The AI Value Gap

AI tools are easy to deploy. AI value is hard to capture.

Many organisations now have access to generative AI tools, agents, dashboards, copilots and automation platforms. The harder question is whether those tools are changing decisions, workflows, costs, revenue and operating discipline.

What the evidence says

BCG describes a widening gap between companies that are generating substantial AI value and companies still reporting minimal material gains. McKinsey's 2025 State of AI survey points in the same direction: use is widening, but scaled enterprise impact remains difficult.

Harvard Business Review's 2026 AI coverage points to practical barriers: data quality, old processes, weak redesign, the gap between executive ambition and managerial reality, and the difficulty of turning pilots into operating change.

The World Bank and OECD evidence on SMEs adds another layer: smaller firms are central to employment and economic activity, but they often face finance and capability constraints. For them, AI value depends on whether tools are tied to cash flow, customers, operations and decisions.

MMK interpretation

The AI value gap is not mainly a technology gap. It is a management gap, a data gap, a workflow gap and a decision gap.

AI creates value when it is tied to a real decision or workflow. The question is not: "Do we have AI?" The better question is: "What business decision will AI improve, and how will we know?"

Five decision questions

1. What decision should AI improve?
2. What workflow will change?
3. What data will the model rely on?
4. Who is accountable for adoption?
5. How will value be measured after 90 days?

AI tools are becoming easier to access. AI value is still earned through discipline: clear decisions, clean data, usable workflows, capable managers and measurable outcomes.

At MMK Consult, we help organisations turn research, data and business evidence into decisions that can be defended.