I joined the GTM team at Ramp in the early days just as finance automation was emerging. What made that experience special was the feeling that we were helping define a category at exactly the moment the market realized the problem was way bigger than anyone had thought.

Finance teams had major gaps in visibility into spending, efficiency, and accountability. Companies had credit cards, expense systems, (manual) approval workflows, and endless email chains of receipts. Leaders couldn't see the full picture.

Ramp made that problem visible. Companies cut spending significantly, streamlined expense workflows, and transformed how finance teams operated. It completely disrupted corporate finance in a good way.

After my first few weeks at Pensero, listening to customer conversations and learning from the founders, I can't shake the feeling that we're witnessing something very similar.

The difference is that today's blind spot is engineering and all the finance that goes with it. For years, engineering has been one of the largest investments inside modern companies, yet it remains incredibly difficult to understand. Most organizations can tell you how many engineers they employ, how much they spend on tooling, and how many projects are in flight. Far fewer can explain how engineering performance is evolving, where productivity is improving, where friction exists, or what impact new investments are actually generating.

AI made it urgent. Over the past year, organizations have rushed to deploy copilots, coding assistants, agents, and increasingly sophisticated AI workflows. Budgets are growing rapidly. Adoption metrics are everywhere. Executives are being asked about AI strategy in board meetings.

Most companies can tell you how much they're spending on AI. They can tell you how many licenses they've purchased. They can tell you how many tokens have been consumed.

Very few can actually connect those investments to engineering outcomes. That gap is the blind spot. That's what makes Pensero interesting to me.

Pensero is focused on measuring impact, not only usage. It gives leaders visibility into how engineering organizations perform, how AI changes that performance, and where opportunities exist to improve delivery, quality, and efficiency.

When I look at the conversations happening across the market today, I see many of the same signals I saw during my Ramp days. A rapidly growing problem. Urgency from buyers. A realization that existing approaches aren't enough.

The reality is simple: AI spending will be scrutinized the same way every major business investment is scrutinized. Organizations will need to understand what they spent and what they gained in return. The companies that can answer that question will move faster, spend smarter, and build stronger competitive advantages than those operating on assumptions.

I've only been here a short time, but the momentum is undeniable. The more conversations I have, the more convinced I become that visibility into engineering performance and AI impact will be table stakes for modern organizations.

It feels like we're still early.

And that's exactly what makes it exciting.