I lead at the point where product direction, data, operating models, and technical architecture must move together. These cases use only outcomes already supported by my experience and operating evidence.
Global enterprise-planning transformation across five regions, with AI-enhanced forecasting embedded into live supply-chain cycles.
Read the case studyCustomer-data integration, segmentation, and offer optimisation that increased engagement and revenue.
Read the case studyAI-powered planning dashboards, cloud analytics, and order-to-cash process mining that improved decision speed and cycle time.
Read the case studyA hands-on proving ground for how identity, permissions, approvals, evidence, and verification let autonomous systems operate with trust.
Read the case studyGlobal enterprise-planning transformation across five regions, with AI-enhanced forecasting embedded into live supply-chain cycles.
Demand and supply planning spanned regions, stakeholders, data, and operational cycles. AI-enhanced forecasting could only create value if the organisation aligned around a shared product and operating model.
As Global Product Manager for Enterprise Planning, I led the transformation of demand and supply planning and introduced product-led ways of working across the global planning landscape.
Enterprise Planning moved towards a shared product model, with AI forecasting connected to the work and decision cycles it was intended to improve.
Five regions aligned around the Enterprise Planning product model; AI-enhanced forecasting deployed into live supply-chain cycles.
Scaling AI is an operating-model decision as much as a technology decision. Regional alignment, ownership, and live workflow integration are what turn forecasting capability into enterprise practice.
Customer-data integration, segmentation, and offer optimisation that increased engagement and revenue.
Shell Marketplace needed a clearer view of customers and a scalable way to optimise hundreds of offers across a diverse B2B portfolio.
As Lead Product Manager, I owned customer-data integration and segmentation for the marketplace and the product capabilities used to scale engagement and analytics.
The marketplace gained a stronger data and platform foundation for matching customers, offers, and measurable commercial actions.
Product engagement increased by 45% and revenue increased by 5%.
AI and analytics create commercial value when they are attached to a product decision loop: integrated data, usable segmentation, optimised offers, and a platform capable of repeating the result.
AI-powered planning dashboards, cloud analytics, and order-to-cash process mining that improved decision speed and cycle time.
Global supply-chain teams needed faster planning insight while operational friction in order-to-cash processes constrained cycle time.
As Supply Chain Data & Analytics Product Lead, I led the products and platform changes needed to make analytics more timely, global, and operationally useful.
Decision-makers gained faster planning insight while process-mining evidence connected analytics to concrete operational bottlenecks.
Decision speed improved by 25%; order-to-cash cycle time reduced by 20%.
Better prediction is not enough. Enterprise value appears when analytics, platform modernisation, and workflow redesign are treated as one product problem.
A hands-on proving ground for how identity, permissions, approvals, evidence, and verification let autonomous systems operate with trust.
Capable models can perform work, but enterprises still need to know what happened, who authorised it, whether completion was verified, and how failures can be diagnosed.
I set out to design, build, and operate a fully instrumented multi-agent platform that treats those non-model concerns as first-class architecture.
The platform became an empirical environment for testing how verification, observability, trust, and engineering discipline behave under real operating scale.
The instrumented system produced 52.1 million hash-chained evidence records over 174 days, providing the measured foundation for the working papers published on this site.
Arqera figures are internal operating measurements from an independent research-and-development system. They are not presented as client outcomes, customer adoption, or independently audited market traction.
Model capability is only one term in delivered AI value. The architecture around the model determines whether that capability becomes observable, governable, and trustworthy operation.
I’m open to senior enterprise AI, product, and transformation leadership opportunities; selected advisory work; and thoughtful research or speaking collaborations.