Identifying AI Use Cases

Before building or buying AI solutions, organisations need a clear framework for identifying where AI will actually create value — and where it will not.

A simple value filter

AI is most likely to create meaningful value where tasks have some combination of: high volume, repetitive processing, large-scale pattern recognition, or quality improvement in human-written outputs. AI is unlikely to create value where tasks require physical presence, strong human relationship, or novel ethical reasoning.

The highest-impact use cases

Document intelligence. Processing, classifying, extracting, and summarising large volumes of documents. High ROI in law, finance, insurance, government, and healthcare.

Customer communication. AI-assisted or AI-first first-line support. Significant volume reduction possible; quality management is the key operational challenge.

Internal knowledge access. AI systems that let employees query internal documentation, policies, and institutional knowledge. Often the highest-perceived-value internal tool for knowledge workers.

Code assistance. AI-assisted coding (GitHub Copilot, Cursor) is showing consistent 20-40% productivity improvements in software teams. One of the best-evidenced AI productivity gains.

The ROI calculation

AI ROI calculations should include: cost of the tool and implementation, cost of quality assurance and human review, value of time saved or quality improved, and cost of errors and the processes to catch them. Many AI ROI calculations are overstated because they count output volume without accounting for the human oversight still required.

The most common mistake in AI business cases is counting the volume of tasks AI can assist with, rather than the net time saved after factoring in quality review, error correction, and prompt maintenance.