AI is a multiplier. But only if the foundation is already there.
87% of enterprises missed their 2025 revenue targets. They also spent more on AI than any year in history. That gap is worth understanding.
Those two numbers sit uncomfortably close together. But correlation isn’t causation, and assuming it does can lead revenue executives to the wrong conclusions. The interesting question isn’t what caused the miss. It’s what the timing of that miss, alongside record AI investment, tells us about where most organisations actually are in their AI readiness.
I’ve been in enough forecast reviews to see the same pattern. When a quarter ends short, the retrospective naturally focuses on what was most visible: market conditions, deal timing, rep activity. What gets less attention is the operating foundation underneath: the data hygiene, the accountability structures, the ownership of outcomes. Those are harder to see and harder to fix. They’re also where the real leverage is.
This is where RevOps becomes critical, not as a reporting function, but as the organisational layer that sits between revenue ambition and AI performance. The teams getting the most from their AI investments aren’t the ones with the biggest budgets. They’re the ones where RevOps has evolved from producing pipeline reports to owning the data architecture, governance standards, and cross-functional alignment that AI depends on.
AI is a multiplier. That’s its nature. Layer it onto a disciplined revenue organisation with clean data, clear ownership, and genuine CIO/CRO alignment, and it compounds what’s already working. The organisations that close the readiness gap don’t just avoid the downside, they get compounding returns that widen the gap between them and everyone still catching up.
Most organisations are at an earlier stage of readiness than their AI investment assumes. That’s not a failure of ambition. It’s an operational readiness challenge, but certainly fixable.
What “ready” actually means
There are three things that separate organisations where AI moves the number from those where it produces confident-looking outputs that still miss the quarter.
1. A unified data architecture. AI is only as reliable as the data behind it. When revenue data lives in siloed systems with inconsistent field definitions, when “closed won” means something different in your CRM than in your CS platform, AI outputs become unreliable regardless of model quality. Nearly half of revenue leaders admit their data isn’t AI-ready. 55% report conflicting pipeline signals across teams.
Revenue leaders should be able to point to one place where data reconciles, explain exactly how conflicts get resolved, and know how quickly signals become available. If those answers aren’t clear, start there.
2. Formal revenue governance. Of all the investments that drive AI performance, governance is the hardest to prioritise. It has no vendor, no launch date, and no demo. But it has the highest leverage. The most disciplined revenue organisations treat data quality as a KPI, reviewed regularly, with clear owners and real accountability when it degrades. That discipline is what allows AI to do its best work. Without it, even a well-designed model is operating on assumptions nobody is actively maintaining.
Governance isn’t the precursor to AI. It’s the foundation that determines how much value AI can actually deliver. This is a meaningful shift in what RevOps is asked to own. Historically the function lived downstream of decisions, reporting on outcomes after the fact. The organisations pulling ahead have repositioned RevOps upstream: setting the standards, owning the definitions, and holding the business accountable for the data quality that everything else runs on. That’s a different job, and it requires a different level of organisational investment in the function.
3. A genuine CIO/CRO partnership. AI readiness isn’t only a revenue problem. It’s an infrastructure problem. When CIOs are directly involved in revenue operations, 96% of leaders say forecast accuracy improves. The organisations seeing the strongest results are the ones where IT and revenue have moved beyond tool procurement into shared ownership of outcomes, where both functions are accountable for what the data produces, not just what the systems cost.
When CIOs and CROs operate in genuine lockstep, decisions get made faster, assumptions get stress-tested sooner, and the gap between AI investment and AI return closes considerably.
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Five questions to assess where you actually stand
Before evaluating any AI investment, answer these honestly:
1. Do we have one source of truth for our revenue data? If marketing, sales, finance, and customer success are pulling different numbers from different systems, you don’t have the consistency AI requires. Everything else is downstream of this.
2. Can we trust our forecast data? Only about one-third of RevOps leaders say they fully trust their revenue data. If forecasts require manual reconciliation or regular debate about which number is right, that’s not a forecasting problem. It’s a data problem, and one worth solving independent of any AI investment.
3. Who owns data quality and what happens when it degrades? The organisations with the most reliable AI outputs have clear owners, defined validation rules, and regular accountability checkpoints. That structure is what keeps the model’s assumptions current as markets and buyer behaviour evolve.
4. Is my CIO a strategic partner or a vendor of record? When CIOs and CROs jointly own systems, data, and outcomes from the start, organisations move faster, make fewer decisions on faulty assumptions, and get more from every AI investment. The earlier that partnership forms, the more it compounds.
5. Has RevOps been given the mandate to match its expanded role? If your RevOps team is still primarily measured on report production and CRM hygiene, there’s a gap between what the function is being asked to deliver and the authority it’s been given to deliver it. The organisations where AI is compounding returns have elevated RevOps into a strategic function with cross-functional accountability. That elevation doesn’t happen by default. It happens by design.
What changes from here
2025 was an inflection point. AI raised the stakes of getting the fundamentals right, and made the return on doing so higher than it’s ever been. The organisations that treat this moment as a prompt to close their readiness gaps will be the ones that pull ahead.
The companies that move fastest over the next two years won’t necessarily be the ones with the most sophisticated AI. They’ll be the ones who invested in what AI depends on: unified revenue data, clear accountability for quality, real collaboration between IT and revenue leadership, and a RevOps function with the mandate, the authority, and the organisational standing to hold all of it together.
AI accelerates what’s already working. The question every revenue leader should be asking isn’t which AI tool should we buy next? It’s what would have to be true for any AI to actually work here?
Answer that question first. Everything else follows.
About the Author of this Article
Kylie Fuentes is Chief Product Officer at Salesloft
About Clari / Salesloft
Together, Clari and Salesloft create a category-transforming AI company for revenue, building the foundation for a Predictive Revenue System — a system that guides revenue teams to accelerate growth. The company combines the broadest dataset, capturing both structured and unstructured signals. End-to-end revenue orchestration capabilities unlock new levels of AI-driven productivity and predictability. Thousands of the world’s most successful companies — including Adobe, IBM, 3M, and Zoom — trust Clari and Salesloft to drive predictable revenue growth.
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