The company that uses ChatGPT the most isn’t necessarily the one that profits the most from generative AI. It sounds counterintuitive — but it’s exactly what shows up once you look closely at what most companies are actually doing with these models today.
In most companies, “using generative AI” has come to mean opening a chat tab to draft an email, summarize a document, or generate an image for a post. That’s real use, and it saves typing time — but it doesn’t change how the company decides, prioritizes, or executes anything. It’s the same person, making the same decision, through the same process, just typing faster.
The mechanism behind the hype: what a generative model actually does
It’s worth understanding the mechanism before deciding where it matters. A generative language model doesn’t “know” facts the way a database does — it predicts, token by token, the statistically most likely continuation of a text, based on patterns extracted from a massive volume of training data. That’s why it’s excellent at tasks of form (writing in a specific tone, structuring an argument, translating register) and less reliable at tasks of fact — it errs, confidently, exactly in the domains underrepresented in that training data: a contract with a specific regional clause, an internal figure that only exists inside your company.
That already points to where the real value is: not in asking the model to “know” something about your business — it doesn’t, unless you feed it that information — but in using it as a reasoning layer over data you already have, formalized and structured. An open chat with zero context about your business is the weakest version of that layer. Wired to a real process and real data, it’s a different category of tool.
One example: the same tool, two different outcomes
Picture a marketing team at a services company that adopts generative AI to write posts, emails, and sales proposals. The result is real: text comes out faster, drafts arrive more polished. Six months later, though, the team still approves proposals the same way, still prioritizes clients by the same informal criteria as always, still decides what becomes a campaign based on whoever’s intuition is in the room. The tool sped up typing. It never touched a single decision.
Now picture the same technology used differently: the generative model reads the company’s structured proposal history — deal size, time to close, reason for loss when a deal was lost — and doesn’t just write the next proposal, it flags which prospect in the pipeline matches the historical pattern of someone who closes fast, and which matches the pattern of someone who historically demands a discount and drags out. The decision of “who do I prioritize this week” stops being pure intuition from whoever’s in the room and gains a new input. The text the model generates is the same kind of text as before. What changed is what the company does with its output — from draft to decision input.
Surface use is not structural application
That’s the distinction separating the two companies above: surface use of generative AI swaps the keyboard for an assistant, but leaves the process, the decision criteria, and the priority structure exactly where they were. Structural application uses the same model as an input inside a decision, a prioritization, or a process step that previously depended solely on one person’s undocumented judgment.
As João Paulo Batistella, innovation executive and former CEO of EISA, argues, the premise that used to justify how a company organized itself was “I don’t have all the world’s knowledge available internally” — and that premise no longer holds. The question left isn’t “how do I get the right people for this task” anymore, it’s “what do I ask this near-unlimited reasoning to do.” But that question only pays off when it’s asked inside a real process, with the company’s real data — not inside a generic chat tab with zero context about what the company actually does.
Where to look for the next structural use
The right question to open that search isn’t “what can generative AI write for me.” It’s: which decision, prioritization, or process step in my business today depends on a person turning unstructured information into judgment — and does that judgment already lean on data the company has, even if it’s scattered (proposal history, customer complaints, a tracking spreadsheet)? That’s exactly where it’s worth plugging in a generative model — not as a chat window next to business as usual, but as part of the flow that produces the decision.
This works well for repetitive, high-volume decisions where historical pattern is a good predictor (lead prioritization, ticket triage, risk classification). For rare, high-impact decisions — an acquisition, a layoff, a strategy pivot — the model can organize information and surface patterns, but the final judgment stays human, and it should.
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