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The AI Centre / Insights

Our Latest Thinking

Clear thinking for leaders making AI decisions.

INSIGHTS

The role helps the leadership team connect AI activity to business priorities, coordinate the people involved, maintain proportionate control and judge whether investment is creating value. Executive AI Leadership is the capability. The fractional CAIO is how that capability is delivered.

New weekly series

Read the model market before you follow it.

AI Model Market Watch examines changes in model pricing, capability and competition, then considers what those changes mean for business leaders making investment and operating decisions.

By Ged Leigh

Published weekly

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Our Editorial Standard

Useful insight should improve a decision.

Authored

Written by people who lead the work.

Practical

Focused on decisions, not AI commentary.

Evidence-informed

Clear about what is known and what is changing.

Leadership questions

The questions business leaders are asking.

Start with the question in front of you. Each guide connects practical information to the relevant leadership decision and the next part of The AI Centre’s approach.

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How AI can strengthen exit value and what buyers will need to see

AI can strengthen ordinary drivers of business value, but a buyer will test whether the effect is proven, repeatable and transferable. This guide shows owners how to prepare evidence on performance, operating continuity, data rights, supplier dependence, governance and internal capability before due diligence begins.
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The importance of change management in becoming an AI-enabled business

AI adoption changes roles, decisions and workflows before it changes an organisation. This practical guide shows leaders how to involve employees, build role-based capability, adjust the conditions around behaviour and reinforce new ways of working with evidence so adoption becomes reliable, sustainable operating practice across teams.
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Choosing AI models by cost per successful business outcome

Token prices and public rankings cannot select the right AI model for a business workflow. This article shows technology leaders how to define acceptance thresholds, run fair task-specific tests, count retries and human review, route work by consequence and procure for model change rather than a permanent winner.
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If AI makes operational excellence easier to copy, what should businesses compete on instead?

Widely available AI can improve speed and consistency without giving customers a reason to choose one supplier. This article separates competitive parity from durable advantage and shows how proprietary context, learning loops, trusted relationships and accountable human judgement can make AI more valuable and harder to imitate.
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AI saved the time. What did the business do with it?

Hours saved prove that a task changed, not that the business benefited. This article gives leaders a five-stage benefit chain, a practical scorecard and a proportionate way to measure whether released capacity became a meaningful commercial or operational outcome before deciding whether to scale it.
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The customer journey is fragmenting: what SME leaders should do about AI discovery

Search remains important, but buyers now use AI assistants and other sources to frame problems and compare suppliers. SME leaders should focus on a clear position, credible evidence, consistent company information and better sales feedback, then test how the business appears across real buyer questions.
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