AI ambition is high, but enterprise readiness needs to catch up
Enterprise AI ambition is accelerating, but AI projects are stalling because the operating foundations — data, workflows, governance and measurement — are too fragmented. To move AI beyond pilots, leaders need to address the foundations that determine whether AI can scale safely, efficiently and commercially.
What are clients saying?
say AI is stuck in small pilots or deployed in specific functions but not unified.
describe their demonstrated AI return as anecdotal.
cite organizational change and adoption management as the most important capabilities in a partner.
FAQ
What is the NTT DATA ServiceNow Insights and Vision Report 2026/27?
It is a primary research report, conducted independently for NTT DATA in 2026 with over 350 ServiceNow clients. The report focuses on how these clients are scaling AI on enterprise service-management platforms.
Who should read this report?
This report is relevant to C-suite leaders, business and IT executives, AI practitioners, IT strategists, HR and workforce planners, and anyone else using platforms like ServiceNow for enterprise operations.
Who was surveyed to get these insights?
Over 350 verified global ServiceNow clients (decision-makers at director-level and above) from six industries — banking and financial services; insurance; life sciences; manufacturing; public sector; and telecom media and technology — were surveyed.
Key findings
Key findings
Fix fragmented foundations before scaling AI
The gap between AI ambition and AI readiness in enterprise service management is where ROI is currently being lost. Before scaling their AI capability, organizations must address the infrastructure challenges that are silently undermining AI investment.
Redesign service processes before deploying agents
Fragmentation and legacy processes are impacting AI value realization. Layering AI over broken workflows does not make them intelligent — it makes them unreliable and inefficient.
Define governance control points early
As AI use grows across business units, gaps in ownership, access, data use and oversight will widen. To manage adoption on their own terms, organizations must establish clear decision rights, guardrails and monitoring now.
Build measurement models from day one
47% of leaders describe demonstrated AI return as anecdotal. Organizations that will get the most value from AI investment will be the ones that measure it in terms a CFO will fund.
Fund workforce readiness alongside platform investment
Having addressed infrastructure, fragmentation, architecture, and measurement, the most urgent remaining imperative is not technical. The technology, in most cases, is ready. The workforce is not.
The research from the NTT DATA Insights and Vision Report 2026/27 shows that AI cannot deliver its full value when it is layered onto broken processes. Scaling AI starts with the end-to-end value chain: identifying the bottlenecks that matter, applying AI where it can materially improve outcomes, and embedding governance from the outset rather than retrofitting it later.
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