Why smarter, smaller steps beat grand digital leaps. Published December 2025, revised January 2026.
The paper challenges the standard transformation playbook. Organisations get more from applying commoditised AI models to specific business problems than from pursuing a grand technology leap.
As AI capability becomes abundant and affordable, competitive advantage shifts away from model sophistication and toward the proprietary application of it — built into workflows the organisation already runs, and instrumented from the start.
That has three practical consequences, which the paper works through in turn: how to choose the process, how to baseline it before you build, and how to keep the result correct once the novelty has worn off and the system is simply part of the estate.
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Every figure is attributed. Where a claim comes from industry reporting rather than a named analyst house, we say so rather than dressing it up.
v1.1 — Published December 2025. Revised January 2026. Figures are as at publication. Where a number has since moved, the direction of the argument has not.
The practice that applies this — enterprise search, agents, document intelligence, biometrics, on our own IP.
The practice →The assurance half: evals, guardrails, drift detection and audit trails for systems that are not deterministic.
The practice →Bring it and we will baseline it with you — what it costs and how long it takes today, before anyone proposes a build.
