RevOps leaders are being asked to solve a much broader set of problems than even a few years ago.

Leadership exceedingly wants to see more ambition. More precision. Faster decisions. Better seller productivity. And a stronger answer for where AI should change the operating model.

The danger's assuming these are separate problems, though.

In our latest episode of The Sales Compensation Show, Dominic Ballinger, VP of Revenue Operations at Sage, offers a useful way to look at this. Dominic came to RevOps through finance and restructuring before moving into GTM operations, where today he helps run a $2B go-to-market plan. He knows what happens when the optimism in the spreadsheet meets the reality of execution.

As he's observed, there's an important role emerging for senior RevOps and sales compensation leaders, insofar as the the job keeps becoming one of translation.

• Translating executive ambition into a plan people can execute.
• Translating field reality back into an honest forecast.
• Translating strategy into quotas and seller economics that reinforce the same definition of success.

And now, translating rapidly expanding AI capabilities into workflows that create actual business value.

Do all this well, and RevOps starts to look a lot less like an operations layer and much more like an architect of the revenue system.

Here are some of the highlights from Dominic’s conversation worth carrying into your own sales planning discussions. As always, you can bookmark the episode to listen in anytime on Spotify, Apple Podcasts, and YouTube.

Episode resources

• Connect with Dominic on LinkedIn  

• Dom's book recommendations: The Five Dysfunctions of a Team by Patrick Lencioni and Essentialism by Greg McKeown

Protect the forecast’s ability to tell you something you don’t want to hear

Stretch targets are supposed to create productive pressure. But Dom notes they become dangerous when that pressure reaches the forecast itself.

Dominic points to a difficult period in 2018, when Sage ultimately issued a profit warning after missing guidance. In hindsight, he says, there were signals that the miss could happen. The bigger problem was cultural: people struggled to surface a forecast that wasn’t what others wanted to hear. Dominic puts the lesson plainly:

This distinction matters for anyone involved in sales planning.

A target is an expression of ambition. A forecast, on the other hand, is information about the system.

When leaders implicitly require the second to validate the first, the organization loses one of its most useful early-warning signals.

Beyond creating psychological safety, Senior RevOps leaders need to preserve enough separation between what the business is committed to achieving and what the current evidence says it will achieve that both can coexist in the same operating conversation.

A field leader should be able to say, “We are still accountable to this number, and based on what we know today, this is where I think we are going to land.”

If those two figures are forced to converge politically, the plan becomes harder to manage precisely when intervention matters most. For planning leaders, this suggests a useful operating test:

• Can the forecast deteriorate without immediately becoming a debate about commitment? If not, signal quality is probably being compromised.
• Can leaders trace the gap between ambition and forecast to concrete assumptions? A gap without an operational explanation is simply unresolved risk.
• Does bad news trigger diagnosis early enough to matter? The purpose of surfacing a yellow flag or miss in advance is to create options. Waiting preserves optimism while eliminating maneuvering room.

The stronger the growth ambition, the more important all of the above becomes. Because, while hope can motivate people, as Dom shares, it makes a poor sensing system.

Dominic is careful not to swing too far in the opposite direction though. As he shared on stretch targets...

A stretch target only works if the operating plan and seller economics agree on what “success” means

According to Dom, the answer to unrealistic planning isn’t to let historical performance dictate the future.

He argues that credible stretch should have a fact base. Think: TAM, prior performance, competitive context and the other data available to form a reasonable view of what could happen. At the same time, he has seen organizations outperform what the spreadsheet suggested was possible because people genuinely believed they could reach beyond the trend.

Which makes for a hard design problem. Your organization needs enough evidence to make the ambition believable without turning the evidence into a ceiling.

You'll want to pay close attention to one layer later, too. That is, CEOs are expected to set ambitious direction. Dominic sees the VP layer as the translators: pressure-testing the ambition, adding context, debating what it requires, then converting it into an executable plan for the org.

And that translation has to reach sales compensation.

Consider the scenario Dominic and Nabeil discuss.

Leadership consciously sets a moonshot target where reaching 70% would still represent an extraordinary business outcome. Then that same stretch gets pushed directly into individual quotas. A seller reaches 70% and, because of the payout curve, earns only 25% of OTE. The business has now encoded two definitions of success.

• At the executive level, 70% is exceptional progress.
• At the seller level, 70% feels like failure.

Quota setting therefore has to be more than allocation math. Before cascading an aggressive corporate target, follow the logic through the entire system:

Growth ambition → capacity and opportunity assumptions → territory and quota design → expected attainment distribution → payout economics → seller experience

Work to isolate where the story changes.  

If leadership is intentionally accepting a wider range of outcomes in pursuit of breakthrough growth, the downstream mechanisms need to reflect that decision. Otherwise, the company is asking sellers to personally absorb uncertainty that executives have already acknowledged exists.

Sage’s AI lesson: treat the work like product development, not a company-wide building contest

Like many businesses, Sage has been deliberately building AI into its RevOps organization to improve workflows.

And one of the more interesting choices is structural. Dominic described pairing product-management and engineering capabilities around specific outcomes. i.e.:

1. Who is the internal customer?
2. What pain are we solving?
3. What outcome should change?

Only then does the team build anything.

That framing becomes important because the first wave of enterprise AI experimentation encouraged almost the opposite behavior (i.e. Give everyone the tools. Democratize building. Let hundreds of use cases bloom).

As Dom shared, the result of this first approach often leads to a large amount of activity with surprisingly little leverage. Impressive demos can still land with an internal customer who says, effectively, “That’s interesting, but it doesn’t materially change my day.”

Sage’s emerging approach is more disciplined. They stay close to the internal customer and map the jobs and tasks being done. They're finding painful, administrative work that pulls GTM teams away from customers and higher-value judgment, then they remove it.

Dominic shares one example where his team automated a complicated workflow and built the supporting software in a week. The team estimated that the same work previously could have taken roughly two months.

Now, ,RevOps teams have always had backlogs full of useful things that never became important enough to justify scarce engineering resources, analyst hours or implementation effort. Because AI can suddenly make some of those improvements economically viable, this creates a prioritization problem for leaders, so you need to exercise judgement here.  

A practical AI portfolio should start with questions like:

• Where is high-value talent spending time on low-value work?
• Which workflows recur often enough that removing friction compounds?
• Where does better access to existing data change the quality or speed of a decision?
• Is the internal customer’s pain acute enough that changing the workflow will actually alter behavior?

Whats more, AI makes far more data and metadata feasible to bring into planning. But Dominic’s concern is that this newfound possibility can encourage teams to over-engineer the system simply because they now can. The output still has to be understandable enough for humans to execute. Remember: more computational power doesn’t repeal the cost of complexity.

RevOps’ next advantage is context

Taken together, Dominic’s ideas point toward a more consequential version of RevOps in the near future.

As he put it, the function’s advantage won’t come simply from possessing more data, deploying more AI, or producing more sophisticated models. It'll come from understanding enough of the business to connect these things. All to know:

• When the forecast is a signal leadership needs to hear.
• When a stretch target is supported by an executable hypothesis.
• How that hypothesis should show up in territories, quotas and incentive economics.
• Which workflows are worth rebuilding with AI and which are merely interesting.

And, how to reorganize execution when technology changes the assumptions the existing operating model was built around.  

As Dom predicts, it'll all become a way bigger remit than administering the revenue engine. it's definitely more about architecting it.

Want more insights like this? Subscribe to The Sales Compensation Show on Spotify or Apple Podcasts, or YouTube for bi-weekly episodes featuring the revenue leaders behind today’s fastest-growing companies.