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The next chapter of revenue AI: lessons from two years as a Gartner Magic Quadrant Leader

gong.io·Source published

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A year ago, Gartner published the first-ever Magic Quadrant for Revenue Action Orchestration (RAO). At the time, the category was still taking shape, emerging from the convergence of sales engagement, conversation intelligence, and revenue intelligence. A lot has changed in the past year. AI agents went from demos to daily work. Sellers started using enterprise AI assistants alongside their core tools. And the question every revenue leader asks us shifted from "What can AI do?" to "Can I trust what it's telling my team?" As the market has changed, so have we. Our focus has stayed the same: building Revenue AI our customers can trust, and that delivers real outcomes. We believe that’s why Gong has been named a Leader in the 2026 Gartner® Magic Quadrant™ for Revenue Action Orchestration for the second year in a row. The most interesting part of this year's report, to me, is the direction of the market. Gartner describes a shift from application-centric platforms to context-centric ones, and says Leaders are building toward "context as a service." We agree, and it's how we've been building Gong from the start. Here's how we see the next phase of this category.

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These are observations in our catalog around the company, not claims made by the article. A publication does not establish why hiring, search interest, or share prices moved.

Checked 2026-11-02 UTC

Current roles

16

30-day current-market scope

Company stories · 30d

7

7 official · 0 independent

Attention records · 7d

0

0 Google · 0 Polymarket

Coverage mix

Official 7Independent 0

Attention sources

Google Trends 0Polymarket 0

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Open roles

16

Technical roles

5 of 16

Observed hiring change

+11 roles

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Open roles

Vacancy history

Current role mix

5 technical · 11 other

31% of currently observed roles are technical.

Vacancy history uses saved daily snapshots through 2026-11-02. News counts are dated publications; attention counts are matched records, not search volume or prediction probabilities.

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