There’s a scenario that plays out every day across the enterprise. A salesperson is about to close a major deal. They want to know what their commission will be. They type the question into ChatGPT or their favorite AI assistant. What comes back is a thoughtful, well-written explanation of how software companies typically structure sales compensation.
The one thing it won’t tell them is what their commission will actually be if they close this specific deal. That gap, between what AI can reason over and what it knows about your business, is the defining challenge of enterprise AI adoption right now.
I call it the logic layer. And without it, AI gives you impressive sounding outputs that are often disconnected from how your business runs.
Why business logic lives with the analyst
One of the more persistent myths in AI is that analysts are on the verge of becoming unnecessary.
The reality is the opposite, and the logic layer is exactly why.
In an AI-enabled enterprise, analysts become more essential because they are closest to the logic and context that governs the business. They know which definition of pipeline matters and which edge cases matter in audit, merchandising, finance, or marketing.
I believe enterprises that succeed in the AI era will not be defined by how much AI they deploy but whether the people who understand the business own and control the intelligence that runs it.
If that ownership defaults entirely to IT or to a vendor’s black box, companies risk scaling systems they cannot fully adapt or audit. Giving business teams the tools and mandate to own their logic is what makes the AI system trustworthy and responsive to how your business runs.
That is why I see analysts as the architects of this next phase.
What the logic layer looks like in practice
Let me return to the commissions example, because it illustrates the concept precisely. Right now, when a salesperson needs to know their commission on a deal, they send a message to the commissions analyst. That analyst has their own spreadsheet — because comp plans change every quarter, with spiffs and special programs layered on top. They run the math manually and send back an answer.
What if that same analyst built a simple, well-defined calculator that encoded their commission logic — the actual rules for your company, your plans, your programs — and connected it to the AI systems your salespeople are already using? Now when a rep asks what their commission will be on a specific deal, they get the right answer. Not a generic explanation of how commissions work.
And here’s the compounding value: that same logic can then be used by the annual planning agent to model the operational cost implications of different comp plans. It can feed the scenario planning model that runs hundreds of simulations for financial planning. The analyst who built it enables an entire network of AI systems to act on accurate, business-specific logic.
That’s the logic layer in practice: curated, purpose-built data assets and calculators that encapsulate how your business works, maintained by the people who understand it, deployable to every AI system that needs it.
What the logic layer requires
This is where I think most companies are still stuck. They’ve made the infrastructure investments. They have cloud data platforms and approved LLMs. But they’re asking those systems to do things they were never designed to do on their own.
The logic layer requires three things:
Purpose-built data assets. A narrow, clean, well-defined data set that reflects how you actually measure a specific business process.
Encoded business logic. This is the part that lives in people’s heads right now — the policies, the edge cases, the context that makes data mean something.
The ability to update it. Nobody runs a business to keep it the same. The logic layer has to be something that domain experts can update when the business changes.
A pragmatic path forward
The good news is that you don’t have to wait for a perfect architecture before you start building a logic layer.
Start with your highest-value, most-repeated business processes — the ones where an analyst is currently fielding the same questions week after week. These are the processes where encoding logic into a curated, AI-ready data asset delivers immediate, measurable value.
Then, empower your analysts to own that encoding — not IT. Give them low-code tools to do the work, and the mandate to treat that encoded logic as a strategic asset they own and evolve as the business changes.
This is also where leadership posture matters.
I have said for a while that this should not be framed as a choice between business and IT. It is both. IT should set standards, manage infrastructure, establish security boundaries, and make approved AI capabilities available across the organization. But IT should not become the bottleneck for every piece of business logic the company needs to operationalize.
If this feels familiar, it should. We have seen this pattern before in enterprise technology. Infrastructure and platforms matter. But the last mile, the part that turns capability into business value, always depends on the people closest to the work.
AI is no different.
The companies that get the most from AI will be the ones that treat it like an operating model. They will automate core workflows, curate the right data, and empower analysts and domain experts to define the logic that makes AI useful and generate answers the business can use.
I recently had a chance to go deeper on these ideas on the Talking AI podcast. If you want to hear more of my thinking on the analyst’s evolving role, how the logic layer connects to agentic workflows, and why I think the next 18 months will be pivotal for getting this right, it’s worth a listen.