State of AI Trust 2026: Why Responsible AI Governance Is a Must-Have for UAE Businesses (McKinsey Survey Insights)
The financial function is changing. Not in increments. In structure.
For years, transformation in finance followed a predictable path – automation, digitization, incremental efficiency gains. Systems became faster. Processes became cleaner. But decision-making? That still sat firmly with people.
That boundary is starting to move.
Across industries, the shift is now visible. We are moving from generative AI “assistants” toward something more consequential-what can only be described as agentic accounting. Systems that don’t just assist workflows, but execute them. End-to-end. Often without interruption.
A reconciliation is no longer something a team completes at month-end. It runs continuously.
Fraud detection is no longer reactive. It sits embedded in live transaction streams.
Reporting? It updates as the business moves, not after it stops.
At first glance, this looks like progress. And it is. But it also introduces a new tension.
Adoption has accelerated rapidly. A large share of individuals and organizations now use AI in some form. Yet confidence has not kept pace. Trust lags behind usage, by a noticeable margin.
This is the paradox. We are relying more on systems we do not fully trust. For CFOs and audit committees, the implication is immediate. If decisions – financial, operational, strategic – are increasingly driven by autonomous systems, then the question is no longer about capability.
It is about confidence in those decisions. And this is where the role of audit begins to shift. Audit can no longer sit at the end of the process, validating outputs after the fact. It has to move upstream. Into the systems themselves. Into how decisions are generated, not just how they are recorded.
In this environment, trust stops being abstract. It becomes something that must be designed, tested, and demonstrated. For firms operating in this space, the expectation is clear. Not just to verify numbers. But to safeguard the integrity of the systems producing them.
Global Insights: The McKinsey 2026 AI Trust Maturity Survey
Organizations are following a pattern worldwide. There is movement. Real movement. Maturity levels around Responsible AI are improving, and investment is rising alongside it. This looks quite encouraging. The deeper analysis shows something different, though.
Capabilities are advancing quickly. Governance structures, less so.
In many organizations, AI strategy exists. Policies exist. Committees exist. Yet control is lacking in complex environments. Responsibility is distributed. Sometimes intentionally, sometimes by default.
The result is fragmentation. This is particularly visible in environments where AI is embedded deeply into operations. Systems interact. Decisions cascade. And oversight struggles to keep up.
The financial sector, to its credit, remains ahead of the curve. Regulatory pressure has forced earlier adoption of governance frameworks. But even here, the strain is visible. Internal oversight mechanisms are being stretched by the speed and complexity of autonomous systems.
At the same time, a second pattern is emerging – one that is harder to ignore.
Organizations that are investing meaningfully in governance and Responsible AI are seeing tangible returns. Not just in reduced risk, but in financial performance. Stronger margins. Better decision quality. More consistent outcomes. This challenges an old assumption.
Governance is not slowing organizations down. In many cases, it is what allows them to move faster with confidence. Which reframes the conversation entirely. The question is no longer whether to invest in governance. It is whether organizations can afford not to.
The Rise of Agentic Accounting and Continuous Auditing
The impact of this shift is perhaps most visible within the audit and finance function itself.
Historically, auditing has been built around limitation. Large volumes of data made it impractical to test everything, so sampling became the standard approach. Test a subset. Extrapolate. Form a conclusion.
That constraint is disappearing.
With agentic systems, full-population analysis is no longer theoretical. It is operational. Every transaction can be evaluated. Every anomaly identified. And importantly, this can happen continuously, not just during audit cycles.
This changes the nature of assurance.
Audit is now a real-time monitoring. Issues can now be seen before they emerge. In some cases they can be prevented very efficiently. The ripple effects are significant.
Close cycles, once measured in weeks, are shrinking. Days are becoming the new benchmark. In some environments, near real-time closing is starting to feel achievable. Reconciliations are automated. Exceptions are identified and resolved earlier. The process becomes less about catching up and more about staying aligned.
At the same time, the role of finance leadership is evolving. With better visibility into cash flows, liquidity, and operational drivers, CFOs are moving beyond reporting. They are shaping outcomes. Using predictive insights to guide decisions, rather than relying solely on historical performance.
But this shift introduces a dependency that cannot be ignored. If systems are continuously generating outputs, then those outputs must be consistently reliable. And reliability, in this context, is not just about accuracy. It is about trustworthiness. Because without trust, even the most advanced system becomes difficult to rely on.
Navigating Global Standards: IAASB, PCAOB, and IFRS in 2026
Regulatory frameworks are now showing a clear shift. Tech impact is now leaving its mark on audit quality standards.
Under frameworks such as ISQM 1 and ISA 220 (Revised), the focus is now identifying and managing risks. This applies not only to the tools used by auditors, but increasingly to the systems being audited themselves.
That distinction is important. And often overlooked. Historically, audits have concentrated on outputs, transactions, balances, disclosures. That worked when systems were largely deterministic. Agentic environments change that dynamic. Outputs are important. But the focus has changed.
Now we focus on how it was produced. Which brings the conversation closer to system integrity. At the same time, financial reporting standards are facing a different kind of pressure.
IFRS 18 is reshaping how performance is presented. It improves clarity, yes but it also raises expectations around consistency and transparency. Alongside this, IAS 38 is coming back into focus as organizations attempt to recognize and value internally developed assets, particularly AI models and datasets. This is where things stop being straightforward. Unlike traditional assets, these are not fixed. They evolve over time. Models improve. Datasets expand. In some cases, their value increases precisely because they are changing.
That creates tension. What actually constitutes cost in such an environment? How do you measure future economic benefit when the asset itself is dynamic? And when it comes to impairment – what exactly are you impairing?
No consistent answers. Lack of consistency is a problem itself.
Layered on top of this is the growing intersection between AI governance and sustainability reporting. ESG disclosures are expanding, and expectations are shifting quickly. Non-financial information is no longer treated as supporting context – it is expected to meet the same level of rigor as financial data.
Which, in reality, changes the scope of assurance. Audit is no longer confined to the ledger.
It extends into systems. Into processes. Into decisions. And with that expansion comes a new expectation. Understanding financial standards is still essential. That hasn’t changed.
But on its own, it is no longer sufficient. There is now an equally important requirement – to understand the technologies that are shaping financial outcomes in the first place.
ADEPTS : Global Expertise for UAE’s Digital Economy
As financial systems evolve, expectations from advisory and audit firms are shifting just as quickly.
Compliance, on its own, is no longer enough. What the market increasingly demands is something more integrated – firms that can operate across accounting, technology, and regulation without treating them as separate domains. This becomes particularly relevant in the UAE.
Operating within jurisdictions such as DIFC requires more than technical knowledge of standards. It requires interpretation. Alignment. The ability to take global frameworks and apply them within a local regulatory environment that is itself evolving.
ADEPTS operates within this space.
The focus is not limited to financial reporting or audit execution. It extends into ensuring that AI-driven financial systems are understood, controlled, and defensible – not just operational.
To do that, a structured approach is necessary. Not a checklist. A framework that reflects how these systems actually function.
The 7-Step AI Audit Framework
It starts with something deceptively simple: visibility.
In many organizations, AI adoption happens organically. Different teams deploy tools, models are updated, datasets evolve and over time, no single view exists of what is actually in use. This creates blind spots. And blind spots, in audit terms, translate directly into risk.
1. Algorithm & Model Inventory
Establishing a centralized inventory of models and algorithms becomes the first step. Not just a list, but a structured record, capturing datasets, training approaches, version histories, and deployment contexts. Without this, traceability is difficult. With it, assurance becomes possible.
2. Data Governance & Lineage
From there, attention shifts to data governance. Data in AI systems is not static. It moves through ingestion, transformation, training, and inference. At each stage, risks emerge. Accuracy can degrade. Bias can be introduced. Regulatory obligations can be triggered.
Tracing this movement, understanding where data originates, how it is processed, and how it influences decisions is essential. Particularly in environments governed by data protection regulations, where lineage is not optional but expected.
3. IP Valuation & Financial Recognition (IAS 38)
The next layer addresses valuation and recognition. Organizations are investing heavily in building proprietary models and datasets. Yet translating that investment into financial reporting under IAS 38 remains complex. It requires more than accounting judgment. It requires technical understanding of development processes, cost attribution, and future economic benefit.
4. Tax Alignment & Compliance
Closely linked to this is tax alignment.
AI-driven operations do not sit outside tax frameworks. They interact with them – through transfer pricing, cost allocations, and the treatment of intangibles. When systems are designed without considering these implications, misalignment tends to surface later. Often during audit. Sometimes during assessment.
Embedding tax considerations early reduces that risk.
5. Technology Assurance (ITGC & Cloud Controls)
The framework then moves into technology assurance.
As financial processes become embedded within cloud environments and DevOps pipelines, the boundary between IT risk and financial risk begins to blur. A configuration issue is no longer just technical – it can affect financial reporting, data integrity, and control effectiveness.
Testing IT General Controls, reviewing cloud configurations, and assessing system resilience becomes part of the assurance process, not separate from it.
6. Bias, Ethics & Explainability Assessment
Another dimension often underestimated is bias and ethics assessment. Agentic systems optimize based on defined objectives. But if those objectives are incomplete or misaligned, outcomes can diverge in ways that are difficult to detect. Fairness, explainability, and transparency must be evaluated deliberately.
Left unchecked, these risks do not remain theoretical. They materialize sometimes in ways that are difficult to reverse.
7. Board-Level Reporting & Strategic Insight
Finally, everything converges into board-level reporting.
At this stage, complexity needs to be translated into clarity. Leadership does not need technical depth. It needs perspective where the risks are, how they are managed, and what actions are required.
This is where the framework completes its purpose.
Not by documenting systems.
But by making them understandable and, more importantly, trustworthy.
Regional Compliance: The UAE AI Act 2026 and UDARS
Global standards set direction. Regional regulation, however, defines the operating reality. In the UAE, that reality is becoming more structured.
The introduction of the UAE AI Act in 2026 signals a clear shift toward formal oversight particularly for systems classified as high risk. These include applications in areas such as credit assessment, hiring, and financial decision-making.
For such systems, annual audits are no longer optional. They are expected.
These audits go beyond technical validation. They examine governance structures, decision accountability, and system transparency. In effect, AI systems are being brought closer to the regulatory discipline traditionally applied to financial reporting.
At the same time, the introduction of the Unified Digital Audit Reporting System (UDARS) reflects a broader transition toward digital-first compliance. Traditional reporting methods, manual submissions, and static documentation are gradually being replaced by integrated, digital audit trails. Records are expected to be structured. Accessible. Tamper-resistant.
This changes the nature of audit readiness. It is no longer something organizations prepare for at year-end. It becomes something they maintain continuously. For many, this requires a shift in mindset.
Controls must be embedded within systems, not layered on top. Documentation must be generated as processes occur, not reconstructed later. And audit trails must exist by design, not by effort.
At the same time, the UAE continues to encourage innovation. R&D incentives, including tax credits, are designed to support investment in emerging technologies such as AI. But access to these incentives depends on one thing: evidence. Not just activity, but documented, verifiable activity.
This creates a dual requirement. Organizations must innovate. And they must demonstrate that innovation in a way that withstands scrutiny.
Strategic CFO Advisory: Beyond the Ledger
The implications of these changes extend directly into the CFO’s role. Finance leadership is no longer defined solely by oversight of reporting. It increasingly involves shaping how decisions are made and how systems support those decisions.
In this environment, traditional models begin to feel limiting.
High-growth organizations, particularly in technology sectors, are turning toward more flexible structures. Fractional CFO services provide access to strategic expertise without requiring full-time appointments. This allows organizations to scale financial leadership alongside business growth, rather than ahead of it.
At the same time, the risk landscape is becoming more complex. Digital environments introduce new forms of exposure. Transactions move faster. Systems interact more deeply. And the potential for hidden anomalies or deliberate manipulation expands.
Addressing this requires more than traditional audit techniques. Forensic capabilities, enhanced by AI, are becoming increasingly relevant. These tools allow organizations to analyze patterns across large datasets, identify irregularities, and surface risks that might otherwise remain hidden.
Beyond risk, however, lies transformation. Many organizations are integrating AI into existing processes. Fewer are stepping back and redesigning their finance functions entirely. The distinction is subtle but important. An AI-assisted function improves efficiency. An AI-native function changes how decisions are made.
This involves rethinking workflows. Aligning processes with continuous data flows. Embedding controls directly within systems, rather than applying them externally. For CFOs, this represents a shift in perspective. From managing processes to orchestrating systems. And that shift is likely to define the next phase of financial leadership.
Conclusion: Trust as a Competitive Advantage
Across all of these developments, one theme continues to surface. The challenge is not capability.
The technology is advancing. Systems are becoming more powerful, more efficient, more integrated. In many cases, the tools required to transform finance already exist. What remains uncertain is something else.
Trust. Can these systems be relied upon? Can their decisions be explained? Can they withstand regulatory scrutiny? These questions are no longer theoretical. They are practical and increasingly urgent.
Organizations that address them effectively will move ahead. Not necessarily because they have better technology, but because they have greater confidence in how that technology operates. In 2026, that distinction matters.
Trust is no longer a secondary outcome of compliance. It is something that must be designed, embedded, and continuously validated. For organizations operating in AI-driven environments, this creates a clear requirement.
To work with partners who understand both sides of the equation – financial accuracy and technological governance. Because in the agentic era, leadership will not be defined by adoption alone. It will be defined by the ability to trust, explain, and defend the systems that drive decisions.
FAQs:
Agentic accounting refers to AI-driven systems that don’t just assist with accounting tasks, but autonomously execute them in real-time. Unlike traditional automation, which merely streamlines tasks like reconciliations or fraud detection, agentic systems continuously perform these functions without interruption, ensuring accurate, up-to-date financial data at all times.
A mature AI governance model, according to the McKinsey 2026 survey, is one that provides clear oversight and responsibility for AI systems, ensuring transparency, accountability, and ethical standards. It involves a unified approach, moving away from fragmented governance structures to one that integrates AI decision-making with financial strategy and compliance.
ADEPTS’ DIFC Approved Auditor status highlights our expertise and compliance with the highest standards of audit and financial reporting, especially within the UAE’s financial ecosystem. This certification offers your business credibility and assurance, ensuring your operations meet both local and global regulatory requirements, critical for international expansion.
IFRS 18 introduces stricter reporting requirements for businesses using AI, particularly around the recognition and measurement of AI-related assets and liabilities. It emphasizes transparency in how AI models and datasets are valued, tracked, and reported, impacting how these assets are reflected in financial statements from 2026 onwards.
Under the UAE AI Act 2026, non-compliance with AI-related audit requirements could result in significant penalties, including fines and sanctions. These penalties are designed to ensure that AI systems in critical sectors like finance adhere to established governance and transparency standards.
Yes, ADEPTS can assist you in claiming the new 50% UAE R&D Tax Credit. We provide comprehensive support, from verifying your eligibility to preparing and submitting the necessary documentation, ensuring your claim is optimized and compliant with the latest regulations.
A data governance audit in accounting focuses on assessing how financial data is collected, stored, processed, and secured. It ensures that systems are compliant with regulatory requirements, such as GDPR and the UAE’s AI Act 2026, and that data integrity is maintained throughout its lifecycle, crucial for reliable financial reporting.
UDARS is a digital-first approach to audit reporting, replacing traditional manual submissions with integrated, real-time audit trails. It ensures that all data and compliance records are structured, accessible, and tamper-resistant, making audit processes more efficient and transparent.
Full population anomaly scanning allows auditors to analyze every transaction in real-time, rather than relying on traditional sampling methods. This enhances accuracy, enabling auditors to identify and address discrepancies or anomalies proactively, resulting in more thorough and timely audits.
Algorithmic explainability ensures that CFOs can understand how AI systems make decisions, which is critical for maintaining transparency and trust in financial operations. It allows for better decision-making, reduces the risk of bias, and enhances compliance with regulatory frameworks, such as the UAE AI Act 2026.
Yes, ADEPTS offers “CFO-as-a-Service,” providing strategic financial leadership without the need for a full-time CFO. This service is especially beneficial for tech startups, where scalable financial guidance is critical to growth, managing complex regulatory environments, and optimizing financial performance.
Forensic auditing goes beyond standard AI audits by investigating potential fraud, financial misconduct, or anomalies that may not be immediately visible in routine audits. It uses advanced data analytics, often incorporating AI tools, to uncover hidden risks and ensure complete financial transparency.
“Human-on-the-Loop” oversight refers to maintaining human judgment in AI-driven processes. While AI can execute tasks autonomously, human oversight ensures that decisions align with ethical standards, regulatory compliance, and strategic goals, particularly in complex or high-risk areas.
Valuing AI datasets under IAS 38 involves assessing their potential future economic benefits, which can be challenging given the dynamic nature of these assets. Organizations must consider the development costs, usage rights, and market potential of the data, ensuring accurate financial recognition and reporting.
Audit readiness refers to maintaining continuous compliance and having systems in place to ensure audits can be conducted at any time, not just at year-end. As regulations evolve, particularly with the introduction of UDARS and the UAE AI Act 2026, audit readiness becomes essential for organizations relying on AI-driven financial systems to ensure data integrity and avoid penalties.
References
- McKinsey & Company. State of AI Trust in 2026: Shifting to the Agentic Era. March 25, 2026.
https://www.mckinsey.com/capabilities/tech-and-ai/our-insights/tech-forward/state-of-ai-trust-in-2026-shifting-to-the-agentic-era
- McKinsey & Company. The State of Organizations 2026: Three Tectonic Forces Reshaping Organizations. February 19, 2026.
https://www.mckinsey.com/capabilities/people-and-organizational-performance/our-insights/the-state-of-organizations
- McKinsey & Company. The State of Organizations 2026 (Full Report PDF). 2026.
https://www.mckinsey.com/~/media/mckinsey/business%20functions/people%20and%20organizational%20performance/our%20insights/the%20state%20of%20organizations/2026/the-state-of-organizations-2026.pdf
- PwC. 2026 AI Business Predictions: Unlocking Value Through Responsible AI. 2026.
https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html
- KPMG. The Future of AI Governance: Trusted AI Framework and UAE AI Charter. July 2025.
https://assets.kpmg.com/content/dam/kpmgsites/ae/pdf/the-future-of-ai-governance.pdf.coredownload.inline.pdf
- International AI Safety Report 2026. Assessing Risks and Capabilities of General-Purpose AI Systems. February 3, 2026.
https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026
- Institute of Internal Auditors (IIA). Agentic AI and the Future of Internal Audit (CAE Bulletin). January 20, 2026.
https://www.theiia.org/en/content/newsletter/cae-bulletin/cae-bulletin-issue-january-20-2026/
- International Association of Privacy Professionals (IAPP). AI Governance Vendor Report 2026. 2026.
https://assets.contentstack.io/v3/assets/bltd4dd5b2d705252bc/blt386189207a33dc5d/ai_governance_vendor_report_2026.pdf
- Lupo, Guy, Bao Quoc Vo, and Natania Locke. Trustworthy AI Posture: Continuous Assurance for Agentic Systems. 2026.
https://arxiv.org/abs/2603.03340 - Batool, Amna, Didar Zowghi, and Muneera Bano. Responsible AI Governance: A Systematic Literature Review. 2023.
https://arxiv.org/abs/2401.10896
