IT & Data
Test case generation and regression coverage
Generate test cases from requirements and existing code, identify coverage gaps in the regression suite, and draft automated tests for the paths that are currently only tested manually.
30–50%
reduction in test authoring effort
Opportunity assessment
Minor improvement. Small efficiency gain with limited effect on overall turnover or bottom line.
Relatively straightforward. Off-the-shelf tools handle the core task. Implementation is well-understood with good examples to follow.
Light data requirements. Uses straightforward inputs — documents, product descriptions, customer records — that are usually accessible with minimal prep.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Light people impact. A new tool slots into an existing workflow. Minimal training needed. Most people will adapt quickly.
Tooling required
Things to consider
Generated tests are good at covering the paths that exist and poor at imagining the ones that should not. Keep experienced testers on exploratory and negative testing, and let the model handle breadth.
Coverage percentage is easy to inflate with tests that assert nothing meaningful. Review a sample of generated tests for assertion quality before trusting the coverage number.
The highest value is usually on legacy code with no tests at all, where the alternative is not a better test but no test.
Experiment starter: Pick one poorly covered module that changes often. Generate tests, have a senior engineer review them, and then deliberately introduce three realistic bugs to see whether the suite catches them.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Experimentation FrameworkThis is an augmentation opportunity — see the relevant playbook section for how to approach it.More in IT & Data
IT service desk triage and deflection
Classify and route inbound IT tickets, resolve common requests through self-service, and draft first-line responses for the remainder using the internal knowledge base and past resolutions.
AI coding assistance for internal development
Equip internal developers with an AI coding assistant for code generation, refactoring, test writing, and code review, integrated into the existing development environment and review process.
Legacy system documentation generation
Generate readable documentation for undocumented legacy systems — data models, integration points, business logic, and dependencies — from source code, database schemas, and configuration.
AI Transformation Playbook
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The playbook gives you the full 150-opportunity directory, scoring tools, and 100+ templates for every stage of an AI transformation programme.