Walk into almost any go-to-market planning session this year and the mandate is the same. More AI, faster, across everything. Score the leads, draft the outreach, prioritize the accounts, route the pipeline, and start handing real decisions to agents that act without waiting for a human to weigh in. The speed is real. I am less sure about the direction.
Here is the part the AI conversation keeps skipping. Before any of this arrived, go-to-market teams had already worked out that the signals we were running on were not good enough. We knew that topical, probabilistic data told us where there might be interest, not where there was a real buyer with a real problem. The whole discipline was trying to move past it, and we were making progress.
Then AI arrived. Because it is so easy to point an agent at the data you already have, it pulled us straight back onto the signals we had been trying to outgrow, and now we act on them instantly, at scale, and without second-guessing. AI does not fix bad data. Instead, it operationalizes it. The technology everyone is treating as the accelerator quietly put the car in reverse. We are going backward at speed.
AI Is Speeding Up the Wrong Signals
It does not feel that way, because we are moving fast. But fast is not the same as progress, and when a whole industry mistakes one for the other, the tool that promised to speed us up sets the field back. Faster wrong is still wrong.
The mechanics are simple. A signal that someone in an account read a piece of content was never proof of much. For years that was fine, because a person used it as a hint, glanced at it, brought their own read of the account, and decided whether it was worth a call. The signal’s weakness was absorbed by the judgment of the person holding it.
When Human Judgment Leaves the Loop
An agent does not hold it that way. It treats the same thin signal as an instruction. It drafts the email, moves the lead, and fires the sequence, then moves to the next one before anyone asks whether the premise was sound. The signal did not get worse. The thing acting on it stopped pausing. We took the one step in the process that carried the judgment and automated it away.
The result is a firehose pushed through a one-inch funnel, vast amounts of mass-produced, topical data expected to yield precise, autonomous decisions. The error rate did not change. The volume of error did.
So the question is no longer how much we can process. For twenty years that was the bottleneck, and we built our entire data model around managing the shortage. AI removed the shortage. Now the bottleneck is the quality of what we choose to ask. That is a better problem to have, but only if we actually upgrade the questions.
And the questions an AI-driven motion has to answer are not the ones our old signals were built for. Not whether someone in the account showed interest, but who specifically is involved in the decision, and whether they are a real person with a real problem or a topic that happened to trend inside the org. Not whether there is activity, but whether research has meaningfully accelerated lately or gone quiet. Not whether the account looks like a fit, but what is actually driving it, whether that is a cost problem, a compliance deadline, or a transformation mandate coming down from their own board.
The Real Test for Autonomous GTM Decisions
If your intelligence cannot answer those, it was not built for a world where software acts on it directly. That is the test I would put to any leader right now. Look at the signals feeding your agents and ask one plain question. Would you let this thing act on that, with nobody checking. If the honest answer is no, it does not belong in an autonomous workflow yet, however fast that workflow is.
None of this is an argument against AI. Forrester calls this shift the GTM singularity, where the lines between marketing, sales, and the systems acting for them collapse, and where old intent signals crack under the pressure. It is coming. The teams that win will not be the ones that automated the most, but the ones most honest about what their data could support, and most disciplined about what they let run on its own.
AI can propel us forward, and the power is real. But propulsion is not steering, and the steering is still ours (as in, us humans). The opportunity is not to act faster on the answers we already had. It is to finally ask better questions, now that we have something capable of acting on better ones.
Fast was never the goal. Being right, at the speed the moment allows, is. That is the standard worth building toward.
Anna Eliot is a marketing executive with deep expertise in B2B go-to-market strategy, brand transformation, demand generation, and revenue growth. As Chief Marketing Officer at pharosIQ, she leads global marketing, positioning, and growth initiatives, helping organizations leverage first-party buyer intelligence, AI-driven insights, and data-powered engagement strategies to accelerate business outcomes. Throughout her career, Anna has specialized in aligning marketing, sales, and product organizations around clear market narratives, scalable demand generation programs, and measurable revenue impact. She is a frequent contributor to industry discussions on modern B2B marketing, buyer intelligence, AI, and go-to-market innovation.
