AI Spending Jumps 110% but Data Silos Stall Enterprise Progress, Says ServiceNow Report

ServiceNow’s Enterprise AI Maturity Index Reveals a Data-Ready Divide

ServiceNow’s third annual Enterprise AI Maturity Index climbed 16 points to 51 out of 100 in 2026, registering what the firm calls an impressive recovery. But beneath that headline number lies a glaring disconnect: companies are pouring money into artificial intelligence without modernizing the fragmented systems and data landscapes that those tools need to function.

Holly Briedis, senior vice president of global industries and solutions at ServiceNow, describes the problem as a “data patchwork quilt” where seams show as soon as workflows try to run across the enterprise. A global survey of 4,500 executives and 2,000 employees found that AI spending surged 110%, yet foundational capabilities—connected data, governance protocols, integrated platforms—have not kept pace. The result is stalled adoption, unreliable outputs, and mounting frustration.

The study identifies a small group of “Pacesetters,” the 21% of organizations scoring highest on maturity. These companies do not discover fewer data problems; they fix them earlier. Before deploying AI, they prioritize data integration, clean governance frameworks, and a shared strategic vision that goes well beyond chasing efficiency gains. Sixty-four percent of Pacesetters digitally integrate and optimize data, compared to just 14% of all other firms.

For the rest, the gap between investment and infrastructure is turning into a business risk. Briedis compares it to “trying to run Formula 1 on a go-kart infrastructure.” Without a connected data backbone, AI cannot deliver context-aware, cross-functional results. And as employees grow skeptical of unreliable AI, they work around the technology, further eroding the return on a 110% spending increase.

Why Pacesetters Are Winning the AI Race with Connected Data

The survey reveals that enterprises are hitting a wall not because the AI technology fails, but because the data underneath it does.

The Data Patchwork Quilt That Trips AI Deployments

Briedis notes that most organizations are still treating data modernization as an IT line item to be postponed. The consequence: when AI models access information from siloed, inconsistent sources, they lack the context needed for reliable decisions. This leads to piecemeal successes that never scale. For agentic AI—where autonomous agents must orchestrate across functions—the problem is existential. If every department’s data is a separate fiefdom, the agents simply cannot perform.

Pacesetters: Integrating Data and Governance from the Start

The 21% of Pacesetters demonstrate a clear alternative. They are far more likely to have digitally connected data (64% vs. 14%) and to have established enterprise-wide governance protocols. Briedis explains that these organizations “treat AI as a design decision,” rethinking how work would flow if they were building the business from scratch. Fifty-seven percent of Pacesetters set a shared strategic vision for AI that reaches beyond simple efficiency gains—they redesign processes rather than automating broken ones. This intentional focus on solving cross-functional challenges, rather than wrestling with single-domain issues, is what lets them accelerate while others stall.

The People Barrier: Workforce Trust and Leadership Gaps

Technology shortcomings are only half the story. Half of employees surveyed believe their jobs will become less necessary as AI evolves, and they do not feel their organizations are preparing them for what comes next. Briedis calls this “more than a skills gap, that’s a leadership gap.” When workers cannot trust AI outputs because of poor governance, they route around the tools, making the investment worthless. The survey underscores that AI transformation will succeed or fail based on people. Without a culture of continuous learning and visible support, even the best data infrastructure will go unused.

What Enterprise Leaders Must Do Next to Close the AI Readiness Gap

• Modernize data before deploying the next AI tool. As Briedis puts it, “fix the process before you automate it, standardize the data before you train on it.” Accelerating spend without a connected data layer will only compound the fragmentation that the survey documents.
• Establish enterprise governance protocols now. The survey shows that Pacesetters build governance early, giving AI a “scaffolding within which to operate.” Leaders should define clear rules for data access, model outputs, and accountability so that employees can trust the results and stop circumventing AI.
• Break down data silos by mapping cross-functional challenges. Briedis advises starting from the top three to five business problems and tracing their “data anatomy” across departments. That reveals the real loopholes that block horizontal AI, rather than optimizing each silo in isolation.
• Invest in workforce reinvention, not just reskilling. With 50% of employees anxious about their future and feeling unprepared, executives must pair technology rollouts with visible career pathways, continuous learning programs, and incentives for risk-taking. The goal is organizational adaptability, not predicting the next model release.
• Adopt a design-led strategy, not a bolt-on one. Follow the Pacesetters’ example: 57% set a shared strategic vision beyond efficiency. Redesign how work flows before automating it, so AI is not simply accelerating broken processes but enabling fundamentally better ways of working.