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Everyone’s talking about AI. From the boardroom to the breakroom, it’s being pitched as the next big thing. But between the buzzwords and bold promises, one question often gets lost: are we solving real business problems, or just playing with shiny tools?
For many organisations, the excitement around AI has led to rushed pilots, unclear goals and underwhelming results. Without a strong link to decisions, data and adoption, even the most promising AI use cases fall flat. It’s time to get practical.
The challenge
AI has officially made it to the boardroom. It’s no longer just a pet project for innovation labs or data scientists with too much time on their hands. But as the hype grows, so does the risk of missing the point. Too many organisations still approach AI as a technology exercise, not a business change.
Ambitious projects take off — generative AI, copilots, predictive models — but without a clear link to decisions, processes or outcomes. Budgets get eaten, time disappears, and valuable expert capacity is spent on pilots that never leave the lab.
The most common pitfalls? Unclear business relevance, poor data quality, and solutions that simply don’t fit how people actually work. Somewhere along the way, teams discover that the required data doesn’t exist or isn’t reliable, that the Azure or Fabric setup can’t support what’s needed, or that users don’t trust — let alone understand — the AI output.
The result? A trail of disconnected pilots, sceptical stakeholders and the growing sense that “AI is expensive and doesn’t deliver”.
The solution
Before you build, pause. LACO uses a practical, structured framework built around three deceptively simple questions:
- Does it truly matter for your business?
Start with problems worth solving. Together with business and IT stakeholders, we identify the decisions and processes where AI can actually make a difference. Is the goal to reduce manual work, improve forecasting, detect risks earlier or personalise customer interactions?By defining what success looks like – fewer errors, shorter lead times, higher conversion, lower cost – we make sure the initiative is anchored in business priorities, not technology curiosity. - Do you have the data and platform to make it work?
Next, we assess the current data landscape and platform readiness. Are the required data sources available, reliable and governed? Can they be integrated into your Azure or Fabric environment? Are performance, security and cost manageable?This step covers everything from data models and pipelines to monitoring and lifecycle management. The goal: ensure every use case is grounded in a solid, scalable foundation. - Will people actually use it?
AI that nobody uses is just an expensive demo. That’s why we consider adoption from day one. Who will use the solution? How will it impact their daily work? What’s needed in terms of transparency, controls and training?We think through user journeys, interfaces (Power BI, Fabric, apps…) and guardrails, so AI becomes part of the process and not something bolted on as an afterthought.
These three questions address business value (viability), data and platform readiness (feasibility) and user adoption (desirability). LACO applies this framework through focused workshops and assessments, always grounded in your existing Microsoft Azure and Microsoft Fabric setup.
The outcome? A shortlist of AI use cases that are technically achievable, strategically relevant and supported by the data and governance to actually succeed.
The results
Organisations that apply this framework move beyond experimentation. Instead of spreading resources across disconnected pilots, they focus on a small number of use cases with real business impact. Each initiative is backed by the right data, a scalable platform and clear outcomes — turning AI from a theoretical exercise into a strategic tool.
By building on existing Microsoft Azure and Microsoft Fabric components, time to value is shortened and adoption becomes easier. Business users are involved from the start, ensuring trust, usability and relevance. The result? A portfolio of AI solutions that deliver measurable value and actually support day-to-day decisions.
