What to look for in go to market software, and how often anyone answers

AI Centrality separates a product whose value depends on a model from a platform that would work without one. 100 of 314 vendors document it well enough to tell the difference from outside.

Autonomy without a disclosed oversight model is the shape of most incidents. High autonomy with a documented escalation path grades well here; any autonomy with nothing written grades poorly.

94 of 314 publish a methodology alongside the figure. 62 carry a D, meaning a material claim with no verification path.

The thinnest axis in the index at 12 percent, three weeks after Article 50 of the EU AI Act became enforceable. Silence here is the single most common gap in the whole dataset.

70 of 314 answer this in public. It is the question with the least published detail and the highest cost of being wrong.

What features should I look for in an AI SDR agent?

Start with the four the index can actually separate vendors on, because the feature list every AI SDR agent publishes is close to identical. First, whether the AI is the product or a layer on a sequencer: 26 of the 27 AI SDR agents in the index document that at a checkable standard, 96 percent, which is the highest share of any category and means this is not the item that separates them. Second, the oversight model, meaning what the agent is allowed to send without a human approving it: 14 of 27 document it. Third, outcome evidence behind the meetings booked figure, where only 5 of 27 publish a methodology and 8 carry a D. Fourth, and this is the one to press hardest, whether the agent identifies itself as AI to the person receiving the message: not one of the 27 documents that, 11 carry a D, and Article 50 of the EU AI Act has been enforceable since 2 August 2026. Outreach Compliance Posture is nearly as thin at 3 of 27. The capability everyone advertises is the one everyone can prove. The obligations arriving with it are undocumented across the whole category.

What features should B2B teams look for in an account based marketing platform?

Judge these platforms on execution discipline rather than on intelligence claims, because the index data says the intelligence claims are not evidenced here. Deliverability and Sending Discipline is the strongest axis in the category at 6 of 8 primary ABM vendors documenting it, 75 percent, so campaign hygiene, list handling and sending controls are the features you can genuinely compare. AI Centrality is the weakest: not one of the 8 primary ABM vendors documents AI as anything the buyer can verify, and every one of them sits at C, making this the least AI evidenced category in the index despite AI appearing in most of the marketing. Outcome evidence is similarly thin at 2 of 8, and commercial transparency at 2 of 8. The practical shortlist test: ask for the account selection logic in writing, ask what the model was trained on, and treat an unanswered version of either as the answer.

What features matter most in conversation intelligence software for sales calls?

This is the best documented category in the index, so the checklist is worth running in full: a typical conversation intelligence vendor documents 5 of the 17 axes against an index median of 5. Transcription and summarization are table stakes and not a differentiator. The three that separate vendors here are the model behind the analysis, where 11 of 11 document AI as the product itself; the privacy posture around recorded conversations, documented by 6 of 11; and the data stewardship boundary, meaning whether your calls are used to train models that serve other customers, where 4 of 11 state a position. That third one is the question to put in writing before a pilot, because a recorded sales call contains your pricing, your objection handling and your customer's words. Integration depth matters next, at 7 of 11, since analysis that never reaches the record system is analysis nobody reads.

What features should I look for in a LinkedIn outreach automation tool?

One feature matters more than the rest and it is the one the category does not document. Platform Terms Exposure asks whether the vendor states plainly how its automation operates against the professional network's terms, whether it runs through an official surface or a user session, and what the account risk is. Not one of the 36 primary LinkedIn tools in the index documents it, and 26 carry a D. Recipient disclosure is nearly as dark at 1 of 36. Meanwhile 28 of 36 publish real pricing, 78 percent, one of the most open categories on the board. The category is transparent about what it charges and silent about the risk it transfers to your account. This is also the thinnest category in the index overall, a typical vendor documenting 3 of 17 axes, with 5 documenting nothing at a checkable standard. Ask for the operating method in writing before you ask about features.

How does the GTM Tech Index decide what counts as a feature worth grading?

An item earns an axis if it is gradeable from public artifacts and if a buyer would regret not knowing it. That produces 17 axes in four groups, applied identically to all 314 vendors, which is what makes the counts on this page comparable across categories. Every axis carries a letter grade and a source basis, and a grade records what the vendor has published rather than what the product does: a low grade means the public record is thin, not that a control is absent. Grades are never aggregated into a composite score. A grade also belongs to a vendor rather than to one of its features, which matters on a vendor listed in more than one category, because the grade may have been earned on a different product line.