AI readiness & ROI benchmark
Your teams use AI, but is it moving the numbers? We assess your AI use cases, data, governance and measurement, and benchmark your maturity against McKinsey's State of AI and Gartner's research on why AI projects stall.
What the market looks like.
| KPI | Market reference | Best-in-class / note | Source |
|---|---|---|---|
| Organisations using AI in at least one function | 88% | 78% a year earlier | 1 |
| Report any EBIT impact from AI | 39% | — | 1 |
| Finance functions using AI | 59% | 91% report low or moderate impact initially | 3 |
| Agentic-AI projects cancelled by 2027 | 40%+ | Reasons: cost, unclear value, weak controls | 2 |
Adoption is no longer the gap; value is. The organisations that capture it measure a baseline, pick use cases with a clear cost line and put controls in place before scaling1,2.
Is this benchmark for you?
Industries
Every industry
Buyer
CEO & founder · COO & operations · CIO, CTO & IT
Company profile
Any size
Good first benchmark if you are new to AI
You need it if…
- Teams use AI tools but nobody measures the value
- You do not know which workflow to automate first
- Pilots stall before production
What we measure. What you get.
What we measure
- Inventory of AI tools and use cases in production
- Baseline and measured value for each use case
- Data readiness: access, quality, ownership
- Governance: approvals, human checkpoints, audit trail
- AI spend vs value delivered
What you receive
- Your maturity vs the McKinsey and Gartner references
- Use cases ranked by value, feasibility and risk
- The three workflows to fund first, with business cases
- 90-day plan with owners and success metrics
The full ai readiness & roi study.
Planned contents. Everything behind this page, in depth: the numbers by sector and company size, what top performers do differently, and a model to price your own gap.
- Market size and growth, every analyst estimate reconciled
- KPI quartiles by company size, sector and region
- Original survey data from decision-makers
- Cost-of-the-gap model with worked examples
- How top-quartile companies close the gap
- Solution landscape and a 12-month roadmap
- Self-assessment scorecard
Four steps. No disruption.
Intake call
30 minutes with a senior engineer to agree scope, KPIs and the data we need.
Data, read-only
Exports or read-only access to the systems involved. Nothing is changed.
Analysis
Your numbers placed against the published market references, KPI by KPI.
Readout
Your position, the cost gap in dollars and a costed roadmap to close it.
Reserve this report.
Reserve the ai readiness & roi industry report, or ask for a custom benchmark on your own data.
- Your maturity vs the McKinsey and Gartner references
- Use cases ranked by value, feasibility and risk
- The three workflows to fund first, with business cases
Sources
- 1McKinsey & Company, The state of AI in 2025 (1,993 participants). mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
- 2Gartner press release, “Over 40% of Agentic AI Projects Will Be Canceled by End of 2027” (25 Jun 2025). gartner.com/en/newsroom/press-releases/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027
- 3CFO Dive, reporting a Gartner survey of 183 CFOs and finance leaders (19 Nov 2025). cfodive.com/news/cfos-ai-adoption-slows-challenges-mount-gartner/805949/