Executive reporting shapes where an organisation invests, which business areas need support and how performance is judged. Across functions, business units and acquisitions, apparently familiar KPIs can rest on different definitions, reporting periods and customer or product structures. When those differences are hidden, a consolidated view can lead to misleading comparisons and misplaced confidence. AI can amplify the problem by carrying those inconsistencies into forecasts, explanations and recommendations at speed, making conclusions sound more certain than the underlying data allows.

In many conversations with senior leadership teams, we encounter situations where they have clear strategic objectives, such as increasing active customers, improving retention or growing average revenue per customer. However, these strategic objectives tend to fade or become disconnected when we look at the underlying business processes and how operational teams interact with KPIs and reports. The organisation has data and reports; it simply lacks the connection to strategic objectives, and so the data is not delivering business value.

The requirement is to establish which KPIs matter to the business, how they are calculated and whether the data behind them supports the decisions leaders need to make. That means connecting measures to their commercial drivers, agreeing what can be compared across the organisation and retaining the detail needed to explain meaningful differences. Business teams must own those judgements. With that foundation, reporting and AI can help leaders investigate performance and choose a response with a clear understanding of the evidence.

Most organisations have some KPI reporting in place at strategic and operational level. What they lack is the “so what”: their reports do not prompt specific business actions and decisions. Many organisations have already started implementing AI or are on the journey of enabling and applying it. However, their AI investments do not really roll up to strategic business value, so either the investments stall or the ROI measurements fail to justify them.

This is where I find the idea of an Insights Value Map particularly useful. It makes the connection between a strategic objective and the supporting information explicit, giving business leaders and data teams a shared view of what matters.

What is an Insights Value Map and why is it valuable?

An Insights Value Map connects a business goal to the information people need to achieve it. It sets out the processes involved, the insights required, the KPIs used to monitor performance, and the analysis and actions those measures should support.

It acts as a functional accelerator for a consultant like me, who has to work at different levels of an organisation – strategic and operational – and recommend data and AI technology enablers that deliver operational outcomes while staying aligned to strategic objectives.

It can also act as a very powerful prioritisation tool for business leaders, who are often asked which reports, KPIs and dashboards should be prioritised. If we align every epic and user story to the top strategic levers and business value, value-based prioritisation becomes possible, helping to resolve conflicting priorities among business leaders. We have seen this work in many strategic assessments.

The structure follows six connected steps: Goal, Process, Insights, KPIs, Analysis and Actions. Figure 1 shows the question each part needs to answer. Together, they establish why information belongs in the report and how the business expects to use it.

How it builds the picture across the business

That connection between a goal, the information it needs and the action it supports becomes more valuable when followed across the business. A group growth objective may depend on sales winning and retaining customers, product teams shaping the offer, and finance understanding the return. Discovery brings those perspectives together, examining the processes, existing reports and decisions behind each map.

Comparing the maps then reveals where those needs meet. Sales may need to understand account growth, finance the profitability of those relationships, and product teams demand across the portfolio. These are different questions, but they depend on connected customer, product and performance data. Working through the detail shows which definitions can be shared and where a local distinction changes the interpretation.

Together, these requirements inform a functional map: a joined-up view of what teams need to understand and do, and the information they share. It guides how records should connect across business units and acquired companies, who owns the definitions, and what detail must remain available beneath the group totals. Each part of the reporting can then be traced back to a business question and the decision it supports.

How AI-assisted discovery accelerates the work

AI-assisted discovery helps organise existing material, compare definitions and prepare initial maps, accelerating the work needed to bring those connections into view. Business teams test the findings and resolve the choices behind them. How performance is measured and where resources are directed have commercial consequences, so their judgement remains central throughout.

Traditional discovery typically starts with questions such as: can you explain the as-is process? What are your pain points and challenges? What are your to-be objectives and aspirations? This leads to a gap analysis and further definition of which use cases need to be enabled.

AI-assisted discovery can do the pre-work of analysing existing documents, SOPs, process charts, technical design diagrams, reports and more, taking me straight to the next line of questioning, such as: why do you think customer churn is due to “low product relevance”?

What this means for executive reporting

Consider a group whose strategy combines profitable growth in its subscription and services businesses with the integration of a recent acquisition. That strategy sets the reporting agenda: understanding the sources of growth, the contribution of each business and the capacity needed to deliver the plan. These questions need a consistent answer through successive planning and performance reviews.

The map translates those priorities into shared reporting requirements: revenue against plan, growth on a comparable basis and margin using an agreed treatment of costs. The framework also enables every investment to be mapped to business value, giving traceability of every penny spent towards business outcomes. Finance and business leaders agree the reporting periods, currency basis and which businesses belong in each comparison. Customer and product records are linked to shared group classifications, with local detail retained.

This creates a standardised view of total group performance alongside a like-for-like view of the same businesses over the same periods, keeping the acquisition’s contribution visible and providing the “so what” for business leaders. Beneath those common measures, each unit retains the analysis needed to explain its results: renewals and expansion for subscriptions, or backlog and delivery capacity for services. Leadership can follow progress against the strategy, distinguish changes in the group’s composition from changes in trading, and adjust priorities as the evidence develops.

That shared foundation supports ongoing planning, investment and performance management across the group. Agreed definitions and ownership keep the view useful as businesses and priorities change. AI can then help investigate variances or draft commentary using the same measures and traceable evidence, with business teams able to check the interpretation.