In the three years ServiceNow has been measuring enterprise AI maturity, 2026 registered an impressive comeback, with the Enterprise AI Maturity Index climbing 16 points to reach 51 out of 100.
Underneath the surface, though, there’s a lot of furious paddling to gain meaningful outcomes from AI investments. A survey of 4,500 global executives and 2,000 employees found that while AI spending surged 110%, foundational capabilities haven’t kept pace, blocking scale.
“What we have now is a data patchwork quilt so when you try to run workflows across that environment, the seams show immediately,” says Holly Briedis, senior vice president of global industries and solutions at ServiceNow. In other words: Everyone bought AI. Few built for it. Fragmented systems and data are leading to fragmented outcomes.
Pacesetters, the 21% scoring highest on maturity, tend to build connected data and governance before deployment rather than after. Focusing on intention over speed has paid off for Pacesetters, setting an example for enterprises looking to make the most of their AI investments.
Explore five takeaways from the ServiceNow Enterprise AI Maturity Index and learn strategies to scale AI.
The investment is real. The infrastructure to support it isn’t. As Briedis puts it, most organizations are “trying to run Formula 1 on a go-kart infrastructure.”
Data modernization used to be an IT line item, kicked down the road to be attended to later. But yesterday’s strategy is costing companies today.
Disconnected data means a lack of context for AI, so the technology delivers unreliable outcomes. “The adoption curve stalls not because the technology failed but the data underneath it did,” Briedis says.
Learn from the Pacesetters, 64% of whom integrate and optimize data digitally, compared to just 14% of everyone else. Pacesetters are not discovering fewer data problems, they just address them faster, a strategy crucial to making the grand AI experiment work at scale.
For agentic AI to work across a company’s functions, those functions need to be orchestrated on a bed of connected and clean data. Not doing so leads to piecemeal success.
“Don’t just slap AI on top of pre-existing workflows,” Briedis advises. The Pacesetters treat AI as a design decision, a chance to rethink how the business would function differently if they were building it from scratch. Fifty-seven percent of Pacesetters establish a shared strategic vision for AI beyond efficiency gains. They’re not optimizing broken processes, they’re redesigning how work flows.
“Fix the process before you automate it, standardize the data before you train on it and integrate the systems before you try to orchestrate them,” Briedis says.
Picture driving along a highway that’s blocked for construction every three miles. Speed doesn’t get you too far, and the many bottlenecks are enough to throttle progress. That’s precisely what’s happening in companies where data silos are preventing AI from picking up momentum.
While humans can work around data silos, AI can’t. “In an enterprise, AI is meant to solve problems horizontally, from east to west,” Briedis points out. Managers might view their roles as confined to specific domains but one of AI’s superpowers is solving problems across functions.
“Instead of wrestling with challenges in a single domain, start from the top three or five challenges plaguing your company and trace their paths and associated data anatomy to find the loopholes worth addressing,” Briedis advises. Better data visibility across functions and breaking down data silos will make AI deployments more effective with more tangible results.
A lack of transparency and increased potential for misinformation is worrying enough when confined to one department. Moving it across functions risks compounding the problem.
When employees can’t trust the results AI delivers due to poor governance, they will work around the technology, stalling AI deployments. “AI transformation will ultimately succeed or fail based on people, and if employees don’t feel equipped, supported or made part of the journey, organizations are going to struggle to realize the value of their investments,” Briedis says.
When governance protocols are in place, everyone can discern truth from chaos. Autonomy doesn’t mean a lack of control; establishing a set of enterprise governance protocols simply gives it a scaffolding within which to operate.
“If you don’t know what’s around the corner with AI, how do you train your employees for it? Build organizational adaptability,” Briedis recommends.
It’s not a good sign that half of employees surveyed believe their jobs will become less necessary as AI agents evolve and don’t feel like their organizations are preparing them for what’s next. “That is more than a skills gap, that’s a leadership gap,” Briedis points out.
Such a gap is worth paying attention to because the success of AI deployments depends on employee acceptance.
“The question leaders should be asking is, ‘What capabilities do we want our people to be able to develop as AI continues to evolve?” Briedis says. “AI readiness isn’t about predicting the next model release, it’s about creating a culture of continuous learning, rewarding risk-taking and workforce reinvention.”
CREDITS
Writer: Poornima Apte
Designer: Jennifer Ramos
Editor: Nick Clunn
