Two decades in the arena. From trading floors to global commercial leadership.

My career started on the CBOE trading floor in Chicago, then moved to EUREX in Frankfurt, where you learn fast that data without judgment is noise. From there I managed global accounts at Reuters (now LSEG), competing head-to-head with Bloomberg in the institutional data market, an environment where every deal is high-stakes, multi-threaded, and won on relationships, not slide decks. That principle has shaped everything since: scaling Salesforce's UK & Ireland commercial business with 70+ AEs, leading EMEA revenue for blockchain infrastructure, re-architecting GTM at a Big Data fintech, and most recently running revenue execution and white-label GTM at a global multi-asset brokerage serving institutional traders and asset managers.

Along the way, I've held full P&L ownership across two companies: a PE-backed global software business and a multi-asset brokerage. Not just revenue, but cost discipline, margin management, capital allocation, and the operational trade-offs that come with running a commercial organisation at scale.

The through-line across every role: building the operating infrastructure that turns effort into predictable, repeatable revenue. Sales controlling, performance management, CRM architecture, pipeline governance. That is also why the agent question landed here first. I have seen both sides of the digital-asset story: the public-chain version is about money and distribution; the institutional version is about permission, privacy and workflow. The permission side is where I spent my time, and it is where much of the AI-driven economy will have to be made operational.

Commercial systems only work if managers run them. I have led teams through growth and change, including more than 70 account executives through ten first-line managers at Salesforce, and a commercial team I inherited after a private-equity buyout. In an engagement, I build the management cadence alongside the system: the forecast call, the pipeline review, and one-to-ones that end in a decision. The aim is a team that runs the system without me. How I approach it: What a People Manager Actually Does.

I have also run AI inside a commercial organisation. At a global multi-asset brokerage I was executive sponsor of an in-house AI sales-enablement platform for the relationship-manager team, through to weekly rollout. I also designed, with AI-assisted development, a pricing and proposal tool built on version-controlled commercial rules. For clients, that experience turns into practical questions: which workflows change, who owns them, and which commercial decisions leadership has to take.

The public version of that thinking runs in The Operating Margin, the newsletter where I work through who gets paid, who controls the rules, and where margin moves.

Two principles run through the work. The workflow is the unit of truth, not the company and not an AI maturity score: a workflow has an owner, a volume, a cost, an authority level and a failure mode, so it can be priced and governed. And every engagement ends in decisions, with named owners, agreed actions and a 90-day sequence.