A B2B software company with about 40 demo requests a month buys an attribution tool. The dashboard reassigns credit across their channels and shows that LinkedIn, which last-click had been crediting generously, is actually contributing far less than it appeared. The founder cuts the LinkedIn budget by half. Two months later demo requests are down 30% and nobody can explain why.
The tool was not lying. It was doing exactly what it was built to do with a data set far too small to support the conclusion drawn from it.
Attribution software is having a moment again, rebranded around AI, and the pitch is reasonable: last-click is obviously wrong, so replace it with something smarter. The first half of that is true. The second half depends on numbers most small and mid-sized teams do not have.
What last-click actually gets wrong
B2B buyers commonly touch a brand somewhere between eight and twelve times before they convert. Last-click hands 100% of the credit to whatever happened immediately before the form submission, which is very often a direct visit or a branded search. Both of those are effects, not causes. Somebody typed your name into Google because of something that happened earlier.
So last-click systematically overvalues the bottom of the funnel and undervalues everything that created demand. If you optimise against it for long enough, you end up cutting the things that made the branded searches happen, then watching the branded searches decline.
That failure is real and well documented. It is the reason attribution vendors exist.
What AI attribution does differently
Data-driven attribution, which is what most of these tools are running under the branding, assigns credit by comparing paths that converted with paths that did not. Google’s own version is built into Ads and GA4. Google’s GA4 attribution documentation describes it as measuring how each touchpoint changes the estimated probability of a key event, using signals like timing between the interaction and the conversion.
That is a genuinely better question to ask than “what was last?”. And when it works, it works well.
The condition is volume. The model learns from patterns across many converting and non-converting paths. Vendor guidance commonly points at something in the region of 3,000 monthly conversions before a data-driven model is producing output you should act on. Treat that as a rule of thumb rather than a hard line, because it varies by how many channels you run and how varied your paths are. The direction is what matters: fewer conversions, less reliable output.
Here is the uncomfortable part. Below that volume the model does not refuse to answer. It produces a number, formatted identically, with the same confident decimal places. Nothing in the interface tells you that it is essentially interpolating.
The 2026 problem that no attribution model solves
There is a newer issue that makes all of this harder, and it is the one worth understanding before you spend anything.
A growing share of commercial research now resolves inside AI answers. Someone asks ChatGPT or Google’s AI Mode for a shortlist, reads a summary that mentions you, does not click, and arrives at your site three days later by typing your name. That is the mechanic behind getting cited in AI answers, and it is invisible to every measurement system you own. Your analytics records direct traffic. Your attribution model, AI-powered or otherwise, distributes credit among the touchpoints it can see, and that particular touchpoint produced no click, no referrer and no session.
So the model does what it is designed to do: it hands the credit to something else. Usually whatever paid channel was running at the time.
This is the takeaway most vendor content will not give you. AI attribution redistributes credit among the channels you already measure. It does not discover channels you do not. If your largest blind spot is discovery happening inside AI answers, a more sophisticated model does not shrink that blind spot. It makes it less visible, by confidently allocating the missing credit to something you can see. The dashboard gets cleaner while the picture gets less accurate.
What to do instead at small volume
If you are under a few hundred conversions a month, four cheap methods beat any model.
Ask people. A required “How did you hear about us?” field on your form, free text rather than a dropdown, is crude, biased toward recency, and still the single most informative thing most small teams can add. When someone types “saw you mentioned in ChatGPT”, no attribution product on the market would have told you that.
Run holdouts. Turn a channel off completely for three or four weeks and watch total pipeline, not that channel’s reported conversions. It is the only method that measures incrementality rather than correlation. It costs you real volume during the test, which is why almost nobody does it.
Test by geography. If you operate in several regions, run a channel in some and not others. Slower than a holdout, less painful.
Change one thing at a time. Unglamorous, and it works when you have too few conversions for anything statistical.
What can go wrong
The failure mode is not that the software is bad. It is that a confident number invites a decisive action, and the decisiveness is the problem.
Specific risks worth naming. Attribution tools need clean tracking across your site, ad platforms and CRM, and that integration work is usually underestimated by weeks, not days. Consent rates directly limit what can be tracked at all, so in markets with high opt-out rates you are modelling on a partial sample before the model even starts. And if the tool becomes the shared source of truth in a company, it starts settling internal budget arguments, which means the errors in it become organisational decisions rather than analytical ones.
This is not worth buying if you run a single acquisition channel, if your monthly conversion count is in the dozens, or if your sales cycle is long enough that the paths finishing today reflect marketing you ran a year ago. In that last case the model is accurately describing a strategy you have already stopped running.
A more useful starting point
Before evaluating any attribution product, work out what you would actually change if the answer came back differently. If the honest response is that you would keep running all your current channels either way, the tool is buying you comfort, not decisions.
If there is a real budget question at stake, start by adding the self-reported field to your forms and running one four-week holdout on the channel you are least sure about. Do that for a quarter. You will know more about your marketing than any dashboard would have told you, and you will be in a far better position to judge whether the software is worth its licence fee.