Cut the AI spend that does not return value.

The AI Waste Audit defines waste as paid capacity, human rework, vendor dependency, or duplicated tooling that does not improve a measured workflow.

The work is not another scorecard. It turns AI spend, usage, or risk into a decision leadership can act on: what should stop, what should be fixed, what deserves more budget, and what must stay controlled before it expands.

This page is for leaders, finance, operations, IT, and business teams that already have visible AI activity but not enough evidence to decide. The common symptom is not lack of AI; it is too much activity without ownership, full cost, or a shared quality threshold.

It is useful before renewals, when several teams buy similar tools, when pilots stay in permanent demo mode, or when nobody can say whether AI truly improves the workflow that matters.

We use the Four Leaks of AI ROI: spend, adoption, leaks, and role drift. Every signal is tied to cost, owner, risk, and decision. This structure avoids confusing enthusiasm, usage, and measurable return.

The framework also forces a simple discipline: an AI use case needs scope, a pre-AI baseline, a quality threshold, a value measure, and a review date. Without those five elements, the company funds a story instead of an operating asset.

Deliverables are designed to be used in decision meetings, not merely read. They separate available evidence, reasonable assumptions, risks to reduce, and measurements to install. Every item should support a concrete decision.

• AI spend register by tool, team, owner, and renewal date
• Duplicate and dormant-seat map
• Hidden cost estimate including review and maintenance
• Consolidation and renegotiation priorities
• Savings plan with risk notes before cuts

Savings waterfall by subscription, seat, duplicate use case, and process rework.

Decision table for keep, consolidate, renegotiate, stop, or rebuild.

The report avoids broad abstract recommendations. It shows the workflows involved, the evidence behind each conclusion, confidence limits, and the next expected decision. A good Harmondale report should make the next meeting shorter.

7-12 working days

The first phase gathers inventory, cost, past decisions, and field examples. The second qualifies priority workflows, checks assumptions with owners, and separates spend, quality, risk, and adoption issues. The end converts analysis into a decision backlog.

€5k-€14k depending on tool count and teams covered

Budget depends mainly on the number of teams, quality of available traces, data sensitivity, and expected analysis depth. A short scope can be enough when the decision is urgent; a broader scope is justified when several budgets or departments are involved.

Objections are normal because the audit touches budgets, team habits, and sometimes tools people genuinely like. The role of the audit is to make decisions defensible, not to turn AI into a search for blame.

• The audit does not remove tools that teams rely on without a transition path.
• Savings are separated from risk so finance does not cut the wrong control.
• The goal is fewer weak tools, not less useful AI.