Most revenue teams discover their governance problems the wrong way — after a deal closes, when the margin is already gone and the approval chain exists only in someone's memory. The dashboard lights up, the CRO is surprised, and the post-mortem reveals a chain of decisions that were never systematically controlled in the first place.
In a recent RevOps Co-op webinar, Matthew Volm moderated a conversation with David Burns, CTO at TLC Worldwide, and Eran Gross, VP of Customer Success at DealHub, about what it actually takes to build commercial governance into the revenue workflow — not as an audit tool, but as a proactive operating system. TLC Worldwide, a global campaign management organization operating across 14 markets, offers a compelling case study: a business that made the shift not because things had broken catastrophically, but because it could see the risk clearly enough to act before they did.
The session covered pricing authority, approval design, deal visibility, change management, and where AI fits into all of it — in that order, intentionally.
The most common misdiagnosis RevOps and finance leaders make is thinking they have a reporting problem. They build better dashboards. They add more metrics. And they still get surprised by deals with unexpected margins or approval paths that nobody can reconstruct.
Eran Gross has seen this play out hundreds of times across CPQ implementations:
"Usually when they're looking at the dashboard, the problem is somewhat three steps or even more steps removed from the actual dashboard they're observing. And by the time it shows up on the reporting, of course the damage is done. You sealed the deal, the margin has been negotiated. You got an approval Slack from someone, and you have to trace it back to who approved it and why, and what was the reasoning." — Eran Gross
Better dashboards don't fix the underlying issue. If the foundations of how pricing is governed, how approvals are structured, and how commercial decisions are documented are wrong, reporting will only tell you about the problem after it has already cost you. As Gross put it: "It's really a governance gap to begin with."
This connects directly to a pattern RevOps teams encounter across the board: the instinct to layer analytics on top of broken processes produces more noise, not more signal. The fix has to start earlier in the workflow — at the moment the deal is built, not the moment it is reported.
When Matthew Volm asked the audience where pricing authority lives in their organizations today — in a system, in a process, in tribal knowledge, or somewhere undefined — Gross's response was telling: all four answers are common, and often all four are true simultaneously within the same organization.
The challenge isn't just that pricing authority is distributed. It's that the distribution is invisible. Someone owns a pricing spreadsheet built in 2018 that nobody wants to touch. A regional lead has unwritten norms about which deals need a quiet heads-up before formal submission. New sales reps learn the unofficial approval map by osmosis, if they learn it at all.
"Usually when they're looking at the dashboard, the problem is somewhat three steps or even more steps removed from the actual dashboard they're observing." — Eran Gross
For TLC, the starting point wasn't chaos — it was a mature-but-manual process built on Excel macros that had grown organically over time, accumulating market-specific nuances and localized logic that lived nowhere except the spreadsheet itself. Burns described the situation honestly: "The current process had served us well. But when we really looked at ourselves as a business and tried to understand how we were gonna scale and develop as an organization, and the controls that we had in place to really help that scaling and that growth, we then started to take that critical review of the end-to-end process."
The risk wasn't that deals were going wrong. The risk was that the mechanism preventing deals from going wrong was entirely manual, brittle, and non-transferable. As people leave, as markets add complexity, as deal volume grows, the spreadsheet-and-tribal-knowledge model erodes. The question for leadership was: what happens when the people who know how to navigate this leave?
Tribal knowledge about commercial processes feels like institutional memory. In practice, it functions more like a single point of failure.
Burns has seen this play out in other organizations beyond TLC — deals with negative margins signed not through malice or incompetence, but because a particular piece of data wasn't in the model, or because the person who understood the pricing nuance had already left the business:
"No one does this with malice or intent. People sign those deals at the time. It's only in retrospect when they look at it and say, 'Actually, this is just not working out.' And often that's because of the information that they didn't know. It's because a particular piece of data wasn't in the model or in the process." — David Burns
This is a harder problem than it sounds, because the erosion of commercial knowledge is gradual. A cost model that was well-understood four years ago becomes opaque over time. "When you start to go through this journey of bringing in this type of solution," Burns noted, "you will inevitably hit the question as to how do we actually cost, and do we have everything in our cost models? And in actual fact, does that need further refinement?"
For RevOps leaders thinking about why legacy processes break under modern demands, the commercial governance gap is a useful lens: it's not that the old process was wrong when it was designed, it's that manual processes don't compound — they erode.
One of the most important reframes in the session came from Gross's description of how he thinks about CPQ readiness. When a revenue leader comes to him saying "we are here to buy software that's gonna solve our problems," he treats that as a warning sign:
"The ones that are gonna come to us and say, 'We are actually documenting what we're doing today,' or, 'We have it documented already, and we are looking for a platform to implement that and deploy that with the platform's capabilities' — that's a better positioning of an engagement with customers." — Eran Gross
The reason is structural: a CPQ implementation touches pricing logic, approval structures, contracting, negotiation norms, legal review, finance observation, ERP synchronization, and fulfillment workflows. It is not a deal desk project that happens to involve IT. It is a company-wide change management project that requires alignment — and at minimum, awareness — from every team whose work touches a deal.
This maps directly to what RevOps practitioners consistently report about change management: the organizations that struggle most with new system deployments are the ones that treated the implementation as a technical problem rather than an organizational one.
Burns reinforced this from TLC's experience: "If you don't approach this with an intent of trying to bring in a new way of working, a new thinking, and a new approach as well, you miss a great opportunity. The classic mistake here is re-engineering your existing process into a system. The opportunity exists to refine your process to get new ways of working."
One of the more underappreciated benefits of going through a CPQ implementation is what the process of encoding commercial logic forces organizations to discover about themselves. Burns described this as an inflection point for TLC — not a recovery from failure, but a move toward something better than they had realized was possible.
The questions that surface during implementation tend to expose assumptions that had calcified into unexamined conventions:
On the margin visibility question specifically, a question raised by a live audience member, Burns offered a nuanced answer: different organizations have wildly different positions, and neither transparency nor opacity is universally right. TLC's approach involves rolling up certain cost elements so "the price is the price," while allowing a defined tolerance for flexibility within guardrails. The key is that the guardrails are explicit and enforced in the system — not negotiated informally in the moment.
Gross added that in his experience across hundreds of CPQ implementations, the more successful approach is to present information to sales reps when they are being held accountable for it: "If there is information that I'm holding the sales rep accountable for through an approval chain or through other means, then ideally I would want to present it to them so that they will know where they're at."
DealHub's approach to CPQ is built around exactly this kind of governed flexibility — giving sales teams enough visibility to act intelligently within defined boundaries, while ensuring that commercial decisions are captured, triggered, and reviewable at every level of the organization.
Before DealHub, TLC's deal review process began when a completed spreadsheet was submitted — meaning the first time leadership saw the details of a deal was when it was already fully formed. The gap between deal initiation and leadership visibility was structural, not accidental.
Now, Burns described a fundamentally different model: visibility into the entire deal lifecycle, from the moment a sales rep starts building a model to the moment a client opens the proposal.
"Here we're able to see who's gone into the system, what they've started to build, where in the life cycle is that process? So we can almost look right through the whole life cycle of DealHub and say, 'Okay, what's coming towards us, and when is that gonna arrive?'" — David Burns
The approval architecture is tiered by deal size and risk profile. Individual markets can sign off on deals within defined parameters. Deals above certain thresholds route automatically to the global deal team — a group of four or five individuals, including the CFO for the largest deals — where each reviewer can approve asynchronously within the system rather than convening a synchronous meeting. The result is faster processing without sacrificing oversight.
There is also a proposal engagement layer: TLC can see which contacts from the client organization have opened the proposal, what pages they spent time on, and whether senior decision-makers like the CFO or CEO have logged in. That behavioral signal feeds directly into pipeline confidence and follow-up prioritization — something that was completely invisible in the spreadsheet-era workflow.
This kind of structured deal insight connects to a broader RevOps principle about how approval data can actively drive efficiency, rather than simply documenting what already happened.
No governance transformation succeeds by fiat. Burns was candid that TLC's implementation required deliberate effort to bring skeptics along — not just the early adopters who were excited about the change, but the people who were satisfied with the existing process and didn't see a compelling reason to change.
The approach was straightforward but time-intensive: face-to-face conversations with everyone affected, focused on understanding their concerns and explaining the reasoning behind the transition.
"We over-indexed a little bit on our side in terms of engaging with not just the people who were really driving the change and really wanted the change in the business, but all those as well who were happy with the old way of working and didn't really see the need to change. And we spent a lot of time face-to-face just explaining why and getting everyone's opinions and views and perspectives." — David Burns
The payoff was that TLC ended up with no fundamental objectors — not because everyone was equally enthusiastic, but because everyone understood the reasoning. The distribution of enthusiasm was wide, but the distribution of understanding was narrow.
Gross reinforced a related point that is easy to underestimate: sales teams tend to experience governance as friction. "Sales see governance as a friction, simply put." The job of a well-designed commercial governance system is to make that friction purposeful — to slow down the right decisions in the right moments, rather than creating blanket obstacles that frustrate everyone equally.
For RevOps leaders navigating this kind of organizational transition, the framing matters: influencing without authority is a core competency here, because the people who need to change their behavior are rarely the ones who initiated the project.
The session's final topic was AI — and the answer to the obvious question ("Can we skip the governance work and just use AI?") was an unambiguous no from both speakers.
Gross's practical test for whether an organization is ready to start thinking about AI is simple: ask your VP of Sales to show you the pricing authority metrics for the last five deals — who approved them, at what discount level, and why — and see how quickly they can produce that information. If getting the answer requires going through three people, the problem is not a technology gap. It's a governance gap.
Burns extended the point with a more optimistic framing: once you have done the governance work and your deal data is structured, unified, and living in a system, AI becomes genuinely powerful in ways that spreadsheet-era analytics never could be.
"Once you get that type of structured data set, you can actually then start to ask some really quite intriguing questions to AI — not just, 'Give me this report,' or, 'Tell me this insight.' You can actually just point AI at the data and say, 'Tell me what you could do with this data. Tell me what insight is there that maybe we've not even thought of.'" — David Burns
The difference between AI as a reporting layer on top of bad data and AI as an analytical engine applied to structured, governed data is substantial. The former gives you faster wrong answers. The latter can surface patterns in pricing, deal composition, win rates, and margin behavior that human analysis would never find — and that can genuinely change how a business prices, forecasts, and goes to market.
Burns added one necessary caution: deal data is confidential, and the security governance around what can and cannot be fed into large language models is an organizational decision that needs to be made deliberately. The excitement about AI capabilities should not outrun the rigor applied to data security.
For RevOps teams trying to figure out where they actually stand on AI readiness, the underlying message is consistent with what the community has been discussing: the boring work of governance, documentation, and data structure is what makes the exciting work of AI possible. There is no shortcut.
The work TLC Worldwide did was not glamorous — it involved spreadsheet archaeology, market-by-market conversations, cost model reviews, and painstaking change management across 14 different geographies. But the outcome is a revenue workflow where leadership can see every deal in motion, where approvals are triggered automatically based on real risk criteria, and where the data coming out of the system is clean enough to eventually power AI-driven insights that the business never had access to before.
That trajectory — from reactive dashboards to proactive governance to AI-ready data — is available to any organization willing to start with the unsexy stuff. Learn more about how DealHub helps revenue teams build commercial governance into their CPQ and deal room workflows.
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