The Commercial Function Was Never Built to Scale

... Now it just might. - 

The commercial leaders I talk to in chemicals are, almost without exception, doing work that doesn't scale.

They're translating market intelligence from dozens of fragmented sources into go-to-market decisions that have to be remade every quarter. They're building account-specific value propositions from scratch for each major negotiation. The handoffs between market insight, offer design, pricing, and field execution lose something each time they cross a boundary. And they're doing all of it by hand, because there's never been a system that could do it for them.

This isn't a people problem. It's a structure problem. Commercial functions in chemicals were built around deep expertise, long relationships, and tacit knowledge. Scalable processes weren't part of the design. And for most of the industry's history, that was fine. The growth came from the market. The commercial team's job was to not lose it.

That era is over.

The Scaling Problem Has a Shape

There is a five-step arc through which chemicals companies create commercial value: finding the right opportunities, deploying the right go-to-market coverage, presenting the right offer to the right customer, setting the right price, and executing consistently across every account, every rep, every region.

Walk through those five steps and a pattern emerges in four of them.

Finding the right opportunities means scanning patents, competitive filings, and application data, and talking to customers to identify where your existing chemistry portfolio could serve applications you're not in. The inputs are unstructured: technical papers, trade press, public filings, competitive patent activity. No human team reads all of it. The whitespace is there, but it's invisible.

Getting the go-to-market right means synthesizing fragmented intelligence (segment dynamics, channel performance, competitive positioning, qualification screening) into decisions about where to allocate coverage and how. That work shifts with every market, every business cycle, every strategic priority. Systems that apply the same rules everywhere produce the wrong answers. The tailoring is the work.

Building the right offer means creating a value proposition specific enough to move a sophisticated buyer. What lands in one negotiation won't land in the next: the customer's application requirements, their alternatives, their procurement constraints, their risk posture. Every account is different.

Consistent execution means the commercial judgment that lives in your top performers (when to introduce technical support, how to frame competitive alternatives, when to hold on price) is available to every rep, not just the ones who've been doing this for fifteen years. That knowledge doesn't transfer today. Most organizations have accepted that as a given.

Pricing is the exception. It has structured inputs, defined rules, and clear outputs. This is where traditional pricing software earns its keep.

The other four steps share the same profile: unstructured data, significant tailoring at every stage, and handoffs that lose information each time they cross a boundary. Software never solved that combination. AI is built for it.

Actioning the Insight

The question worth putting to your commercial organization isn't "are we using AI?" Most organizations can answer yes, and it means very little. The question is: where in the commercial arc is AI actually working?

If the answer is reporting automation or first-draft sales decks, the real opportunity is being missed. The highest-value applications change what your commercial team can see: the whitespace in your portfolio, the accounts that don't look like accounts yet, the execution gap between your best reps and the rest of the team.

Commercial in chemicals was not built to scale. That has always been the constraint. It's now the opening.

Until next week,

Kendall -

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