This series interprets the 2027 SME GEO White Paper. Part 1 focuses on status and diagnosis: how the user decision path migrates from search to Q&A, the three preferences AI uses to choose sources, and the ten structural gaps SMEs can self-check.
This is Part 1 of the interpretation of the 2027 SME GEO White Paper, focusing on the cognition part of the white paper: status and diagnosis. The purpose of understanding the status is not to create anxiety, but to figure out exactly what basis AI uses to make choices.
I. The change in the decision path: from search to Q&A
The traditional path is linear: need arises - search keywords - browse results - compare - decide. After AI gets involved, this path is compressed into one question and answer: the user states the need and constraints, and AI gives the answer and optional sources.
Public research shows that 80.9% of users turn to AI for answers and 60.8% of consumers make purchase decisions based on AI recommendations. For users this is a big efficiency gain; for enterprises a harsh fact is amplified: AI usually gives only one answer, and if your brand is not in it, you never enter the user's choice set.
II. The three preferences AI uses to choose sources
The white paper distils AI citation preferences into three points - also the bullseye of all subsequent actions:
- Clear structure: whether the information has clear hierarchy and semantic boundaries, parseable smoothly by machines;
- Cross-verifiable: whether the same fact can be found in third-party places beyond the official website;
- Directly extractable: whether there is a segment that can be dropped directly into an answer with self-contained semantics.
Note the common thread: they are all about 'how it is organised', not 'how much content'. This is exactly why SMEs can participate calmly.
III. Diagnosis: a health check on AI perception of you
The diagnosis method the white paper gives can be followed directly:
- Design the question set: cover four types - category questions, parameter comparisons, solution recommendations, after-sales doubts - 20-30 questions in total;
- Cross-engine sampling: ask each question in mainstream generative engines and record the answer;
- Record four metrics: mention rate (does the brand appear), citation rate (is it a source), recommendation position (ranking and phrasing), statement accuracy (is the AI info correct);
- Competitor comparison: under the same questions, who is mentioned and why; is the gap in structure or sources.
This baseline report is the basis for judging all later investment - without it, change cannot be proven.
IV. Ten structural gap self-check
- The official website has no clear information architecture; product parameters scattered across PDFs and images;
- Missing Schema.org structured markup;
- The official website lacks answer pages for high-intent questions;
- Key facts (qualifications, parameters, price ranges, service boundaries) have no unified tone;
- Third-party platforms have no brand info or it is outdated;
- The brand has persons, products and cases, but no semantic connection between them;
- Multiple contradictory old versions of information exist;
- Slow page loading and poor mobile experience raise crawl cost;
- No 'conclusion paragraph' that AI can extract;
- No monthly review mechanism; content is abandoned after launch.
If your self-check hits more than four items, the priority is clear: fix the base first, then talk about content spread.
Closing
Part 1 covered status and diagnosis. Part 2 will enter the most hands-on part of the white paper - the 90-day implementation path and organisational coordination, including deliverables, division of labour and common failure modes for each phase.