January 15, 2026

The Role

In 2025, SQUER (a Vienna-based digital product engineering company) reached out based on the advisory work I was publishing at Flex.Insight. They asked me to lead their Product, Data & AI unit. I wound down Flex and joined. The mandate is as much business as execution: define what we sell, who we sell it to, how we position it, and turn one-off consulting expertise into offers we can deliver again and again. It sits directly on the commercial front line: shaping offers, working strategic accounts, and sitting in the sales conversations myself.

What I Do

• Commercial strategy and positioning for the Product, Data & AI portfolio: deciding which offers exist, how they're priced, and how they're explained to the people who buy them, across product discovery, platform work, data initiatives, and AI services.
• Go-to-market and pre-sales: early qualification, executive workshops, and proposals that tell buyers what will actually be built, why it matters to their business, and in what order.
• Productization of data & AI services: deciding what stays bespoke consulting, what becomes a repeatable offering, and where internal tooling improves both margin and speed.
• Interim delivery leadership where executive credibility was required: I stepped in as interim Head of Engineering on a client's digital logistics platform (millions of transactions across 10+ integrated systems), accountable for keeping execution aligned with business expectations. That operational depth feeds directly back into the commercial work.

What Makes It Hard

Engineering-led firms tend to over-rely on delivery excellence and underinvest in commercial clarity. Strong teams, strong references, but offers too fuzzy or too technical to scale in the market. Fixing that takes more than better slideware: it takes hard choices about positioning, target clients, and what not to sell. Selling product, data, and AI services is especially difficult because buyers are flooded with vague promises. Everyone claims transformation and acceleration. Very few can connect that language to operating models, delivery constraints, and credible commercial outcomes. That translation is the core of the role, and it only works because it is grounded in having personally carried delivery accountability.

Where It Stands

This is a current role, so the honest outcome section is still being written. I'd rather publish numbers when they're shareable than adjectives now. Two proof points so far: a portfolio narrowed to offers we can actually scope and deliver, and an interim engineering mandate on a high-volume logistics platform (millions of transactions across 10+ integrated systems) that ended with clarified team boundaries and teams shipping predictably against them.