David Sunton

  • Why AI Transformation Needs Solution Framing And Change Management

    AI transformation fails when organisations separate solution design from change adoption. A strong AI initiative needs both: solution framing to define the problem, workflow, data, risks and governance; and change management to build trust, adoption, capability and sustained behaviour change. Together, they turn AI ambition into practical business value.

  • Why Data Readiness Can Make Or Break AI Projects

    AI projects often fail because organisations confuse having data with having AI-ready data. Data readiness depends on quality, access, ownership, governance, context and workflow fit. This article explains why leaders must assess data readiness before implementation, and why data problems are often business, process and accountability problems.

  • The AI Use Case Scorecard

    Not every AI idea should become a project. A practical AI use case scorecard helps leaders assess whether an opportunity is valuable, feasible, governable and adoptable before committing time, budget and attention. This article explains how to evaluate AI use cases across problem clarity, business value, data readiness, process fit, people impact, risk and adoption.

  • From AI Idea To AI Use Case

    An AI idea is only a starting point. A use case turns that idea into a practical business initiative by defining the problem, users, workflow, data, value, risks, governance and adoption pathway. This article explains how leaders can move from AI ambition to use cases worth pursuing.

  • Before The AI Project: Why Opportunity Assessment Matters

    AI projects should not begin with tools, vendors or models. They should begin with a clear business problem, measurable value, realistic data requirements, process fit, governance and adoption readiness. This article explains why AI opportunity assessment matters before organisations commit to pilots, platforms or implementation.

  • Why AI Projects Are Change Projects

    AI projects do not succeed through model deployment alone. They change workflows, roles, decisions, governance, trust and behaviour. This article explains why leaders should treat AI initiatives as change projects from the beginning, connecting use case evaluation, human-in-the-loop design, adoption, process redesign and sustained business value.

  • Why AI Strategy Is Failing and What Leaders Can Do Differently

    Many organisations are investing heavily in AI, yet struggling to realise meaningful impact. The issue is rarely technology. More often, AI initiatives fail because leadership, operating models, and decision design have not evolved alongside the tools. This article explores why AI strategy breaks down and what leaders can do differently to strengthen outcomes.