Most companies are AI-curious. That is the expensive one.

Somebody asked me last year which camp people were in on AI. The answer split three ways, and it has stayed useful, except the interesting version is not about people at all.

It is about revenue organizations.

The ignorant company is cheap. The native company is compounding. The curious company is paying full price for motion and calling it progress.

Most companies I look at are curious.

Why the middle costs the most

Because AI does not fix a process. It runs the process you already have, faster and at greater volume.

If stage three means whatever the rep updating the record decided it meant, then a model trained on your pipeline will forecast confidently off that. Confidently and wrongly, at speed, with a chart.

If your ICP is a guess, AI will now execute that guess a thousand times a week, politely and at scale, into a market that will remember you for it.

If nobody ever defined what good discovery sounds like, an AI call summary is a transcript with an opinion attached. You cannot coach against output nobody set a standard for.

None of that is a tooling failure. Every one of those companies bought a competent product. They pointed it at an undefined process and got a faster undefined process.

AI does not fix a short column. It loads it faster.

Where it genuinely earns its place

I am not against any of this. The gains are real, and they are almost all in the same category: work your team already skips because it takes too long.

Two years of closed-lost notes read in one pass, with the patterns nobody had time to find surfaced as themes. Forty minutes of account research compressed to three, so the rep actually does it instead of winging the first call. A weekly sweep of a target list that returns only what changed, short enough that someone reads all of it. A first draft from a real brief, where the human still supplies the point of view and the edit.

That is a genuinely different company after six months. None of it required a new process. It required an existing process to exist.

Where a human still carries more weight

Three things, and they are the three that decide whether you hit the number.

Discovery is judgement under uncertainty. A model will produce a confident, plausible summary of a conversation it did not have to steer. Knowing which thread to pull, and when to stay silent, is not a summarisation problem.

Coaching works because someone whose opinion the rep cares about was watching. That is the entire mechanism. Automate the observation and you have removed the part that changed the behaviour.

Deciding which accounts deserve your team’s time is the highest-leverage call you make all quarter. It is also the one most often handed to a scoring model. If a rep cannot say why an account scored 82, they will not work it. An opaque score is a number, not a decision.

What your buyer wants, and what AI can actually do about it

Strip away the category noise and buyers want three things. It has not changed in twenty years.

Know my business. Not that I run schools or clinics or four hundred restaurants. The risks, the pressures, who I answer to, what my last two quarters looked like. AI is genuinely good at this now. It used to take an hour a call and most reps skipped it.

Know my technology. Not just your product. How it lands in the environment I already have, what it touches, what it replaces. AI helps here too, if somebody has written down what your product actually does.

Make an expert recommendation. Clear, specific, and willing to be wrong. Not a feature dump and not five options with a comparison table.

AI can do the first two well enough that they stop being a differentiator. The third is judgement, and it is the only one that closes anything. Which means the bar moved: the research your rep used to be praised for is now table stakes, and the recommendation is all you have left.

The tell your buyer already recognises

Everyone can spot it now. The long hyphens. The overuse of ensure and leverage. Bold text in strange places. Sentences that are perfectly constructed and say nothing.

Your buyer reads twenty of those a week. When one arrives from your team it does not read as efficient. It reads as nobody here spent two minutes on me, which is a costly impression in a market where trust is most of the purchase.

The failure is not that a model wrote it. It is that nobody had a point of view for the model to express.

The order that works

Define it. Install it. Then let a model run it.

Reverse those and you have bought a very fast way to be wrong. It is not a sophisticated insight and it is not what most companies did, because the tools were available before the discipline was.

Two things are worth adding on top.

Write down the line. Which decisions a model is allowed to make, and which a person makes. Put it somewhere people can read. Almost nobody has done this, and the cost of an ungoverned agent talking to an existing customer is higher than every hour it saves.

Make a place to share what works. A channel where a rep can post that they cut report writing in half, or spotted a use case in prep they would have missed. Governance from the top sets the boundary. The frontline finds the actual value, and they will only find it if there is somewhere to say so.

Where to start

Not with a tool. With an honest answer to one question.

If you automated your current sales process tomorrow, exactly as it is written today, what would you be automating?

If you can answer that in a sentence, you are ready and you should move fast.

If the honest answer is that nobody has written it down, you already know what the first project is, and it is not an AI project.