Finding focus in the age of AI

The organisation was not starting from a blank page. There was clear momentum around AI, and a willingness to innovate. But appetite alone doesn't produce results. 

Operationally, teams were navigating time-consuming processes that limited productivity and diverted attention from higher-value work. Expertise was often concentrated, creating dependencies that slowed down workflows and decision-making. At the same time, clients were expecting faster, more consistent, and more insightful outputs.

Although ideas for AI applications were already emerging across the organisation, they remained fragmented. There was no common approach to identify, reflect on, and surface relevant opportunities, nor a shared language to discuss priorities consistently across the organisation.

As a result, the organisation faced a critical risk: not a lack of ideas, but a lack of direction. What was missing was a clear answer to a deceptively simple question: where should AI be prioritised to deliver the greatest impact? 

We designed and facilitated an AI Opportunity Compass session. The approach was deliberately centred on business priorities rather than technology capabilities. Rooted in the Double Diamond framework and Design Thinking principles, the session was built around a single conviction: the right solutions only emerge once the right problems are properly understood.

Participants were guided through a structured, collaborative process designed to progressively move from exploration to prioritisation.

The session moved through three phases and began by sparking discussions about day-to-day challenges. These were explored across three key dimensions where AI is known to create value: repetitive tasks, knowledge bottlenecks, and areas of ambiguity. This phase created a shared and comprehensive view of the organisation’s friction points.

From there, the group shifted into opportunity identification. Participants explored how AI could address these challenges, both in terms of improving internal efficiency and enhancing the value delivered to clients. The process remained anchored in concrete business contexts, resulting in a diverse set of potential use cases. 

Finally, the strongest opportunities were evaluated, reframed as 'how might we' questions, and developed into concrete use case outlines, each with expected outcomes, data requirements, and implementation considerations clearly defined.

By the close of the session, the organisation had moved from scattered thinking to a prioritised AI use cases list. Across the day, participants identified more than two hundred challenges and opportunities across their operations and client-facing activities. These insights formed the basis for developing over 40 AI use case ideas, and around 15 were selected and defined for further implementation. Three clear themes emerged: automating repetitive workflows to free up capacity, scaling internal knowledge to reduce dependency on individuals, and using AI to deliver faster and more data-driven value to clients.

But the numbers only tell part of the story. What the session also produced was alignment across teams that had previously approached AI from different angles, converging around a shared understanding of priorities and opportunities. 

This shift was reflected in participant feedback after the session, which indicated a high level of satisfaction with both the content and the facilitation. The organisation left with the confidence and structure to pursue a number of initiatives, gaining actionable direction in its AI journey.