The AI readiness gap: why audit and accounting leaders aren’t seeing the value they expect

Many firms have already invested in AI. Fewer are seeing the value they hoped for.  

The problem lives in the gap between adopting AI and preparing people to work effectively with it.  

Training sessions get completed, licences get assigned and new tools are rolled out. Yet adoption stalls, staff revert to old workflows and leaders struggle to connect investment to measurable impact.  

This is the AI readiness gap and it doesn’t show up the same way across roles. A junior auditor learning to prompt effectively faces distinct challenges from a senior manager reviewing AI-assisted work, or a partner leading a firm through the shift. One-size-fits-all training can’t resolve a lag that changes shape by function.

Why training on tools alone is not enough

Many firms treat AI readiness as a training problem.

It isn’t.

Only 28% of senior audit leaders describe their firms as very or extremely prepared to reskill their staff for effective AI use, according to a global IDC study of over 1,000 audit leaders.

Readiness requires capability-building that touches every level of the organization. A one-day workshop may teach someone how to use a tool, but it won’t teach them how to evaluate its outputs and challenge agents when something looks off.

Addressing this gap requires understanding what is creating it:

1. The pace of AI outstrips organizational change. AI evolves faster than firms can absorb it, let alone plan for and invest in it.  

2. Most firms are underinvesting in change management. Without the right skills in place, AI bets can create frustration faster than value.

3. Faster doesn’t always mean better. Speed of tooling without the skill of analysis is a recipe for risk.

Investment in your team is the essential missing piece – reskilling auditors and change management across teams.  

The divide looks different at every level

AI literacy varies across functions, as does the way each role uses AI. Blanket solutions not tailored to unique applications of AI tooling can cause friction or even retention issues on your team, at any level.  

Why procedures exist, not just what procedure is: With AI handling manual tasks like data extraction and reading contracts, junior auditors are no longer honing their eye for anomalies and good judgment through repetition. They need to understand not just what a procedure does, but why it exists and be able to supervise an AI agent executing it. Without that shift, your best junior talent either disengages or leaves for a firm that’s investing in them.

Question AI processes, not just outputs: For senior staff, the role shifts from reviewing work performed by people to reviewing AI-assisted output. Review questions become: What was the tool asked to do? How did it arrive at this conclusion? Is the rationale supported?  

Governance determines whether AI scales responsibly: Partners and leadership teams must treat governance as a core skill to bring accountability into the AI-enhanced process on a systemic and organizational level. For example, at Citrin Cooperman, AI governance at the firm level is handled by the Risk Management Officer, while the domain-specific AI policies sit under practice leaders.  

Measure capability over participation

Once you know what to measure, the picture changes. The right reskilling strategy can drive tangible, scalable gains for your team. That’s why using metrics that show more than ticking a participation box are imperative for meaningful change.  

• Connect learning to the work: the AICPA and CIMA’s Future-Ready Finance survey found that 61% of respondents rank on-the-job training as the most effective upskilling tactic.

• Quality over quantity: metrics comparing AI-supervised outputs to equivalents without AI, staff confidence in evaluating AI outputs and compliant use provide a holistic view of implementation value.

• Skip the offsite: one-time courses, learning disconnected from daily tasks and participation-only metrics miss the opportunity to win real value for your teams. Measure what happens behind the desk each day, not one-time attendance.

The right technology accelerates readiness

Even the most capable teams struggle to adopt when AI lives outside the audit workflow. No matter how well-trained your people are, a disconnected tool still asks them to think in two places at once.  

Caseware Verity is embedded directly in the engagement, so teams apply new skills where they actually work – not by context switching between disconnected tools. It maintains context from the entire engagement to ensure any outputs or suggestions are rooted in data integrity and the nuances of each specific case, so your team can spend less time re-verifying and more time exercising judgment.

That’s what capability-building is truly for – not just cleaner engagements, but people who feel trusted to think, rather than trained to click. Auditors who feel that shift are the strong contributors who stay. And a firm that retains its sharpest people in an ecosystem where many are struggling to hold onto top talent enjoy a compounding advantage that competitors can’t easily copy.  

Closing the readiness gap is a competitive advantage

The firms that pull ahead won't be those that deploy AI the fastest. They’ll be the ones that build the capabilities to use it best.

Caseware Verity is where that capability takes root – inside the workflow, not outside it.

Invest in your team with the long-term mindset it takes to differentiate.

Explore Caseware Verity.

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