Evidence
Case studies
Fifteen anonymised case studies showing how organisations use AI to improve decisions, operations and customer outcomes, plus one illustrative portfolio vignette.
Where a figure is a real CAPIO result it is marked actual. Everything else is marked representative, meaning a typical or target outcome benchmarked to independent research, not a verified per client metric.
- Turning AI activity into a governed programme
- Compliance and competence as the growth engine
- Seeing the whole field, not five percent of it
- An expert made available at scale
- The support desk that answers itself
- An expert on tap for a small firm
- Fluency across a whole small company
- Catching fraud before it lands
- Contracts read in seconds
- More jobs per van, fewer miles
- Cutting the energy bill on what you already run
- A small dev team that ships like a bigger one
- Keeping subscribers who were quietly leaving
- Lifting the whole field, not just the top performers
- Buying at the right moment, not out of habit
Portfolio vignette, one score across the whole book
This is an illustration of how the model works across a portfolio, not a claimed client engagement.
A fund holds several companies in the same broad sector. AI is handled company by company, if at all, with no single view across the book, so every board update is apples to oranges.
Running the CAPIO methodology in each company gives every one its own Twin, its own readiness score and its own pilot portfolio, all built the same way. Those roll up into one portfolio dashboard. The fund can finally see which companies are ahead, which are exposed and where a dollar of attention moves valuation most. At exit, a company with a live Twin, real governance and a defensible score walks into diligence with an asset rather than an open question.
The point a single company cannot buy on its own is comparability. One methodology, one measure, every company.
Evidence base for representative figures
Every representative number above traces to one of these.
- Pilot failure and the partnership advantage. MIT NANDA, The GenAI Divide, State of AI in Business 2025. About ninety five percent of enterprise generative AI pilots reach no measurable impact. Pilots pairing internal specialists with external expertise succeed around sixty seven percent of the time, against twenty two percent for internal only builds. The biggest returns come from operational and back office work, not sales and marketing tooling.
- The value of dedicated AI leadership. IBM Institute for Business Value with the Dubai Future Foundation. Companies with a Chief AI Officer report about ten percent higher return on AI investment, rising to as much as thirty six percent when the role runs a centralised or hub and spoke operating model. IBM's 2026 CEO study puts CAIO adoption at seventy six percent of organisations, up from twenty six percent a year earlier.
- Support and agent productivity. NBER working paper, Generative AI at Work, Brynjolfsson, Li and Raymond. An AI assistant raised issues resolved per hour by fourteen percent on average and thirty four percent for the least experienced workers, while improving customer sentiment and retention.
- Onboarding and time to competence. McKinsey research cited across contact centre deployments, twenty to thirty percent reduction in time to proficiency, with real deployments around forty percent and knowledge assistant cases cutting ramp from thirteen weeks to as few as two.
- Conversation intelligence. Multiple 2024 to 2026 sources including Gong and Gartner cited analyses. Fifteen to twenty five percent win rate improvement, forty to fifty percent reduction in new representative ramp, against a baseline where managers review under five percent of calls.
Case studies are anonymised. Representative outcomes are benchmarked to independent research and illustrate typical results rather than a specific client figure.