From Automation to Transformation: How AI is Reshaping Financial Controlling Roles and Systems

 

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

DAC Democratic Arabic Center GmbH

International Journal of Kurdish Studies : Twelfth Issue – January 2026

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

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Abstract

This study explores the transformative role of Artificial Intelligence (AI) in financial controlling, moving beyond traditional automation to examine AI’s strategic, organizational, and ethical impacts. Utilizing a mixed-methods research design, the study integrates secondary data analysis and a primary survey targeting finance professionals, with a focus on institutions and industries in Germany. The research begins by outlining its methodological foundations, including research philosophy, design, and ethical considerations. Empirical data are drawn from three case studies—Siemens, Deutsche Bank, and PwC—illustrating diverse strategies and outcomes in AI adoption. Key findings from both secondary and primary sources reveal substantial efficiency gains, evolving controller roles, emerging ethical challenges, and the shifting competencies required in the field. A socio-technical lens is then applied, employing frameworks such as the Technology Acceptance Model (TAM) and Socio-Technical Systems Theory (STS) to critically assess how AI is reshaping governance structures, accountability mechanisms, and professional identities in financial controlling. Converging and diverging insights are analyzed to provide a comprehensive understanding of the phenomena. This research contributes to the growing body of knowledge on AI in finance by offering theoretically grounded and data-driven insights. It also provides practical implications for decision-makers, academics, and practitioners seeking to navigate the rapidly changing landscape of digital financial management.

Kurte

Ev lêkolîn rola veguherîner a jîra çekirî (AI) di çavdêriya darayî de vedikole, ji otomasyona kevneşopî derbas dike da ku bandorên wê yên stratejîk, rêxistinî û exlaqî lêkolîn bike. Bi karanîna sêwirana lêkolînê ya pir-rêbaz, lêkolîn analîza daneyên duyemîn û anketek seretayî ya ku pisporên darayî armenc dike, bi balkişandina ser sazî û sektorên li Almanya, entegre dike. Lêkolîn bi avakirina bingehên wê yên metodolojîk dest pê dike, di nav de felsefeya lêkolînê, sêwiran û nirxandinên exlaqî. Daneyên empîrîk ji sê lêkolînên dozê (Siemens, Deutsche Bank, û PricewaterhouseCoopers) têne girtin, ku stratejiyên cihêreng ên pejirandina AI nîşan didin. Dîtinên sereke ji çavkaniyên duyemîn û seretayî destkeftiyên girîng ên karîgeriyê, rolên çavdêriyê yên pêşkeftî, pirsgirêkên exlaqî yên derketî holê, û guhertinek di jêhatîbûnên ku di vê qadê de hewce ne eşkere dikin. Dûv re perspektîfek sosyo-teknîkî tê sepandin, bi karanîna çarçoveyên wekî Modela Qebûlkirina Teknolojiyê (TAM) û Teoriya Sîstemên Sosyo-Teknîkî (STS) da ku were nirxandin ka AI çawa avahiyên rêveberiyê, mekanîzmayên hesabdayînê, û nasnameyên profesyonel di çavdêriya darayî de ji nû ve diguherîne. Perspektîfên hevgirtî û cuda têne analîzkirin da ku têgihîştineke berfireh a vê diyardeyê peyda bikin. Ev lêkolîn bi pêşkêşkirina têgihiştinên teorîk û daneyî beşdarî zanîna zêde ya li ser zekaya sûnî di darayî de dibe. Ew her weha ji bo çêkerên polîtîkayê, akademîsyen û pratîsyenên ku dixwazin bi guhertinên bilez ên di rêveberiya darayî ya dîjîtal de re bigihîjin hev, sepanên pratîkî peyda dike.

1. Introduction:

In recent years, the rapid advancement of Artificial Intelligence (AI) has brought about a paradigm shift in various domains of business operations, particularly in financial controlling. Traditionally perceived as a function rooted in reporting, budgeting, and cost management, financial controlling is increasingly being redefined through the integration of intelligent technologies that enable predictive analytics, real-time decision-making, and value-added strategic support.

While automation has long played a role in enhancing the efficiency of routine financial tasks, the emergence of AI introduces a new dimension—one that goes beyond mechanizing processes to fundamentally transforming the roles, responsibilities, and competencies of financial controllers. Modern AI systems are not only capable of processing vast volumes of structured and unstructured data, but also of identifying patterns, generating insights, and learning from feedback loops, thereby challenging the conventional boundaries of human decision-making in finance.

Despite the growing relevance of AI in the financial sector, most existing research has either focused on the technical capabilities of AI or on its implications for broader financial services such as banking and investment. There remains a significant gap in the literature concerning the application and impact of AI specifically within financial controlling functions, especially from a socio-technical and ethical perspective. Moreover, empirical evidence on how organizations, particularly in the German industrial context, are adopting and adapting to AI in controlling remains scarce.

This study responds to this research gap by exploring the strategic, operational, and ethical implications of AI adoption in financial controlling. By employing a mixed-methods approach, the research combines case studies of leading German companies with a survey of finance professionals to capture both organizational practices and individual perceptions.

The study addresses the following core questions:

    How is AI being applied within financial controlling functions across industries?

    What are the perceived benefits, risks, and barriers to AI adoption in controlling?

    How is the role of the financial controller evolving in light of AI-driven transformation?

    What ethical and governance challenges emerge from integrating AI into financial decision-making?

In doing so, the research contributes to a more holistic understanding of AI’s role in reshaping financial controlling—not merely as a tool for automation, but as a disruptive socio-technical force that reconfigures organizational structures, professional identities, and ethical norms in the digital age.

1- The research method applied for the study of AI and automation in financial controlling

1.1 Introduction

This research outlines the research methodology employed to investigate AI and automation in financial controlling. The study adopts a mixed-methods approach, combining secondary data analysis with a small-scale primary survey. This approach enables both theoretical exploration and practical insights, addressing the research question: “AI and Automation in Financial Controlling: Threat or Opportunity?”

1.2 Research Philosophy and Approach

The study is grounded in a pragmatic research philosophy, emphasizing actionable outcomes for academic and professional audiences. A qualitative-dominant mixed-methods approach was employed to capture both perceptions and observable patterns in AI adoption (Rikhardsson & Yigitbasioglu, 2018, p. 34).

The research combines:

• Secondary analysis of academic literature, industry reports, and case studies.
• Primary qualitative survey of financial controllers to gather real-world experiences and contextualize secondary findings.

1.3 Research Design

The study employs an exploratory mixed-methods design with two components:

1. Secondary Study Component:
   • Sources: Academic literature, industry reports, and case studies from Bank of America, Amazon, and Deloitte (Deloitte, 2024, p. 14; Amazon, 2024, p. 34; PwC, 2022, p. 9).
   • Purpose: Establish theoretical foundations, identify trends, risks, and opportunities, and provide a benchmark for primary data.
2. Primary Survey Component:
   • Design: Small-scale semi-structured survey of 8 financial professionals.
   • Purpose: Capture practitioners’ experiences with AI adoption in financial controlling.
   • Justification: Focus on depth over breadth, allowing detailed exploration of emerging themes (Barredo Arrieta et al., 2020, p. 67).

1.4 Secondary Data Analysis

The secondary data analysis involved a systematic review of:

• Academic journals: Management Accounting Research, Journal of Accounting & Organizational Change, Journal of Management Control (Schäffer & Weber, 2021, p. 89).
• Industry reports: Deloitte, PwC, Bank of America, Amazon (Deloitte, 2024, p. 14; PwC, 2022, p. 9; Amazon, 2024, p. 34).
• Regulatory sources: EU AI Act, OECD AI Principles (OECD, 2021).

Analysis Method:

• Thematic analysis to identify recurring topics such as AI adoption drivers, ethical concerns, governance challenges, and skill shifts (Bostrom & Heinen, 1977, p. 17).
• Triangulation across multiple sources to ensure reliability and comprehensive coverage.

1.5 Primary Survey Design

1.5.1 Target Population

• Financial controllers, CFOs, and finance managers with direct experience in AI or automation in finance.

1.5.2 Sampling Method

• Purposive sampling: Participants selected based on relevant experience.
• Sample size: 8 participants to allow in-depth qualitative exploration.

1.5.3 Survey Instrument

• Semi-structured questionnaire with open-ended questions.
• Focus areas:
  1. Current AI and automation practices in financial controlling.
  2. Perceived opportunities and threats.
  3. Ethical and governance considerations.
  4. Skills, training needs, and workforce implications.

1.5.4 Data Collection Procedure

• Surveys conducted online or in-person, depending on participant availability.
• Each session lasted approximately 20–30 minutes.
• Responses anonymized to ensure confidentiality.

1.6 Data Analysis:

1.6.1 Qualitative Analysis

• Survey responses analyzed using thematic analysis to identify recurring patterns, concerns, and insights (Barredo Arrieta et al., 2020, p. 67).
• Key themes linked to secondary data for triangulation.

1.6.2 Integration with Secondary Data

• Survey insights compared with secondary data trends to examine alignment or divergence.
• Emphasis on illustrative examples and practitioner quotes for richness.

1.6.3 Limitations of Data Analysis

• Small sample size limits generalizability.
• Respondent subjectivity may introduce bias.
• Secondary data may be time-bound due to rapid evolution of AI technologies.

1.7 Ethical Considerations

• Participant consent obtained before survey administration.
• Data anonymized and stored securely.
• Findings reported objectively, respecting confidentiality and ethical guidelines (Davenport et al., 2020, p. 24).

1.8 Rationale for Mixed-Methods Approach

Combining secondary and primary data allows the study to:

1. Validate literature findings against real-world experiences.
2. Explore practitioner perspectives not captured in prior studies.
3. Enhance practical relevance, providing actionable recommendations for controllers and organizations (Schäffer & Weber, 2021, p. 89).

This chapter establishes a robust research framework for examining AI and automation in financial controlling. By integrating secondary analysis with a small primary survey, the study balances theoretical depth with practical insights, addressing the research question comprehensively and providing a foundation.

2- Artificial Intelligence in Finance and Control (Case Studies in German Institutions and  Industry Context )

 2.1 Introduction

This chapter examines the industry context of AI adoption in financial controlling, focusing on global trends, organizational applications, and sector-specific insights and situates financial controlling within real-world organizational and industry practices.
It draws on case studies, industry reports, and academic literature to illustrate how AI is transforming financial functions in leading corporations, including Siemens, Deutsche Bank, PwC, Amazon, and Bank of America.

2.2 AI Adoption Trends in Finance

AI adoption in finance has accelerated rapidly over the past decade:

• Automation of routine processes: Tasks such as data entry, reconciliation, and report generation are increasingly automated using Robotic Process Automation (RPA) and AI-driven tools (Aguirre & Rodriguez, 2017, p. 125).
• Predictive and prescriptive analytics: Organizations leverage AI for forecasting, budgeting, and risk management, enabling more proactive decision-making (Kumar & Renuka, 2025, p. 113).
• Enhanced compliance and fraud detection: AI models identify anomalies and detect compliance risks in real time, supporting regulatory adherence (Deloitte, 2024, p. 18).

Global surveys indicate that financial institutions investing in AI experience faster decision cycles, reduced operational costs, and improved strategic planning (Bank of America, 2025, p. 6).

2.3 Case Study 1: Siemens

Siemens has implemented AI in financial controlling to improve forecasting and operational efficiency:

• Predictive analytics: AI models analyze historical financial data to anticipate cash flow requirements and optimize capital allocation (Maple et al., 2023, p. 47).
• Process automation: Routine reporting tasks have been automated, freeing controllers for strategic advisory roles (Weber, 2019, p. 102).
• Outcomes: Improved accuracy, faster reporting cycles, and more informed investment decisions, demonstrating AI’s dual role as an operational and strategic enabler (PwC, 2022, p. 10).

2.4 Case Study 2: Deutsche Bank

Deutsche Bank leverages AI to enhance financial monitoring, risk management, and compliance:

• AI-driven risk assessment: Algorithms detect irregularities and potential fraud, reducing exposure to financial and operational risks (Davenport & Kirby, 2016, p. 23).
• Role transformation: Financial controllers are increasingly performing analytical and advisory tasks, using AI insights to support senior management (Schäffer & Weber, 2021, p. 91).
• Impact: Enhanced regulatory compliance, improved decision-making quality, and a more agile finance function (PwC, 2022, p. 12).

2.5 Case Study 3: PwC

PwC has integrated AI across consulting and financial controlling functions to support clients and internal operations:

• Automation of repetitive tasks: Audit and reporting processes are streamlined through AI-driven workflows (PwC, 2022, p. 13).
• Advanced analytics: Predictive models support financial planning, cash management, and risk assessment for clients globally (Maple et al., 2023, p. 47).
• Professional skill development: Controllers are trained in AI literacy and data analytics, highlighting the importance of upskilling (Weber, 2019, p. 102).

2.6 Cross-Industry Insights

AI adoption in financial controlling exhibits similar patterns across industries:

• Efficiency gains: Routine transactional activities are automated, reducing human error and operational costs (Aguirre & Rodriguez, 2017, p. 125).
• Strategic value creation: Controllers focus on interpretation, scenario planning, and advisory services, increasing their strategic contribution (Božič & Dimovski, 2019, p. 215).
• Regulatory and ethical challenges: Transparency, bias mitigation, and compliance remain critical considerations for AI deployment (Barredo Arrieta et al., 2020, p. 67).

This demonstrates that AI adoption is not only a technical transformation but also a strategic and organizational evolution across sectors.

2.7 Key Drivers and Barriers for AI Adoption

• Drivers:

1. Efficiency and cost savings: AI reduces the operational cost and enhances efficiency (Brynjolfsson & McAfee, 2017, p. 23).
2. Better decision-making and predictions: AI provides predictive and prescriptive analytics enabling strategic financial choice (Davenport & Ronanki, 2018, p. 110).
3. Innovation and competitive differentiation: Organizations apply AI to innovate and maintain strategic differentiation within the industry (Christensen, 1997, p. 33).

• Barriers:

1. Availability and quality of data: Unavailability of or unreliable data dissuades AI accuracy and reliability (Bhardwaj et al., 2024, p. 50).
2. Training requirement and shortage of skills: Controllers need technical competence, analytical skill, and AI knowledge to communicate effectively with AI systems (Keller, 2021, p. 90).
3. Regulatory and ethical risks: Compliance, fairness, transparency, and accountability concerns must be addressed in order to avoid legal or reputational risks (European Commission, 2021, p. 22).

Understanding these drivers and impediments is critical for organizations to achieve maximum AI benefits while avoiding potential pitfalls.

2.8 Summary

This chapter describes how the adoption of artificial intelligence in financial controlling is subject not only to the nature of individual organizations but also to general industry forces. The case studies show how AI enhances efficiency, accelerates strategic decision-making, and reshapes professional activities. Meanwhile, its success depends on the manner in which organizations manage fundamental drivers such as data quality, employees’ competence, governance, and ethical concerns.

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.

3- Findings / Case Studies:

3.1 Introduction

This chapter presents the findings of the study, integrating insights from secondary data (case studies, industry reports, and academic literature) with responses from a small-scale practitioner survey. The analysis highlights the dual nature of AI and automation in financial controlling, the evolving role of controllers, and the governance and ethical considerations shaping adoption. The mixed-methods approach allows triangulation between literature and real-world experience.

• Secondary Data Findings

The secondary data analysis revealed several emergent themes:

3.2.1 Efficiency and Automation Gains

• Automation of routine processes: AI and RPA automate repetitive processes such as data entry, reconciliations, and report generation.
  By removing the human element from such processes, organizations reduce human error, accelerate processing time, and allow finance professionals to redirect their focus to higher value-added activities.
  This development not only delivers greater efficiency but also results in overall process standardization throughout financial operations (Deloitte, 2024, p. 15).
• Saving time and improved forecasting: Case studies of large organizations such as Bank of America and Amazon reveal that AI adoption saves significant time.
  For example, automated reconciliations that took hours of manual effort are now accomplished in minutes.
  Moreover, predictive analytics feature improves forecasting accuracy by analyzing historical trends and detecting emerging patterns.
  This enables controllers and finance teams to make proactive, data-driven decisions, elevating the strategic value of their work (Amazon, 2024, p. 34).
• Strategic direction from predictive and prescriptive analytics: Beyond operational efficiency, AI provides forward-looking information to inform strategic decision-making. Predictive models project cash flow volatility, probable risks, and resource needs, and prescriptive analytics provide the best course of action to take to achieve financial and operational objectives.
  These capabilities allow controllers to see beyond traditional reporting and contribute directly to investment decisions, risk solutioning, and long-term planning initiatives, demonstrating the broader potential of AI in transforming finance from a reactive to a proactive function (PwC, 2022, p. 12).
  Overall, the secondary data emphasize that AI and automation not only improve the operational efficiency but also the strategic relevance of financial controlling by enabling quicker, more accurate, and forward-looking decision-making

3.2.2  Threats and Challenges

Secondary data analysis also depicted some risks and challenges associated with AI and automation implementation in financial controlling.

• Workforce impact and job substitution: Automation has the risk of substituting junior finance jobs that typically carry out repetitive and routine tasks, such as data entry, reconciliations, and report generation.
  This leads to changes in the workforce, with firms requiring staff to possess hybrid skills that combine finance competence with data literacy, AI interpretation, and ethical decision-making capabilities.
  The shift serves to reinforce the need to adopt active workforce planning and reskilling efforts in order to render workers relevant and engaged in value-added work (Frey & Osborne, 2017, pp. 260-262).
• Compliance and auditability matters: AI models, particularly those powered by advanced machine learning algorithms, produce outcomes that are not transparent or explainable.
  This untransparency becomes an issue for internal and external audits because controllers and auditors cannot trace decision logic or confirm model correctness. Sustaining auditability entails integrating explainable AI practices, documentation processes, and validation systems that maintain transparency and enable regulatory compliance (Barredo Arrieta et al., 2020, p. 67).
• Regulatory uncertainty: The quickly evolving AI regulations, especially for high-risk AI systems, create compliance issues for multinational organizations.
  Companies are confronted with different national and international standards as they integrate AI in financial activities, resulting in increased compliance costs, legal risks, and operational complexity.
  The uncertainty emphasizes the importance of effective governance frameworks, risk assessment procedures, and continuous monitoring to balance AI adoption with compliance (European Commission, 2023, p. 67).

In combination, these threats and challenges indicate that even though AI offers operational and strategic benefits, organizations should take workforce transformation, model transparency, and difficult regulatory environments seriously to overcome threats and ensure trust in AI-based financial controlling.

• 2.2.1 Organizational Chart – Finance Department

Before AI/Automation

CFO

• Financial Controllers
  • Cost Controlling
  • Reporting & Analysis
  • Budgeting & Forecasting
• Accountants
  • Accounts Payable
  • Accounts Receivable
• Data/BI Analyst

After AI/Automation

CFO

• Financial Controllers
  • Cost Controlling (AI-assisted)
  • Reporting & Analysis (automated dashboards)
  • Budgeting & Forecasting (predictive models)
• Accountants
  • Accounts Payable (RPA)
  • Accounts Receivable (RPA)
• Data/BI Analyst (focus on analytics & AI model monitoring)

3.2.3 Governance and Ethical Considerations

Secondary data analysis emphasized that good governance and ethics are the characteristics of sound AI implementation in financial controlling.

• Strong governance frameworks: Organizations that have put in place strong governance frameworks for AI demonstrate higher adoption success, increased operational confidence, and clearer accountability.
  Governance frameworks typically encompass model validation guidelines, risk measurement, performance tracking, and human override controls.
  Embedded governance in AI deployment ensures that the deployment is in alignment with organisational objectives as well as mitigating operation, ethical, and compliance threats (Deloitte, 2024, p. 18).
• Ethically used AI: Active measures to prevent bias, ensure transparency, and ensure ongoing human oversight are required for ethical AI use.
  Bias testing, fairness auditing, and transparent AI models help ensure decision-making is accurate, equitable, and justifiable.
  Ongoing human presence is needed to make AI outputs understandable, correct errors, and follow ethical procedures, particularly in sensitive financial environments (Barredo Arrietta et al., 2020, p. 67).
• Weak governance risks: Weak governance may lead to the loss of stakeholders’ trust, poor-informed decisions, and reputational damage.
  Errors in financial reporting, compliance breaches, and public backlash can befall companies if AI systems operate without proper controls and ethical safeguards.
  It is for this reason that integrating governance and ethical safeguards into the whole AI lifecycle is crucial (Financial Times, 2025, p. 5).

3.2.4 Changing Roles for Controllers

The adoption of AI and automation is in its essence revolutionizing the work and responsibilities of financial controllers:

• Move towards strategic advisory: Controllers are now starting to move away from such routine repetitive processing of transactions to more work that is analytical strategic in nature, advisory to business, and decision supportive in direction. Automation of routine tasks allows them to focus on interpreting data, identifying opportunities, and helping top-level organizational strategy (Weber, 2019, p. 102).
• Hybrid skill sets: The evolving controller’s role necessitates the integration of finance knowledge, data analysis abilities, and moral reasoning.
  Controllers must be able to interpret AI-generated insights, critically analyze results, and communicate findings effectively to facilitate organizational decision-making. Education and continuing professional development are required to equip controllers with these hybrid skills (Schäffer & Weber, 2021, p. 90).
• Accountability value creation: The true value of controllers lies in interpreting AI outcomes responsibly, transparent decision-making, and ethical and regulatory accountability.
  They provide a mediating role between technology and human control to ensure that AI enhances financial control rather than undermining trust or regulation (Maple et al., 2023, p. 48).

Combined, these advancements demonstrate how controllers are being transformed into strategic partners who balance technical, analytical, and moral responsibilities and are at the forefront of effective AI implementation in financial controlling.

• 2.4.1 Workflow Diagram – Monthly Closing Process

Pre-AI Workflow:

1. Collect data from multiple ERP modules → manual consolidation
2. Verify and reconcile accounts → manual checks
3. Prepare management reports → manual formatting
4. Approve reports → management review

Post-AI Workflow:

1. Data automatically extracted and consolidated via RPA
2. Reconciliations assisted by AI anomaly detection
3. Management reports auto-generated with dashboards
4. Management reviews insights (focus on exceptions)

3.3 Primary Survey Findings

The survey involved 8 finance professionals, with consequent real-world knowledge gained in respect of AI usage.

Role	Frequency	% of Total
Financial Controller	2	25%
Senior Controller	2	25%
Head of Controlling	1	12.5%
CFO/Finance Director	1	12.5%
Accountant	2	25%
Data/BI Analyst	0	0%
Other	0	0%

3.3.1 Attitudes towards AI

• AI as an efficiency tool: The players all agreed on the potential of AI to increase efficiency, particularly where there is routine and time-consuming work such as reporting, budgeting, and forecasting.
  AI was seen as a facilitator that will mechanize routines, ensure minimal errors, and free up controllers to perform more valuable tasks that require interpretation and strategic thinking (Davenport & Ronanki, 2018, p. 110).
• Job security concerns: Youth controllers feared losing jobs due to automation. While AI boosts productivity, it also transforms traditional jobs and competence requirements.
  The necessity for firms to manage staff change through reskilling and redeployment was highlighted by the participants to tackle redundancy concerns (Frey & Osborne, 2017, pp. 260–262).
• Role of human judgment: The participants noted that AI should supplement and not replace human judgment, particularly in the event of high-impact financial decisions such as strategic planning, investment analysis, or risk decisions.
  Controllers reaffirmed that accountability, ethical direction, and context awareness continue to be essential, making AI output accurately readable and responsibly used (Weber, 2019, p. 102).

Overall, the evidence supports that while finance professionals as a whole remain positive regarding the efficiency benefits of AI, employment security issues and the need for human control are central to shaping adoption attitudes. The findings emphasize the importance of installing AI in a socio-technical system that integrates automation and human knowledge, including ethical responsibility.

• 3.1.1 AI Usage

Question	Mean Likert	Std Dev	Distribution (1–5)
A1: Finance uses AI/automation	3.5	1.1	1:0, 2:1, 3:2, 4:3, 5:2
A2: Personal AI usage	3.0	1.2	1:1, 2:2, 3:2, 4:2, 5:1

 

• 3.1.2 Tools Used

Tool	Frequency
RPA	3
Predictive forecasting	2
Anomaly/fraud detection	2
Generative AI assistants	1
OCR/IDP	2
Workflow automation	3
None	0
Other	0

• 3.1.3 Primary Processes Automated

 

Process	Frequency
Closing & reconciliation	4
Management reporting	3
Planning/Budgeting/Forecasting	3
Cost controlling	2
O2C	1
P2P	1
Project controlling	1
Data integration/ETL	2
Other	0

 

 

 

3.3.2 Governance and Ethics

The findings of the interview and survey emphasized important issues regarding governance and ethical AI adoption in financial controlling:

• Absent clearly stated internal policies: The absence of well-formalized internal AI regulation policies was raised by the participants.
  Without clearly defined standards and procedures, controllers are at a loss to offer consistent, transparent, and accountable use of AI in financial processes (Schmidt, 2020, p. 102).
• Ethical concerns: The respondents spotted ethical concerns in AI adoption, including data privacy, predictive models being biased, and responsibility for AI-based decision-making.
  These concerns reflect broader organizational and societal expectations that financial processes are compliant, fair, and trustworthy despite automation (Barredo Arrieta et al., 2020, p. 67).
• Need for systematic training: Certain interviewees expressed the view that systematic training programmes in AI governance, ethics, and regulation are imperative to equip controllers with AI-assisted roles.
  Such training was regarded as the determining factor in empowering financial professionals with the learning and discretion necessary in managing ethical concerns and ensuring company accountability (Rossi, 2022, p. 45).

Overall, the findings indicate that ethical governance is an important determinant of successful AI deployment in financial controlling. Firms that invest in policy transparency, risk awareness, and formal training are more effectively able to build trust, compliance, and sustainable AI use, confirming the controller’s role as a strategic and ethical decision-maker.

• 3.2.1 Governance & Maturity

Section	Statement	Mean		Std Dev	Visual (Gradient)
Opportunities	Improves process efficiency	4.0		0.7	█████ (dark green)
Opportunities	Increases data accuracy	3.8		0.8	████▉ (green)
Opportunities	Frees time for analysis	3.6		0.9	████▎ (light green)
Opportunities	Enhances decision-making	3.5		1.0	████▎ (light green)
Opportunities	Improves timeliness	3.7		0.8	████▌ (green)

 

Threat	Risk to job security	3,2	1,0	███▏ (yellow)
Threat	Lack of transparency	3,5	0,9	████▎ (light green)
Threat	Data/privacy risk	3,8	0.8	████▌ (green)
Threat	Model errors	3,0	1.1	██▌ (orange)
Threat	Skills gap	3,5	0.9	████▎ (light green)

 

Governance & Maturity	Clear governance	3.5	0.9	████▎ (light green)
Governance & Maturity	Monitor performance	3.2	1.0	███▏ (yellow)
Governance & Maturity	Regulatory compliance	3.8	0.7	████▌ (green)
Governance & Maturity	Training available	3.0	1.1	██▌ (orange)

• 3.2.2 Benefits vs Risks

 

 

3.3.3 Competences and Skills

The interviews revealed that AI implementation in financial controlling is directly related to the occurrence of new competences and skill sets of controllers:

• Data literacy, AI awareness, and ethical decision-making: Respondents emphasized that modern controllers must be aware of how AI interacts with, analyzes, and draws conclusions from financial information.
  Awareness of AI potential, limitations, and ethical issues—i.e., bias, transparency, and accountability—is critical for its responsible use.
  These competencies enable controllers to interpret AI outputs correctly and to make fully informed decisions in alignment with organizational and societal values (Müller, 2022, p. 148).
• Limitations of traditional financial competencies: Traditional accounting, reporting, and budgeting competencies were found to be lacking in the AI-enabled environment. Controllers now need to complement their financial expertise with capabilities to handle complex datasets, understand predictive models, and translate analytical insights into actionable business plans (Granlund & Malmi, 2020, p. 15).
• IT and data science collaboration: Successful AI use was consistently associated with cross-functional collaboration.
  Controllers must collaborate with IT specialists and data scientists to implement AI tools, validate model outputs, and integrate them with organizational processes. Collaboration ensures that AI solutions are practicable, reliable, and aligned with strategic goals, enabling smooth operational and decision-making processes (Kokina & Davenport, 2017, p. 118).

 In summary, the findings indicate that successful AI implementation for financial controlling is contingent on the creation of hybrid skill sets that combine classical finance expertise, data literacy, ethical judgment, and collaboration with IT and analytics teams. Those organizations that invest in these capabilities position their controllers as strategic advisors who are able to leverage AI effectively while maintaining accountability and transparency.

3.3.3.1 Future Outlook

Statement	Mean Likert	Std Dev
F1: Controller role more strategic	3.6	0.9
F2: Net impact is opportunity	3.5	1.0

Estimated share of tasks automated by 2028

Range	Frequency
0–10%	1
11–25%	3
26–40%	2
41–60%	2
61%+	0

 

3.3.4 Opportunities for Value Addition

The interviews highlighted several ways in which the application of AI can enhance controllers’ role strategic value:

• Facilitating strategic thinking: The participants identified that AI cuts out time- and labor-consuming, mundane tasks such as data inputting, reconciliations, and report generation, thereby allowing controllers to focus on more abstract analysis and decision-making.
  This facilitates finance professionals to create deeper analysis of organizational performance, profitability, and resource optimization, effectively shifting their function from operational management to advisory strategy (Davenport & Ronanki, 2018, p. 110).
• Sophisticated analytical abilities: Participants commented that AI facilitates such functions as fraud detection, scenario planning, and sophisticated financial modeling. Predictive and prescriptive analytics enable controllers to predict risks, analyze different strategies, and simulate business outcomes in a way that would be cumbersome without AI support.
  The functions enhance not just the accuracy but also the speed of strategic counsel (Appelbaum et al., 2017, p. 34).
• Hybrid career profiles: Interviewees sketched out the future of controlling as hybrid in nature, with a mix of traditional finance expertise, data literacy, and AI interpretation skills.
  Controllers are increasingly needed to fulfill the role of AI output interpreters, translating advanced algorithmic data into actionable business strategies. The hybrid career path positions itself with repeated learning and cross-functional skill sets (Keller, 2021, p. 90).

Overall, the research suggests that AI holds vast value-added opportunities through facilitating controllers to act as strategic advisors, ethical stewards, and translators of advanced analytics. Organizations putting money into hybrid skill building and strategic engagement with AI are likely to maximize operational effectiveness and strategic leverage.

• 3.4.1 Monitoring Metrics

v    Metric	Description	Frequency
Bot success rate	% of invoices processed without exceptions	Daily
Forecast accuracy (RMSE)	Deviation between predicted & actual values	Monthly
Data anomalies flagged	Number of exceptions in reconciliations	Weekly
System downtime	RPA/AI tool operational hours	Monthly

        3.3.4.2 RPA Workflow – Invoice Processing

Workflow:

Start

  ↓

Retrieve invoices from ERP

  ↓

Validate invoice data

  ↓

Check for duplicates

   ↓

Apply business rules

  ↓

Post entries to ERP

  ↓

Send confirmation email

  ↓

End

• Exceptions are flagged for manual review.

3.3.4.3 AI Forecasting Model

Inputs:

• Historical revenue
• Market trends
• Budget targets

Algorithm: Time series forecasting (ARIMA or LSTM)

Output: Next quarter revenue & cost forecasts

LSTM Architecture:

Input Layer → LSTM Layer (64 units) → Dense Layer → Forecast Output

3.3.4.4 Sample Python Snippet

import pandas as pd

import matplotlib.pyplot as plt

# Example DataFrame simulation (replace with your real Excel read)

invoices = pd.read_excel(‘invoices.xlsx’)

valid_count = 0

invalid_count = 0

for index, invoice in invoices.iterrows():

    if validate(invoice):

        valid_count += 1

    else:

        invalid_count += 1

# Plotting the chart

labels = [‘Valid Invoices’, ‘Invalid Invoices’]

counts = [valid_count, invalid_count]

plt.bar(labels, counts, color=[‘green’, ‘red’])

plt.title(‘Invoice Processing Summary’)

plt.ylabel(‘Number of Invoices’)

plt.show()

3.4 Integrated Analysis

3.4.1 Diverging Insights

The conjoint analysis indicates areas of convergence between the primary and secondary findings that validate key patterns in AI adoption in financial controlling:

• The AI contribution to decision-making and efficiency: All the interviews and the literature are pointing towards AI as the primary force behind operational efficiency. Robotic processing takes away routine tasks, releasing time spent on transactional work, while predictive and prescriptive analytics help with more strategic decision-making.
  This intersection underscores the reality that AI is not only a technical enhancement but also an enabler for controllers to operate at higher value (Aguirre & Rodriguez, 2017, p. 125).
• Governance and ethics: The two sources stress that the absence of governance and ethics can yield unexpected results, such as biased outputs, compliance violations, or damaged stakeholder trust.
  It requires frameworks of governance, human-in-the-loop controls, and transparent processes in order to mitigate risks and ensure responsible AI uptake (Barredo Arrieta et al., 2020, p. 67).
• Shifts in controllers’ roles: Both primary and secondary data offer proof of the change in the controller role.
  Transactional activities are computerized to a large extent, and controllers take on advisory roles, make strategic recommendations, and take part in organizational decision-making in a more active and moral manner (Weber, 2019, p. 102).

3.4.2 Diverging Insights

There exist differences that emerge in spite of convergence:

• Job security issues: Participants expressed greater anxiety regarding potential job loss than what has been documented in literature.
  While scholars in research target efficiency in automation, professionals are worried about actual threats to junior finance roles, emphasizing human aspect in AI implementation (Frey & Osborne, 2017, pp. 260–262).
• Governance and ethics imperative: Respondents confirmed that ethics and governance are short-term issues in practice, whereas literature sometimes addresses these issues rather theoretically.
  This means that practical AI adoption requires policy worth implementing, scripted training modules, and constant monitoring to manage ethical risks effectively (Financial Times, 2025, p. 5).

3.4.3 Emergent Themes

Three significant themes emerged from the combined analysis:

1. Human-in-the-loop: The resources are in consensus that AI needs to be complementary to human judgment and not a replacement.
   Controllers must interpret outputs, affirm AI-based decision-making, and hold themselves accountable to maintain reliability and compliance.
2. Hybrid skills development: Effective AI deployment hinges on developing hybrid skills with a mix of financial expertise, analytical sophistication, and moral awareness. These skill sets enable controllers to handle sophisticated AI outputs, aid strategic planning, and oversee risk.

Cultural adaptation: Organizational readiness and culture dictate the success of implementing AI.
Effective communication, trust-building, and change management techniques are essential to mitigate resistance and establish AI as a strategic tool.

So, the study indicates that artificial intelligence and automation in financial control present both challenges and opportunities. Automation increases efficiency, accuracy, and decision-making quality, enabling controllers to focus on strategic advisory functions. But it also presents risks around workforce displacement, governance, ethical responsibility, and compliance requirements.

Controllers will need more hybrid skills, which integrate finance, analytics, and ethical judgment, to be effective in AI-enabled systems. Organizations have to invest in formal training, governance structures, and human-in-the-loop processes to promote responsible adoption of AI. Cultural preparedness and change management are also crucial to maintain adoption and utilize AI strategically.

Generally, the implementation of AI in financial controlling must be viewed from a socio-technical change angle. Systems must be transparent, explainable, and ethically governed to maximize benefits while avoiding risks. These findings are a precursor to the following chapter, which treats theoretical implications, managerial recommendations, and directions for future research.

4- Artificial Intelligence in Financial Controlling:
A Socio‑Technical and Ethical Discussion

4.1 Introduction

This chapter interprets the findings in light of theoretical frameworks and scholarly literature. It examines how AI and automation impact financial controlling, considering both opportunities and threats. The discussion draws on the Technology Acceptance Model (TAM), Socio-Technical Systems (STS) theory, and Disruptive Innovation theory to provide a structured understanding of adoption, organizational dynamics, and professional evolution.

4.2 AI Adoption and the Technology Acceptance Model (TAM)

TAM posits that the perceived usefulness and ease of use of technology determine user acceptance (Davis, 1989, p. 320).

• Perceived Usefulness: Both secondary data and survey respondents indicate that AI enhances efficiency, forecasting accuracy, and fraud detection (Kumar & Renuka, 2025, p. 114).
  Participants confirmed that AI reduces repetitive tasks, enabling controllers to focus on strategic analysis (Amazon, 2024, p. 34).
• Perceived Ease of Use: Concerns include model complexity, lack of explainability, and required training (Maple et al., 2023, p. 48).
  Survey participants emphasized data literacy and interpretative skills as crucial for effective AI utilization (World Economic Forum, 2023, p. 12).

Implication: TAM explains why AI adoption is uneven—perceived benefits drive acceptance, while usability barriers limit full adoption.

4.3 AI as a Socio-Technical System (STS)

STS theory emphasizes the interplay between technology, people, and organizational structures (Saunders et al., 2019, p. 87).

• AI in controlling is a socio-technical transformation, not merely a technical upgrade. Its impact depends on human oversight, governance frameworks, and cultural adaptation (Barredo Arrieta et al., 2020, p. 67).
• Survey respondents confirmed that human-in-the-loop mechanisms are essential to maintain accountability and ethical integrity ( World Economic Forum, 2023, p. 12).
• Organizational readiness, including data infrastructure and cross-functional collaboration, shapes adoption success (Deloitte, 2024, p. 18; PwC, 2022, p. 13; Amazon, 2024, p. 34).

Implication: Technology alone cannot generate value; effectiveness depends on aligning AI capabilities with human skills, roles, and governance.

4.4 Disruptive Innovation in Financial Controlling

Disruptive Innovation theory frames AI as a technology capable of reshaping industry norms and professional roles (Christensen, 1997, p. 45).

• Routine tasks previously performed by controllers are increasingly automated, creating potential displacement risks (Frey & Osborne, 2017, pp. 260–262).
• AI enables new value propositions, including predictive insights, prescriptive recommendations, and fraud detection, positioning controllers as strategic partners (Kumar & Renuka, 2025, p. 114).
• Survey respondents expressed both excitement and apprehension: while AI creates advisory opportunities, fear of job redundancy remains (Schäffer & Weber, 2021, p. 91).

Implication: AI represents a disruptive shift: it challenges traditional practices, accelerates hybrid skill requirements, and requires proactive workforce management strategies.

4.5 Governance, Ethics, and Accountability

Across all frameworks, governance and ethics are central to sustainable AI adoption:

• Transparency and Explainability: Opaque AI models reduce trust and auditability (Barredo Arrieta et al., 2020, p. 67).
  Interpretable outputs are essential for decision-making.
• Bias and Fairness: AI systems can introduce bias, requiring systematic evaluation and ethical oversight (Maple et al., 2023, p. 48).
• Regulatory Compliance: Aligning AI deployment with frameworks such as the EU AI Act ensures responsible innovation (European Commission, 2023, p. 67).

Implication: Governance is core to adoption success and professional legitimacy.

4.6 Evolution of the Controller Role

Findings indicate a shift from transactional to strategic roles:

• Controllers are transitioning from “scorekeepers” to interpreters of AI outputs, advisors, and ethical gatekeepers (Schäffer & Weber, 2021, p. 91).
• Hybrid skillsets—financial expertise, analytics, and ethical judgment—are critical for added value (Maple et al., 2023, p. 48).
• Continuous learning is required to keep pace with evolving AI capabilities, emphasizing the lifelong learning imperative (European Commission, 2021, p. 22).

Implication: Professional roles are being redefined, not eliminated, with AI enabling higher-value activities.

4.7 Secondary and Primary Data Combination

Merging secondary sources with survey insights provides a richer understanding:

• Secondary data validates broader trends in AI adoption, governance, and role evolution (Kumar & Renuka, 2025, p. 114).
• Survey participants offer nuanced perspectives, highlighting immediate ethical concerns, perceived job risks, and organizational barriers (World Economic Forum, 2023, p. 12).
• Triangulation confirms that AI adoption is context-dependent, shaped by technology, human skills, organizational culture, and regulatory environment.

4.8  Main Discussion Points

1. AI is both a threat and an opportunity: Automation reduces routine work but can displace roles; it also enhances strategic decision-making.
2. Human oversight remains indispensable: Ethical reasoning, contextual interpretation, and complex decision-making cannot be fully automated.
3. Governance and ethics are central: Trust, accountability, and compliance underpin sustainable adoption.
4. Professional evolution is inevitable: Controllers must embrace hybrid skills and advisory responsibilities.
5. Adoption is incremental and context-dependent: Both technology readiness and cultural adaptation are prerequisites for success.

4.9 Conclusion

This discussion demonstrates that AI in financial controlling cannot be assessed purely as a technological issue.

It represents a socio-technical transformation with disruptive potential. Adoption success depends on human judgment, governance, ethics, and skill development.
By integrating TAM, STS, and Disruptive Innovation theory with empirical findings, the study clarifies that the controller of the future will be a strategic, ethical, and technologically adept professional, supported by AI, not replaced by it.

The next chapter will draw conclusions and actionable recommendations, synthesizing theory, literature, and empirical insights to guide practitioners, policymakers, and researchers.

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