Technology Is the Backbone of Success. You Should Understand How It’s Performing.

When I was at Google in the early days, I became obsessed with something that, at the time, made many people uncomfortable: measuring marketing properly.

Back then, marketing was still treated as a creative function. Campaigns were evaluated by perception, by narrative, by how compelling they felt. Budgets were approved because they “made sense,” not because they were instrumented. Very few organizations had a rigorous understanding of unit economics, attribution, or marginal return.

I didn’t see it that way.

If marketing was going to be a serious lever for growth, it had to be measurable. It had to be accountable. It had to compete for capital like any other function. Over time, the industry caught up. Today, no serious company would operate marketing without dashboards, funnels, and ROI models. The idea sounds obvious now but I can tell it wasn’t back then.

I believe we are living through the same moment with engineering.

And you know what? I couldn’t be more excited.

Engineering has quietly become one of the largest people investment most technology companies make. It builds the product, defines differentiation, determines speed, and increasingly shapes margin structure. In many businesses, it is the business.

And yet, when I sit in boardrooms, I still see engineering discussed largely through narrative. We talk about roadmaps, about hiring plans, about whether teams “feel strong.” We look at outcomes like revenue growth, customer churn, feature launches and we infer that engineering must be performing well if those numbers look healthy. We extrapolate this conclusion because we can not measure it.

Technology today is the backbone of success, I’ve seen it and done it myself many times. If your product is software, your engineering organization is the mechanism through which strategy becomes reality. It is where capital is transformed into code, and code into revenue. If that mechanism is inefficient, misaligned, or fragile, the entire company feels it and sometimes it’s just too late to correct it.

What concerns me is not that engineering cannot be measured because it absolutely can. What concerns me is that many organizations still treat it as a craft discipline that resists instrumentation. We accept proxies, rituals and velocity as a comforting signal. But we rarely ask deeper questions about contribution, sustainability, or systemic risk, or the real return on our engineering payroll and AI investments.

Two decades ago, marketing leaders learned that being “creative” was not incompatible with being accountable. In fact, measurement made marketing stronger: It enabled better decisions, better capital allocation, and more credible conversations at the executive level.

Engineering is now the Trojan horse of this same transformation.

It is the function that determines how effectively a company adopts AI, how quickly it can iterate, how resilient its architecture is under scale. It is where technical debt accumulates quietly. It is where future optionality is either created or constrained.

As AI becomes embedded in development workflows, this conversation becomes even more urgent. Companies are now investing heavily in AI coding assistants, infrastructure, and platform tooling which is often layered on top of their largest cost center: engineering headcount. Yet very few organizations can clearly articulate how those AI investments change productivity, quality, or delivery speed.

If we do not understand how your engineering team is performing, we are governing blind, and this becomes crucial to measure the impact of payroll costs and AI tooling spend. We are trusting outcomes without understanding production. We are allocating millions without visibility into how it converts into value.

I am not advocating micromanagement. I am advocating clarity.

If technology is your backbone, you should know how strong it is. You should know where it bends. You should know where AI amplifies output and where it simply increases cost. You should know where it compounds advantage and where it silently erodes it.

We learned this lesson with marketing and we will learn it with engineering.

The only question is whether we choose to learn it proactively or after it becomes painfully obvious.