Customer and product
Discovery, voice of customer, problem framing, product direction, requirements, roadmaps and service design.
ABOUT
A senior practitioner who stays close to the work.
I have spent 10+ years inside customer-facing SaaS organisations, moving from frontline technical diagnosis into service leadership, product direction and cross-functional delivery.
My strongest work starts where the problem is still messy.
I began in technical product support, where product decisions are experienced in their most concrete form: a customer is blocked, the system behaves unexpectedly and someone has to find out why.
That grounding stayed with me as I moved into management, built specialist teams, shaped Shopify's Optimisation Services and led EMEA technical support operations at SOTI. I learned to treat services as products, operating models as designed systems and implementation as part of the product decision.
Independent software and AI work keeps that judgement current. Building the product forces every abstract requirement to become a data boundary, interaction, test or trade-off.
I am now focused on senior individual-contributor roles where customer discovery, solution design, technical coordination and adoption sit together: forward-deployed product, solutions, implementation, TAM, customer success, technical support, product and applied AI.
RANGE
Implementation and adoption
Technical delivery, operating models, change, enablement, customer recovery and continuous improvement.
Technical and AI
Solution design, workflow automation, data boundaries, evaluation, responsible AI and hands-on product building.
Influence and ownership
Clear decisions, executive communication, multidisciplinary coordination and developing specialist capability.
WORKING PRINCIPLES
How I make decisions when the answer is not obvious.
- Listen for the system behind the request.
The stated ask is often a symptom. Customer behaviour, operational constraints and team incentives reveal the actual problem.
- Make the maturity visible.
A working product, a tested prototype and a convincing model are different kinds of evidence. The language should preserve that distinction.
- Design for the people who must operate it.
A product or service only scales when ownership, knowledge, exceptions and feedback have somewhere to live.
- Keep consequential decisions human.
Automation should make good judgement easier to apply, not obscure who owns the outcome.