The situation
The business had grown through a series of acquisitions and had become fragmented across products, platforms, and teams. Commercial integration had run well ahead of technical integration. Engineering was siloed around legacy product boundaries, execution discipline was inconsistent, and the company lacked the telemetry needed to make fast, confident decisions.
Scale was still meaningful. Growth had stalled, capital efficiency was weak, and the portfolio was carrying more complexity than the operating model could support.
What the real problem was
Not a roadmap problem. A portfolio, operating-model, and growth-architecture problem: three things that look like one thing from the outside and have to be untangled in a specific order.
Every acquired product had a defender inside the business and a customer list outside it. Nobody had been willing to say out loud which ones were structurally constrained, so investment stayed spread thin across all of them. That is the tax a company pays for every decision it declined to make.
What I changed
- Rationalized product investment against growth potential, strategic fit, and technical viability, and named, explicitly, which areas we were reducing and what we were giving up.
- Reoriented the business around its strongest commercial wedges rather than defending the full surface area.
- Reorganized engineering into three motions, product, infrastructure, and AI/data, to break silos inherited from the acquisitions and let capability scale across the platform rather than within one product.
- Built an AI-led roadmap tied to measurable revenue and efficiency levers, not to a general capability narrative.
- Installed operating discipline that did not exist: unified roadmap planning, estimation, defect tracking, execution dashboards, and customer-response metrics.
- Raised internal AI maturity from close to zero by introducing tooling and working methods across the organization, not just the AI team.
- Used selective co-funding with anchor clients to extend the roadmap under tight resource constraints.
What happened
The business moved from steep contraction toward renewed growth. Two funding rounds closed against a product and AI roadmap tied to revenue levers. The portfolio became materially simpler, the operating model materially more accountable, and the growth story materially more investable.
Why this one matters
It is what portfolio simplification looks like when the pressure is real and the runway is not theoretical. Incremental fixes were no longer available, and the work had to hold up under diligence while it was still in progress.