Most comp plans buy the wrong behavior in a usage-based world—and the results show up as stale pipelines, noisy dashboards, and hunters who drift into farming. We sat down with seasoned sales ops leader Chuck Lee to unpack how to pay for outcomes that actually matter: a clean start, a predictable ramp, and a scalable hand-off that sticks.

We trace a real transformation inside a large inbound motion where reps were incentivized to chase the oldest leads and obsess over consumption they didn’t own. By stripping the plan to a simple, action-focused design and shutting off post-implementation pay for AEs, the team saw a 40%+ lift in conversion. Chuck explains why high-velocity sales demands fewer choices, not more; how to align quotas to volatile demand without eroding trust; and the telltale signs your plan is buying noise instead of revenue.

Then we go deep on usage-based mechanics. The true “deal won” is when the customer starts using the product, and the second milestone is the ramp to forecast. Chuck shares how to set the AE’s window in the deal using historical ramp curves, why FP&A should co-own the model, and how SLAs between sales and CS prevent credit confusion and dropped hand-offs. We also confront the perpetual commission trap that turns hunters into farmers, and outline a cleaner split: hunters own start and ramp-to-target, farmers own adoption, expansion, and problem-solving.

If you’re wrestling with comp design for usage-based sales, this conversation gives you practical guardrails, from monthly quota tuning and points-based payouts to role clarity that protects new logo growth. Subscribe, share with your revenue team, and tell us: what behavior is your comp plan really buying?

The loudest AI often isn’t the smartest. We dig into fresh research with Curtis Schroeder, Head of Research and Insight at Varicent, to unpack a striking pattern among 150 revenue leaders: most expect the biggest ROI from system-level AI—forecasting, territory design, quota setting, incentive modeling—while investments still chase seller-facing tools that look great in a demo but struggle to compound impact.

Curtis explains why AI for revenue is really two markets. Seller AI promises instant productivity stories yet demands training, process change, and continuous behavior shifts. System AI upgrades decision quality at the core, creating compounding gains across the org—better coverage, cleaner attainment, faster re-planning, and more credible forecasts. We explore how to separate hype from value, why human skepticism remains the top blocker, and why adoption improves when AI becomes invisible inside workflows rather than another tool reps must learn.

You’ll hear how leaders pair quick visible wins with deeper system investments, how to make forecasting an always-on signal rather than a month-end ritual, and how to link territory potential to quota for fairer, higher-yield plans. We also get real about ROI proof: attribution is messy, but speed, decision quality, and resilience to market shocks are measurable and persuasive. If you’re navigating mandates to “do AI” while chasing durable growth efficiency, this conversation offers a practical blueprint to build trust, compress planning cycles, and invest where results compound.

If the episode sparks ideas, follow the show, share it with a teammate, and leave a quick review—what’s one system-level decision you’d upgrade with AI next?

Download the report mentioned in this podcast:

https://www.varicent.com/info/ai-roi-sales-revops

Headlines scream about AI every day, but the real story is quieter: the teams winning with AI aren’t chasing shiny tools, they’re rebuilding how revenue work gets done. We sat down with Dan Morgese, Director of Content Strategy and Research at Gong, to unpack the new State of AI report and reveal what separates impact from noise. The report pairs a survey of 3,000 director-plus leaders with Gong Labs analysis of 7.1 million closed opportunities, giving us both market sentiment and inside-the-workflow evidence.

What stood out first is a mindset shift: productivity just jumped to the number one growth lever, reframed from time saved to revenue per rep. That changes everything. Instead of using AI to draft more emails, top teams use it to guide seller actions, expose deal risk, and align coaching with what actually moves win rates, cycle time, and ASP. Depth of adoption beats breadth—leaders who treat AI as a core driver of strategy, not a sidecar, see stronger commercial outcomes across the board.

We also dig into the underappreciated frontier: forecasting, strategic planning, and initiative tracking. Adoption for these systemic use cases surged as teams realized forecasting improves when you combine call intelligence, pipeline dynamics, and engagement signals. Planning gets smarter when AI informs territory design and compensation scenarios. And tracking initiatives in the wild lets leaders see whether new messaging lands with customers and whether it moves revenue, closing the loop from strategy to impact.

Trust inevitably comes up. Sixty-seven percent of leaders say they trust AI, but the smarter framing is trust in data. Domain-specific systems that capture reality—conversations, signals, and activity—beat manual CRM fields when accuracy and explainability matter. With AI quickly becoming table stakes, the advantage shifts from “Are you using AI?” to “Are you using it well?” If you’re ready to move beyond pilots, this conversation offers a blueprint: pick systemic use cases, build depth, measure what matters, and let revenue per rep be your scoreboard.

If this resonated, follow the show, share it with a colleague who owns forecast or RevOps, and leave a quick review so more revenue leaders can find it.