I work at the intersection of enterprise AI, data and analytics—helping translate complex business challenges into practical systems, clearer decisions and intelligent workflows.
A focused combination of strategic thinking and hands-on technical understanding—designed around the work organisations actually need done.
Identify valuable use cases, clarify readiness, connect technology choices to business priorities and shape realistic paths from experimentation to implementation.
Structure data, define useful metrics and turn information into dashboards and insights that support confident operational and strategic decisions.
Explore AI-enabled search, chat and workflow automation with attention to data access, human oversight, evaluation and practical usefulness.
A growing portfolio of practical work. Each case study should make the problem, contribution, technical choices and evidence clear.
Developing a Google Cloud-first approach to connect data foundations, analytics and applied AI around real business questions—with a clear path to a demonstrable end-to-end case study.
Experience with AI-enabled search and chat concepts for public-service and finance-related information workflows. A full case study can document the verified architecture, role, safeguards and delivery status.
A portfolio direction for demonstrating SQL, data modelling, KPI definitions and decision-ready dashboards using synthetic or appropriately shareable data.
Building practical learning experiences that help people explore where AI can support their work—starting with a real problem, testing ideas and reviewing outputs critically.
Portfolio note: examples are presented as initiatives and areas of experience, not as claims of quantified impact or production deployment. Add approved details and verified outcomes as each case study is finalised.
Public conversations on AI, inclusion and the developing world offer an independently viewable record of how I think about technology and its wider implications.
A conversation about learning to code, representation in technology and the possibilities AI could create for developing parts of the world.
Watch the interview ↗A discussion of AI’s opportunities and risks, including who benefits from the technology and how its development may affect equality across nations.
Watch the full debate ↗The best solution is not automatically the most complicated one. I begin by understanding who needs what to change, then work backwards to the data, tools and delivery approach that fit.
Continue the conversation ↗Define the real problem, users, constraints and what success would look like.
Choose an appropriate solution, data foundation and way to test the key assumptions.
Create a useful prototype or workflow with appropriate security, oversight and evaluation.
Check whether the solution works in practice, learn from users and iterate based on evidence.
I’m open to conversations about enterprise AI, data and analytics roles, consulting engagements and thoughtful collaborations.