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There is a version of this conversation I have had more times than I can count. A CRO or VP Sales walks me through their go-to-market process. It is well-documented. The stages are named. The ICP is described somewhere. The qualification methodology has an acronym. The team has been trained on all of it.
Then I ask: what percentage of your pipeline meets your SQL criteria right now? What is your ICP adherence rate this quarter? How many open deals have exceeded the maximum sales cycle for their segment?
Silence. Not because the answers don’t exist somewhere in the data — they do. But because nobody has looked, and the system has not been designed to surface them.
Your GTM system is not the process you designed. It is the sum of all individual interpretations of that process, running simultaneously, without a controlling mechanism to detect or correct the drift. Understanding why this happens — and how to fix it structurally — is the most important thing most revenue organisations are not doing.
The first structural enemy of GTM execution is the assumption that the process is being followed.
Revenue Operations (RevOps) defines the shared data model, process layer, and metrics framework that the go-to-market engine runs on. But definition is not the same as execution. A defined process that is not verified is an assumption. And a business run on assumptions is not a business under control — it is a business where every metric is telling you what people believe is happening, not what is actually happening.
The pattern I see most often: companies manage financial outputs with rigour but leave operational inputs entirely uncontrolled. They review ARR, pipeline coverage, and win rates in every leadership meeting. They do not systematically check whether ICP criteria are being applied, whether SQL gates are holding, whether close dates reflect real customer buying timelines or wishful thinking by sales reps under quota pressure.
Nobody checks — often because checking implies distrust. The result is collective, invisible failure. Everyone on the team is doing what they believe is right. The aggregate of those individual beliefs is a process that has drifted significantly from the design. And nobody can see the drift until the forecast misses.
A factory built on consensus is not a factory. It is a workshop where individuals are doing their best.
The second structural enemy is compromise at the definition level.
Every exception weakens the factory. Every time a senior AE pushes back on a qualification criterion and the criterion is softened, the gate degrades. Every time a manager approves a deal advancement that does not meet exit criteria “just this once,” the exit criteria become optional. Every time a team is allowed to run their own version of a stage definition, the shared data model fractures.
Compromise happens for understandable reasons. Sales teams resist criteria they perceive as arbitrary. Senior performers expect latitude. Managers under pressure to show pipeline numbers prefer to approve than to debate. Each individual decision feels reasonable. The aggregate effect is a process that exists on paper but not in practice.
The fix is not motivational. It is architectural. Exit criteria must be enforced in the CRM, not by managers. When the system does not permit a deal to advance without meeting defined criteria, the question of whether to enforce the rule does not arise. The rule is the infrastructure. You do not negotiate with infrastructure.
The third structural enemy is the most commonly misunderstood, and the one that creates the most organisational damage over time: functional fragmentation.
Your customer buys from one company. They experience one journey — from first awareness through evaluation, decision, onboarding, and renewal. But inside most revenue organisations, three separate functions are each optimising their piece of that journey independently.
The customer feels every gap between the islands. The handoff from MQL to SQL feels like a different company. The post-sale experience feels disconnected from the promises made in the sales process. The renewal conversation happens in a different context from the one the customer was onboarded into.
The resolution is architectural, not collaborative. It requires someone to hold the holistic view — the CRO or CommOps function — and to design the customer journey from the outside in (reflecting how the buyer actually moves) and the process layer from the inside out (reflecting how the organisation serves that journey). These are two distinct layers. They must be designed and maintained separately.
No consensus. No committee. An architectural decision, made by someone with the authority to enforce it. This is why the CRO role is fundamentally an architectural role.
Governance is the controlling mechanism that makes the gap between designed process and actual process visible — automatically, continuously, and in time to act.
Effective governance does not require more meetings. It requires three things: defined rules, automatic surfacing of deviations from those rules, and defined response protocols when deviations occur.
A governance deviation is any deal state that violates a structural rule: a deal past the maximum sales cycle corridor, an open opportunity with no contact in 30 days, a deal marked SQL without required binary qualification fields completed.
In our implementations, governance deviations surface in dashboards within 24 hours — not because a manager looked for them, but because the system is designed to find them automatically. The AE sees their own view. The team lead sees all deviations across the team. No manual report. The deviation is visible before it becomes a problem.
The question to ask of your current system is simple: what governance deviations are invisible in your reporting right now? Deals past maximum cycle. Contacts outside SLA. Stage criteria not met. If you cannot answer this in two minutes using your current dashboard, your controlling layer is missing.
Governance absence — specifically, the lack of a controlling mechanism that makes process deviations visible automatically. Most GTM designs fail not because the design was wrong but because there is no system to detect when the design is not being followed. The process drifts, the data degrades, and the forecast becomes unreliable, all without any single visible failure event.
ICP adherence is the percentage of deals in the active pipeline that meet all defined ICP criteria. If your ICP definition cannot be checked against CRM fields, it cannot be measured — which means it cannot be managed. In our engagements, ICP adherence below 70% consistently correlates with forecast inaccuracy and extended sales cycles.
Enforcement of criteria is experienced as a check on individual judgment, which is uncomfortable for experienced salespeople. The most effective reframe is to move enforcement from the manager level to the system level. When the CRM does not permit advancement without criteria being met, enforcement becomes a property of the tool, not a leadership behaviour.
Metrics measure what has already happened — ARR growth, win rate, pipeline coverage. Signals measure what is happening now, before it moves the headline metrics. Time-in-stage drift, contact frequency gaps, and post-SQL consistency are signals. They fire before the forecast deteriorates. Companies with only a metrics layer are managing the past. Companies with a signals layer are managing the present.
The island problem shows up as definitional inconsistency across the customer lifecycle. Marketing’s lead statuses do not map cleanly to Sales’ pipeline stages. The handoff data required for a high-quality SQL is not captured at the MQL stage. Customer Success does not have visibility into the commitments made during the sales process. The CRM becomes three separate databases with loose connections.
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