A new benchmark study of 1,000 professional services and IT leaders finds that the strongest predictor of AI payoff in the revenue organization is not how much a company spends on tools. It is whether sales, delivery, finance, and customer success run on one connected system instead of four disconnected ones. For revenue leaders who assumed the AI gap was a model problem, the data says otherwise: it is a handoff problem, and it starts the moment a deal closes.
The Data Behind the Divide
The 2026 Global Service Dynamics Report, commissioned by Certinia and conducted by Sapio Research, surveyed 1,000 professional services and IT/technology decision makers across the US, Canada, UK, Australia/New Zealand, and Singapore in June 2026. Its headline finding: companies with successful AI outcomes are more than three times as likely to be fully integrated across systems as those with mixed results, 28 percent versus 8 percent. Firms with profit margins above 40 percent are three times more likely to be fully integrated than barely profitable competitors. Among organizations achieving net revenue expansion above 100 percent, 68 percent are aligned or fully integrated, far above the 22 percent sector-wide rate of full integration.
“You cannot scale a modern services business on fragmented infrastructure,” said DJ Paoni, Chief Executive Officer of Certinia. “Connection is the true differentiator for the organizations pulling ahead this year. This data shows that organizations fully integrated across sales, delivery, finance, and customer success are consistently outperforming on AI adoption and profitability. Removing that fragmentation frees people to focus on what matters most: delivering exceptional value to customers.”
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Where Forecasts Actually Break Down
The report’s sharpest finding for revenue leaders is not about technology at all. It is about who believes the numbers. Executives rate their own organization’s forecasting confidence 24 points higher than the practitioners doing the work rate it. They rate their AI success 16 points higher and their ability to grow revenue without adding headcount 10 points higher. That gap compounds downstream: 62 percent of executive leadership teams have defined net revenue retention goals, compared with just 45 percent of services and delivery teams, the group closest to the customer relationships that actually determine retention.
The most costly zone sits in the middle. Just 38 percent of partially aligned organizations report high confidence in their resource, demand, and revenue forecasts. Fully siloed companies report 75 percent confidence, and fully integrated companies report 78 percent. A partial system connection, in other words, carries the coordination cost of interdependence without the forecasting benefit of a shared source of truth. For a sales leader that means the mid-migration state, common after a CRM consolidation or an M&A integration, is where forecast accuracy is most likely to fail.
What This Means for the Revenue Leader
This finding lands on top of an argument this publication has already made from a different data set. Salesloft’s own 2026 benchmark found that 100 percent of US revenue teams now use AI somewhere, but only 20.6 percent call it production-ready, with CRM data hygiene as the leading bottleneck. Certinia’s data supplies the mechanism: production-readiness is not a model quality problem, it is an integration problem, and the same organizations that cannot connect sales, delivery, finance, and customer success are the ones reporting AI as unreliable. That also lines up with the case this publication has made that a 100 percent adoption figure is a meaningless number on its own, since adoption without a shared operating system just multiplies the confidence gap rather than closing it.
For a RevOps leader, the practical read is to audit the handoff before buying the next AI seat. If sales, delivery, finance, and customer success are not looking at the same account record, no forecasting tool or copilot layered on top will close the 24-point confidence gap the report measures, because the gap is a data problem, not a dashboard problem. With 62 percent of executive teams setting net revenue retention goals but only 45 percent of delivery teams doing the same, a large share of organizations are running a retention target that the people closest to the customer relationship have never been handed. That gap all but guarantees that retention becomes something finance discovers after the fact rather than something the front line manages toward.
The Uneven Payoff, and Where Budgets Are Already Moving
AI success is not distributed evenly even among adopters. Among organizations that have deployed AI, the share reporting moderate or significant success ranges from 43 percent among accounting, tax, and audit firms to 74 percent among IT service providers, a 31-point spread that tracks closely with how digitized and API-connected each sector’s underlying workflows already were before AI arrived. Pricing and hiring are both shifting in response: 75 percent of organizations expect to increase outcome-based pricing over the next 12 months, even as nearly a third already report difficulty managing hybrid or complex billing models, and 82 percent name AI and data specialists as their top hiring priority for the year, even as the same share expects to grow revenue without a proportional increase in billable headcount.
“Organizations that move directly to AI deployment without first establishing a clean, unified data environment consistently encounter the same problems: AI outputs that are unreliable, workflows that break under edge cases, and a rapid deterioration of user trust,” said Mickey North Rizza, Group Vice President, Enterprise Software and Agents at IDC. “The failure is not the AI, it is the data infrastructure beneath it.”
What to Do Before the Next AI Purchase
Three moves separate the fully integrated 22 percent from everyone else, according to the report: pushing net revenue retention targets down to the delivery and services teams that own the customer relationship, treating a partial system migration as a forecasting risk rather than a temporary inconvenience, and measuring AI success by whether it closed the confidence gap between leadership and the front line, not by adoption rate alone. Revenue leaders evaluating a new forecasting or revenue intelligence tool this quarter should ask the vendor one question the report implies most companies still cannot answer honestly: does this run on the same account data that sales, delivery, finance, and customer success all see, or does it add a fifth system to reconcile.
Source: Certinia
