Measuring AI Adoption Across Large Engineering Organisations
Helping leadership understand how emerging AI tools were being adopted across a large engineering organisation and creating the evidence needed to inform future investment decisions.
Adoption is easy to measure. Value is harder.
As generative AI tools began appearing across engineering environments, leadership faced a familiar challenge. Interest was growing rapidly, but evidence was limited. Questions emerged around adoption, productivity, workforce impact and long-term investment priorities. The organisation needed to move beyond anecdotes and isolated success stories. Leadership required a clearer understanding of how tools such as GitHub Copilot and emerging coding assistants were being used across a large engineering population. The challenge was not deploying AI. The challenge was understanding whether it was creating meaningful value.
"Enthusiasm can accelerate adoption. Evidence determines investment."
- 01Established visibility into AI tooling adoption patterns.
- 02Connected workforce, engineering and operational data sources.
- 03Created executive views of usage and engagement trends.
- 04Explored relationships between adoption and engineering outcomes.
- 05Supported leadership decision-making with evidence-based insights.
- Improved visibility into AI adoption trends.
- Evidence-based discussions around technology investment.
- Better understanding of workforce engagement with AI tooling.
- Early insight into productivity and behavioural signals.
- More informed decisions regarding future AI strategy.
- Stronger executive confidence in emerging technology assessments.
Most organisations can tell you whether AI tools have been deployed. Far fewer can explain whether they are changing behaviours, influencing outcomes or creating measurable value. Understanding adoption is useful. Understanding impact is strategic.