By the end of 2025, AI had infiltrated nearly every corner of the software world. The incentive compensation management (ICM) category was no exception. If you saw an ICM demo last year, you likely saw a chatbot from every vendor.
But as we move into 2026, the tide is going out. The hype cycle is ending, and the deployment cycle is beginning. This is the year we find out which of these AI features can survive in production, and which were built strictly for the stage.
I am incredibly optimistic about AI in ICM. We are already seeing it transform how admins troubleshoot and how sales reps understand their pay. But I also believe we are about to see a major fracture in the market.
The divide won't be between those who have AI and those who don't. It will be between software applications that have the architecture to support AI, and those that are simply layering a chatbot over a mess of custom scripts or formulas.
"The divide won't be between those who have AI and those who don't. It will be between software applications that have the architecture to support AI, and those that are simply layering a chatbot over a mess of custom scripts or formulas."Grayson Morris
Chief Executive Officer @ Performio
The physics of ICM: Tables and logic
To understand why AI struggles in our industry, you have to look at what ICM actually is. At its core, incentive compensation systems contain two key elements: tables and logic.
The tables store your data—transactions, payments, product details, territories, OTCs, hierarchies, etc. The logic is the engine that transforms that raw information into dollars and cents—applying crediting rules, eligibility criteria, hold-and-release payouts, clawbacks, SPIFs, draws, true-ups, etc.
For AI to be valuable, for it to diagnose a payout error or model a plan change, it must understand the intent behind those tables and that logic. It needs to know that one specific table represents the commission rates for the regional sales manager plan, and what the payout rules for that plan mean for commission results this month.
This is where architecture comes into play.
The problem with "custom"
For decades, the standard approach in ICM has been extreme customization. If you needed a complex plan, vendors built it for you using custom scripts, nested formulas, and database tables unique to your business.
To the humans building those systems, that customization made sense. But to an AI, it is a black box.
If an AI looks at a system built on custom scripts, it doesn't see "commission logic"; it sees a tangled web of code without context. The AI is forced to guess in its responses based on limited information. And in the world of commissions, where accuracy is binary, a guess is a liability. You cannot "hallucinate" a paycheck.
"In the world of commissions, where accuracy is binary, a guess by AI is a liability. You cannot 'hallucinate' a paycheck."Grayson Morris
Chief Executive Officer @ Performio
The Performio difference: Context by design
At Performio, we took a different path long before the current AI boom. We bet our business on an Adaptable ICM Core and a component-based architecture.
Our Adaptable ICM Core is a standardized, extensible data model—consistent across customers and shaped by more than 20 years of compensation experience across dozens of industries. It is designed to be understood by AI.
On top of that foundation, we use a component-based architecture. Instead of writing custom scripts for every plan, we assemble compensation logic from a consistent set of well-defined components.
Because both the data structures and the components are standardized—and their intent is explicitly documented—our AI doesn’t have to guess what your data means. It understands the business purpose of your logic, because that purpose is built directly into the system.
This architecture is the difference between an AI that offers generic advice and an AI that can say, "Jennifer’s April commission payout is less than she thinks because 60% of the Initech deal was excluded by the hold & release rule in her plan and the unpaid amount is stored in the ‘commissions held’ table."
Moving beyond the veneer
In 2026, you will see many vendors treating AI as a veneer—a slick interface meant to distract from a customized backend.
At Performio, our AI capabilities—from the Admin Assistant that helps answer any question about your system to the MCP Server that connects your comp data to your enterprise ecosystem—are not just add-ons. They are the realization of an architectural bet we made years ago.
When it comes to ICM, we believe AI can do more than summarize documents or data. It should reduce the manual burden of the "tables and logic" that have weighed teams down for decades. It should make compensation transparent, diagnosable, and agile.
But that future is only possible if the foundation is solid.
If you are evaluating how AI fits into your compensation strategy this year, look past the chat window. Look at the architecture. That is where the real value lives.
—Grayson Morris, Performio CEO
If you’re evaluating how AI fits into your compensation strategy, see how Performio is building it the right way. Explore our AI capabilities →
Grayson Morris is CEO and Board Member at Performio, with deep experience building and scaling sales compensation solutions across hundreds of customers in dozens of industries. He has guided the transformation of Performio to help organizations manage complex incentive plans with accuracy and flexibility. Grayson previously co-founded Stables Partners and held leadership roles at Sunrun and SolarCity. He holds an MBA from Stanford University and engineering degrees from UC Berkeley and Rice University.