Questions of accountability
- Who can authorize an AI initiative?
- Who owns the risk?
- Who can stop a system?
- What requires governance approval?
- Which decisions belong to IT?
Professional profile
Delivery at the intersection of business, software and AI.
Technical Project and Delivery Manager with extensive experience leading software development, modernization, migration and digital transformation initiatives across complex business and technology environments.
The work combines delivery leadership with technical depth, strong client relationships and the ability to engage effectively with technical teams, manage risk and connect business objectives with technology execution.
Current areas of work include software and infrastructure project delivery, Applied AI across projects and the SDLC, and the development and application of practical approaches to AI Governance.
Capabilities
Capabilities
Core areas of professional contribution across technology delivery, transformation and AI.
01
Technical Project & Delivery Management
Leading complex technology initiatives across Traditional and Agile delivery approaches, including Fixed-Price and Time & Materials engagements, connecting business objectives, technical execution, teams, risk and client relationships.
02
Applied Artificial Intelligence
Applying AI capabilities to Technical Delivery and the SDLC to improve execution, analysis and repeatability.
03
AI Governance
Translating frameworks, standards and regulation into practical governance approaches, decision mechanisms and controls.
04
Software Modernization & Transformation
Modernization, migration, rehosting, refactoring and Knowledge Recovery from Source Code across enterprise environments.
AI & AI Governance
Applied AI → Accountability
Applying AI within real delivery and software-development activities creates opportunities for improvement, but also raises governance questions that technology alone cannot answer.
From “What” to “How”
My work in AI Governance focuses on moving from what frameworks, standards and regulation recommend toward how organizations can make governance operational through decisions, responsibilities, controls and evidence.
It also explores how AI is adopted and applied in organizations, including its integration into processes and decision-making while addressing organizational objectives and governance considerations.
This includes the development, testing and use of practical governance approaches, informed by research and evaluated through practical use.
Insights
Insights
Articles and practical perspectives emerging from work in AI Governance, Applied AI, technology delivery and software modernization.
Experience
Experience
Professional breadth supporting current work across delivery, transformation and AI.
Professional experience spans technology delivery, software development and modernization, enterprise systems, product and requirements work, Agile practices and risk management, together with the evolving use and governance of AI across complex business and technology environments.
This experience includes work across banking, retail, housing & construction, food & beverage, automotive, service desk, workplace management, e-commerce and government environments, providing exposure to different operating models, technology landscapes and business needs.
This background provides the combination of delivery, business and technical perspective used today to lead technology initiatives, work effectively with clients and technical teams, and incorporate emerging capabilities such as AI without losing sight of execution, risk and accountability.