Find out what your AI tools are actually delivering.
Most AI deployments underperform because no one has measured them. Our AI Performance Audit gives you a clear, evidence-based picture of adoption rates, output quality, and the gap between what you are paying for and what you are getting.
Every dimension of AI performance.
We look at adoption, output quality, team-level usage, and the gap between what your AI tools can do and what they are currently doing.
Usage and Adoption Analysis
We pull usage data from your AI platforms and map actual adoption against licensed users. You see exactly who is using AI, how often, and for which tasks.
Output Quality Assessment
We review a sample of AI-generated outputs across your key use cases and score them against quality benchmarks. Poor output patterns are identified and documented.
Team-Level Adoption Mapping
Adoption is rarely uniform. We map usage by team, role, and department to identify where AI is embedded and where it has not taken hold.
Gap and Risk Identification
We identify the gaps between current performance and the potential value of your AI tools, including risks from inconsistent usage or poor output quality.
Audit Report and Findings
A clear written report covering findings, evidence, and prioritised recommendations. Suitable for sharing with leadership and licence decision-makers.
Baseline for Ongoing Measurement
The audit establishes a performance baseline so that future improvements can be measured against a defined starting point. Essential for ROI reporting.
From access to actionable findings.
A structured audit process that delivers clear evidence and prioritised recommendations within two to three weeks.
Scope and Access Setup
We agree the scope of the audit, the AI tools to be reviewed, and obtain read-only access to usage reports and admin dashboards.
Usage Data Collection
We extract usage metrics from your AI platforms, including active users, feature usage, session frequency, and output volumes.
Output Sampling and Scoring
We review a representative sample of AI outputs across your key use cases and score them against defined quality criteria.
Stakeholder Interviews
Short structured interviews with team leads and end users to understand barriers, frustrations, and what is working well.
Findings and Prioritisation
We compile findings into a prioritised list of issues and opportunities, ranked by business impact and ease of resolution.
Audit Report Presentation
We present the audit findings to your leadership team with a clear summary, evidence, and a recommended action plan.
Audits that led to real improvement.
Legal Services Firm
A 90-person law firm had deployed Microsoft 365 Copilot to all fee earners but had no visibility of whether it was being used or delivering value after four months.
Audit revealed 22% active usage. Key barriers identified: lack of training and no approved prompt guidance. Targeted programme launched. Usage reached 61% within six weeks.
Financial Services Business
A financial services company was concerned that AI outputs in client-facing documents were inconsistent and potentially introducing errors.
Output quality audit found three recurring prompt patterns producing unreliable results. Prompt library updated. Output quality scores improved from 58% to 91% acceptable on first review.
Higher Education Institution
A university had invested in AI tools across three departments but leadership had no evidence to justify expanding the programme to the wider institution.
Audit produced a clear ROI baseline and adoption map. Leadership approved institution-wide rollout based on audit evidence. Expansion programme launched within eight weeks of audit completion.
Not sure if your AI tools are delivering their potential?
Book a free initial consultation. We will discuss your current AI deployment, the tools you are using, and whether a full performance audit would give you the evidence you need to improve or expand your AI programme.