Whitepaper The AI Fitness Test
Why operating models, not tools, will decide who captures value from AI.
Why operating models, not tools, will decide who captures value from AI.
The AI Fitness Test examines what two years of enterprise AI adoption has actually revealed — not about the technology, but about the operating models it is landing in. Drawing on published research from Accenture, Goldman Sachs, the Bank of England, Stanford HAI and a BCG field experiment with 758 consultants, alongside Remarkable’s own programme experience, this paper argues that the tools are rarely the constraint, but the system they enter almost always is.
Fourteen pages of diagnostic argument, structured across four sections. The report sets out why organisations are adopting AI faster than they are redesigning how they work, and why the gap between those two speeds is where most value is being lost. The key findings examine shadow AI as an operational signal, the difference between individual productivity gains and organisational ones, the jagged frontier of model capability, governance at speed, and why successful pilots can still fail the business. The strategic priorities section sets out what fit organisations do differently — and why advantage is moving from access to absorption.
86% of organisations plan to increase their AI investment, yet only 21% have redesigned their end-to-end processes around the technology. A tool pointed at broken or inherited work does not repair the work — it makes it faster, more embedded and harder to remove. The organisations that are getting the most from AI are not the ones with the most tools; they are the ones that diagnosed their operating models before they started buying.
This whitepaper provides a practical framework for AI fitness: diagnosing before adopting, routing work to the right intelligence, reading shadow AI before punishing it, and measuring outcomes rather than usage. Leadership teams that read this and act will have a clearer view of which parts of their business are genuinely ready to absorb AI capability — and which are not. Those that shelve it will be asking the same questions in a year’s time.
