Revenue ops is still evolving in real time. So while Finance has established principles, and manufacturing has mature operating systems, go-to-market operations, by comparison, can still feel like the Wild West. That is, two companies can use nearly identical tech stacks, yet run their revenue engines entirely differently.

It's often in sales compensation, however, where inconsistencies become impossible to ignore.

Downstream of all the functions put together is where ambiguous ownership turns into exceptions or a poorly chosen measure can reward behavior the business never intended to encourage.

Mollie Bodensteiner, VP of Revenue Operations at ZoomInfo, has seen sales comp tensions play out across multiple organizations. And in this candid conversation with Forma.ai Founder and CEO Nabeil Alazzam, she brings strategic perspective to a function too often discussed primarily in terms of plan mechanics.

For Mollie, a comp plan is really a live operating system. Its exceptions reveal how the company governs. Its usability determines whether sellers can act on it. And its data can show leaders how performance is really being created.

This conversation covered a ton of ground; from governance, to plan rollout, AI, and the human judgment required to make compensation work in practice. So below we've distilled some of the episode’s most useful ideas for a holistic incentive program that works in the real world.

Episode resources

• Connect with Mollie on LinkedIn  

• Mollie's resource recommendations:  You Win in the Locker Room First by Jon Gordon and Mike Smith. Additionally, books by Jim Collins (including Good to Great, Great by Choice, and Built to Last)

Stop processing endless comp exceptions. Start governing them.

One of the first things Mollie points out is that comp plans can become a liability when leaders don't zoom out to standardize governance.

As she observes, compensation exceptions often start as routine operational work: A request comes in. The compensation team gathers context, checks the rules, routes approvals, updates the calculation, and communicates the outcome. The queue clears...until the next exception arrives.

What looks like simple administrative cleanup is actually often a signal that the underlying compensation design isn’t working as intended.

There's a situation where there are effectively two compensation plans: the one that was designed and communicated, and the one that emerges through undocumented overrides, selective enforcement, and escalations that depend on who is asking:

To Mollie's point, every exception sets a precedent.

When top performers successfully negotiate around the rules, sellers learn the plan's flexible. Managers learn to escalate instead of coach. And the compensation team is left enforcing decisions that sales leadership has already implicitly undermined.

Over time, the written plan loses authority (and so does the system it’s meant to run).

For senior leaders, you should question: “what does this request reveal about how the system is working?”

• Is the policy misaligned with reality?
• Is the rule unclear or inconsistently applied?
• Is this a true strategic exception or a way to avoid a difficult conversation?
• What precedent does this set for the next request?

Exceptions will always exist, that's a given. But your goal is to prevent independent judgement from becoming invisible system design. A useful way to manage this is to classify exceptions into three types:

1. ‍Data corrections. These restore the outcome the existing plan already intended. These should be resolved quickly and monitored for systemic upstream issues.
2. ‍Policy interpretations. These expose ambiguity in the rules of engagement, crediting logic, or plan language. Repetition signals clarification or redesign is indeed needed. ‍
3. Strategic overrides. These consciously depart from policy because leadership believes a different outcome serves the business. These require explicit ownership, documented reasoning, and a clear decision on whether the precedent should alter the plan.

That last category is critical. Sales leaders can't treat compensation as “someone else’s system” while continuously rewriting how it behaves through exceptions.

As Mollie advocates, they need to help design it, stand behind it, and reinforce it in the field, because every override is effectively a change to the company's operating system. Once leaders start patching the system informally, Mollie warns, they’ve effectively “lost the locker room”—and rebuilding that trust is far harder than fixing the original architecture.

Stress-test what 100% team-wide attainment would actually imply for the business

Salespeople are supposed to optimize their compensation plans. That is the point.

The most commercially aware sellers will study the mechanics, identify the highest-value path, and concentrate their effort accordingly. Calling that behavior “gaming” can obscure the more useful question: what happens to the business when the seller succeeds?

So, Mollie uses a sharp test when designing sales compensation plans:

If someone finds a way to reach 200% attainment, will the company be pleased with the revenue, margin, customer profile, and behavior that produced it?

Her test becomes even more revealing when applied across the entire field. Before approving a plan, imagine every rep discovers its most profitable strategy. Would the company win at scale?

A well-designed incentive program should survive rational optimization. If a seller maxes their earnings by producing exactly the customer and economic outcomes the business needs, the plan is functioning. The organization should be eager to study that performance and help more sellers replicate it.

You should only be worried when the easiest route to higher pay inadvertantly generates low-quality acquisition, weak margin, short-lived consumption, channel conflict, or other outcomes that become expensive after the commission has been paid.

Caps can limit exposure, sure, but they do not repair faulty incentive logic. In some cases, a cap simply punishes a seller for producing an outcome the company should have wanted.

Scenario planning should, therefore, go beyond projecting total payout expense. For every proposed measure, ask:

• What's the fastest legal route to maximizing this component?
• What happens if every seller takes that route?
• Does the behavior remain profitable at 150% or 200% attainment?
• Is there a quality, margin, retention, or consumption outcome that must accompany volume?
• Would leadership celebrate the result, or attempt to change the rules after seeing the payout?

This challenge (discovering when a comp plan stands to reward the wrong behaviors) becomes sharper as companies experiment with acquisition, usage, consumption, and other measures where economic value may not be fully understood at the moment of sale. A unit can be easy to count while still being difficult to value.

Where the relationship between an action and its eventual value is uncertain, leaders should be cautious about assigning too much incentive weight too early. Pilot the measure. Observe quality. Build history. Then increase its role as confidence in the economics grows.

Ultimately, design for the intelligent seller who will find the edge case. Then make sure the edge case is one the company can afford to scale.

Use activity to reveal the path to quota, not replace it.

Mollie is candid about activity-based compensation: she does not like the idea of paying on activity. During ramp, she sees a case for activity measures helping a new seller build the required motion before enough outcome data exists to evaluate performance properly. But beyond that stage, she argues activity should usually serve a different purpose.

It should help the organization explain the path to quota.

The best companies Mollie's worked with have made that path visible at any point in time. Sellers can see how their inputs connect to pipeline and how pipeline connects to the outcome they're expected to produce. This visibility helps leaders coach the motion without confusing movement with progress.

A blanket requirement to make 75 calls per day doesn't automatically create revenue (just compliance).

As Maggie shares, a high-performing seller who reaches quota with fewer calls may have found a better message, channel, account profile, or sequence; so that variation is worth investigating. RevOps can identify what is working and determine whether the behavior can be transferred to the rest of the team.

The opposite is equally informative. A seller making 500 calls while remaining near the bottom of the attainment distribution doesn't need a reward for exceptional activity. They need help diagnosing why the activity is producing so little value.

This is where activity data can become an operating instrument. It can help leaders distinguish among several very different problems:

• Insufficient effort
• Poor targeting
• Weak conversion
• Ineffective messaging
• Inadequate pipeline coverage
• A quota or territory that was unrealistic from the beginning

As Mollie argues, though, paying directly on activity can blur these distinctions. The measure quickly becomes the goal, even when the activity has stopped contributing meaningfully to the outcome. Instead you want to make the causal chain visible. Show sellers how much pipeline they need, what conversion assumptions underpin the path, where they currently stand, and which inputs have historically helped comparable reps close the gap.

This creates a much stronger coaching conversation than a generic volume target.

It also creates a continuous learning loop for RevOps. When someone outperforms the expected input-to-output relationship, it's time to study them. When someone dramatically underperforms it, intervene. When the relationship changes across the entire field, update the operating assumptions.

One more diagnostic: average attainment can hide a broken plan

Mollie challenged another familiar sales performance metric toward the end of the episode: average quota attainment.

As she points out, an organization may report that the team achieved 90% of plan and conclude its targets and incentive design were broadly successful. But the average actually reveals very little about how that result was produced.

One exceptional seller may be pulling the result upward. The field may be divided between a large group at the bottom and a small group dramatically overachieving. A cluster immediately above quota could indicate healthy calibration, an exploitable threshold, or an uneven allocation of opportunity.

To understand plan health, Mollie recommends you review the full attainment distribution.

• Where are sellers clustering?
• How wide is the spread?
• Are results consistent within comparable roles and territories?
• Is one manager relying on a single “golden pony” while the rest of the team remains underwater?

The distribution helps leaders determine whether they're looking at a plan problem, a quota problem, an opportunity problem, or a management one. It also shifts the leadership objective. Instead of depending on isolated heroics, managers should be accountable for building more consistent performance across the team.

Overall, the average tells you where the organization landed. But the distribution more granularly begins to explain how it got there.

Want more takeaways from Mollie? Watch the full conversation for more on productizing the compensation process, building field ownership, applying AI to scenario planning, and much more.

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