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Trusted AI in the financial close does not come from a more confident model. It comes from limiting AI to well-defined tasks, giving it reliable and appropriately protected data, retaining human authority over material accounting judgments, and monitoring the controls as systems and models change. Start with one measurable close problem, then expand only when the evidence and control environment support it.
Start with a close problem, not an AI tool
Choose a recurring task with a clear owner, baseline, and measurable outcome. Examples include reducing reconciliation time, resolving exceptions sooner, or shortening a specific part of the close cycle. Define how success will be measured and what errors or delays would make the use case unacceptable.
Separate productivity assistance from decision support. An AI tool that drafts commentary or flags unusual transactions is not doing the same work as one that proposes journal entries or determines an accounting treatment. The closer a use case gets to a consequential accounting conclusion, the stronger its review, approval, and evidence requirements need to be.
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ACCA and CA ANZ’s 2026 report, based on a global survey of 1,600 finance professionals, identifies data quality, analytical capability, and integrating multiple data sources as challenges to effective analytics and AI. It also distinguishes using AI to improve productivity from using it to deliver broader value, while noting concerns about reliability, trust, and explainability. Those findings support a practical sequence: define the business problem and expected value first, then check whether the data and workflow can support it. Read ACCA and CA ANZ’s findings.
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Build the control environment before expanding access
Map where AI and automation already touch financial reporting, not just the tools the finance team selected. Embedded features in vendor systems and automated steps in connected workflows can affect the same data or controls. A useful inventory records the process, tool, data involved, purpose, owner, affected accounting assertions or reporting steps, and whether a person reviews the output.
Assign accountability and map controls
Management should own the AI strategy and control environment, with appropriate board and audit committee oversight. For each use case, name an accountable business owner and identify who approves access, validates outputs, handles exceptions, and authorizes changes. Include third-party tools in vendor oversight, privacy, and security reviews.
KPMG’s 2024 financial reporting implementation guide applies internal-control considerations to automation and AI. COSO’s GenAI resource, described by AICPA & CIMA, offers a practical way to translate the Internal Control—Integrated Framework into use-case inventories, risk assessments, control mapping, and monitoring of model changes. These are implementation resources, not a universal control design or a substitute for applicable law and company policy. KPMG’s financial reporting guide and AICPA & CIMA’s overview of COSO’s GenAI resource provide further detail.
Protect data and access
Determine what information the tool can access, where it is processed, who can retrieve outputs, and whether submitted data may be retained or used beyond the close task. Apply access controls suited to the sensitivity of financial and personal data. Assess cybersecurity, privacy, and third-party risks before production use, and ensure the data sources feeding the process are complete, reconciled, and fit for purpose.
Rank #3
Make outputs and changes reviewable
Define how reviewers will verify the output against source records and policy, what evidence is retained, and how the team can reconstruct what happened. Record relevant inputs, output, reviewer action, approval, and exceptions in a way that supports the organization’s documentation and audit needs. Reassess the controls when prompts, models, connected data, permissions, or vendor features change.
Set firm boundaries for human judgment
Decide in advance what the system may do, what it may recommend, and what requires human approval. AI can be assigned bounded work within policy, such as identifying anomalies or preparing a draft for review; it should not silently acquire authority over material accounting judgments. Define escalation triggers, override rights, approval thresholds, and a fallback procedure if the tool is unavailable or produces unreliable results.
In a January 30, 2025 webcast poll of more than 3,300 finance and accounting professionals, Deloitte found that 21.3% cited trust in agentic AI as a barrier to use. On autonomy, 59.7% said they trusted agents to decide within a defined framework while people retained judgment calls; 2.7% trusted agents to make decisions including judgment calls, and 19.9% did not trust them to make decisions. Answer rates varied by question, and the poll is not a population estimate or a rule for every finance team. Deloitte’s poll results illustrate why decision boundaries should be explicit rather than assumed.
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Court Watson, Controllership & Treasury Transformation leader, Deloitte & Touche LLP
Compare implementation approaches against the same criteria
There is no source-based ranking of individual products. Compare a proposed approach—whether built into an existing system, added as a specialist tool, or developed internally—against the workflow and control needs that matter to your organization.
- Integration: Can it work with the ERP, close platform, and data sources already in use without creating fragile handoffs?
- Evidence and auditability: Can the team trace inputs, outputs, reviews, approvals, and exceptions?
- Human review: Can permissions and workflow steps enforce the agreed limits on autonomous action?
- Privacy, security, and third-party risk: Are data handling, access, and vendor oversight acceptable for the information involved?
- Validation and explainability: Can finance staff test outputs against records and understand enough to challenge errors?
- Ownership and skills: Are there named process owners and staff able to operate, review, and monitor the system?
- Resilience: Is there a workable manual or alternate process if the AI service or its data connection fails?
- Measured value: Does the approach improve the chosen measure, such as reconciliation time, exception resolution, or close-cycle time, without weakening control?
KPMG’s 2024 intelligent-close paper describes potential applications such as anomaly detection, integrated processes, and generative-AI assistance with inconsistency detection, reconciliation, and initial financial commentary. It presents a conceptual framework—not independent proof that these applications will produce the same results at every organization. See KPMG’s intelligent-close paper.
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- Document the baseline: Record the current process, owner, time or other chosen measure, exception rate, review steps, and known data limitations.
- Define the permitted task: State what the AI may access and produce, what it may not decide or execute, who must review it, and which conditions require escalation.
- Test with controlled cases: Check representative normal and exception cases against source records and established accounting policy. Include failure scenarios such as incomplete data or unavailable connections.
- Require human sign-off: Keep approval with the designated finance owner for consequential outputs, and retain the evidence needed to explain acceptance, correction, or rejection.
- Review results and control performance: Compare the pilot with its baseline, including exceptions and review burden. Expand only if the measured benefit is meaningful and the controls work as designed.
- Monitor after launch: Assign responsibility for ongoing output checks, access reviews, incident handling, and reassessment after material model, data, or workflow changes.
Plan for integration, talent, and cost constraints
Implementation obstacles are not limited to model performance. The Bank of Canada’s 2026 Financial System Survey reports that respondents planning to expand AI use cited difficulty integrating AI into existing infrastructure and workflows (58%), talent constraints (56%), data security and privacy concerns (33%), and high implementation and use costs (31%). These figures describe Canadian financial-system survey respondents, not corporate accounting departments generally. The survey also identifies data quality and bias, cybersecurity and privacy, and model risk or lack of explainability among leading operational risks. Read the Bank of Canada survey.
For finance teams, the practical implication is to include integration effort, training, data readiness, privacy review, and ongoing operating costs in the business case—not to treat them as post-pilot details. The Financial Stability Board’s June 10, 2026 publication proposes 12 sound practices for organization-wide governance and AI lifecycle management. It is a consultation report, not final binding regulation, and is directed to financial-sector adoption rather than serving as a universal corporate accounting rule. Read the FSB consultation report.
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