Prepared by the researche : Rony Meslem  – A researcher at the Professional Master’s level at the College of Applied Interdisciplinary LTD, London, UK

DAC Democratic Arabic Center GmbH

International Journal of Kurdish Studies : Eleventh Issue – October 2025

A Periodical International Journal published by the “Democratic Arab Center” Germany – Berlin

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Abstract

This article examines the role of artificial intelligence in the context of global economic transformations, with a focus on its impact on financial oversight. It explores the following: Theoretical frameworks: Technology Acceptance Model (TAM), Disruptive Innovation Theory, Socio-Technical Systems Theory (STS), and an integrated adoption framework. Economic dimensions: AI as a driver of growth, its impact on labor markets, efficiency in decision-making, restructuring of global market structures, ethical/regulatory considerations, and its specific implications for financial controlling. This study’s approach builds a framework using the Transactional Asset Management (TAM) model, Disruptive Innovation (TI), and STS methodologies to form an integrated framework for AI adoption at the micro (individual), meso (organizational), and macro (industrial/economic) levels. Empirical grounding: It relies on secondary data (reports from PwC, Deloitte, the OECD, the European Commission, and others) and references from theory and practice to illustrate its arguments. The research findings summarize the automation of routine work as AI replaces repetitive finance and control tasks. Its role in enhancing strategic capabilities is that AI enables controllers to provide forward-looking insights through predictive analytics. Regarding its role in labor market transformations, automation is displacing routine jobs but creating new advisory, managerial, and analytical roles. There are ethical and regulatory concerns that the responsible adoption of AI must prioritize fairness, transparency, accountability, and compliance with global AI regulations (EU AI Act, OECD, etc.).

– Kurte

Gotar rêvîwane sûnî (ZS) di çarçoveya veguherînên aborî yên cîhanî de, bi tekezîyek taybetî li ser rola wê di kontrolkirina darayî de, vedikole. Ew li ser lêkolîna: Çarçoveyên teorîk: Modela Qebûlkirina Teknolojiyê (TAM), Teoriya Nûjeniya Têkder, Teoriya Sîstemên Civakî-Teknîkî (STS), û çarçoveyek pejirandina yekgirtî. Pîvanên aborî: ZS wekî ajokerek mezinbûnê, bandora wê li ser bazarên kar, karîgeriya di biryardanê de, ji nû ve avakirina avahiyên bazara cîhanî, nirxandinên exlaqî/rêkûpêk, û bandorên wê yên taybetî li ser kontrola darayî. Nêzîkatiya vê lêkolînê çarçoveyek bi karanîna modela Rêveberiya Sermayeyên Danûstandinê (TAM), Nûjeniya Têkder (TI), û rêbazên STS ava dike da ku çarçoveyek yekgirtî ji bo pejirandina ZS di astên mîkro (takekesî), mezo (rêxistinî) û makro (pîşesazî/aborî) de ava bike. Bingeha empîrîk: Ew xwe dispêre daneyên duyemîn (raporên ji PwC, Deloitte, OECD, Komîsyona Ewropî, û yên din) û referansên ji teoriyê û pratîkê da ku argumanên xwe nîşan bide. Encamên lêkolînê otomasyona xebata rûtîn kurte dike ji ber ku ZS şûna karên darayî û kontrolê yên dubare digire. Rola wê di baştirkirina şiyanên stratejîk de ew e ku AI rê dide kontrolkeran ku bi rêya analîtîkên pêşbînîkirî têgihîştinên pêşerojê peyda bikin. Di derbarê rola wê di veguherînên bazara kar de, otomasyon karên rûtîn diguhezîne lê rolên nû yên şêwirmendiyê, rêveberiyê û analîtîk diafirîne. Fikarên etîkî û rêziknameyî hene ku pejirandina berpirsiyar a AI divê pêşîniyê bide dadmendî, şefafî, hesabpirsîn û pabendbûna bi rêziknameyên gerdûnî yên AI (Qanûna AI ya YE, OECD, hwd.).

1- Introduction

Artificial Intelligence (AI) and automation are transforming finance functions across industries. For financial controllers, this transformation is an important question: must these technologies be viewed as a threat to traditional roles or as an opportunity to advance the profession? This thesis answers that question by examining how AI and automation transform controlling processes, skills, and firm expectations, and by prescribing ways in which controllers and firms can successfully adapt.

Context and Relevance

Financial control has long been a core part of business management, providing planning, reporting, and performance monitoring. Controllers not only ensure that organizations are looking back at results but also ahead at opportunities and risks. However, a lot of the underlying work involved in controlling—reconciliations, variance analysis, standard reporting—is highly repetitive and rule-based in nature. These are exactly the kind of activities for which AI and automation can do better than humans.

At the same time, businesses are faced with mounting complexity: cross-border operations, regulatory scrutiny, faster business cycles, and a growing need for evidence-driven decision-making. AI capabilities can review enormous sets of data, develop predictive solutions, and recognize anomalies in real-time. This allows controllers to shift from being “scorekeepers” to strategic allies who provide forward-looking insights. For financial controllers and organizations, the question is not whether AI will conquer finance—it has already done so—it’s one of implementation: how to use it in a way that provides both efficiency and human judgment.

Research Aims

This research pursues three broad goals:

1. Assess the impact of AI and automation on financial controllers’ everyday responsibilities and future tasks.
2. Establish the capabilities and mindsets controllers need to become and stay relevant and add value in an AI environment.
3. Reflect on how organizations may apply AI responsibly, aligning efficiency with ethics, culture, and governance.

Research axes:

1- Introduction

2- Artificial intelligence in economic literature

2-1- Introduction

2-2- Technology Acceptance Model (TAM)

2-3- Disruptive Innovation Theory

2-4- Socio-Technical Systems Theory (STS)

2-5- AI Adoption Integrative Framework

3- Artificial intelligence and its dimensions in economic transformations

3-1- Introduction

3-2- AI as a Driver of Global Economic Transformation

3-3- AI and Labor Market Implications

3-4- AI in Decision-Making and Economic Efficiency

3-5- AI and Global Market Structures

3-6- Ethical, Regulatory, and Societal Considerations

3-7- AI in Financial Controlling: Economic Implications

3-8- Conclusion.

Key Findings

AI has a double effect on financial controlling.

1.Routine work is automated

Functions such as reconciliations, journal postings, and regular reports are increasingly being automated. Though this is cost-saving and error-reducing, it also means much of the function that used to belong to controllers is disappearing.

2. Strategic potential increased

AI-based solutions provide predictive forecasting, modeling of scenarios, and advanced analytics. This makes it possible for controllers to become more advisory in nature, with a focus on insight rather than transaction.

3. Evolution of skills needed

Controllers need to become digitally competent—especially in analytics, AI literacy, and communication. Business partnering and influencing soft skills also become more important as the role shifts towards advisory work.

4. Organizational support is imperative

Controllers cannot transform alone. Companies need to invest in training programs, career progression, and cultural change. Leadership also needs to address ethical matters related to bias, transparency, and accountability in AI-augmented decision-making.

5. Varied attitudes among professionals

Survey results show that there is optimism toward gains in efficiency but fear of losing jobs and identity loss in profession. Acceptance of AI by employees is heavily based on whether or not they feel supported in adapting to change.

Practical Implications

The message to financial controllers is clear:

• Practice lifelong learning in AI, data analytics, and visualization software.
• Become a strategic business advisor, not just a data processor.
• Improve communication and advisory capacity to enable technical insights to drive management action.

To organizations, success with AI means:

• Creating systematic reskilling and career development programs.
• Embedding ethical considerations into AI deployment.
• Synchronizing AI programs with business strategy, not cost savings.
• Implementing a culture of trust in which personnel see technology as an enabler and not as a threat.

2- Artificial intelligence in economic literature:

2.1 Introduction

The research provides a comprehensive overview of the theoretical literature and practitioner insights related to artificial intelligence (AI), automation, and their application to financial controlling.
The overview combines theoretical models, empirical studies, and practical observations to obtain a holistic understanding of the topic field. The overview highlights how AI technologies are transforming accounting and controlling processes, redesigning professional roles, and influencing organizational decision-making. Furthermore, the chapter discusses ethical, governance, and regulatory factors, which are at the heart of responsible AI uptake. By synthesizing current research and frameworks, this chapter sets the stage for the empirical examination in the study and positions the research issue in the context of wider academic and industry debates.

The literature is structured around several grand themes: the technological evolution of AI in finance, theoretical frameworks of understanding technology adoption, socio-technical implications of automation, and redefining the controller function. It also addresses ethical and governance concerns, along with empirical results from case studies and surveys in financial organizations. In so doing, the review situates AI adoption as a socio-technical phenomenon that actively interacts with human actors, organizational structures, and external regulatory contexts.

Second, this chapter underscores the intertwinement of technological innovation and organizational adaptation. It not only explores how AI and automation are embedded in financial processes but also looks at how such technologies influence human behavior, managerial decision-making, and strategic planning. The review also uncovers gaps in current research, namely concerning the concrete introduction of AI in different organizational contexts and the evolving expectations of financial professionals. By providing a systematic overview of the literature, this chapter enables an informed comprehension of the complex dynamics inherent in AI adoption and clears the path for the subsequent empirical study.

2.2 Technology Acceptance Model (TAM)

The Technology Acceptance Model (TAM) proposes that technology acceptance by users is a function of perceived ease of use and perceived usefulness (Davis, 1989). In financial controlling, perceived usefulness looks at the extent to which AI helps improve process efficiency, accuracy of forecasts, and support in decision making. Perceived ease of use addresses complexity of AI systems, clarity of outputs, and intensity of training to enable controllers to use technology effectively.

Empirical evidence indicates that perceived usefulness is a primary motivator for AI adoption in finance, since professionals envision the capacity to automate repetitive tasks, realize predictive power, and maximize overall performance (Deloitte, 2021). Conversely, perceived ease of use, uninterpretability, and poor data literacy limit adoption, pointing towards the need for led training programs and easy-to-use interfaces of AI (Burrell, 2016).

In addition, TAM emphasizes the psychological and behavioral dimensions of technology acceptance. Controllers’ trust in AI systems, their receptivity to new tools, and the perceived reliability quality of AI outputs fuel adoption. Feedback loops are also highlighted by the model: as users experience payoff and witness tangible performance improvement, their acceptance and use increase, creating a reinforcing loop of adoption and know-how. TAM thus provides a valuable lens for understanding both cognitive and motivational drivers of AI adoption by finance professionals, emphasizing the importance of aligning technology design, training, and support with individuals’ abilities and expectations.

2.3 Disruptive Innovation Theory

Disruptive Innovation Theory sets the context for the disruption of traditional industry standards and professional roles by AI. AI has the potential to automate routine controlling functions, which may displace junior-level jobs, while simultaneously creating roles in strategic advisory, predictive intelligence, and ethical advice.

Finance literature highlights twofold perspectives among experts: AI adoption is considered both as a driver of efficiency and strategic intelligence and a potential cause of employment insecurity and established work habits (Frey & Osborne, 2017; Kumar & Renuka, 2025). Companies, which implement disrupting technologies proactively, reassess processes, implement reskilling initiatives, and establish governance frameworks to maintain efficiency gains while promoting ethical as well as professional responsibilities. The theory recognizes that firms that fail to embrace the potential of AI risk losing their competitive advantage, while firms that are successful in managing adoption can leverage AI for long-term strategic growth, decision-making, and more sophisticated professional roles

2.4 Socio-Technical Systems Theory (STS)

Socio-Technical Systems Theory (STS) emphasizes that technology is not independent but interacts with human actors, organizational processes, and social structures (Bostrom & Heinen, 1977). Financial controlling using AI is a good case in point for this perspective, since success depends on technology alignment with organizational culture, workflow design, governance structures, and human expertise.

Research shows that human-in-the-loop processes are vital to maintaining accountability, ethical quality, and decision-making excellence (Schäffer & Weber, 2021). Controllers must interpret AI output, review algorithmic conclusions, and integrate insights with overall organizational decision-making. Organizational readiness—in infrastructure, cross-functional coordination, and change management—is therefore an absolute requirement for effective AI implementation (Deloitte, 2021).

STS also recognizes the importance of social adaptation alongside technological embedding. Adoption means adjustments in responsibility, re-writing processes, and fostering a cooperative culture between finance, IT, and data science teams. Leadership, communication strategies, and education initiatives are crucial in ensuring that human players are able to use AI tools productively. STS theory places AI as a socio-technical intervention rather than a technologically led enhancement, and thus even more weightage is given to integrated adoption strategies considering people, process, and technology in harmony.

2.5– AI Adoption Integrative Framework
Combining TAM, Disruptive Innovation, and STS, this research establishes an integrative framework for AI adoption in financial controlling:

• Individual adoption: Why controllers adopt or reject AI tools (TAM).
• Industry transformation: Why AI disrupts jobs and processes (Disruptive Innovation).
• Organizational alignment: Ensures AI adoption is socially and technically integrated for long-term benefits (STS).

This systemic approach allows exploration of AI adoption at the micro (individual), meso (organization), and macro (industry/economy) levels (Davis, 1989, p. 324).

3- Artificial intelligence and its dimensions in economic transformations

3.1 Introduction

Here discusse the application of AI in the real world in the context of global trends, organizational behavior, and industry-level innovation in financial controlling.
With rich case studies and industry insights, it paints a picture of how AI is redefining finance functions, driving efficiency, innovation, and strategic decision-making in global industry champions.
The chapter stresses that AI adoption is not just a technical change but a socio-technical change that calls for organizations to recast workflows, professional roles, governance, and employee skills.

3.2 AI as a Driver of Global Economic Transformation

AI technologies are recognized as disruptors in both economic theory and practice:

• Efficiency and productivity: AI increases productivity by automating redundant work, optimizing resource allocation, and improving decision-making capabilities across industries worldwide (Brynjolfsson & McAfee, 2017, p. 23).
• Competitiveness and innovation: Firms such as Amazon, Tencent, and Siemens employ AI for analytics-based forecasting, faster market response, and improved customer intelligence to attain strategic excellence in global markets (Christensen, 1997, p. 33).
• Economic growth and structural change: Macroeconomic studies estimate that AI adoption drives reallocation of labor and capital, leading to growth and inducing transition dislocation and disparity in both advanced economies and emerging markets (Frey & Osborne, 2017, pp. 254–280).

Economic theories increasingly feature AI as a core driver of aggregate factor productivity and global growth dynamics, each with respective advantages and distributional concerns (Brynjolfsson & McAfee, 2017, p. 23).

3.3 AI and Labor Market Implications
Adoption of AI transforms labor markets worldwide:

• Jobs displacement: Dull and repetitive roles are increasingly automated, with implications of temporary unemployment in specific sectors, including banking, manufacturing, and transport (Frey & Osborne, 2017, pp. 254–280).
• Role formation and adaptation: AI generates new roles that require digital, analytical, and managerial capabilities. Financial controllers are being transformed into strategic advisors who interpret AI outputs (Weber, 2019, p. 101).
• Skilling and reskilling gap: Constant upskilling is required to prepare the workforce for hybrid human-AI work, which is a problem experienced in Europe, Asia, and North America (European Commission, 2021, p. 22).

Global surveys indicate that countries investing in education and training in AI have more harmonious labor market transitions and successful adoption (World Economic Forum, 2023, p. 12).

3.4 AI in Decision-Making and Economic Efficiency
AI facilitates micro- and macroeconomic decision-making across borders:

• Forecasting analytics: AI enables accurate forecasting of market trends, patterns of demand, and exposures to risk in multinational banks and hedge funds (Amazon, 2024, p. 34).
• Prescriptive models: AI prescribes the optimal course of action and improves allocation of resources and firm-level decision-making across worldwide supply chains (Kumar & Renuka, 2025, p. 56).
• Behavioral insights: Machine learning algorithms generate insights on economic behavior and inform policy intervention in taxation, trade, and investment (Brynjolfsson & McAfee, 2017, p. 23).

Researchers caution against algorithmic bias, transparency issues, and overdependence on automation, which can distort outcomes and exacerbate inequalities (Barredo Arrieta et al., 2020, p. 67).

3.5 AI and Global Market Structures
AI is responsible for new global market structures:

• Platform economics: AI enables global platform-based business models, where data-driven insights dominate competition (Brynjolfsson & McAfee, 2017, p. 23).
• Capital markets: Computerized risk assessment, predictive modeling, and high-frequency trading reshape efficiency and capital allocation in international markets (Sutton, 2021, p. 112).
• SMEs and diffusion of innovation: AI uptake in SMEs can optimize efficiency and innovation, though policy action is required to fill gaps, especially in developing economies (OECD, 2021, p. 78).

Studies emphasize the interplay between technology, firm strategy, and international competitiveness (Christensen, 1997, p. 33).

3.6 Ethical, Regulatory, and Societal Considerations
The adoption of AI raises important governance and societal issues:

• Transparency and accountability: Financial and economic AI models must be interpretable and auditable to maintain trust (European Commission, 2021, p. 22).
• Fairness and bias: Imbalanced datasets can worsen differences in labor markets, credit access, and economic opportunity (Barredo Arrieta et al., 2020, p. 67).
• Regulatory frameworks: The EU AI Act, OECD AI Principles, and Chinese AI guidance aim to tie innovation with societal safeguards (European Commission, 2023, p. 67).

These considerations link economic efficiency with societal welfare, emphasizing that AI adoption must be both technically effective and ethically responsible (Schäffer & Weber, 2021, p. 89).

3.7 AI in Financial Controlling: Economic Implications
Financial controlling illustrates AI’s dual economic role on both firm and global scales:

• Efficiency gains: Automation reduces effort and operational cost in repetitive reporting, observed in Bank of America and Amazon (OECD, 2021, p. 78).
• Value creation at the strategic level: Predictive analytics improves investment decisions, budgeting, and forecasting, enhancing organizational performance and macroeconomic efficiency (Kumar & Renuka, 2025, p. 56).
• Compliance and risk: AI drives regulatory compliance and fraud detection to maintain financial stability within multinational companies (Barredo Arrieta et al., 2020, p. 67).

Financial controlling is a microcosm of global AI adoption, linking operational advantages with far-reaching economic impacts (Brynjolfsson & McAfee, 2017, p. 23).

3.8 Conclusion

Literature underlines that AI and automation in financial controlling both have opportunities and challenges. Technologically, AI is able to automate repetitive work, improve accuracy, and provide predictive insights supporting decision-making. Organizationally, successful adoption requires workflow alignment, governance architecture, and capability of the workforce. Professionally, controllers are transitioning towards strategic advisory, ethical governance, and data-driven decision-making.

Theory models—TAM, STS, and Disruptive Innovation—provide effective lenses for framing drivers of adoption, organizational change, and professional role evolution. Ethical, governance, and regulatory dimensions remain at the core of responsible AI deployment, prioritizing transparency, fairness, and accountability.

The literature overall positions AI adoption in financial controlling within the context of a dynamic socio-technical change that engages dynamically with people, processes, and technology.

By situating financial controlling in its sectoral background, the chapter provides a realistic connection between the theoretical concepts treated in the preceding parts and the empirical evidence analyzed in later sections. In so doing, it illustrates the two-faced character of AI in finance—as both a threat of disruption and a gigantic promise of development. Deployment of AI is thus a socio-technical transformation that entails deliberate planning, human touch, and frequent adaptation to achieve lasting value.

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