I write often about technology and organizational behaviour, and when I first read the recent reports that a partner at KPMG Australia was fined for using AI to pass an internal AI training test, I felt compelled to step back and consider what this moment means for firms, regulators and professional trust.
What happened — a concise summary
KPMG Australia discovered that an unnamed partner had uploaded internal training material into an external AI tool to generate answers for a mandatory course on artificial intelligence. The firm imposed a financial penalty on the partner and required a retake of the assessment. KPMG also reported that dozens of employees had been identified using AI inappropriately during internal exams over the past year Business Standard.
Internal policies and the nature of the test
From what has been disclosed, the training was designed to improve staff competency on AI — a sensible and necessary initiative for an audit and advisory firm. The course included a downloadable reference manual that participants were instructed to consult. However, the firm’s policy prohibited uploading those materials to external or uncontrolled AI platforms during closed assessments.
Two elements are worth noting:
- The test was both technical and ethical in nature: it aimed to establish baseline knowledge about AI capabilities and responsible use. The goal was not to trick staff but to ensure they could advise clients and exercise professional judgement when AI is deployed.
- The policy distinction mattered: download and read the manual was permitted, but transfer the manual into an uncontrolled AI service to generate answers was explicitly disallowed.
Why the partner (and others) may have used AI
Motivations are rarely binary. In this case they likely included a mix of factors:
- Time pressure and productivity expectations. Professionals under deadline may view shortcuts as pragmatic, especially when the tool promises fast, seemingly authoritative answers.
- Overconfidence in AI outputs. Many users assume a polished, fluent response implies correctness — a dangerous cognitive shortcut.
- Ambiguity in acceptable AI use. If firms have not clearly articulated boundaries for different tasks, employees may assume internal training is low-risk to leverage tools.
Understanding these drivers is critical: this is not simply a problem of a single errant actor but of incentives, clarity and training.
Ethical and legal implications
Ethically, the case undermines core professional duties: competence, integrity and accountability. For auditors and consultants, those duties are not optional; they underpin public trust.
Legally, several angles matter:
- Professional reporting obligations. Depending on jurisdictions and professional bodies, partners may be required to self-report breaches. Firms also face scrutiny if misconduct suggests systemic control failures.
- Contract and independence risks. If AI-assisted training or deliverables are used in client engagements without proper disclosure and validation, that can create liability.
There is also a reputational effect that cascades: clients, regulators and the public read these incidents as signals about a firm’s culture and controls.
Industry context
This episode is not isolated. Professional services firms globally are racing to integrate AI while also facing earlier exam-integrity scandals and AI-related errors in client work. The paradox is stark: firms selling AI-enabled efficiency must also show they govern it effectively.
Regulators and professional bodies are increasingly attentive. In some jurisdictions, regulators expect disclosure and remediation when AI misuses could affect competence or client outcomes. That elevates internal training integrity from an HR issue to a regulatory concern.
Possible consequences for KPMG and the partner
For the partner:
- Direct financial penalty and mandatory remediation (retraining, retesting).
- Potential professional disciplinary processes if self-reporting regimes or professional bodies are engaged.
- Career and reputational consequences within the firm and the market.
For the firm:
- Heightened regulatory scrutiny and potential requirement to demonstrate strengthened controls and transparent reporting.
- Damage to client trust, especially for engagements where AI is a component of methodology or deliverables.
- An internal cultural reckoning: firms will need to ensure senior leaders model compliance with AI governance, not just mandate it.
Lessons for corporations
This incident offers several practical lessons that apply beyond one firm:
- Make AI policies task-specific. Differentiate between open research, drafting, client work and closed-book assessments. Vague language invites misuse.
- Design assessments to test judgment, not rote recall. Scenario-based and individualized tasks are less amenable to one-size-fits-all AI prompts.
- Log and monitor AI-enabled workflows where appropriate. Transparent logs (prompt and output retention) help with validation and learning.
- Align incentives. If speed and throughput are rewarded more than demonstrated competence, people will look for shortcuts.
- Invest in ethical training and cultural reinforcement. Rules without reinforcement and visible leadership adherence will be ignored.
Final reflection
There is an uncomfortable irony in using AI to pass an AI test — but the deeper story is about governance and trust in a fast-changing technical landscape. Technology will continue to amplify human capability, but it also magnifies misalignment between stated values and lived incentives.
I don’t think punitive measures alone will solve the problem. Firms need better policy design, assessment formats that validate judgment, and transparent tracking of AI use. Only then can organizations credibly claim that staff are competent to advise others on AI — and that clients and the public can rely on that advice.
I welcome your thoughts: what should firms prioritise first — stronger technical controls, reworked assessments, or cultural change? Please reflect and comment below.
Regards,
Hemen Parekh
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