7 Ways Leading Comp Teams Are Using AI (Beyond the Basics)
At this point, saying you use AI is like saying you use email.
81% of comp professionals in our 2026 State of Incentive Comp Report say they're using AI in some capacity, up 16 points year over year. The more useful question is how many use it extensively. The answer is 28%. Those organizations report a 67% very prepared rate.
Seven of the top 20 predictors of organizational preparedness in our data are AI-related; no other category comes close to that concentration. And the comp leaders running those teams? They're not just more prepared — they're more visible, more strategic, and a lot harder to replace.
So what are those teams actually doing with AI that the other two-thirds aren't?
We extracted the patterns from the data. Here are seven use cases that distinguish the deep-end users from the rest.
1. Pressure-testing plan design before it ships
The old version of plan design: build the model, walk it through committee, send it to the field, find out at quarter-end whether it worked.
The new version: ask AI to recommend changes before the plan goes live, model the second-order effects, and ship a version that's already been stress-tested.
52% of AI users in our SOIC data are now using AI for strategy recommendations, up 10 points year over year. More than half of AI users are asking the system to weigh in on what the plan should look like, not just calculate against it.
Plans fail most often where designers didn't think to look. A SPIFF that accidentally rewards discounting, a quota structure that pushes reps toward smaller deals, an accelerator that's too easy for high performers and impossible for everyone else. AI can run thousands of scenarios against historical performance data in the time it takes to schedule a comp committee meeting.
Start by feeding AI your last four quarters of performance data and asking it to flag plan elements that would have produced different outcomes, which SPIFFs over- or underperformed, which accelerators bunched at certain attainment levels, which territories consistently missed. The exercise tells you whether the AI has enough context to be useful before you build it into your design process. If the outputs match what you already knew, the data foundation is solid. If they're noise, the problem is upstream, and worth fixing before you trust AI with forward-looking design.
2. Pulling insights out of reports nobody has time to read
Comp leaders are sitting on more data than ever and have less time to interpret it. Quota attainment by segment. Plan-type performance by region. SPIFF ROI by quarter. The reports get built. Whether they get read is a different question.
67% of AI users — up 11 points YoY — are now using AI to summarize insights from reports. That's the most widely adopted advanced use case in the dataset, and it ranks #15 among our preparedness predictors. Teams that do this report a 48% very prepared rate.
"What does this report actually say?" is a real question. Most dashboards bury the lede in 14 charts. AI pulls the signal: what changed, what's anomalous, what deserves a closer look, and saves the deep reading for what warrants it.
3. Drafting plan communications that reps actually understand
53% of AI users are using it to communicate plans. That's not a small operational improvement. It's one of the biggest unlocks in the entire AI use-case list.
Reps tune out comp plans for a specific reason. And it's not the math. It's the legalese, the outdated PDFs, and the portal still showing last quarter's plan.
Clarity is the single biggest motivation factor in our State of Sales data. 67% of salespeople say they stay motivated regardless of how often targets change, as long as expectations are clear.
AI helps comp teams translate plan logic into language reps actually read. Personalized examples. Plain-English breakdowns of accelerators. Pre-built answers to "how does this affect me?"
The comp plan doesn't drive behavior. The understood comp plan drives behavior. AI shortens the distance between the two.
4. Auto-resolving the easy 80% of rep inquiries
Most of those aren't really questions. They're verifications: checking whether a deal got credited, whether a SPIFF applied, whether the math holds. The information exists. The rep just can't get to it.
AI agents that can read commission logic and answer questions in plain language eliminate the easy 80% of inquiries before they ever reach a comp admin's inbox. The hard 20% still come through, but those are the ones worth actual human attention.
Audit your last pay period's inquiries and categorize them into calculation questions, missing deals, plan confusion, and status checks. If 70% or more fall into three or four repeatable categories, those are the ones to automate first. Build the AI workflow against the highest-volume category, measure the deflection rate over two pay cycles, then expand. Don't try to automate everything at once. The wins compound, but only if you can prove the first one worked.
5. Recalibrating quotas based on AI-driven productivity gains
This is the use case most comp teams haven't worked through yet.
43% of organizations in our SOIC report are already setting quotas based on the assumption that reps using AI will be more productive. Another 41% plan to. That means roughly 84% of comp orgs are either pricing AI productivity into their targets or about to. This is also a top-five predictor of revenue growth in our data.
If AI tools are making reps faster at research, prep, and follow-up, that capacity has to go somewhere. Organizations are now using AI to model what the new realistic quota should look like, based on actual productivity data. They run connected systems where quota changes flow directly into the plan configuration, so the recalibration is live in the field during the cycle, not waiting in a spreadsheet for the next implementation window.
6. Building dashboards on demand instead of waiting for the BI team
53% of AI users are using AI to create dashboards.
The traditional model: a comp leader needs visibility into a specific thing. Say, performance against accelerator thresholds by segment. They file a request with the BI team, wait two weeks, get a dashboard that's almost what they asked for, request changes, wait another week.
The AI model: ask the question, get the dashboard, iterate in real time.
This connects to a quieter pattern in our data. Basic programming and scripting (SQL, Python) is a top-eight predictor of preparedness, a skill that would have been irrelevant for comp teams five years ago. Teams that rank programming as important report a 59% very prepared rate. Nobody's asking comp admins to become developers. But the teams that can query their own data aren't waiting on anyone.
7. Pairing embedded AI with general-purpose tools
Here's the one that surprised us most.
74% of AI users in the SOIC report use AI built into their comp software. 72% also use general AI tools. The teams getting the most out of AI aren't choosing between embedded and general-purpose. They're using both.
This runs counter to how many vendors talk about AI ("our embedded AI replaces the need for outside tools"). The reality is more nuanced. Embedded AI is good at the things it has context for: calculations, plan logic, payee questions. General AI is good at the things it doesn't need that context for: drafting comms, summarizing meetings, sketching out scenarios. The combination is what compounds.
Map which AI tools your team is already using outside the comp platform. If general-purpose tools are doing work that should live in your ICM (answering payee questions, calculating scenarios, pulling commission data), that's a sign your embedded AI isn't pulling its weight. If embedded AI is doing things general tools handle better, like drafting plan communications or summarizing leadership readouts, you're paying twice for the same output. The right stack uses each tool for what it's actually good at.
What deep adoption looks like
Looking at the seven use cases above, the through-line isn't complexity. None of these require a data science team or a custom build. They require commitment, application, and the discipline to actually integrate AI into how the work gets done.
The preparedness data shows the same arc playing out at different stages.
Teams that committed early — extensive use (67% very prepared), AI built into the platform (49%), AI as a 2026 priority (49%) — are the ones now applying it across the workflow.
Teams applying it (48% very prepared for summarizing insights, 48% for strategy recommendations) are starting to see downstream results: better commission accuracy (50%) and fewer rep inquiries (49%).
Seven stats. One curve. Whichever stage your team is at, the next stage is on the same trajectory.
Cadence predicts AI adoption better than any feature comparison. Weekly adjusters use AI extensively at 3x the rate of as-needed adjusters. The weekly teams had to lean on AI; adjusting that often is hard to do manually. The as-needed teams didn't, because nothing forced them to.
Most teams are in the middle of this right now. Past the basics, not yet at extensive use. If that's you, pick one use case from the seven above and run it for a quarter. Not three. One. The comp leaders who've moved into more strategic roles didn't get there by boiling the ocean — they got there by proving what AI could do in a specific, measurable context, and then expanding from there. Winning teams got to extensive use the same way: carefully, one use case at a time.
Want to go deeper on where AI is heading in sales planning and incentives? Download our 2026 AI in Sales Planning and Incentives Report.
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Suspendisse varius enim in eros elementum tristique. Duis cursus, mi quis viverra ornare, eros dolor interdum nulla, ut commodo diam libero vitae erat. Aenean faucibus nibh et justo cursus id rutrum lorem imperdiet. Nunc ut sem vitae risus tristique posuere.
