Before You Scale: The AI Audit Framework Every Company Should Use

With over 30 years advising companies on accounting, financial reporting and complex business challenges, I’ve seen technology transform operations. As president of Blythe Global Advisors (BGA), our controls evaluation practice helps companies align innovation with accountability. Below is a practical framework for internal AI evaluation grounded in lessons from my decades of guiding clients through strategic and technical transformations.

Why Internal AI Audits Are Now A Business Imperative

AI tools often enter organizations through formal projects, software updates or employee experiments. However they arrive, these tools affect real outcomes and must be evaluated with the same rigor as any other strategic investment.

Since the release of ISO/IEC 42001 in December 2023, the first global standard for AI management systems, we have seen an uptick in urgency around building structured oversight. However, despite growing awareness, many companies have yet to adopt the standard, leaving them vulnerable to regulatory and operational risks.

At BGA, I see a recurring issue: Firms often implement AI tools without defining clear rules and lines of responsibility. This lack of structure can lead to inconsistent outcomes, such as unreliable forecasts that disrupt budgets or valuations. Our approach is to help companies view AI as a strategic investment—one that requires careful evaluation to make sure it supports business goals and stays within acceptable risk boundaries.

Implementing a structured audit process helps provide clarity and control, enabling companies to scale AI responsibly.  

Legal And Regulatory Drivers: Why Oversight Matters

Recent legal developments highlight the growing risks associated with AI, particularly in employment contexts. The ongoing class action lawsuit against Workday illustrates how courts are willing to treat companies—and sometimes their AI vendors—as accountable for discriminatory outcomes produced by AI hiring systems. In parallel, new state laws, such as those enacted in California and Colorado, are raising the bar for transparency and auditability in AI-driven decisions.

Workday’s attainment of ISO/IEC 42001 certification for its AI management system further underscores the importance of proactive governance. While the certification was not specifically a result of litigation, pursuing rigorous, third-party validation of AI practices is increasingly viewed as an industry best practice. Across the board, regulatory scrutiny is intensifying, with both customers and lawmakers demanding auditable and explainable AI systems.

At BGA, our controls evaluation group assists clients in implementing robust AI audit trails to ensure compliance and transparency. Sometimes leaders are skeptical of governance. By educating them on how oversight closes gaps before they become risks, we are able to shift their perspective, turning initial reluctance into real support for proactive, effective controls.

Five Core Areas Of Internal AI Evaluation

Industry best practices highlight five core pillars for responsible AI scaling:

• Ownership And Inventory: Track all AI systems and assign ownership to prevent unchecked tools.
• Business Case And Financial Impact: Document each tool’s purpose and measure ROI against benchmarks.
• Operational Alignment: Test tools in real-world workflows with human oversight.
• Vendor And Tool Risk: Scrutinize vendor contracts for transparency and liability clauses.

• Visibility And Shadow AI: Monitor unsanctioned AI use and provide approved alternatives.

Among these, ownership is most critical. Controls evaluation at BGA begins with a mapping process that ties every AI system to its responsible owner, aligning solutions with strategic goals. 

Putting The Framework Into Practice: Seven Tactical Actions

Once these five areas are defined, effective operationalized oversight of AI systems begins with seven practical actions grounded in leading industry guidance:

1. Inventory all AI systems, prioritizing those impacting finances.
2. Monitor outcomes using internal data to catch biases.
3. Validate vendor and team accountability with audit trails.
4. Keep audit-ready records, including system logs.
5. Review contracts for data use and liability terms.
6. Establish a cross-functional governance group to review tasks.
7. Reassess tools periodically, especially after major changes in data, scale or functionality.

Cross-functional teams are necessary but not always aligned, and this can be a challenge. We have found that by facilitating workshops to bridge gaps between IT, finance and compliance teams, we can create frameworks within the organization that reduce friction and foster trust.

ISO/IEC 42001: A Blueprint For AI Governance

ISO/IEC 42001 provides a robust framework for AI management, emphasizing system inventories, role clarity, lifecycle controls and regulatory compliance. Adoption is growing. According to A-LIGN’s 2025 Compliance Benchmark survey of over 1,000 professionals, 76% of organizations plan to pursue AI-specific certification within 24 months.

The surge is driven by more than regulation. Customers, partners and investors increasingly expect evidence of responsible AI practices. Many companies are now being asked to demonstrate governance readiness through formal certification.

In my experience working with audit committees, discussions around ISO/IEC 42001 are becoming a common agenda item. Boards are increasingly focused on ensuring that their organizations are aligned with emerging standards, not only to meet compliance needs but also to demonstrate robust governance and establish themselves as responsible leaders in AI adoption.

Evaluate First, Scale Second

AI is now fundamental to business operations, but scaling these technologies without appropriate oversight introduces avoidable risks. By applying a disciplined, structured evaluation framework, companies can drive sustainable value while minimizing exposure.

The key point we make to our clients who are implementing AI is this: Investing in oversight is not simply due diligence—it is essential to successful and responsible innovation.