As artificial intelligence transforms how businesses operate, a new divide is emerging amongst startups: Companies with clean, governed data versus those drowning in fragmented information across disconnected tools. For early-stage founders facing increasing pressure to prove capital efficiency, this isn’t just a technical challenge, it’s an existential one.

John Queally, Senior Director of Revenue Operations at Clari, has witnessed this evolution firsthand. His vision of revenue operations (RevOps) as the “data nervous system,” connecting every go-to-market (GTM) team, represents a fundamental shift from the customer relationship management (CRM) admin role most companies still assign to the function. Rather than serving as a report factory, RevOps should be the unbiased arbiter of truth that enables companies to shift from fighting about data to executing strategy.

At the recent Gartner Chief Sales Officer Conference, the message was clear: AI is no longer optional, and the quality of data feeding these systems will directly determine competitive advantage. For founders building companies today, the choice is stark: Establish intentional data governance now, or spend years unwinding technical debt while competitors race ahead.

John’s roadmap offers a path forward for scaling beyond founder-led growth with systems designed for the AI-driven era.

From banking precision to tech chaos

John’s transition from the financial services world to tech revealed a jarring contrast in how organizations approach data. “In the banking world, data was very controlled. It was very governed, especially on the business operations side,” he said. “You need to know everything that’s happening with precision, mainly because the regulatory environment forces that on you.”

When John joined the tech world, everything shifted. In his view, most tech companies operate in one of two distinct buckets: The product and engineering side typically maintains rigorous data standards and governance – they have to build products effectively and scale engineering teams. The business side, however, operates with less efficiency.

“Every department grew in silos. And within the growth of those silos, everything was a little different and uniquely shaded all over the place,” John said. This fragmentation creates a fundamental decision-making problem: “How do you make decisions if you can’t agree on the basic data points and inputs? You spend your time fighting about those, not fighting about the decisions that matter and are going to move the needle.”

The solution, in John’s view, lies in RevOps serving as the unbiased truth-teller, a function with no allegiance to any particular department, only to the company’s goals and establishing a foundation for strategic decision-making.

When founders should engage RevOps talent

For seed-stage companies still driven by founder-led sales, the question isn’t whether they need RevOps, it’s when and how to bring that expertise in-house. John’s advice: Don’t rush to hire full-time, but avoid two common traps that can set a company back years.

“Don’t go hire a CRM admin or a freelancer admin and call that your operations function. You will need that at some point, but it is not a true RevOps function,” he said. “Don’t go hire a RevOps analyst, very junior in their career, and call that your RevOps person, either. Those individuals, while great, are not going to have the experience to help guide you with the decisions that you need.”

Instead, John recommends engaging someone at a leadership level on a part-time basis to build foundational elements before bringing on someone full-time. The key is to focus on measurement capabilities that enable better strategic decisions. “Every dollar matters that you’re spending. Capital, especially in those early stages, is precious.”

The trap founders fall into is doing something that works, then scaling it without understanding why it worked. “You may roll out a campaign and it’s wildly successful. But if you can’t answer the question of ‘why was it successful’ and you don’t understand if it was successful with the specific audience you intended, how are you going to make sound investment decisions?”

This is where RevOps becomes critical: Establishing the measurement foundation to determine if you’re hitting your ideal customer profile, and what adjustments will drive better returns on future investments.

Data governance as a competitive advantage

John’s banking background gives him a unique perspective on why data governance has become mission-critical for modern businesses. At JPMorgan Chase, he could trust every data element in production databases because massive governance functions guaranteed accuracy. While John doesn’t advocate that startups adopt banking-level rigor, he acknowledges the recent industry shifts that have made data quality a competitive imperative.

At the Gartner Chief Sales Officer Conference, John witnessed a pivotal moment in the AI conversation. CROs received a direct call to action: “If you think AI is not your priority, you are mistaken. You are wrong. AI is absolutely your priority, and you can no longer wait for IT or other departments to carry this forward.”

The conference went further with tactical guidance that fundamentally ties to data governance. Leaders must understand their processes end-to-end because they “have to place AI with intention.” More critically, “the quality of data that you pump in is directly going to be tied to the return you get out of it.”

“Bad data into AI systems does not yield magic. AI does not clean up your data and just say, ‘Oh, look, I just fed this a slop of junk and all of a sudden a diamond came out and it was amazing.’ It does not work like that, you get garbage out with garbage in” John said.

This reality makes data governance the foundation for AI success. “AI is here. It’s happening. It’s going to control so many of the processes of business. We must make sure it’s fed with relevant data, as well as intellectual property data from your own company, because that’s what’s going to move you ahead.”

The challenge? Governance is “the most unsexy thing on the planet to talk about.” But as John puts it: “It’s time to take our vitamins and eat our vegetables.”

The 3 pillars of revenue data governance

When it comes to implementing effective revenue data governance, John identifies three critical pillars: Cross-platform data consistency, defining key metrics, and building reliable forecasting systems. Of these, cross-platform consistency presents the biggest challenge for most organizations.

“Every leader is being outreached to buy every tool under the sun. More tools do not equal more success,” John said. The problem emerges when departments implement tools independently without considering the horizontal data layer. “As you’re adding those, you have to do it with intention and you have to have an underlying data architecture that all those are fitting into.”

The consequences of poor cross-platform consistency are immediate: Conflicting data and fundamental errors. “You’re going to have two things telling you what you think should be the same number as completely different numbers,” he said. This becomes especially critical as companies prepare for AI implementation since the underlying data model will feed all AI solutions.

For practical governance without constraining growth, John advocates focusing sales teams on essential data points rather than comprehensive field completion. Don’t ask for 100 fields – ask for 5 that tie directly to business outcomes. Modern tools can help by listening to conversations and suggesting field updates, making it easier for sales reps to react rather than create from scratch.

The biggest technical debt trap? The impulse to solve tracking problems by adding new CRM fields. “If your urge is to say, ‘Just add a new field with the CRM so you can track something,’ you’re probably not doing it the right way. You are also building tech debt that you’re going to suffer from 5 years from now.”

Key metrics and forecasting capabilities, while equally important, depend entirely on this solid data foundation to provide accurate business intelligence and enable confident strategic decisions.

Beyond the CRM admin misconception

Despite RevOps’ strategic potential, most organizations still relegate the function to tool oversight. “We’re the CRM admins, which in some cases, yes, that function does fall under RevOps, but it is so much more,” John said. This misconception stems from RevOps’ origins as Salesforce or HubSpot administrators tasked with generating reports.

The “report factory” mentality creates a fundamental inefficiency in decision-making. “If your RevOps leaders are not sitting in the room where decisions are being made, you are elongating the timeline,” John said. Instead of having strategic input during discussions, teams make decisions, realize they need data, send requests to RevOps, then reconvene – a cycle John describes as “playing this game of hacky sack” instead of moving efficiently.

John measures his effectiveness differently: “Am I engaging with my counterparts in those strategic decisions? If I am not, I am not being effective.” RevOps should be the nervous system enabling real-time strategic guidance, not the department to call after decisions are made.

The shift from narrow CRM analytics to holistic revenue orchestration reflects this broader misconception. “Revenue data is everywhere. It’s not just in the CRM,” John said. “If your strategy is just to bring it all to the CRM, you’re going to fail. There’s too much data out there today.”

Modern revenue operations require integrating data from financial planning & analysis systems, product telemetry, customer success platforms, and marketing tools to understand the complete customer journey. “The database layer is one of the most important assets that you have at your company, and you need to treat it as such,” John said.

This holistic approach becomes critical for AI success, as companies need comprehensive data ecosystems rather than CRM-centric views to feed machine learning models effectively.

Future-proofing for the AI era

Looking ahead, John sees RevOps professionals becoming increasingly tied to AI strategy implementation. The function won’t be replaced by AI, but rather will become essential for managing AI’s integration across revenue processes. “The biggest change from a capability perspective is that 10 years ago, there were a lot of RevOps professionals who didn’t have coding experience, who didn’t have database skills experience. That’s going to be everything. You will not survive if you enter this field and do not have those skills.”

Technical skills alone, however, aren’t enough in John’s view. “RevOps has to have a sense for business strategy,” he said. “So many data individuals will say, ‘I live in code.’ Well, that’s great. But if you can’t tie that code to something and articulate it to a sales executive, you will not be successful.”

This dual requirement, technical proficiency and sharp business acumen, positions RevOps at what John calls “the fulcrum between the strategic and the technical.” As AI capabilities evolve rapidly, RevOps professionals must balance which element leads the decision-making process at any given moment.

For founders building companies today, the message is clear: “Don’t think your RevOps is your CRM admin. Start early, because if you do these things right with intention now, it’s going to drive your ability to scale and your future success.” The companies that establish solid data foundations now will be the ones that can effectively leverage AI to move ahead of competitors still wrestling with fragmented, unreliable data systems.