S-I-C-T Framework: A Scientific Theory That Asks to Be Broken

The market rule they were still managing

Meta description: The market keeps the same rules under new packaging: discoverable, intelligible, credible — and worth recommending.

The market review was reassuring, as it had been for years. Share: stable. Distribution: strong. The category map: familiar names in familiar positions. I have sat through enough of these reviews to know what they cannot show, and in this company the thing they could not show was already happening: the buyer’s journey had acquired a new first step — an AI answer to the category’s founding question — and that step was owned by a thin specialist with no share, no shelf, and no presence on the category map. The market’s physics had changed at the front of the deal. The review measured the back of it, where the physics still looked intact.

[AWAITING MIKLOS — scene: a market review on share and shelf while a thin specialist owns the question that starts the deal]

What rule is the market review still using?

The reviews I read run on a set of inherited rules: that buyers enter the market through known channels, that consideration forms through exposure and sales contact, that share of voice predicts share of market. Each rule described the physics of a market where information was scarce and vendors supplied it. The AI answer layer inverted the scarcity: the buyer now arrives informed, with criteria already set, having consumed a synthesis the vendor did not produce. The rules are not wrong about the market they describe. They describe a market that now begins later — after the step nobody in the review is measuring.

Where has that rule already failed in the buyer’s path?

The rules I would tell the team have not changed: a business still needs a technically functioning website, content that explains its value, and credible ways for people to discover and trust it. The packaging of online discovery may change radically; these foundations do not become irrelevant because a new interface attracts attention. I do not see the AI answer layer as permission to abandon old disciplines. It is a reason to apply them with more care and to observe new behavior.

The failure I trace is upstream and quiet. The founding question of the category — the one that defines what kind of solution a buyer believes they need — is now answered by AI, sourced from whoever the engines trust. In the companies I am describing, that answer increasingly comes from a specialist who frames the category around their own approach. Buyers arrive at the incumbent’s sales team pre-framed: asking for the specialist’s vocabulary, evaluating against the specialist’s criteria. The incumbent still wins its share of these deals. It is winning them on someone else’s map, and each win confirms the review’s rules while the map changes underneath.

Why do share slides update late?

The false explanation is measurement lag: share data takes quarters to compile, so the review is inherently backward-looking. I have watched companies respond with faster data — real-time share proxies, weekly pipeline analytics — and still miss the distortion, because the lag is not in the data’s speed. It is in the data’s location. Share measures the end of the journey: who signed. The distortion lives at the beginning: who framed. By the time framing losses appear in share, the frame has been lost for years — and the review, watching the end of the journey with faster instruments, experiences the arrival of the loss as sudden.

What pattern marks unseen distortion?

The marker I trust is vocabulary drift in the pipeline. I read win-loss notes and sales transcripts looking at the language buyers use: whose terms, whose categories, whose criteria. When the pipeline’s vocabulary drifts away from the company’s own framing — when buyers consistently arrive speaking a rival dialect — the distortion has already happened upstream. The company is competing inside a frame it did not set, on criteria it did not write. The share slide may still be green. The vocabulary drift is the early signal, and it is in documents the market review never opens.

The market review should remember the goalkeeper problem. The person responsible for the foundations can feel like a goalkeeper: when the defense works and the expected save is made, few people celebrate; when the team concedes, everyone notices. Technical and strategic work has the same asymmetry — problems become visible when something fails, but the effort that prevented failures may remain outside the headline numbers. A review that reads the interface shift and forgets the foundations will misprice both.

[AWAITING MIKLOS — scene: second scene]

What question would show the rule is over?

Market reviews ask how we are performing — a question answerable within the inherited rules. The question that tests the rules themselves asks where the buyer’s beliefs now come from — who teaches our market what to want? If the answer is no longer substantially the company and its peers, the rules the review runs on have expired, and no amount of performance within them will announce that fact. Expired rules fail silently. That is what makes them expensive.

The question that separates rule from packaging: would this online presence still be worth visiting, understanding, and recommending if the AI layer vanished tomorrow?

PR excerpt: [PR — awaiting Miklos material; cut 80–120 words from his paragraphs once available.]

Miklos Roth, author of Signal Over Noise

By Miklos Roth — AI marketing consultant, Budapest. More: his field notes.

© Copyright Roth Creative