Data, AI and automation
AI strategy and evaluation
Most organizations now have AI pilots. Far fewer know whether those pilots save time, improve quality or simply move work around. We help leadership choose AI use cases on evidence, and we evaluate deployed AI tools with the same causal methods we use for any program.
Questions this answers
- Which of our candidate AI use cases are worth pursuing, and in what order?
- Is our data ready for them?
- Did the AI tool we deployed actually improve speed, quality or cost, compared with what would have happened anyway?
- What governance do we need for accuracy, privacy and accountability?
Methods
Use-case discovery and prioritization, data-readiness assessment, business-case modelling, pilot design with proper comparison groups, quality and accuracy evaluation of model outputs, and practical AI governance frameworks.