Salesforce surveys 2,025 agentic AI leaders: being first to launch isn't being first to see ROI
Published by Salesforce News on September 6, 2026 under the headline "New Study of 2,025 Agentic AI Leaders: First To Launch Isn't Fastest to ROI," the study surveyed 2,025 agentic AI decision-makers across 20 countries on 5 continents (May 14-28, 2026): 30% have already deployed, 47% are piloting, 23% are still evaluating. The core finding fits in one line — the sector that deployed least, Professional & Business Services, reaches measurable ROI in 6.5 months, while High Tech, a leading deployer, takes 10.1 months. Launch speed and payback speed are not the same thing.
The methodology is solid: 2,025 agentic AI decision-makers surveyed between May 14 and 28, 2026, across 20 countries and 5 continents, split clearly between those who've already deployed (30%), those piloting (47%), and those still evaluating (23%). The aggregate numbers look good overall — customer satisfaction up 29%, issue resolution 31% faster, operational costs down 29%, employee adoption at 53%, and an average measurable ROI timeline of about 8 months. But the number that breaks the "first mover wins" narrative sits elsewhere: Professional & Business Services, the sector with the lowest adoption, hits measurable ROI in 6.5 months, while High Tech — one of the heaviest deployers — takes 10.1 months, nearly double. Shibani Ahuja, SVP of Data & AI Strategy at Salesforce, sums it up: "The advantage was never in starting first; it's in starting deliberately." Joe Inzerillo, President of Enterprise & AI Technology, spells out the method: "You can go use case by use case: get the data accurate, mechanized, and semantically described." One detail in the study explains a good part of the gap: only 31% of organizations had unified their data before deploying their agents.
What this study confirms at enterprise scale is exactly what we tell the SMBs who come to us thinking "installing an agent" is the project: it isn't — it's the first line of a longer one. The discipline Inzerillo describes — going use case by use case, with clean, well-described data — is literally what AppManager does when we configure an automation with a client: we scope one real use case, test it, and have the business owner explicitly approve it before it touches anything real — never an agent acting alone on real consequences, always a human approving first. That's not a compliance box we tick; it's, very concretely, the same "deliberate slowness" the Salesforce study ties to a faster payback, not a slower one. Moving fast on deployment and moving fast on results are two different things — and the second is the one that matters to an SMB owner who has neither the time nor the budget to start over.
For AppH
- A large, independent study (2,025 leaders, 20 countries) empirically confirms what AppH has argued all along: accompanying a rollout — use case by use case, clean data, real adoption — saves time to payback; launch speed alone does not.
- AppManager's mandatory approval click on every automation enforces, in practice, the same "use case by use case" discipline Joe Inzerillo describes as the right method — not by accident, but by product design since its very first module.
Against / the honest limit
- The study covers large enterprises (High Tech, Professional & Business Services) with budgets and data teams no AppH SMB customer has — an average 8-month timeline measured across corporate programs doesn't transfer as-is to an SMB automation.
- AppH has not yet published its own internal measurement of time-to-ROI for its customers — recommending "deliberate slowness" is easier to say than to prove with our own numbers.
The study's headline reads almost like a warning to us as much as to the reader: "first to launch" isn't "first to win." We see it regularly in our own conversations with SMB owners in a hurry to "have an agent" before they've even decided what that agent should do, for whom, and who gets to approve it. Shibani Ahuja's line — "the advantage was never in starting first; it's in starting deliberately" — isn't an abstract lesson in caution, it's a number: 6.5 months versus 10.1, nearly double, depending on whether a sector took the time to prepare its data or not. We're not claiming AppH has already measured that same gap across its own customers — we haven't, and saying so plainly is part of the same honesty principle we're asking of this study. What we can say is that the approval click we require on every automation isn't there to slow things down on principle — it forces, every time, the same question this study raises: do we actually know what this agent is going to do, before we let it do anything?
Verified by a human at AppH