in machine learning (ML), which is the core component of AI, challenges towards FIDA still persist (Nand et al., 2025). This is majorly due to the non-availability of data sets reflecting real world conditions for training and validating AI models. Real financial data is confidential and sensitive and not publicly available. AI trained on synthetic data fails to generalise in real world. Bionic advisor coalescing his expertise with the ability to reason through human principles (Zaidan & Ibrahim, 2024) will be able to judge the recommendations generated with the use of AI. He will play an intermediary role in identifying the reasons behind the recommendations made by the AI tool before putting them into implementation. He will contextualise the recommendations to the objectives of the decision to validate the recommendations. In the role of BA the person will complement and mitigate the risk in the use of AI due to its myopic nature. The study on human involvement in decision-making under psychology has decade-long history. In the Judge-Advisor System, the Judge takes the decision based on the advice provided by the advisor. In business decisions, the business management teams are the judges, taking business decisions, and the advisors are the finance head who plays the role of strategic advisor. We have empirical evidence towards improvement in decision-making due to the involvement of multiple advisors (Lu et al., 2024; Steyvers & Kumar, 2024). The concept of BA has these characteristics imbued in it. The recommendations of AI are first evaluated and then recommended for decisions based on it. This brings into the decision-making process a dual advisory system making it more robust. The reliability of the advice of the Finance manager as a BA will further enhance when it is AI assisted as a second advisor. At the same time, trust in the use and adoption of AI in business decisions will foster when backed by advice from a BA who has the qualifications and expertise in financial analysis. It is evident in past studies that AI assisted human performance is better than the performance of humans or AI singularly (Steyvers & Kumar, 2024). Business decisions in the current VUCA (Volatile, Uncertain, Complex, and Ambiguous) and BANI (Brittle, Anxious, Nonlinear, and Incomprehensive) business environment are complex and critical. Quick and accurate decision is the challenge for all businesses. With AI assisted decision-making, RTDS (Real-time decision support) is the remedy for this challenging business environment. RTDS is a fusion of information management with data, assessing the situation for alternatives and recommending the output (Séguin et al., 1997) In the current evolving business environment with complex business challenges, decision makers and influencers need to interpret and apprehend the output derived with the use of technology (Trunk et al., 2020). BA is a hybrid model in Finance where the qualification and expertise are combined with the use of AI. Bionic advisor, in his capacity, acts as an interpreter and translator of the recommendation made by AI (Khan et al., 2025) rather than supervising the execution process of machines. Robo Advisor is a wealth management or pure investment portfolio management model introduced during 2008 (Hodge et al., 2021). Robo advisors is an online platform model catering to the investment advice needs of retail investors. In this model, client’s personal financial information is shared, based on which the robo advisor develops recommendations (Brenner & Meyll, 2020). Robo advisors use information technology to provide investment advice with minimal or no human support (Cardillo & Chiappini, 2024). Robo advisors offered portfolio options based on standard risk assessments. Robo advisors came up as competitors to stock advisory brokers or commission agents. Robo advisor model was a low cost advisory option developed for the majority. In research on robo advisors, it has been recommended that Trust in the human being using the advice from machines and technology can be built by modifying the interface modality between human and machine (Hildebrand & Bergner, 2020).. Most of the investment advisory and financial firms are scaling down pure robo advisory options while adopting a hybrid approach. History drives the future, and we learn from mistakes and embrace inspirations from success. With this thought, in order to gain insight into the future, an SLR and content analysis of existing research has been executed. The main focus is to identify, investigate, and analyse the reasons that will drive the adoption of hybrid technology in the role of Finance Manager. 3. Research Methodology Data Collection The conceptualisation of the model is based on Socio-Technical System Theory (Cooper & Foster, 1971), Hybrid Intelligence – Human-AI Collaboration Theory (Dellermann et al., 2019). This research is based on a systematic literature review (SLR) and a content analysis approach of peer reviewed articles, books and articles published on professional websites. A wide spread search was initiated on major academic research databases, including Scopus, Web of Science, and Science Direct for the retrieval of the research documents. Qualitative factors from research articles have been derived through manual content analysis. This derivation is based on their relationship to AI, need for human interaction with AI, adoption and resistance to AI, and their relevance to the hybrid intelligence. Related contents so derived from the research articles has been analysed using the fishbone cause-and-effect analysis to ascertain how these factors have been instrumental in shaping the conceptual foundation of the proposed model. Articles prior to 2019 pertain to conceptual shifts evolved in past and theoretical background related to the current research. As the objective of the research is linked to human AI collaboration in finance, the articles were targeted in specific subject areas, viz., Business, Management and Accounting, Economics, Econometrics and Finance, Computer Science, Arts and Humanities, Social Science, Sociotechnical, Multidisciplinary, Materials Science, Biochemistry, Genetics and Molecular Biology, Psychology and Law. (Fig. 1 – Subject wise contribution of research Articles. Author generated illustration) Figure 1 indicates the analysis of the subject areas from which research articles are selected for this study. Emphasis is given on articles related to Business Management and accounting, which contribute to 38% of the total research articles reviewed. Economics, Econometrics and Finance add 10% and Social Science adds 5% to the research articles, making it a sum of 53% related to finance and accounting. As the research subject involves AI articles related to the subject of Computer Science, it contributes 19% of the research articles. As the concept involves human interaction with AI, Articles related to Multidisciplinary, Material Science, Arts and humanities, Sociotechnical, Biochemistry, Genetics and Molecular Biology, Psychology, and Law contribute to the human element in the research. Keywords used in searching research articles are Artificial intelligence, Human-AI, Hybrid Intelligence, Digitalisation, Digital Transformation, Technology, Robo-advisors, Bionic Models, Bionics, Business Partner role, Controllers, Management Accountant, Economic Planning, Knowledge, Soft Skills, Risk, Explainability, False Data Injection Attacks, and Poisoning attack. The key words evolved from preceding literature review guided the subsequent search of research articles. In this search process on Scopus, WOS and Science Direct, not a single research article is available for the key word “Bionic Advisor”. A combination of the subject area and keyword was made to facilitate identifying related past research. The combination used is listed in Table 1 below. SUBJECT KEY WORD Multidisciplinary Artificial intelligence Arts and Humanities Artificial intelligence Arts and Humanities Bionics Biochemistry, Genetics and Molecular Biology Artificial intelligence Business, Management and Accounting Artificial intelligence Business, Management and Accounting Business Partner role Business, Management and Accounting Controllers, Management Accountant Business, Management and Accounting Digital Transformation Business, Management and Accounting economic planning Business, Management and Accounting Knowledge Business, Management and Accounting Management Accountants Business, Management and Accounting Robo-advisors Business, Management and Accounting Soft Skills Computer Science Artificial intelligence Computer Science False Data Injection Attacks Computer Science Human-AI Computer Science Hybrid Intelligence Computer Science Poisoning attack Economics, Econometrics and Finance Artificial intelligence Economics, Econometrics and Finance Digitalisation Economics, Econometrics and Finance Explainability Economics, Econometrics and Finance Robo-advisors Materials Science Bionic Models Multidisciplinary Digitalisation Psychology Technology Social Science Artificial intelligence Psychology Artificial intelligence Sociotechnical Technology Law Risk (Table 1) To ensure transparency, traceability, and enhance validity with consistency in reporting PRISMA approach has been adopted (Sohrabi et al., 2021) as depicted in the Fig. 2 below. (Fig. 2 - PRISMA Flow chart reporting phases of SLR) Inclusions & Exclusions Criteria Table 2 below summarises the inclusion and exclusion criteria of articles considered for this study. Criteria Code Reason Description Inclusion IN 1 Classical Conceptual Paper Conceptual papers of 1997 having 101 Citations Conceptual papers of 2007 having 568 Citations IN 2 Theoretical Paper Related to Knowledge Theory, Foundation of Bionics and Socio-technical theory IN 3 Conceptual Paper Papers contributing development of Bionics Advisor concept. Exclusion EX 1 Relevance Not related to hybrid technology in Finance management (Table 2 ) Studies published from 2019 to June 2025 have been considered to align with the era of artificial intelligence. As the concept of BA is unexplored and does not have much discussions on websites, only professional websites discussing specifically over “Bionic Advisor” and “Robo-Advisor” have been considered. Web articles on “Bionic” and “Robo Advisors” considered relate to the period prior to 2020. Theoretical and classical articles of the period prior to the year 2019 have been used for the theoretical background. 4. Content Analysis The Literature has been reviewed with an objective to ascertain the factors that influence AI adoption, stimulate hybrid technology, and the change in the role of Finance Manager in the era of AI. The literature reviewed in 42 research articles indicates 35 factors as listed in Table 3 below. These factors drive adoption of hybrid technology in the process of decisions making, indicate the role of Finance Manager in influencing business decisions, and highlight and compare the characteristics of AI and humans. Sr No. Factors identified through SLR No of Articles 1 AI - Generic, Explicit, Myopic, Non-contextual 1 2 Conversational integration more effective 1 3 Hybrid approach - Increase combined performance 2 4 AI autonomy & adoption - conflicting dynamics 1 5 AI - Human interdependency 1 6 AI - Bias 1 7 AI - Weak Security 2 8 AI - Lack of contextual approach 1 9 AI Lacks emotional intelligence & contextual ethics 1 10 AI - Human intelligence - complementary 1 11 AI - Sustainability, Fairness & Explainability 1 12 Humans more trustworthy 1 13 Human Intervention - Increase reliability 1 14 AI - Needs Regulatory Control 2 15 Governing Laws 1 16 Hybrid approach - Explanatory 1 17 Advisory Interface increase adoption 1 18 Ownership of Decisions 1 19 Management Accountant - Critical advisory role. 1 20 Management Accountant - Skill & Business Acumen 1 Sr No. Factors identified through SLR No of Articles 21 Finance - Key influencer 2 22 Hybrid approach - Mitigation against weak security 1 23 Data Scarcity - Problem solving Human Intervention 1 24 Data Scarcity - Bionic Advisor efficient 1 25 Humans more coherent than AI 1 26 Business mandates hybrid approach 1 27 Strategic requirement - Human AI collaboration 2 28 Human Experience - Cognitive & Technical 1 29 Role Change Driver 3 30 Quintessential skills 1 31 Cognitive capabilities - Superiority over AI 1 32 Uncertainty in business - human cognitive ability 1 33 Complexity in Decisions - Human cognitive ability 1 34 Combination - Sensory Neural Network with Motor neural network 1 35 Bionics - Interdisciplinary Science : Life Science & Engineering 1 Grand Total 42 (Table 3) The above factors listed in Table 3 above, based on their technological dimensions, behavioural and cognitive dimensions, and Integration dimensions driving the adoption of technology are reclassified into three major driving factors: AI, Human, and Hybrid as explained below in Table 4 below. Sr. No. Major Factors Category basis 1 AI AI pros and cons driving adoption of technology 2 Human Humans using AI driving the adoption of technology 3 Hybrid Characteristics of Hybrid technology driving its adoption (Table 4) Further to the above major categorization, for the convenience of analysis, these 35 factors identified from SLR have been regrouped into 12 major categories as listed in Table 5 below. This categorisation is on the basis of broad characteristics of the individual factors influencing the adoption and resistance of AI, stimulating hybrid technology towards a role model of BA. Sr No. Major categories – Drive Factors No. of Factors AI Human Hybrid Total 1 Critical Insights 1 1 1 3 2 Risk in AI 4 4 3 Individual Weakness 2 1 3 4 Trust Perspective 1 2 3 5 Need for Governance 2 1 3 6 Adoption Determinant 2 2 7 Decision Influencer 3 3 8 Risk Mitigation 2 1 3 9 Efficiency Optimization 1 2 3 10 Value Addition 3 3 11 Cognitive Perspective 3 3 12 Conceptual Dimension 2 2 Grand Total 10 17 8 35 (Table 5) Framework Derivation and Rationale To address the research question, it is necessary to draw relationship in terms of cause and effect between the factors derived from SLR. The cause and effect analytical approach empowers the investigation of relationships that set a structured pathway for model development. In view of the same, a cause and effect analysis and framework has been constructed. The 35 factors identified through SLR of 42 research articles have been initially grouped into three major categories based on the characteristics of the factors as listed in Table 4 above and indicated in the index of the illustration in Fig. 3 below.. These three categories have also been colour-coded for ease of identification in the framework illustration in Fig. 3. Further, these 35 factors have been regrouped into 12 major root cause areas driving hybrid technology and evolution of BA model. (Fig. 3 - Cause and effect relationship framework. Author generated illustration ) The cause and effect analysis illustrated in Fig. 3 provides insights into research question 1 indicating interaction of multiple factors influencing adoption of AI in business decisions. The framework also addresses research question 2 by highlighting the beneficial factors of hybrid technology, triggering the concept of BA. The illustration in Fig. 3 enables comprehension of technological, behavioural, and a combination of both, contributing the evolution of BA role for Finance Managers. The Fig. 3 also illustrates, the how factors behind the transformation of finance in AI driven business environment, where human and AI converge to enhance business decision-making and efficiency. Deeper exploration and comprehension from the literature reviewed are elaborated in succeeding discussion. It highlights the how and why behind each factors driving the hybrid human-AI ecosystem in finance. This extensive analysis and synthesis drives the transformation of Finance Manager role to BA. Critical Insights It is empirically evident that AI is generic, explicit, and myopic, as a result, needs control and contextual application,(Li et al., 2023) making it necessary for human AI collaboration in order to adopt AI in decision-making for optimizing performance. It is also empirically evident that conversational interaction has proved more trustworthy in the adoption of AI under the robo-advisor modelling (Hildebrand & Bergner, 2020). Data driven statistical analysis using Bayesian modelling highlights that, including human confidence over AI increases performance by eliciting explicit error and bias (Steyvers et al., 2022). It is empirically proven that performance through joint utilization of the unique intelligence of humans and AI is more than the individual performances (Lu et al., 2024). It also complies to the judge-advisor concept. These critical insights from past researches drive for a fusion of AI with human towards building hybrid technology. BA model is the stimulation of these critical insights for the role of Finance Manager, as the key influencer over business decisions, combining human cognition and AI to increase the efficiency in business. Risk in AI It is evident from research that a degree of autonomy in the use of AI stimulates adoption of AI in decision-making (D’Acunto et al., 2019). Humans encounter reluctance towards 100% acceptance of recommendations from algorithms. The fifth industrial revolution is expected to drive human cognitive abilities like creativity and critical thinking in increasing efficiency by utilizing the abundance of information generated by AI (Poláková et al., 2023). The adoption of AI in decision-making is embedded with the risk of pre-existing bias, such as contextual bias, algorithmic bias, and data bias (Roy et al., 2025). Adoption of AI also drives challenges of data security and privacy, data dependency, and interpretability (Yi et al., 2023), which is a core of finance function and any regulatory activity. AI is also vulnerable to poisoning attacks(Tian et al., 2023) that can mislead the decisions and recommendations. The risk envisaged in the past research towards the optimum utilization of AI appeals for risk mitigation through unification of humans with AI in hybrid models. BA is one such model that will mitigate the risk envisaged in past research towards adoption of AI. Individual Weakness Dynamically evolving business environment offers contextual challenges to AI in terms of data requirement for real time recommendation with limited resources affecting the quality of the AI decision (Séguin et al., 1997). AI, due to its artificial characteristics, lacks human like cognition, empathy, and operates purely on algorithms. This feature of AI deprives the system from comprehending the emotional and ethical impact on the society (Stewart, 2024). It is evident from past research that the performance of hybrid technology combining AI with human is grander than their individual performance. It is also ascertained that humans when interact with AI the perform more as compared to the individual performance of AI or humans (Steyvers & Kumar, 2024). These research findings indicate that weakness of humans is compensated by utilization of AI, and at the same time the weaknesses in AI are compensated by humans through their cognition and contextual ability. This complementary action in hybrid approach drives the evolution of BA model in the role of a Finance Manager and elevates the efficiency by influencing business decisions with the use of AI, converting individual weaknesses of humans and AI into strong hybrid approach. Trust Lack of explainability in AI restrains trust and surges risk in it, as a result, it also needs regulations to build in it sustainability, accuracy, fairness, and explainability (Giudici & Raffinetti, 2023). Explainability has been at the core of control and governance especially in the finance management as major responsibility of Finance Manager which inculcates trust in his role and responsibility. Management Accountants, by virtue of their role and responsibility as controllers and compliance officers, supplement the meaning and relevance of the information before sharing the same for processing decisions based on the said information (Jarvenpaa et al., 2023). This makes the role more trustworthy. Research on robo-advisor recommends that financial institutions build trust and relationship and recognize robo-advisors role as complementary to human advice instead of a substitute (Brenner & Meyll, 2020). The concept of robo-advisors could not make ubiquitous influence as different levels of investors had different levels of belief on algorithms (Krause, 2025). Trust has a direct relation with transparency. The transparency in process enables comprehension in general, making it more trustworthy and reliable. Lack of transparency in AI due to its size, structure, and complex process leads to lack of trust. To inculcate trust in the use of AI in finance, we need intervention of the Finance Manager who is most trusted member in the organization from compliance and risk perspective to support the recommendations made by AI. This drives a role change for Finance Manager as BA who uses his domain expertise and uses AI for influencing business decisions. Need for Governance Research on robo-advisors has made it evident that there is a need for regulations to secure investors from the conflict of interest of robo-advisory service providers and establish control over the service providers (Liu et al., 2023). Research also recommends exploring human and robo-advisor interaction for prolific association. The Monetary Authority of Singapore FEAT Principles (Fairness, Ethics, Accountability and Transparency) recommend ensuring decisions of AI are fair, explainable, transparent, and human centric. Article 22 of GDPR (the European General Data Protection Regulation) recommend the right of person whose data is used for insisting human intervention (Buckley et al., 2021). Interpreting AI outputs emphasize for strong domain knowledge. Research highlights a trend towards evolving hybrid models to compensate for interpretability and explanatory deficiencies through Explainable Artificial Intelligence (Khan et al., 2025). This trend will bridge the gap between AI and human harmonizing into a hybrid technology. Governance has always been the most important perspective of finance in reporting and transparency. Research on robo-advisory model and regulatory authorities in different multiple geography, highlight the need for strong governance in adoption of AI over transparency, explainability, and data privacy. The embedding of these aspects in utilization of AI in finance initiates BA model for Finance Manager. This is a hybrid model with the responsibility of ensuring governance and compliance in which the Finance Manager in the role of BA has the professional expertise. Adoption Determinant Research highlights that an advisory interface between robo-advisors and investors tends to improve conviction over the recommendation and adoption of the robo-advisor concept (Cardillo & Chiappini, 2024). This drives the concept of hybrid intelligence, combining man and machine for optimization of benefits with the use of AI. The Socio-technical system theory explains the man-machine complementarity aspect, stating that man will not be accountable for operations of machines or any other activities unless and until he has control over it and his responsibilities are clearly specified (Cooper & Foster, 1971). Ownership of the decision involves the decision maker and enhances results. Adoption acts as a mediating factor in inculcating ownership. The bionic advisory role of the Finance Manager will reinforce the aspects of ownership in the Finance Manager over the utilization of AI in recommendations towards improving business efficiency. This bionic advisory model will also encourage the adoption of the use of AI in finance and persuade faith over AI by integrating human oversight and demonstrating value creation. Decision Influencer The role of Accountant has expanded from the traditional role of compliance owner to strategic business advisors. The new role has a larger perimeter in business decisions, including strategy, risk, technology including cyber security, and forensic accounting (Yigitbasioglu et al., 2023). As per the role theory, organizational roles are based on the expectations of other members in the organization. In the same line the accountants are expected to adopt service role beyond book-keeping making them an important element in decision-making processes. This is driven by their characteristics of ensuring alignment of all strategies and recommendations with environment and consequences by virtue of professional technical skills and business knowledge gained by deep involvement in the business processes (Byrne & Pierce, 2007). The business partner role, also called as a strategic business partner, adds meaning to the information derived from AI, converting the Management information system into Meaningful information system, taking the position of a key influencer in the organization (Boerner et al., 2025). Overall finance has been a core and integral part of business planning(Sorg, 2025) as a risk assessor to guard business from uncertainty and ensure compliance to economic and social planning. With the utilization of AI and its insights from large volume of data, BA model can enhance the efficiency of Finance Manager and in turn benefit organization in enhancing optimum utilization of resources in uncertain and dynamic business conditions. Risk Mitigation False data injection attacks is a crucial hazard built into the generic characteristics of AI and can lead to major disruption in areas of AI adoption. A hybrid approach coalesces the supervised learning (Labelled data provided by humans) with unsupervised learning (self-acquired) to mitigate the risk in AI (Nand et al., 2025). The core of AI operation is the analysis of historical data and pattern development from the repetition of events and making inferences. This facilitates automation of routine tasks; however, new incidents or solutions for new problems without sufficient history obligate human intervention (Raisch & Fomina, 2025). New challenges and problems, such as security in cloud computing, downtime impact, changing customer expectations with scarce back data, make it critical for AI to generate patterns. Research referring to a report from Pricewaterhousecoopers highlights that Bionic advisory concept will be a new evolution where, human insights over the AI output add contextual understanding and augment decision-making to mitigate risk involved in AI due to scarcity (Reepu, 2019). This drives a shift or restructuring of the role of Finance Manager for optimization of business efficiency. Human intervention through BA model with its domain expertise in professional analytics, cognition, governance, and compliance will enhance resilience towards malicious data. Context analytics and behavior pattern interpretation characteristics of human will enable BA to safeguard business. Efficiency Optimization Research recommends AI as a persuasive agent in decision-making, more than an analytical tool, highlighting that persuasion when attempted through humans is more adaptable and impactful than through only AI (Sun et al., 2025). Recent research highlights that trust and compliance challenges complete automation, expecting a hybrid approach in future. The research also states that a Hybrid approach with a fusion of human judiciary and AI process is counseled by industry experts (Khalil et al., 2025).Information generated through AI without communication to humans for interpretation and deployment will not serve the purpose. Research states that hybrid approach will be crucial for stimulating management efficiency in the era of Industry 4.0 (Alam & Khan, 2024). Human AI collaboration is warranted to circumvent inefficiency in management due to errors and miscalculations through sole dependency on AI (Dumitru & Halpern, 2023). Bionic advisor will act as a catalyst in emphasizing the use of AI for optimization of efficiency in business. In this role, the Finance Manager will interpret and evaluate the recommendations of AI and, leveraging the trust conferred in his role, communicate tangible benefits to the stakeholders. Value Addition Humans acquire tacit knowledge through experience, and their cognitive abilities are called socialization. Humans create explicit knowledge through internalization, traditionally known as learning. Conversion of tacit knowledge to explicit is externalization. AI has explicit knowledge created through the process of combination; in this process, existing knowledge is collated and restructured. Pure combination is a superficial revelation of existing information without correlation to reality. A continuous cycle of all four modes through interaction between tacit and explicit knowledge adds and creates value in the organization (Nonaka, 1994). The exponential growth in the availability of information and data is driving analytical challenges in business management. The analytical skill of management accountants offers new terrains of opportunity from analytics to advisors (Abbas, 2025). Successfully adopting technology envisages a change of role as translators and interpreters of information provided by AI, as against only supervising the execution of machines (Trunk et al., 2020). Technological skills have become indispensable over past four decades, and AI in the last decade is driving disruption through automation. Management accountants to retain the position of key professionals need to keep a close pace with technology (Berry & Routon, 2020) and explore a role change from analytic to advisor. The risk of job displacement and the quintessential skills like emotional intelligence, empathy, and creativity in the management accountants drive him for a role change from manager to advisor with AI taking over the routine management tasks (Cady et al., 2024). The role of BA will not only add value to the role of Finance Manager but will also enrich efficiency in business decisions by compensating individual weaknesses of human and AI. The BA model is the need of the hour and contemplated by management for optimization of resources invested in AI. Cognitive Perspective Bionic models are a combination of technology and human biology. The human like behavior and activities of bionic models make them superior to AI. The biological ability of brain, such as emotions and contextual observations, makes bionic models and humans superior over AI (Ren & Liang, 2014). Uncertainty in business drives complex hindrances, calling for domain expertise along with cognitive abilities like intuition and empathy to identify the best suited solutions generated through AI. Human cognitive abilities complement AI and create psychological ownership, enhancing trust (Dellermann et al., 2019). It is evident from research that AI used to resolve complex tasks can inculcate critical drawbacks in decisions. However, in relatively high task complexity tasks, the ability of the advisor determines the trust in the advisor (Hodge et al., 2021). Human cognition, such as thinking, reasoning, and comprehending, enables humans in resolving complex tasks. Bionic advisor model is enriched with the combination of technology and human cognition. Contextual and ethical reasoning backed by domain expertise is the dynamism of BA, which compensates for the weakness of AI, which lacks sensitivity and ethical reckoning. Conceptual Dimension Hybrid technology concept is based the use of technology for processing of big data and pattern assessments, identifying repetitions using a sensory neural network. Multiple recommendations are shared through machines to humans for interaction with the subject to use a motor neural network to resolve complex tasks requiring cognition (Dushkin & Stepankov, 2021). Bionics is an interdisciplinary science combining life science with engineering science. The application of this combination of science enables optimization of benefits. This science will accelerate technology and its benefits, creating complementary intelligence (Roth, 1983). Combination of two or more aspects always have the objective to create compensatory benefits which exceeds benefits from either of the two or more aspects. In bionic advisory model the benefits of technological advancements is coalesced with benefits of life sciences. Finance management has been the crucial and important element in optimization of resources and bionic advisory model for the role Finance Manager will prove towards optimum utilization of AI as a resource to improve business efficiency. Analytical Derivation – Bionic Advisor The above detailed analysis indicates the evolution of the role of BA as the key role in enhancing the responsibilities of a Finance Manager. The Trust, compliance, governance, and risk mitigation have been the key responsibilities of a Finance Manager. The risk involved due to the weakness in the AI system, due to its architecture, drives the need for a regulatory and trusted partner as a BA. The trustworthiness imbued in the role of Finance Manager as controller, ensuring governance and compliance, enhances the efficiency in decision-making with the use of AI, adding value to the organisation. The cognitive abilities and the professional tacit knowledge of the Finance Manager creating complementary benefits mitigate the risks involved in the AI. The evidences from past research towards the characteristics of AI and its impact drives the need for a hybrid technology directly supporting the evolution of BA. The fast evolving industrial revolutions also emphasize a vital and essential need for a collaboration of man and machine to confront the challenges in the future due to uncertainty and volatility in business. Necessity is the mother of all invention. In the same line of thought, a BA is an evolution from this necessity of using AI for improving efficiency, simultaneously compensating for the risk involved in AI. Bionic advisor evolves from the interdisciplinary field of bionics, which is a combination of life science and engineering. The need for optimization of advantages from the use of sensory neural network involved in AI, and the advantages of motor neural network are driving the concept of BA. The high investment cost challenges involved in the adoption of new technology and the reliability are augmenting the concept of BA. In the next section, this research evaluates the BA concept on the theoretical background. 5. Conceptual and Theoretical Underpinning Artificial Intelligence in Finance Finance forms an integral function in all domains of business operation. Any adverse impact in the area of finance reflects directly upon the operating efficiency of the business irrespective of the business domain. The use of AI in Finance is increasing exponentially, and so is the risk involved in the use of AI in Finance. As of date, there are no standard norms to ensure or assess the trustworthiness of AI applications in Finance. (Giudici & Raffinetti, 2023). This is mainly contributed by and commonly known as black box problem. Black box problem refers to the challenge or impossibility of understanding through tracing the internal workings and processes involved in producing the results with the use of AI. Due to the opacity of the system, it becomes critical to link how specific inputs are transmuted into decisions, thereby limiting transparency and trust in AI generated results. AI operates independently outside human supervision and involvement. This is driving global focus and concern on the use of AI in Finance. Though AI is driving high precision computation with accuracy and speed, accelerating complex financial processes also brings ethical challenges such as bias, equity, and transparency, which calls for strong governance. (Roy et al., 2025). The 2018 World Economic Forum (WEF) suggested collaboration of multiple stakeholders to resist the potential social and economic risk of AI enabled systems in Finance. Similarly, in 2019, WEF emphasised governance explicitly on AI explainability, systemic risk, AI biases, and algorithmic collusion as a prominent basis of risk in finance. The core concern of all is that humans still need to play an important role in ensuring how AI is used, watched over, and that it works safely in the interest of the society. The Singapore FEAT (Fairness, Ethics, Accountability and Transparency) principles highlighted that AI should be human centric. Article 22 of the European General Data Protections Regulation (GDPR) indicates insistence of human intervention in purely AI driven decisions. (Buckley et al., 2021). AI should not be autonomous but should be controlled by humans (Zaidan & Ibrahim, 2024). Socio-Technical System Theory - Bionic Advisor Model Socio-Technical system theory, formulated by Eric Trist and Fred Emery at Tavistock Institute of Human Relations in London around the 1950 is theorised with a combination of social and technical elements in the organisation. It indicates that there is an interrelationship between humans and technology in organisations (Cooper & Foster, 1971). The theory indicates that optimization of results can be derived through a combination of humans and technology at work. ( Fig. 4 Author Illustration ) ( Fig. 5 Author Illustration ) The BA model is also grounded in the principles of Socio-Technical System Theory as indicated in Fig. 4 and Fig. 5. The Finance Manager as an human being in the role of strategic business partner has the characteristics of ensuring compliance to processes, ethics and organisation culture. As a trusted partner, the relationship is at the core of the role. With the adoption of digitalisation and automation, the use of AI in business finance management drives a role change from the strategic business partner to BA. The communication of recommendations made by AI under the role and responsibility of BA will assign the accountability and responsibility in the BA. The bionic advisory role model will leverage complementary intelligence (Dellermann et al., 2019) to optimise results. Hybrid Intelligence – Human-AI Collaboration Theory: Bionic Advisor Hybrid Intelligence is the ability to accomplish resolution of complex aspirations, coalescing the use of AI and human intelligence (Dellermann et al., 2019). Due to this, both human intelligence and AI evolve together and attain superior results. The rationale behind this combination of human intelligence and AI is to overcome the individual limitations in each of them when working independently. The hybrid intelligence model enables leveraging the power of AI in terms of speed, analysis, and processing of big data, simultaneously also uses its domain expertise, cognitive ability, intuition, and empathy to make rational decisions. (Fig. 6) (Fig. 7) (Fig. 6 & Fig. 7 are amended (Dellermann et al., 2019)) BA is a model also linked to the principles of Hybrid Intelligence. Finance Manager in this role model as BA will collaborate with AI, using it as a tool to increase his efficiency at work. In this course of collaboration, BA will enhance his cognitive abilities and also enable the AI to acquire the basis and consideration on which he amends the AI recommendation, refer Fig. 6. This will enhance the ability of AI in future undertakings and analysis. This collaboration of AI with Finance Manager in the role of BA will have the benefits of complementary intelligence refer Fig. 7. Bionic advisor model enables use of cognitive abilities, intuition and empathy in reference to context and the environment in selection of outcomes shared by AI. 6. Findings and Implications The in-depth analysis and interpretation, along with the theoretical support, reveal key insights from this research. The findings indicate that BA’s are human beings using AI as a tool to analyse data for making comparative analysis and recommending predictions. These predictions are further analysed in reference to the context and consequences using human cognitive abilities and domain expertise. In this model, the communication of the AI output is not directly shared with the client or advice seeker, but through a human being in the role of a BA. This human being has the empathy and emotions to understand the objectives of the advice seeker and also interact with them for mutual understanding. This model of BA is not restricted to only individuals seeking investment advice but is more macro in nature and will play an important role in overall business operation management. This model will develop a strategic advantage in adopting a hybrid intelligence module where human intelligence is blended with AI and the recommendations are optimised for results. Also, during this process of optimisation, both humans and AI will develop individual cognitive abilities for the future. Hybrid decision-making The above research on AI and the challenges faced in the use of AI in Finance drive us towards modelling a combination of human and AI. AI coalesced with bionic can increase the efficiency in finance, accounting, and decision-making (Reepu, 2019). Wealth Professional a Canadian advisory professional and a publishing company, featured a report “ The dawn of the bionic advisor ” in 2018 by Donna Bristow (Burton, 2018). Donna states that a combination of human cognition and technology can bring transparency and reliability to the advisory profession. She states that in the era of BA, human expertise will complement technology to enhance effective communication and foster efficiency with informed decision-making. Bionic advisory model is a union of technology and human experience (Mitra, 2021). Empirical studies have evidence towards encouraging performance in decision-making due to human-AI collaboration (Li et al., 2023). The analytical understanding and insights makes it evident that performance through a combined or hybrid approach of an Individual and AI is greater than the performance of an individual that has outperformed the AI performance. It is also evident that performance through a combined or hybrid approach of Individual and AI is greater than performance driven solely by AI (Steyvers et al., 2022). The Biological advantage of humans is the central nervous system (CNS), which cannot be manufactured or simulated, as it is a biological construct. It is this CNS that drives emotions in humans, which makes them different from AI (Stewart, 2024). Emotional understanding of the consequences makes humans different from AI. It is this difference between AI and human that will generate complementary advantage when combined in an hybrid approach as BA in business decisions. BA is an evolution of a judicious model wherein the individual who takes this role has expertise in the specific field and uses technology for analysis to advise his or her internal or external customers/clients. This evolution emerges from the trivial gap in research focused in explaining and distinguishing the strategic role of humans and AI during collaboration (Alam & Khan, 2024; Steyvers & Kumar, 2024). This model of BA provides a nomenclature to the hybrid approach envisioned by past researchers towards coalescing AI with humans in decision-making. (Abbas, 2025). A role change in Finance Management. In Finance management, Bionic advisor is a conceptualization resulting from implication of AI technology in management accountant role. In the field of Finance the role of Business Finance Manager has also evolved from bean counter to strategic business partner (Byrne & Pierce, 2007). In this role as strategic business partner, with prime owner of confidential data, the key responsibilities of the Finance Managers are providing current financial insights to support strategic decisions and drive business performance. With most of the analytical activities that can be transferred to AI, the adoption of the role of BA will be the future of Finance Managers. The adoption of the BA role embodies strategic alignment for the finance business partner as an enhanced role contributing to enhanced value creation for the entire organisation. Enhancing efficiency at work with the adoption of automation and digitalisation in the field of finance has been a successful history. Adoption of AI will further enhance the efficiency in business finance management. Activities such as budgeting, forecasting, performance monitoring, and analysis can be managed with AI, adding a human element before communicating to the business management. A Finance Manager, as the strategic business partner and owner of critical and confidential data, is the trusted role for all stakeholders (Jarvenpaa et al., 2023). Adding the trusted human element to AI output will not only enhance the trust of the business team in the use of AI, but will also increase the trust, reliability, and importance of the advisory role of the Finance Manager. Bionic advisor will create cohesiveness between the human and AI relationship. He will monitor and regulate the autonomy of AI in business in contextual reference and also with reference to the evolution of technology over time. Human cognitive constraints limit solutions to a small set of searches.(Raisch & Fomina, 2025) In the role of bionic Advisor, the use of AI will compensate for these constraints in enabling a large set of solutions. An important aspect of the Man-Machine relationship is that Man will never take responsibility for Machines. Man owns the responsibilities clearly defined and expressed, and more importantly, has control over the areas of responsibility (Cooper & Foster, 1971). In the role of BA this responsibility will be clearly defined by process. In this model, recommendation and outputs from AI is channelised to the business decisions maker through the Finance Manager in the role of a BA. As the Finance Manager has the role of a strategic business partner and an important influencer over business decisions, this model of BA is a best fit and a right on time role changeover. This model also has the objective of optimisation of results, which is always the core interest of the organisation’s financial management. Management Information System. MIS abbreviation for Management Information System, with the use of AI and human intervention as BA, will evolve into a Meaningful Information System. The information so derived will drive the management and related individuals to make well-informed business and investment decisions. Such information will have the element of having considered human emotions and will remove the drawback of transparency involved in the use of AI. It will also have the characteristics of the trust and compliance due to the human involvement. Due to automation we have massive and complex data. Traditional regression models involving multiple assumptions are inadequate in handling intricate context-dependent relationships involved in data (Yi et al., 2023). Corelating intricate relationships like fairness, ethics, and policy needs human involvement. AI techniques can model linear and non-linear relationships and derive complex patterns in big data; however, they do not explain why the pattern exist. BA, with his knowledge backed by context-specific experience correlating with the environment and the objective of the decisions, can change the attributes of the management information system into a meaningful information system. He can analyse the patterns and correlate them with the related environment to provide reasoning for the pattern. As a BA, the person will mitigate the risk due to a lack of contextual awareness of the business environment in the AI algorithms. He will also complement the generic nature of AI, making the recommendation specific to the objectives. Robo Advisor - Innovation to Obsolescence Robo Advisor system or tool has major pitfalls resulting in its downfall. One of the major pitfalls was the ability of the advice seeker to change the algorithm by way of altering the risk factor or even portfolio mix. This alters the recommendations adversely. The companies managing these robo advisors were more concentrated on their business profits as against investors interest (D’Acunto et al., 2019). This also developed an adverse bias in the robo advisory system. Post covid, investors moved from traditional stock portfolio to ETF’s (Mackintosh. & Robert Jankiewicz, 2025). ETFs were cheap and easy to use, which significantly influenced the investment strategies, driving robo advisors towards redundancy and insignificance. It is empirically evident that during the market downfall, the robo advisor users outperformed non robo advisor users due to its adaptability and autonomy to the investor (Liu et al., 2023). This justifies that robo advisor can perform well with the intervention of humans. This also indicates that the robo advisor system was not dynamic and self-sufficient in managing a volatile market, which is one of the most important characteristics of the stock market. This developed a sense of unreliability among the users. Robo advisor tool, system, or module was developed as a low-cost option, which made it highly price sensitive and could not stand the competition from big investment portfolio managers. Investors also moved to human plus digital approach which again added cost to the robo advisor system making survival critical. In an interview with ThinkAdvisor, Nexus Strategy founder, president, and CEO Timothy Welsh said, “I think it is the final nail in the robo-advisory coffin. If a firm of the size, strength, brand, and reach of Blackrock couldn’t make it work, then no one can.” (FinTech Global, 2023). As a low-cost service model, the number of entrants were also many. This resulted in a survival crisis and a shift of service to other business streams, driving towards the end of the robo advisors era. 7. Conclusion This research highlight that the factors adversely responsible for adoption of AI are driving hybrid technology. The beneficiary characteristics of hybrid technology are driving evolution of BA concept The study also reveal that the emerging need for hybrid technology is driving the evolution of BA model. This will be a significant shift of role for finance professionals. The BA will lead the journey in the era of AI by developing complementary Intelligence augmenting human and AI (Dellermann et al., 2019). Bionic advisor, as a human being possessing cognitive ability, by leveraging technology, will complement AI and enhance reliability. Bionic advisor will play an important role in enhancing AI adoption. Change in the volatile and uncertain economy is inevitable. As the economy evolves, the roles of people involved also need to change. Finance is the most important aspect of any economy (Sorg, 2025). Adoption and change of role from Strategic business partner to a BA role for finance management will improve the efficacy in influencing business decisions. The trust levels over the Finance management as well as AI will further increase, due to involvement of BA as a human being(Steyvers & Kumar, 2024) in validating and communicating the decision for optimisation of results. We have empirical evidence in past research that performance through a combined or hybrid approach of Individual and AI is greater than performance driven solely by AI (Steyvers et al., 2022). The role of BA will have full control and responsibility towards the adoption, use, and development of AI for recommending decision-making in optimising results. This concept of BA is conceived and developed for business finance management and as a role development for a strategic finance business partner. This can be further explored on other areas of business operation such as human resource management, Marketing management and even in investment management advisory. Declarations Author contribution declaration :- The author confirms that, Material preparation, data collection, data analysis, and manuscript writing was performed by Mahesh Sulakhe. The author confirms that, review of the manuscript and final approval of the manuscript was performed by Mugdha Shailendra Kulkarni Dual Publication :- The author confirms that this submission is not published in any journal and is also not submitted for consideration elsewhere. Authorship :- The author confirms that the journal policies have been read and the submission is being made in accordance to the journal policies. Clinical Trial :- The author confirms that this research do not involve any type of clinical trial, hence approval not required. Competing interest declaration :- The author confirms and declare that they have no competing interest relevant to the content of this article. Funding declaration :- The author confirms that, this research is not funded by any specific grant from any person, organisation or from any type of funding agency in whole or in part. 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Humanities and Social Sciences Communications, 11 (1). https://doi.org/10.1057/s41599-024-03560-x Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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Author generated illustration\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8272781/v1/a7650459b601355dc53e2ccf.png"},{"id":99321274,"identity":"7445efc1-f718-4d10-bac2-73864df7dbca","added_by":"auto","created_at":"2025-12-31 16:39:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87284,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA Flow chart reporting phases of SLR\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8272781/v1/9f9896d5d12d81fafbecf4ce.png"},{"id":99299929,"identity":"2792438f-3dc7-4427-9004-ed9a6e912502","added_by":"auto","created_at":"2025-12-31 13:20:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":296858,"visible":true,"origin":"","legend":"\u003cp\u003eCause and effect relationship framework. Author generated illustration\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8272781/v1/521aa5dac5bca3382a1a998d.png"},{"id":99320193,"identity":"1aafda1d-d85d-4a9a-bb2f-caee1c274f55","added_by":"auto","created_at":"2025-12-31 16:38:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":65120,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"45.png","url":"https://assets-eu.researchsquare.com/files/rs-8272781/v1/a422cb417da0b468a79d9591.png"},{"id":99299936,"identity":"8f8f3aaa-9eca-40a6-b632-06755c199eae","added_by":"auto","created_at":"2025-12-31 13:20:24","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":149358,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"67.png","url":"https://assets-eu.researchsquare.com/files/rs-8272781/v1/6b5657569552a294b91900e7.png"},{"id":104403121,"identity":"2a4ee620-3e7f-4198-984d-100f461af5f6","added_by":"auto","created_at":"2026-03-11 12:17:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1671318,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8272781/v1/100988ef-f26f-44f9-b8d7-aeecc85a396f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evolution of Bionic Advisor from Collaboration of Human \u0026 Artificial Intelligence in Finance","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eBionic advisor (BA) is a human being with domain expertise, who will interact, coordinate, and communicate with AI. In this model, Human Intelligence and AI offset their individual weaknesses. Bionic models are hybrid systems that combine human intelligence with machine. It is a combination of biological intelligence with AI. The term bionics was proposed by US Air Force Col Jack E. Steele in 1960 in a conference at Wright-Patterson Air Force base in Dayton, Ohio. The word originates from the Greek word \u0026ldquo;bion\u0026rdquo; for \u0026ldquo;life\u0026rdquo; and \u0026ldquo;ic\u0026rdquo; for \u0026ldquo;having the nature of\u0026rdquo;. Though it is claimed that Bionics as a Science was launched in 1960, the basic principles were known and applied since the beginning of human civilisation. (Roth, 1983). Bionics is a science for the application of biological mechanisms and processes to Human technologies. Human technologies not only include Machines but also include technological solutions developed to support and replicate human biology and cognition. This core basis of the science of bionics drives the development of multiple models and products. Models such as bionic limbs, eyes, and cochlear implants are used to restore functions of human organs, leveraging technology and biological mechanisms. Such models are categorised as products with living parts. Technological system products, such as AI, are categorised as non-living products devised to simulate cognitive or behavioural processes. Bionics is a science that uses biological principles to construct artificial technology having characteristics of biology (Ren \u0026amp; Liang, 2014). Bionic advisor, using his domain knowledge, evaluates the recommendation made by AI contextually before putting it into action. The results, recommendations, and options of the AI will be analysed and weighted using its cognitive abilities.\u003c/p\u003e \u003cp\u003eAI operates on a cyclic repetition of processes over the perception of data from the environment. It lacks the cognitive abilities of human beings. AI lacks intuitive and context sensitivity (Dushkin \u0026amp; Stepankov, 2021). AI does not possess the characteristics of consciousness and is a technological tool inspired by living intelligence. Empirical evidence reveals enhanced bias in decisions driven by the use of AI (Trunk et al., 2020). With cognition at the core, perception and intelligence work at logical levels in humans (Dushkin \u0026amp; Stepankov, 2021). With this cognitive ability, humans have evolved. It is evident in history that humans imitated nature and invented tools and machines for survival and quality of life. AI is a tool developed by humans to ease complex functions and improve efficiency at work in various fields such as Healthcare, E-Commerce, Finance, Education, Manufacturing, Entertainment, and Natural language processing, and many others.\u003c/p\u003e \u003cp\u003eAI is expanding in the automation of repetitive and routine tasks in finance. Optimal utilisation of resources and monitoring of risk has been the core focus of finance. The Finance Manager has been the most trusted partner in an organisation (Jarvenpaa et al., 2023). With these characteristics, the role of Finance Manager has evolved from bean counter to Strategic business partner. With the advent of AI in finance and its associated risks (Roy et al., 2025), the role of Finance Manager is driving towards BA. The benefit of compensatory intelligence using a combination of human and AI to the organisation is the foundation of this role.\u003c/p\u003e \u003cp\u003eThe objective of this research is to develop a conceptual framework revealing the benefits of human and AI collaboration in the role of Finance Manager and to propose a conceptual model of BA that will enhance the efficacy in business decisions and performance. Comprehending the factors driving the adoption and the resistance towards AI in Finance, this research conceptualises a unique hybrid model of BA for the role of Finance Manager. This research, with theoretical support, highlights the upscaling of efficiency due to the adoption of the hybrid model in influencing business decisions. This research features the evolving role of Finance Manager in the era of AI and addresses following research questions.\u003c/p\u003e \u003cp\u003eQ1. What are the factors that are adversely influencing adoption of AI in business decisions?\u003c/p\u003e \u003cp\u003eQ2. What are the benefits of using hybrid technology in business decisions?\u003c/p\u003e \u003cp\u003eQ3. Conceptualise the BA role model for Finance Manager to influence AI adoption in business decisions, enhancing efficacy and performance in business?\u003c/p\u003e \u003cp\u003eThis research is organised as follows. Section 2 briefs on literature reviewed, Section 3 denotes the methodology used in the research, Section 4 will highlight the Content analysis drawn from the literature reviewed, Section 5 will comprise of the Conceptual and Theoretical underpinning, with Section 6 discussing the findings and implication, and Section 7 drawing a conclusion.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eUse of technology is not new in business. The initial stage of technology driving business was machines replacing humans. The current technological environment is witnessing continuously evolving machines towards intelligence driven by AI and Machine learning. In this epoch of Automation and AI, AI assisted decision-making is widely in use. Application of AI in decision-making was predicted in past research indicating that technology will drive automation in decision-making (Raisch \u0026amp; Fomina, 2025).\u003c/p\u003e \u003cp\u003eDigitalisation and AI technologies like advanced analytics and process automation are currently transforming the role of Finance Managers and decision-making (Yigitbasioglu et al., 2023). The role of Finance Managers is changing from controllers to advisors. AI technology is fast expanding its prominence in the field of financial planning (Sorg, 2025). Automation has moved routine and mundane tasks from humans to machines (Raisch \u0026amp; Fomina, 2025). This has resulted in promoting and generating opportunities for quality and creativity in human action. It is empirically evident that the unique intelligence of humans and AI complements each other (Lu et al., 2024). This is driving human AI collaboration towards hybrid technology. The rise of robo-advisors was due to the adoption of AI, and the decline is due to non-adoption of hybrid technology (Cardillo \u0026amp; Chiappini, 2024; D\u0026rsquo;Acunto et al., 2019). In finance, as AI is spearheading the optimization of efficiency, hybrid technology represents the future.\u003c/p\u003e \u003cp\u003eThe profession of Accountant has evolved over time from bean counter to information analyst to strategic business partner. Finance Manager as a strategic business partner is an integral part of business decision-making (Byrne \u0026amp; Pierce, 2007). With the adoption of Automation and artificial intelligence in business management, role of the Finance Manager is further evolving from a strategic business partner to an advisor (Yigitbasioglu et al., 2023). As a custodian of important business data and information Finance manager has also developed the skills of business analytics (Boerner et al., 2025).\u003c/p\u003e \u003cp\u003eCritical analytical skill is most needed to drive decisions in an ambiguous context. Efficient use of technology fundamentally relies on human cognitive ability, which machines and AI inherently lack (Pol\u0026aacute;kov\u0026aacute; et al., 2023). Critical Analytical skills are crucial in Business Finance or any finance management. AI can complement by assisting in processing a large volume of data, identifying trends, patterns, and relationships. With the core responsibility of finance management towards financial risk management, he needs to ascertain whether the patterns generated by AI are relevant or misleading. Finance Manager also needs to evaluate the AI output with the objective context in terms of economic and organisational strategy. AI can assist in making predictions based on past data. However, Finance Manager needs to evaluate the AI output in the context of long-term financial and non-financial consequences.\u003c/p\u003e \u003cp\u003eAI has changed business operations by automating most of the repetitive and monotonous tasks. This automation is transforming business operations and is necessitating a hybrid approach in complex tasks involving analysis and investigation for new insights (Raisch \u0026amp; Fomina, 2025). The technology-driven business is also driving the need for business decisions to be based on human intervention in deciphering the AI assisted results (Trunk et al., 2020). The adoption of BA model, having a combination of humans and machines, by the Finance Manager, imbued with his tacit knowledge, will be a perfect fit for this demand in business operations.\u003c/p\u003e \u003cp\u003eBA epitomises a hybrid intelligence model where human tacit knowledge is merged with AI driven explicit knowledge. This integration of human Tacit knowledge with AI explicit knowledge aligns with the SECI (Socialisation \u0026ndash; Externalisation \u0026ndash; Combination - Internalisation) process of the theory of organisational knowledge creation. Tacit knowledge is the knowledge that is acquired by practice. It is a combination of cognitive and technical attributes of a person. Tacit knowledge is an unceasing activity of learning. Explicit knowledge refers to knowledge that is integrated, formalised, data driven, and standardised (Nonaka, 1994). The analytical skill and context specific knowledge of Finance Manager acquired by experience in the volatile and uncertain business environment is the tacit knowledge inculcated and deeply embedded in his character. This has driven him from a bean counter to strategic business partner and will further position him strongly in the BA model. Bionic advisor, as a human being, will have the characteristics and skills such as empathy, collaboration, creativity, intricate problem solving, and critical thinking, which make him quintessential. Such qualities cannot be easily replicated by machines (Dumitru \u0026amp; Halpern, 2023).\u003c/p\u003e \u003cp\u003eThe idiom \u0026ldquo;Garbage in, Garbage out\u0026rdquo; was the basis of computers. AI, which is a tool that runs on computers including, embedded systems, servers, cloud platforms, and the algorithms, models, and data processing are powered by computing hardware. The core operation of AI tools is that it is influenced by patterns through repeated acquaintance with data. As the data size involved in such iterative learning is massive and complex, the quality of the data becomes crucial. With business and technology expanding exponentially, the quantum of data is not expected to abate. The environment in which AI operates is also vast, which has the risk of data poisoning through unsocial elements like hackers. Intentionally introducing biased, incorrect, and misleading data in the AI operating environment can lead to erroneous predictions and recommendations (Tian et al., 2023). These characteristics of AI tool make it highly vulnerable, inheriting the basics of \u0026ldquo;Garbage in, Garbage out\u0026rdquo;.\u003c/p\u003e \u003cp\u003eFalse Data Injection Attacks (FIDA) embody a critical threat where adversaries manipulate data to disrupt estimation processes and can lead to severe economic disruption. Despite extensive research and advancements in machine learning (ML), which is the core component of AI, challenges towards FIDA still persist (Nand et al., 2025). This is majorly due to the non-availability of data sets reflecting real world conditions for training and validating AI models. Real financial data is confidential and sensitive and not publicly available. AI trained on synthetic data fails to generalise in real world.\u003c/p\u003e \u003cp\u003eBionic advisor coalescing his expertise with the ability to reason through human principles (Zaidan \u0026amp; Ibrahim, 2024) will be able to judge the recommendations generated with the use of AI. He will play an intermediary role in identifying the reasons behind the recommendations made by the AI tool before putting them into implementation. He will contextualise the recommendations to the objectives of the decision to validate the recommendations. In the role of BA the person will complement and mitigate the risk in the use of AI due to its myopic nature.\u003c/p\u003e \u003cp\u003eThe study on human involvement in decision-making under psychology has decade-long history. In the Judge-Advisor System, the Judge takes the decision based on the advice provided by the advisor. In business decisions, the business management teams are the judges, taking business decisions, and the advisors are the finance head who plays the role of strategic advisor. We have empirical evidence towards improvement in decision-making due to the involvement of multiple advisors (Lu et al., 2024; Steyvers \u0026amp; Kumar, 2024). The concept of BA has these characteristics imbued in it. The recommendations of AI are first evaluated and then recommended for decisions based on it. This brings into the decision-making process a dual advisory system making it more robust. The reliability of the advice of the Finance manager as a BA will further enhance when it is AI assisted as a second advisor. At the same time, trust in the use and adoption of AI in business decisions will foster when backed by advice from a BA who has the qualifications and expertise in financial analysis. It is evident in past studies that AI assisted human performance is better than the performance of humans or AI singularly (Steyvers \u0026amp; Kumar, 2024).\u003c/p\u003e \u003cp\u003eBusiness decisions in the current VUCA (Volatile, Uncertain, Complex, and Ambiguous) and BANI (Brittle, Anxious, Nonlinear, and Incomprehensive) business environment are complex and critical. Quick and accurate decision is the challenge for all businesses. With AI assisted decision-making, RTDS (Real-time decision support) is the remedy for this challenging business environment. RTDS is a fusion of information management with data, assessing the situation for alternatives and recommending the output (S\u0026eacute;guin et al., 1997) In the current evolving business environment with complex business challenges, decision makers and influencers need to interpret and apprehend the output derived with the use of technology (Trunk et al., 2020). BA is a hybrid model in Finance where the qualification and expertise are combined with the use of AI. Bionic advisor, in his capacity, acts as an interpreter and translator of the recommendation made by AI (Khan et al., 2025) rather than supervising the execution process of machines.\u003c/p\u003e \u003cp\u003eRobo Advisor is a wealth management or pure investment portfolio management model introduced during 2008 (Hodge et al., 2021). Robo advisors is an online platform model catering to the investment advice needs of retail investors. In this model, client\u0026rsquo;s personal financial information is shared, based on which the robo advisor develops recommendations (Brenner \u0026amp; Meyll, 2020). Robo advisors use information technology to provide investment advice with minimal or no human support (Cardillo \u0026amp; Chiappini, 2024). Robo advisors offered portfolio options based on standard risk assessments. Robo advisors came up as competitors to stock advisory brokers or commission agents. Robo advisor model was a low cost advisory option developed for the majority. In research on robo advisors, it has been recommended that Trust in the human being using the advice from machines and technology can be built by modifying the interface modality between human and machine (Hildebrand \u0026amp; Bergner, 2020).. Most of the investment advisory and financial firms are scaling down pure robo advisory options while adopting a hybrid approach.\u003c/p\u003e \u003cp\u003eHistory drives the future, and we learn from mistakes and embrace inspirations from success. With this thought, in order to gain insight into the future, an SLR and content analysis of existing research has been executed. The main focus is to identify, investigate, and analyse the reasons that will drive the adoption of hybrid technology in the role of Finance Manager.\u003c/p\u003e"},{"header":"3. Research Methodology","content":"\u003cp\u003e \u003cb\u003eData Collection\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe conceptualisation of the model is based on Socio-Technical System Theory (Cooper \u0026amp; Foster, 1971), Hybrid Intelligence \u0026ndash; Human-AI Collaboration Theory (Dellermann et al., 2019). This research is based on a systematic literature review (SLR) and a content analysis approach of peer reviewed articles, books and articles published on professional websites. A wide spread search was initiated on major academic research databases, including Scopus, Web of Science, and Science Direct for the retrieval of the research documents.\u003c/p\u003e \u003cp\u003eQualitative factors from research articles have been derived through manual content analysis. This derivation is based on their relationship to AI, need for human interaction with AI, adoption and resistance to AI, and their relevance to the hybrid intelligence. Related contents so derived from the research articles has been analysed using the fishbone cause-and-effect analysis to ascertain how these factors have been instrumental in shaping the conceptual foundation of the proposed model. Articles prior to 2019 pertain to conceptual shifts evolved in past and theoretical background related to the current research.\u003c/p\u003e \u003cp\u003eAs the objective of the research is linked to human AI collaboration in finance, the articles were targeted in specific subject areas, viz., Business, Management and Accounting, Economics, Econometrics and Finance, Computer Science, Arts and Humanities, Social Science, Sociotechnical, Multidisciplinary, Materials Science, Biochemistry, Genetics and Molecular Biology, Psychology and Law.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;1 \u0026ndash; Subject wise contribution of research Articles. Author generated illustration)\u003c/p\u003e \u003cp\u003eFigure 1 indicates the analysis of the subject areas from which research articles are selected for this study. Emphasis is given on articles related to Business Management and accounting, which contribute to 38% of the total research articles reviewed. Economics, Econometrics and Finance add 10% and Social Science adds 5% to the research articles, making it a sum of 53% related to finance and accounting. As the research subject involves AI articles related to the subject of Computer Science, it contributes 19% of the research articles. As the concept involves human interaction with AI, Articles related to Multidisciplinary, Material Science, Arts and humanities, Sociotechnical, Biochemistry, Genetics and Molecular Biology, Psychology, and Law contribute to the human element in the research.\u003c/p\u003e \u003cp\u003eKeywords used in searching research articles are Artificial intelligence, Human-AI, Hybrid Intelligence, Digitalisation, Digital Transformation, Technology, Robo-advisors, Bionic Models, Bionics, Business Partner role, Controllers, Management Accountant, Economic Planning, Knowledge, Soft Skills, Risk, Explainability, False Data Injection Attacks, and Poisoning attack. The key words evolved from preceding literature review guided the subsequent search of research articles. In this search process on Scopus, WOS and Science Direct, not a single research article is available for the key word \u0026ldquo;Bionic Advisor\u0026rdquo;.\u003c/p\u003e \u003cp\u003eA combination of the subject area and keyword was made to facilitate identifying related past research. The combination used is listed in Table\u0026nbsp;1 below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUBJECT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKEY WORD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultidisciplinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArts and Humanities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eArts and Humanities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBionics\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiochemistry, Genetics and Molecular Biology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness Partner role\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControllers, Management Accountant\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Transformation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eeconomic planning\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKnowledge\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement Accountants\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobo-advisors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBusiness, Management and Accounting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSoft Skills\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFalse Data Injection Attacks\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman-AI\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid Intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eComputer Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoisoning attack\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomics, Econometrics and Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomics, Econometrics and Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigitalisation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomics, Econometrics and Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eExplainability\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomics, Econometrics and Finance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRobo-advisors\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterials Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBionic Models\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMultidisciplinary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigitalisation\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial Science\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArtificial intelligence\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSociotechnical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLaw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;1)\u003c/p\u003e \u003cp\u003eTo ensure transparency, traceability, and enhance validity with consistency in reporting PRISMA approach has been adopted (Sohrabi et al., 2021) as depicted in the Fig.\u0026nbsp;2 below.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;2 - PRISMA Flow chart reporting phases of SLR)\u003c/p\u003e \u003cp\u003e \u003cb\u003eInclusions \u0026amp; Exclusions Criteria\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ebelow summarises the inclusion and exclusion criteria of articles considered for this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReason\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInclusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClassical Conceptual Paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConceptual papers of 1997 having 101 Citations \u003c/p\u003e \u003cp\u003eConceptual papers of 2007 having 568 Citations\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTheoretical Paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRelated to Knowledge Theory, Foundation of Bionics and Socio-technical theory\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIN 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConceptual Paper\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePapers contributing development of Bionics Advisor concept.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExclusion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEX 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRelevance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNot related to hybrid technology in Finance management\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eStudies published from 2019 to June 2025 have been considered to align with the era of artificial intelligence. As the concept of BA is unexplored and does not have much discussions on websites, only professional websites discussing specifically over \u0026ldquo;Bionic Advisor\u0026rdquo; and \u0026ldquo;Robo-Advisor\u0026rdquo; have been considered. Web articles on \u0026ldquo;Bionic\u0026rdquo; and \u0026ldquo;Robo Advisors\u0026rdquo; considered relate to the period prior to 2020. Theoretical and classical articles of the period prior to the year 2019 have been used for the theoretical background.\u003c/p\u003e"},{"header":"4. Content Analysis","content":"\u003cp\u003eThe Literature has been reviewed with an objective to ascertain the factors that influence AI adoption, stimulate hybrid technology, and the change in the role of Finance Manager in the era of AI. The literature reviewed in 42 research articles indicates 35 factors as listed in Table\u0026nbsp;3 below. These factors drive adoption of hybrid technology in the process of decisions making, indicate the role of Finance Manager in influencing business decisions, and highlight and compare the characteristics of AI and humans.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors identified through SLR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo of Articles\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Generic, Explicit, Myopic, Non-contextual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConversational integration more effective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid approach - Increase combined performance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI autonomy \u0026amp; adoption - conflicting dynamics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Human interdependency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Bias\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Weak Security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Lack of contextual approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI Lacks emotional intelligence \u0026amp; contextual ethics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Human intelligence - complementary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Sustainability, Fairness \u0026amp; Explainability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumans more trustworthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Intervention - Increase reliability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI - Needs Regulatory Control\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGoverning Laws\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid approach - Explanatory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdvisory Interface increase adoption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOwnership of Decisions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement Accountant - Critical advisory role.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement Accountant - Skill \u0026amp; Business Acumen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSr No.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eFactors identified through SLR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eNo of Articles\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFinance - Key influencer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid approach - Mitigation against weak security\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Scarcity - Problem solving Human Intervention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eData Scarcity - Bionic Advisor efficient\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHumans more coherent than AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBusiness mandates hybrid approach\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStrategic requirement - Human AI collaboration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Experience - Cognitive \u0026amp; Technical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRole Change Driver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuintessential skills\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive capabilities - Superiority over AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUncertainty in business - human cognitive ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplexity in Decisions - Human cognitive ability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCombination - Sensory Neural Network with Motor neural network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBionics - Interdisciplinary Science : Life Science \u0026amp; Engineering\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrand Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;3)\u003c/p\u003e \u003cp\u003eThe above factors listed in Table\u0026nbsp;3 above, based on their technological dimensions, behavioural and cognitive dimensions, and Integration dimensions driving the adoption of technology are reclassified into three major driving factors: AI, Human, and Hybrid as explained below in Table\u0026nbsp;4 below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMajor Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCategory basis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI pros and cons driving adoption of technology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHumans using AI driving the adoption of technology\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCharacteristics of Hybrid technology driving its adoption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;4)\u003c/p\u003e \u003cp\u003eFurther to the above major categorization, for the convenience of analysis, these 35 factors identified from SLR have been regrouped into 12 major categories as listed in Table\u0026nbsp;5 below. This categorisation is on the basis of broad characteristics of the individual factors influencing the adoption and resistance of AI, stimulating hybrid technology towards a role model of BA.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSr No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMajor categories \u0026ndash; Drive Factors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eNo. of Factors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHuman\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHybrid\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCritical Insights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk in AI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndividual Weakness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrust Perspective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNeed for Governance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdoption Determinant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDecision Influencer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRisk Mitigation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEfficiency Optimization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue Addition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCognitive Perspective\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConceptual Dimension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrand Total\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e(Table\u0026nbsp;5)\u003c/p\u003e \u003cp\u003e \u003cb\u003eFramework Derivation and Rationale\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo address the research question, it is necessary to draw relationship in terms of cause and effect between the factors derived from SLR. The cause and effect analytical approach empowers the investigation of relationships that set a structured pathway for model development. In view of the same, a cause and effect analysis and framework has been constructed.\u003c/p\u003e \u003cp\u003eThe 35 factors identified through SLR of 42 research articles have been initially grouped into three major categories based on the characteristics of the factors as listed in Table\u0026nbsp;4 above and indicated in the index of the illustration in Fig.\u0026nbsp;3 below.. These three categories have also been colour-coded for ease of identification in the framework illustration in Fig.\u0026nbsp;3. Further, these 35 factors have been regrouped into 12 major root cause areas driving hybrid technology and evolution of BA model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;3 - Cause and effect relationship framework. Author generated illustration )\u003c/p\u003e \u003cp\u003eThe cause and effect analysis illustrated in Fig.\u0026nbsp;3 provides insights into research question 1 indicating interaction of multiple factors influencing adoption of AI in business decisions. The framework also addresses research question 2 by highlighting the beneficial factors of hybrid technology, triggering the concept of BA. The illustration in Fig.\u0026nbsp;3 enables comprehension of technological, behavioural, and a combination of both, contributing the evolution of BA role for Finance Managers. The Fig.\u0026nbsp;3 also illustrates, the how factors behind the transformation of finance in AI driven business environment, where human and AI converge to enhance business decision-making and efficiency.\u003c/p\u003e \u003cp\u003eDeeper exploration and comprehension from the literature reviewed are elaborated in succeeding discussion. It highlights the how and why behind each factors driving the hybrid human-AI ecosystem in finance. This extensive analysis and synthesis drives the transformation of Finance Manager role to BA.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCritical Insights\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIt is empirically evident that AI is generic, explicit, and myopic, as a result, needs control and contextual application,(Li et al., 2023) making it necessary for human AI collaboration in order to adopt AI in decision-making for optimizing performance. It is also empirically evident that conversational interaction has proved more trustworthy in the adoption of AI under the robo-advisor modelling (Hildebrand \u0026amp; Bergner, 2020). Data driven statistical analysis using Bayesian modelling highlights that, including human confidence over AI increases performance by eliciting explicit error and bias (Steyvers et al., 2022). It is empirically proven that performance through joint utilization of the unique intelligence of humans and AI is more than the individual performances (Lu et al., 2024). It also complies to the judge-advisor concept. These critical insights from past researches drive for a fusion of AI with human towards building hybrid technology. BA model is the stimulation of these critical insights for the role of Finance Manager, as the key influencer over business decisions, combining human cognition and AI to increase the efficiency in business.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk in AI\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIt is evident from research that a degree of autonomy in the use of AI stimulates adoption of AI in decision-making (D\u0026rsquo;Acunto et al., 2019). Humans encounter reluctance towards 100% acceptance of recommendations from algorithms. The fifth industrial revolution is expected to drive human cognitive abilities like creativity and critical thinking in increasing efficiency by utilizing the abundance of information generated by AI (Pol\u0026aacute;kov\u0026aacute; et al., 2023). The adoption of AI in decision-making is embedded with the risk of pre-existing bias, such as contextual bias, algorithmic bias, and data bias (Roy et al., 2025). Adoption of AI also drives challenges of data security and privacy, data dependency, and interpretability (Yi et al., 2023), which is a core of finance function and any regulatory activity. AI is also vulnerable to poisoning attacks(Tian et al., 2023) that can mislead the decisions and recommendations. The risk envisaged in the past research towards the optimum utilization of AI appeals for risk mitigation through unification of humans with AI in hybrid models. BA is one such model that will mitigate the risk envisaged in past research towards adoption of AI.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIndividual Weakness\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDynamically evolving business environment offers contextual challenges to AI in terms of data requirement for real time recommendation with limited resources affecting the quality of the AI decision (S\u0026eacute;guin et al., 1997). AI, due to its artificial characteristics, lacks human like cognition, empathy, and operates purely on algorithms. This feature of AI deprives the system from comprehending the emotional and ethical impact on the society (Stewart, 2024). It is evident from past research that the performance of hybrid technology combining AI with human is grander than their individual performance. It is also ascertained that humans when interact with AI the perform more as compared to the individual performance of AI or humans (Steyvers \u0026amp; Kumar, 2024). These research findings indicate that weakness of humans is compensated by utilization of AI, and at the same time the weaknesses in AI are compensated by humans through their cognition and contextual ability. This complementary action in hybrid approach drives the evolution of BA model in the role of a Finance Manager and elevates the efficiency by influencing business decisions with the use of AI, converting individual weaknesses of humans and AI into strong hybrid approach.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTrust\u003c/b\u003e \u003c/p\u003e \u003cp\u003eLack of explainability in AI restrains trust and surges risk in it, as a result, it also needs regulations to build in it sustainability, accuracy, fairness, and explainability (Giudici \u0026amp; Raffinetti, 2023). Explainability has been at the core of control and governance especially in the finance management as major responsibility of Finance Manager which inculcates trust in his role and responsibility. Management Accountants, by virtue of their role and responsibility as controllers and compliance officers, supplement the meaning and relevance of the information before sharing the same for processing decisions based on the said information (Jarvenpaa et al., 2023). This makes the role more trustworthy. Research on robo-advisor recommends that financial institutions build trust and relationship and recognize robo-advisors role as complementary to human advice instead of a substitute (Brenner \u0026amp; Meyll, 2020). The concept of robo-advisors could not make ubiquitous influence as different levels of investors had different levels of belief on algorithms (Krause, 2025). Trust has a direct relation with transparency. The transparency in process enables comprehension in general, making it more trustworthy and reliable. Lack of transparency in AI due to its size, structure, and complex process leads to lack of trust. To inculcate trust in the use of AI in finance, we need intervention of the Finance Manager who is most trusted member in the organization from compliance and risk perspective to support the recommendations made by AI. This drives a role change for Finance Manager as BA who uses his domain expertise and uses AI for influencing business decisions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eNeed for Governance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eResearch on robo-advisors has made it evident that there is a need for regulations to secure investors from the conflict of interest of robo-advisory service providers and establish control over the service providers (Liu et al., 2023). Research also recommends exploring human and robo-advisor interaction for prolific association. The Monetary Authority of Singapore FEAT Principles (Fairness, Ethics, Accountability and Transparency) recommend ensuring decisions of AI are fair, explainable, transparent, and human centric. Article 22 of GDPR (the European General Data Protection Regulation) recommend the right of person whose data is used for insisting human intervention (Buckley et al., 2021). Interpreting AI outputs emphasize for strong domain knowledge. Research highlights a trend towards evolving hybrid models to compensate for interpretability and explanatory deficiencies through Explainable Artificial Intelligence (Khan et al., 2025). This trend will bridge the gap between AI and human harmonizing into a hybrid technology. Governance has always been the most important perspective of finance in reporting and transparency. Research on robo-advisory model and regulatory authorities in different multiple geography, highlight the need for strong governance in adoption of AI over transparency, explainability, and data privacy. The embedding of these aspects in utilization of AI in finance initiates BA model for Finance Manager. This is a hybrid model with the responsibility of ensuring governance and compliance in which the Finance Manager in the role of BA has the professional expertise.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAdoption Determinant\u003c/b\u003e \u003c/p\u003e \u003cp\u003eResearch highlights that an advisory interface between robo-advisors and investors tends to improve conviction over the recommendation and adoption of the robo-advisor concept (Cardillo \u0026amp; Chiappini, 2024). This drives the concept of hybrid intelligence, combining man and machine for optimization of benefits with the use of AI. The Socio-technical system theory explains the man-machine complementarity aspect, stating that man will not be accountable for operations of machines or any other activities unless and until he has control over it and his responsibilities are clearly specified (Cooper \u0026amp; Foster, 1971). Ownership of the decision involves the decision maker and enhances results. Adoption acts as a mediating factor in inculcating ownership. The bionic advisory role of the Finance Manager will reinforce the aspects of ownership in the Finance Manager over the utilization of AI in recommendations towards improving business efficiency. This bionic advisory model will also encourage the adoption of the use of AI in finance and persuade faith over AI by integrating human oversight and demonstrating value creation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDecision Influencer\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe role of Accountant has expanded from the traditional role of compliance owner to strategic business advisors. The new role has a larger perimeter in business decisions, including strategy, risk, technology including cyber security, and forensic accounting (Yigitbasioglu et al., 2023). As per the role theory, organizational roles are based on the expectations of other members in the organization. In the same line the accountants are expected to adopt service role beyond book-keeping making them an important element in decision-making processes. This is driven by their characteristics of ensuring alignment of all strategies and recommendations with environment and consequences by virtue of professional technical skills and business knowledge gained by deep involvement in the business processes (Byrne \u0026amp; Pierce, 2007). The business partner role, also called as a strategic business partner, adds meaning to the information derived from AI, converting the Management information system into Meaningful information system, taking the position of a key influencer in the organization (Boerner et al., 2025). Overall finance has been a core and integral part of business planning(Sorg, 2025) as a risk assessor to guard business from uncertainty and ensure compliance to economic and social planning. With the utilization of AI and its insights from large volume of data, BA model can enhance the efficiency of Finance Manager and in turn benefit organization in enhancing optimum utilization of resources in uncertain and dynamic business conditions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRisk Mitigation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFalse data injection attacks is a crucial hazard built into the generic characteristics of AI and can lead to major disruption in areas of AI adoption. A hybrid approach coalesces the supervised learning (Labelled data provided by humans) with unsupervised learning (self-acquired) to mitigate the risk in AI (Nand et al., 2025). The core of AI operation is the analysis of historical data and pattern development from the repetition of events and making inferences. This facilitates automation of routine tasks; however, new incidents or solutions for new problems without sufficient history obligate human intervention (Raisch \u0026amp; Fomina, 2025). New challenges and problems, such as security in cloud computing, downtime impact, changing customer expectations with scarce back data, make it critical for AI to generate patterns. Research referring to a report from Pricewaterhousecoopers highlights that Bionic advisory concept will be a new evolution where, human insights over the AI output add contextual understanding and augment decision-making to mitigate risk involved in AI due to scarcity (Reepu, 2019). This drives a shift or restructuring of the role of Finance Manager for optimization of business efficiency. Human intervention through BA model with its domain expertise in professional analytics, cognition, governance, and compliance will enhance resilience towards malicious data. Context analytics and behavior pattern interpretation characteristics of human will enable BA to safeguard business.\u003c/p\u003e \u003cp\u003e \u003cb\u003eEfficiency Optimization\u003c/b\u003e \u003c/p\u003e \u003cp\u003eResearch recommends AI as a persuasive agent in decision-making, more than an analytical tool, highlighting that persuasion when attempted through humans is more adaptable and impactful than through only AI (Sun et al., 2025). Recent research highlights that trust and compliance challenges complete automation, expecting a hybrid approach in future. The research also states that a Hybrid approach with a fusion of human judiciary and AI process is counseled by industry experts (Khalil et al., 2025).Information generated through AI without communication to humans for interpretation and deployment will not serve the purpose. Research states that hybrid approach will be crucial for stimulating management efficiency in the era of Industry 4.0 (Alam \u0026amp; Khan, 2024). Human AI collaboration is warranted to circumvent inefficiency in management due to errors and miscalculations through sole dependency on AI (Dumitru \u0026amp; Halpern, 2023). Bionic advisor will act as a catalyst in emphasizing the use of AI for optimization of efficiency in business. In this role, the Finance Manager will interpret and evaluate the recommendations of AI and, leveraging the trust conferred in his role, communicate tangible benefits to the stakeholders.\u003c/p\u003e \u003cp\u003e \u003cb\u003eValue Addition\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHumans acquire tacit knowledge through experience, and their cognitive abilities are called socialization. Humans create explicit knowledge through internalization, traditionally known as learning. Conversion of tacit knowledge to explicit is externalization. AI has explicit knowledge created through the process of combination; in this process, existing knowledge is collated and restructured. Pure combination is a superficial revelation of existing information without correlation to reality. A continuous cycle of all four modes through interaction between tacit and explicit knowledge adds and creates value in the organization (Nonaka, 1994). The exponential growth in the availability of information and data is driving analytical challenges in business management. The analytical skill of management accountants offers new terrains of opportunity from analytics to advisors (Abbas, 2025). Successfully adopting technology envisages a change of role as translators and interpreters of information provided by AI, as against only supervising the execution of machines (Trunk et al., 2020). Technological skills have become indispensable over past four decades, and AI in the last decade is driving disruption through automation. Management accountants to retain the position of key professionals need to keep a close pace with technology (Berry \u0026amp; Routon, 2020) and explore a role change from analytic to advisor. The risk of job displacement and the quintessential skills like emotional intelligence, empathy, and creativity in the management accountants drive him for a role change from manager to advisor with AI taking over the routine management tasks (Cady et al., 2024). The role of BA will not only add value to the role of Finance Manager but will also enrich efficiency in business decisions by compensating individual weaknesses of human and AI. The BA model is the need of the hour and contemplated by management for optimization of resources invested in AI.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCognitive Perspective\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBionic models are a combination of technology and human biology. The human like behavior and activities of bionic models make them superior to AI. The biological ability of brain, such as emotions and contextual observations, makes bionic models and humans superior over AI (Ren \u0026amp; Liang, 2014). Uncertainty in business drives complex hindrances, calling for domain expertise along with cognitive abilities like intuition and empathy to identify the best suited solutions generated through AI. Human cognitive abilities complement AI and create psychological ownership, enhancing trust (Dellermann et al., 2019). It is evident from research that AI used to resolve complex tasks can inculcate critical drawbacks in decisions. However, in relatively high task complexity tasks, the ability of the advisor determines the trust in the advisor (Hodge et al., 2021). Human cognition, such as thinking, reasoning, and comprehending, enables humans in resolving complex tasks. Bionic advisor model is enriched with the combination of technology and human cognition. Contextual and ethical reasoning backed by domain expertise is the dynamism of BA, which compensates for the weakness of AI, which lacks sensitivity and ethical reckoning.\u003c/p\u003e \u003cp\u003e \u003cb\u003eConceptual Dimension\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHybrid technology concept is based the use of technology for processing of big data and pattern assessments, identifying repetitions using a sensory neural network. Multiple recommendations are shared through machines to humans for interaction with the subject to use a motor neural network to resolve complex tasks requiring cognition (Dushkin \u0026amp; Stepankov, 2021). Bionics is an interdisciplinary science combining life science with engineering science. The application of this combination of science enables optimization of benefits. This science will accelerate technology and its benefits, creating complementary intelligence (Roth, 1983). Combination of two or more aspects always have the objective to create compensatory benefits which exceeds benefits from either of the two or more aspects. In bionic advisory model the benefits of technological advancements is coalesced with benefits of life sciences. Finance management has been the crucial and important element in optimization of resources and bionic advisory model for the role Finance Manager will prove towards optimum utilization of AI as a resource to improve business efficiency.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAnalytical Derivation \u0026ndash; Bionic Advisor\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe above detailed analysis indicates the evolution of the role of BA as the key role in enhancing the responsibilities of a Finance Manager. The Trust, compliance, governance, and risk mitigation have been the key responsibilities of a Finance Manager. The risk involved due to the weakness in the AI system, due to its architecture, drives the need for a regulatory and trusted partner as a BA. The trustworthiness imbued in the role of Finance Manager as controller, ensuring governance and compliance, enhances the efficiency in decision-making with the use of AI, adding value to the organisation. The cognitive abilities and the professional tacit knowledge of the Finance Manager creating complementary benefits mitigate the risks involved in the AI. The evidences from past research towards the characteristics of AI and its impact drives the need for a hybrid technology directly supporting the evolution of BA. The fast evolving industrial revolutions also emphasize a vital and essential need for a collaboration of man and machine to confront the challenges in the future due to uncertainty and volatility in business. Necessity is the mother of all invention. In the same line of thought, a BA is an evolution from this necessity of using AI for improving efficiency, simultaneously compensating for the risk involved in AI. Bionic advisor evolves from the interdisciplinary field of bionics, which is a combination of life science and engineering. The need for optimization of advantages from the use of sensory neural network involved in AI, and the advantages of motor neural network are driving the concept of BA. The high investment cost challenges involved in the adoption of new technology and the reliability are augmenting the concept of BA. In the next section, this research evaluates the BA concept on the theoretical background.\u003c/p\u003e"},{"header":"5. Conceptual and Theoretical Underpinning","content":"\u003cp\u003e \u003cb\u003eArtificial Intelligence in Finance\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFinance forms an integral function in all domains of business operation. Any adverse impact in the area of finance reflects directly upon the operating efficiency of the business irrespective of the business domain.\u003c/p\u003e \u003cp\u003eThe use of AI in Finance is increasing exponentially, and so is the risk involved in the use of AI in Finance. As of date, there are no standard norms to ensure or assess the trustworthiness of AI applications in Finance. (Giudici \u0026amp; Raffinetti, 2023). This is mainly contributed by and commonly known as black box problem. Black box problem refers to the challenge or impossibility of understanding through tracing the internal workings and processes involved in producing the results with the use of AI. Due to the opacity of the system, it becomes critical to link how specific inputs are transmuted into decisions, thereby limiting transparency and trust in AI generated results. AI operates independently outside human supervision and involvement. This is driving global focus and concern on the use of AI in Finance.\u003c/p\u003e \u003cp\u003eThough AI is driving high precision computation with accuracy and speed, accelerating complex financial processes also brings ethical challenges such as bias, equity, and transparency, which calls for strong governance. (Roy et al., 2025). The 2018 World Economic Forum (WEF) suggested collaboration of multiple stakeholders to resist the potential social and economic risk of AI enabled systems in Finance. Similarly, in 2019, WEF emphasised governance explicitly on AI explainability, systemic risk, AI biases, and algorithmic collusion as a prominent basis of risk in finance. The core concern of all is that humans still need to play an important role in ensuring how AI is used, watched over, and that it works safely in the interest of the society. The Singapore FEAT (Fairness, Ethics, Accountability and Transparency) principles highlighted that AI should be human centric. Article 22 of the European General Data Protections Regulation (GDPR) indicates insistence of human intervention in purely AI driven decisions. (Buckley et al., 2021). AI should not be autonomous but should be controlled by humans (Zaidan \u0026amp; Ibrahim, 2024).\u003c/p\u003e \u003cp\u003e \u003cb\u003eSocio-Technical System Theory - Bionic Advisor Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSocio-Technical system theory, formulated by Eric Trist and Fred Emery at Tavistock Institute of Human Relations in London around the 1950 is theorised with a combination of social and technical elements in the organisation. It indicates that there is an interrelationship between humans and technology in organisations (Cooper \u0026amp; Foster, 1971). The theory indicates that optimization of results can be derived through a combination of humans and technology at work.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e( Fig.\u0026nbsp;4 Author Illustration ) ( Fig.\u0026nbsp;5 Author Illustration )\u003c/p\u003e \u003cp\u003eThe BA model is also grounded in the principles of Socio-Technical System Theory as indicated in Fig.\u0026nbsp;4 and Fig.\u0026nbsp;5. The Finance Manager as an human being in the role of strategic business partner has the characteristics of ensuring compliance to processes, ethics and organisation culture. As a trusted partner, the relationship is at the core of the role. With the adoption of digitalisation and automation, the use of AI in business finance management drives a role change from the strategic business partner to BA. The communication of recommendations made by AI under the role and responsibility of BA will assign the accountability and responsibility in the BA. The bionic advisory role model will leverage complementary intelligence (Dellermann et al., 2019) to optimise results.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHybrid Intelligence \u0026ndash; Human-AI Collaboration Theory: Bionic Advisor\u003c/b\u003e \u003c/p\u003e \u003cp\u003eHybrid Intelligence is the ability to accomplish resolution of complex aspirations, coalescing the use of AI and human intelligence (Dellermann et al., 2019). Due to this, both human intelligence and AI evolve together and attain superior results. The rationale behind this combination of human intelligence and AI is to overcome the individual limitations in each of them when working independently. The hybrid intelligence model enables leveraging the power of AI in terms of speed, analysis, and processing of big data, simultaneously also uses its domain expertise, cognitive ability, intuition, and empathy to make rational decisions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;6) (Fig.\u0026nbsp;7)\u003c/p\u003e \u003cp\u003e(Fig.\u0026nbsp;6 \u0026amp; Fig.\u0026nbsp;7 are amended (Dellermann et al., 2019))\u003c/p\u003e \u003cp\u003eBA is a model also linked to the principles of Hybrid Intelligence. Finance Manager in this role model as BA will collaborate with AI, using it as a tool to increase his efficiency at work. In this course of collaboration, BA will enhance his cognitive abilities and also enable the AI to acquire the basis and consideration on which he amends the AI recommendation, refer Fig.\u0026nbsp;6. This will enhance the ability of AI in future undertakings and analysis. This collaboration of AI with Finance Manager in the role of BA will have the benefits of complementary intelligence refer Fig.\u0026nbsp;7. Bionic advisor model enables use of cognitive abilities, intuition and empathy in reference to context and the environment in selection of outcomes shared by AI.\u003c/p\u003e"},{"header":"6. Findings and Implications","content":"\u003cp\u003eThe in-depth analysis and interpretation, along with the theoretical support, reveal key insights from this research. The findings indicate that BA\u0026rsquo;s are human beings using AI as a tool to analyse data for making comparative analysis and recommending predictions. These predictions are further analysed in reference to the context and consequences using human cognitive abilities and domain expertise. In this model, the communication of the AI output is not directly shared with the client or advice seeker, but through a human being in the role of a BA. This human being has the empathy and emotions to understand the objectives of the advice seeker and also interact with them for mutual understanding. This model of BA is not restricted to only individuals seeking investment advice but is more macro in nature and will play an important role in overall business operation management. This model will develop a strategic advantage in adopting a hybrid intelligence module where human intelligence is blended with AI and the recommendations are optimised for results. Also, during this process of optimisation, both humans and AI will develop individual cognitive abilities for the future.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHybrid decision-making\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe above research on AI and the challenges faced in the use of AI in Finance drive us towards modelling a combination of human and AI. AI coalesced with bionic can increase the efficiency in finance, accounting, and decision-making (Reepu, 2019). Wealth Professional a Canadian advisory professional and a publishing company, featured a report \u0026ldquo;\u003cem\u003eThe dawn of the bionic advisor\u003c/em\u003e\u0026rdquo; in 2018 by Donna Bristow (Burton, 2018). Donna states that a combination of human cognition and technology can bring transparency and reliability to the advisory profession. She states that in the era of BA, human expertise will complement technology to enhance effective communication and foster efficiency with informed decision-making. Bionic advisory model is a union of technology and human experience (Mitra, 2021). Empirical studies have evidence towards encouraging performance in decision-making due to human-AI collaboration (Li et al., 2023).\u003c/p\u003e \u003cp\u003eThe analytical understanding and insights makes it evident that performance through a combined or hybrid approach of an Individual and AI is greater than the performance of an individual that has outperformed the AI performance. It is also evident that performance through a combined or hybrid approach of Individual and AI is greater than performance driven solely by AI (Steyvers et al., 2022). The Biological advantage of humans is the central nervous system (CNS), which cannot be manufactured or simulated, as it is a biological construct. It is this CNS that drives emotions in humans, which makes them different from AI (Stewart, 2024). Emotional understanding of the consequences makes humans different from AI. It is this difference between AI and human that will generate complementary advantage when combined in an hybrid approach as BA in business decisions.\u003c/p\u003e \u003cp\u003eBA is an evolution of a judicious model wherein the individual who takes this role has expertise in the specific field and uses technology for analysis to advise his or her internal or external customers/clients. This evolution emerges from the trivial gap in research focused in explaining and distinguishing the strategic role of humans and AI during collaboration (Alam \u0026amp; Khan, 2024; Steyvers \u0026amp; Kumar, 2024). This model of BA provides a nomenclature to the hybrid approach envisioned by past researchers towards coalescing AI with humans in decision-making. (Abbas, 2025).\u003c/p\u003e \u003cp\u003e \u003cb\u003eA role change in Finance Management.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eIn Finance management, Bionic advisor is a conceptualization resulting from implication of AI technology in management accountant role. In the field of Finance the role of Business Finance Manager has also evolved from bean counter to strategic business partner (Byrne \u0026amp; Pierce, 2007). In this role as strategic business partner, with prime owner of confidential data, the key responsibilities of the Finance Managers are providing current financial insights to support strategic decisions and drive business performance. With most of the analytical activities that can be transferred to AI, the adoption of the role of BA will be the future of Finance Managers.\u003c/p\u003e \u003cp\u003eThe adoption of the BA role embodies strategic alignment for the finance business partner as an enhanced role contributing to enhanced value creation for the entire organisation. Enhancing efficiency at work with the adoption of automation and digitalisation in the field of finance has been a successful history. Adoption of AI will further enhance the efficiency in business finance management. Activities such as budgeting, forecasting, performance monitoring, and analysis can be managed with AI, adding a human element before communicating to the business management. A Finance Manager, as the strategic business partner and owner of critical and confidential data, is the trusted role for all stakeholders (Jarvenpaa et al., 2023). Adding the trusted human element to AI output will not only enhance the trust of the business team in the use of AI, but will also increase the trust, reliability, and importance of the advisory role of the Finance Manager.\u003c/p\u003e \u003cp\u003eBionic advisor will create cohesiveness between the human and AI relationship. He will monitor and regulate the autonomy of AI in business in contextual reference and also with reference to the evolution of technology over time. Human cognitive constraints limit solutions to a small set of searches.(Raisch \u0026amp; Fomina, 2025) In the role of bionic Advisor, the use of AI will compensate for these constraints in enabling a large set of solutions.\u003c/p\u003e \u003cp\u003eAn important aspect of the Man-Machine relationship is that Man will never take responsibility for Machines. Man owns the responsibilities clearly defined and expressed, and more importantly, has control over the areas of responsibility (Cooper \u0026amp; Foster, 1971). In the role of BA this responsibility will be clearly defined by process. In this model, recommendation and outputs from AI is channelised to the business decisions maker through the Finance Manager in the role of a BA. As the Finance Manager has the role of a strategic business partner and an important influencer over business decisions, this model of BA is a best fit and a right on time role changeover. This model also has the objective of optimisation of results, which is always the core interest of the organisation\u0026rsquo;s financial management.\u003c/p\u003e \u003cp\u003e \u003cb\u003eManagement Information System.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMIS abbreviation for Management Information System, with the use of AI and human intervention as BA, will evolve into a Meaningful Information System. The information so derived will drive the management and related individuals to make well-informed business and investment decisions. Such information will have the element of having considered human emotions and will remove the drawback of transparency involved in the use of AI. It will also have the characteristics of the trust and compliance due to the human involvement. Due to automation we have massive and complex data. Traditional regression models involving multiple assumptions are inadequate in handling intricate context-dependent relationships involved in data (Yi et al., 2023). Corelating intricate relationships like fairness, ethics, and policy needs human involvement. AI techniques can model linear and non-linear relationships and derive complex patterns in big data; however, they do not explain why the pattern exist.\u003c/p\u003e \u003cp\u003eBA, with his knowledge backed by context-specific experience correlating with the environment and the objective of the decisions, can change the attributes of the management information system into a meaningful information system. He can analyse the patterns and correlate them with the related environment to provide reasoning for the pattern. As a BA, the person will mitigate the risk due to a lack of contextual awareness of the business environment in the AI algorithms. He will also complement the generic nature of AI, making the recommendation specific to the objectives.\u003c/p\u003e \u003cp\u003e \u003cb\u003eRobo Advisor - Innovation to Obsolescence\u003c/b\u003e \u003c/p\u003e \u003cp\u003eRobo Advisor system or tool has major pitfalls resulting in its downfall. One of the major pitfalls was the ability of the advice seeker to change the algorithm by way of altering the risk factor or even portfolio mix. This alters the recommendations adversely. The companies managing these robo advisors were more concentrated on their business profits as against investors interest (D\u0026rsquo;Acunto et al., 2019). This also developed an adverse bias in the robo advisory system. Post covid, investors moved from traditional stock portfolio to ETF\u0026rsquo;s (Mackintosh. \u0026amp; Robert Jankiewicz, 2025). ETFs were cheap and easy to use, which significantly influenced the investment strategies, driving robo advisors towards redundancy and insignificance. It is empirically evident that during the market downfall, the robo advisor users outperformed non robo advisor users due to its adaptability and autonomy to the investor (Liu et al., 2023). This justifies that robo advisor can perform well with the intervention of humans. This also indicates that the robo advisor system was not dynamic and self-sufficient in managing a volatile market, which is one of the most important characteristics of the stock market. This developed a sense of unreliability among the users. Robo advisor tool, system, or module was developed as a low-cost option, which made it highly price sensitive and could not stand the competition from big investment portfolio managers. Investors also moved to human plus digital approach which again added cost to the robo advisor system making survival critical. In an interview with ThinkAdvisor, Nexus Strategy founder, president, and CEO Timothy Welsh said, \u003cem\u003e\u0026ldquo;I think it is the final nail in the robo-advisory coffin. If a firm of the size, strength, brand, and reach of Blackrock couldn\u0026rsquo;t make it work, then no one can.\u0026rdquo;\u003c/em\u003e (FinTech Global, 2023). As a low-cost service model, the number of entrants were also many. This resulted in a survival crisis and a shift of service to other business streams, driving towards the end of the robo advisors era.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis research highlight that the factors adversely responsible for adoption of AI are driving hybrid technology. The beneficiary characteristics of hybrid technology are driving evolution of BA concept The study also reveal that the emerging need for hybrid technology is driving the evolution of BA model. This will be a significant shift of role for finance professionals. The BA will lead the journey in the era of AI by developing complementary Intelligence augmenting human and AI (Dellermann et al., 2019). Bionic advisor, as a human being possessing cognitive ability, by leveraging technology, will complement AI and enhance reliability. Bionic advisor will play an important role in enhancing AI adoption. Change in the volatile and uncertain economy is inevitable. As the economy evolves, the roles of people involved also need to change. Finance is the most important aspect of any economy (Sorg, 2025). Adoption and change of role from Strategic business partner to a BA role for finance management will improve the efficacy in influencing business decisions. The trust levels over the Finance management as well as AI will further increase, due to involvement of BA as a human being(Steyvers \u0026amp; Kumar, 2024) in validating and communicating the decision for optimisation of results. We have empirical evidence in past research that performance through a combined or hybrid approach of Individual and AI is greater than performance driven solely by AI (Steyvers et al., 2022). The role of BA will have full control and responsibility towards the adoption, use, and development of AI for recommending decision-making in optimising results. This concept of BA is conceived and developed for business finance management and as a role development for a strategic finance business partner. This can be further explored on other areas of business operation such as human resource management, Marketing management and even in investment management advisory.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contribution declaration :-\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe author confirms that, Material preparation, data collection, data analysis, and manuscript writing was performed by Mahesh Sulakhe.\u003c/li\u003e\n \u003cli\u003eThe author confirms that, review of the manuscript and final approval of the manuscript was performed by Mugdha Shailendra Kulkarni\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eDual Publication :-\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe author confirms that this submission is not published in any journal and is also not submitted for consideration elsewhere.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship :-\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe author confirms that the journal policies have been read and the submission is being made in accordance to the journal policies.\u0026nbsp;\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial :-\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe author confirms that this research do not involve any type of clinical trial, hence approval not required.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest declaration :-\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe author confirms and declare that they have no competing interest relevant to the content of this article.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eFunding declaration :-\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThe author confirms that, this research is not funded by any specific grant from any person, organisation or from any type of funding agency in whole or in part.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eData availability declaration :- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;NOT APPLICABLE\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Declarations :- \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;NOT APPLICABLE\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate \u0026amp; Consent to Publish \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;NOT APPLICABLE\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAbbas, K. 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AI Governance in a Complex and Rapidly Changing Regulatory Landscape: A Global Perspective.\u0026nbsp;\u003cem\u003eHumanities and Social Sciences Communications, 11\u003c/em\u003e(1). https://doi.org/10.1057/s41599-024-03560-x\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Bionic Advisor, Hybrid Intelligence, Strategic business partner, Finance business partner, Artificial Intelligence, Finance Management, Decision-Making","lastPublishedDoi":"10.21203/rs.3.rs-8272781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8272781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose \u0026ndash;\u003c/h2\u003e \u003cp\u003eArtificial Intelligence (AI) is an automation and analytical tool that lacks human intuition and specific contextual sensitivity. The adoption of AI for business decision-making in Finance is crucial, necessitating human-AI collaboration. The research aims to understand the factors behind the adoption and the resistance towards AI and explore the evolving role of Finance Manager in the era of AI. This research proposes a hybrid intelligence conceptual model of Bionic Advisor (BA) wherein human intelligence coalesces with AI.\u003c/p\u003e\u003ch2\u003eMethodology \u0026ndash;\u003c/h2\u003e \u003cp\u003eThe research adopts a systematic literature review (SLR) combined with cause-and-effect analysis. Qualitative factors from research articles have been derived through manual content analysis. Fishbone analytical framework has been used to derive relationships among the factors influencing the model. The model is grounded in multidisciplinary theories.\u003c/p\u003e\u003ch2\u003eFindings \u0026ndash;\u003c/h2\u003e \u003cp\u003eThe insight from this research conceptualises a unique hybrid model of BA. This research and analysis reveal that the BA model will develop synergistic intelligence. This synergetic intelligence will influence business decisions to elevate business results efficiently. Finance Manager in this role of BA, will integrate his domain expertise and cognitive ability with AI, enhancing efficacy in business decisions.\u003c/p\u003e\u003ch2\u003eOriginality -\u003c/h2\u003e \u003cp\u003eConceptualisation of BA as a role changer to strategic finance business partner contributes to the emerging literature on hybrid intelligence. In this model, the Finance Manager acts as an interpreter and translator for AI. BA model is an emerging paradigm to equip businesses with AI. This research opens avenues for future empirical study on the influence of BA on business decisions.\u003c/p\u003e","manuscriptTitle":"Evolution of Bionic Advisor from Collaboration of Human \u0026amp; Artificial Intelligence in Finance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 13:20:20","doi":"10.21203/rs.3.rs-8272781/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"15c26933-f241-487b-bc83-c2abf18f1433","owner":[],"postedDate":"December 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-06T12:27:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-31 13:20:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8272781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8272781","identity":"rs-8272781","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}