AI attribution: finally getting honest numbers for multi-touch B2B

Attribution in B2B has been broken since the term was invented. Buying cycles are long, multiple stakeholders touch multiple channels, and most of the actual influence happens off-platform — in conversations, in private rooms, in podcasts you don't measure.

Last-touch attribution is comforting and wrong. It credits the channel that closed, not the channel that did the work. First-touch is equally wrong in the opposite direction. Linear and time-decay models split the difference with no claim to accuracy.

AI doesn't solve attribution. But it does something useful: it stops pretending. Modern AI-assisted attribution combines deterministic tracking, probabilistic modelling, and explicit qualitative signal (surveys, sales conversations, self-report) into a more honest blended view.

The starting point is shifting from 'attribution' to 'allocation.' The question is not 'which channel deserves credit?' It's 'where should the next dollar go?' That reframing changes everything.

Run a marketing mix model quarterly. AI-assisted MMM has come down in price by 90% in two years. Even small companies can now afford a proper statistical look at channel contribution.

Layer in self-reported attribution at the bottom of every form. 'How did you hear about us?' is the most honest signal you have, especially when triangulated against MMM and last-touch data.

Stop optimising at the channel level. Optimise at the play level — content campaign, outbound segment, event placement. Plays are the unit at which decisions can actually be made.

Accept that 30–40% of pipeline will always be 'dark social' — influenced by sources you can't measure. The job is to make peace with that, fund the channels you can measure, and resist the temptation to over-attribute the unmeasured majority.