Stop Making Platform Teams Pretend to Be Revenue Teams

Every quarter, a ritual plays out across Product and Engineering organizations: platform teams sit down to write their OKRs, and the discomfort starts immediately. The objectives that honestly describe their work (reduce deployment time by 40%, improve API reliability to 99.95%, cut onboarding friction for new services) feel too “internal” to survive a leadership review. So the team rewrites them. They stretch. They contort. They find a revenue metric three hops away and build a tenuous story linking their infrastructure migration to it.

The result is a set of OKRs that technically passes muster but describes work nobody actually recognizes. The team spends the quarter doing one thing and narrating another. Leadership reads objectives that feel vaguely correct but never quite explain what is happening or why it matters. And at the end of the cycle, the retrospective lands on a frustrating conclusion: the key results moved (or didn’t), but nobody learned anything useful about whether the platform got better.

This pattern is everywhere, and it is corrosive. Platform work shouldn’t have to masquerade as revenue work to be valued. When it does, the OKR system is broken. The fix requires understanding why the model fails, what the real cost is, and what a better set of objectives actually looks like for teams whose job is to make other teams faster, more capable, and more autonomous.

Why Platform Impact Is Systemic, Not Singular

Platform teams exist to create leverage. They build shared services, tooling, data infrastructure, and developer experiences that enable multiple product teams to move faster. Their value is multiplicative, distributed across every team that depends on them. That is fundamentally different from a product team working on a checkout flow or an onboarding experience, where the line from effort to user outcome is relatively short and direct.

The attribution problem

When a platform team rebuilds the CI/CD pipeline and deployment frequency across the organization doubles, that improvement shows up in every team’s velocity metrics. When a data platform team creates a self-serve analytics layer, the product teams using it discover insights faster and ship better experiments. The value is real, often enormous, but it doesn’t land in a single metric that the platform team can cleanly own. This is what John Cutler has called the problem of teams that connect to revenue through several hops: the work is essential, but the path between effort and business outcome is long and branching.

Systems value versus feature value

Most OKR frameworks are implicitly designed for feature teams. They assume a world where a team works on a discrete capability, ships it, and measures whether users adopt it. That model works beautifully for teams building customer-facing product. It falls apart for teams whose “product” is another team’s ability to do their job well. The value a platform team delivers is systemic: it shows up as reduced cycle times, fewer incidents, faster onboarding of new engineers, and the ability for product teams to experiment without waiting six weeks for infrastructure. These are organizational health metrics, and the best Product organizations already know how to track them. The problem is that the OKR review process often treats them as second-class outcomes.

A shared vocabulary gap

Part of the difficulty is linguistic. Revenue, conversion, and retention are universally understood as “real” outcomes. Deployment frequency, service reliability, and developer experience lack the same organizational status, even when they are upstream of every single revenue metric leadership cares about. Without a shared vocabulary for the value of enablement work, platform teams are left translating their impact into somebody else’s language. The translation always loses something.

The Cost of Forcing False Alignment

When the goal-setting model does not accommodate platform work on its own terms, teams adapt. They find ways to survive the system. Every one of those adaptations has a cost, and over time, the costs compound into serious organizational damage.

Narrative debt

The most immediate cost is what might be called narrative debt: the gap between what a team is actually doing and what their objectives claim they are doing. A platform team that frames a major infrastructure migration as “improve checkout conversion by 2%” has created a story that is technically defensible but functionally dishonest. Leadership reads the OKR and thinks the team is working on checkout. The team knows they are working on infrastructure. Neither side gets the clarity they need to make good decisions. Melissa Perri’s observation about strategy being a decision-making framework rather than a plan is directly relevant here: when the framing is wrong, the decisions it produces will be wrong too.

Misallocated attention

False alignment distorts leadership’s mental model of where effort is going. If three platform teams all write OKRs that reference revenue metrics, the executive team’s portfolio view shows heavy investment in growth, when in reality, most of that effort is going toward reliability and developer experience. This is exactly the kind of roadmap theater that erodes trust between teams and leadership. When executives eventually discover the gap between the narrative and the work, the default response is skepticism toward the teams, when the real problem is a system that made honest reporting feel unsafe.

Lost learning

Good OKRs create a feedback loop: you set a target, you work toward it, and the key results tell you whether your approach worked. When platform teams write OKRs anchored to metrics they don’t directly influence, that feedback loop breaks. The key result might move (checkout conversion might tick up for reasons entirely unrelated to the infrastructure migration), but the team learns nothing about whether their platform work was effective. Or the key result might not move, and the team gets dinged in a review despite doing excellent, high-impact work. Either way, the organization has lost the ability to learn from the cycle.

Talent attrition

Strong engineers and Product Managers gravitate toward platform roles because the work is technically challenging and the impact is broad. Those same people leave when they realize the organization does not actually value what they do on its own terms. If every quarterly review requires a contorted explanation of how their reliability work somehow maps to a revenue metric, the message is clear: the organization considers their work secondary. That is how you lose your most capable infrastructure and platform talent to companies that know how to value systems work.

Designing Objectives That Allow Enablement to Exist

The solution is straightforward in concept, though it requires real commitment from Product and Engineering leadership: design your OKR framework so that enablement objectives are legitimate, first-class outcomes. This means changing what counts as a valid objective, how key results are structured, and how platform work is reviewed.

Create an explicit category for enablement objectives

Most organizations run OKRs with implicit categories: growth, retention, monetization. Platform work does not fit neatly into any of these. Adding an explicit enablement category (with names like “Engineering Velocity,” “Platform Capability,” or “Operational Health”) signals that the organization values this class of work. The category needs executive sponsorship and parity with customer-facing objectives in reviews. Marty Cagan makes a useful distinction between experience teams and platform teams, noting that the purpose of a platform team is to enable experience teams to better solve problems for their customers. The OKR framework needs to reflect that topology.

Write key results the team can actually influence

The single most common failure in platform OKRs is selecting key results that the team cannot directly move. A platform team should not own “increase revenue by X%” as a key result. They might reasonably own “reduce mean time to deploy from 45 minutes to under 10 minutes,” or “enable 80% of product teams to provision a new service without filing a support ticket,” or “reduce P1 incident frequency by 50%.” These are measurable, outcome-oriented, and directly within the team’s sphere of influence. The team can learn from whether these results move. That learning is the whole point.

Use leading indicators, not lagging ones

Revenue is a lagging indicator of many things, including platform health. Platform teams should anchor their key results to leading indicators: deployment frequency, build times, time-to-first-commit for new hires, API latency, error rates, the number of teams able to run experiments independently. These metrics move on a timeline that matches the team’s work cadence and provides actionable feedback. When a team sees that their refactored authentication service cut integration time for downstream teams from two weeks to two days, that is a real, valuable, measurable result.

Frame platform objectives as problems to solve

This is where outcome-based roadmapping maps directly to platform work. Instead of writing a platform objective as “migrate to Kubernetes” (an output), frame it as “enable product teams to scale services independently without waiting for infrastructure support.” The first describes a task. The second describes a problem to solve, and it opens space for the team to discover the best approach. Maybe the answer involves Kubernetes. Maybe it involves something else entirely. The Now-Next-Later roadmap is particularly well-suited for platform teams for exactly this reason: it separates the problem from the solution and gives the team room to experiment.

Build a review cadence that respects different time horizons

Platform investments often pay off over longer time horizons than feature work. A design system takes months to build and years to generate compounding returns. A data platform migration might show minimal measurable improvement in the first quarter but transform organizational capability over the next three. Quarterly OKR cycles work best when they are treated as a cadence of introspection rather than organizational delivery sprints. For platform work, this means setting key results that reflect progress toward a longer arc, reviewed quarterly but understood as part of a multi-quarter investment. Leadership needs to evaluate platform OKRs with that context, asking “are we learning and making measurable progress?” rather than “did we hit the number this quarter?”

What “So What” Looks Like for Internal Products

One of the hardest questions for platform teams is articulating the “so what” of their work in a way that resonates with business leadership. Revenue teams have a built-in “so what”: more revenue. Platform teams need to construct their own, and that construction requires deliberate practice.

The enabling condition framing

The most effective way to communicate platform value is through enabling conditions: “Our work created the conditions that allowed X to happen.” This is different from claiming credit for X directly. A platform team that reduced deployment time from hours to minutes did not “increase feature velocity by 30%.” They created the conditions that made a 30% velocity increase possible across the teams that used the new deployment pipeline. This framing is honest, verifiable, and significantly more compelling than a forced attribution to revenue.

Adoption as a success metric

Internal products succeed when other teams voluntarily adopt them. Adoption rate is one of the most telling metrics for platform work: if product teams actively choose to use the platform’s tools, APIs, and services rather than building their own, the platform is delivering value. Conversely, if teams routinely build workarounds to avoid the platform, that is a signal that the platform is failing to serve its users, regardless of what the OKRs say. Framing platform success around adoption, satisfaction, and time-saved for consuming teams creates an honest accountability structure.

The capacity unlock narrative

Every product leader understands that capacity is finite and expensive. Platform teams can frame their impact in terms of capacity unlocked: “This quarter, our self-serve provisioning tool eliminated an estimated 200 engineering hours of support requests from product teams.” That is 200 hours those product teams could redirect toward customer-facing work. Translated into loaded engineering cost, that is a number any CFO understands. The key is to measure and report these capacity effects explicitly, rather than hoping leadership will infer them.

Treat internal consumers as users, not colleagues

The best platform teams operate with the same rigor they would apply to an external product. They run discovery with their internal users. They measure satisfaction. They maintain a roadmap that is visible and outcome-driven. They treat other engineering teams as customers whose problems they need to understand and solve, and they hold themselves accountable to outcomes that those customers care about. This mindset shift changes everything about how the team operates, and it gives leadership a much clearer lens for evaluating their impact.

Making the Organizational Shift

Recognizing the problem is the easy part. The harder work is changing the systems that created it. This requires coordinated effort from Heads of Product, CTOs, and platform leaders who are willing to redesign how their organizations set, review, and reward objectives.

Give platform teams a seat at the OKR table

In many organizations, company-level OKRs are set by business leadership and then cascaded downward. By the time they reach platform teams, the objectives have been filtered through revenue and growth lenses that don’t reflect infrastructure reality. Platform leaders need to be in the room when top-level objectives are being set, so they can advocate for enablement objectives at the organizational level. An objective like “increase engineering team autonomy and reduce cross-team dependencies” is just as strategically legitimate as “grow revenue by 20%,” and it needs a sponsor with enough authority to defend it in portfolio reviews.

Run platform retrospectives against platform metrics

End-of-quarter reviews for platform teams should center on platform outcomes: reliability, adoption, developer satisfaction, time-to-capability. If the review forces the team to narrate their quarter through revenue metrics they don’t control, the review process itself is the problem. Leadership can still ask how platform work connects to business goals (that is a legitimate and important question), but the primary evaluation should be against the outcomes the team set for itself, framed around the problems they were hired to solve.

Invest in tooling that supports strategic context

One of the anti-patterns that reinforces bad platform OKRs is the use of delivery tools as the primary system of record. When the team’s work lives in Jira tickets disconnected from objectives and product strategy, it is nearly impossible to see or communicate the strategic intent behind the work. A lean roadmapping tool that connects initiatives to objectives makes the strategic context visible by default. When leadership can open a roadmap and see that the platform team’s “Now” column is focused on reducing deployment friction, linked to an enablement objective with measurable key results, the need for narrative contortion disappears. The work speaks for itself because the system surfaces the intent.

How Platform Work Earns Its Place in the Product Portfolio

The organizations that get platform Product Management right share a common trait: they treat platform work as a legitimate product discipline, with its own users, its own outcomes, and its own accountability structure. They don’t force platform teams to borrow credibility from revenue metrics. They create space for enablement objectives to stand on their own, measured by the impact they have on the teams and systems they serve.

This is not a small shift. It requires CTOs who are willing to defend enablement objectives in executive reviews. It requires Heads of Product who design OKR frameworks with explicit room for systemic, non-customer-facing outcomes. It requires platform leaders who invest in measuring and communicating their impact with the same rigor they would apply to an external SaaS product. And it requires tooling that makes strategic context visible, so the connection between platform work and organizational outcomes is always clear.

The payoff is significant. When platform teams can write honest objectives and measure outcomes they directly influence, the quality of their work improves, the trust between platform and product teams deepens, and leadership gets a real view of where organizational investment is going. The OKR system starts doing what it was designed to do: create alignment through clarity, accountability through measurement, and learning through honest reflection.

Platform work is the foundation that makes everything else possible. The goal-setting model should reflect that, loudly and without apology.