Only 26% of enterprises have operationalized AI, FPT-Forrester study finds

Just 26% of enterprises consider themselves advanced in operationalizing AI, even as a majority have committed real budget to it. That gap is the central finding of a global study published July 8, 2026, by Forrester Consulting, commissioned by FPT Corporation, drawing on surveys and interviews with 397 business and technology decision-makers across North America, Europe, Asia Pacific, and Japan.

The report, titled "From Pilots to Reusable Platforms: A Blueprint for Scaling Enterprise AI," covers executives in automotive, financial services, healthcare, manufacturing, energy, and sports. Its core argument: AI scaling is no longer a technology problem. It is an organizational one.

Budget is moving, but outcomes are not

More than half of surveyed organizations, 51%, are directing at least 5% of their IT budgets toward AI. Automation and cost reduction rank as the primary drivers. Yet only 34% are pursuing what the study defines as an AI-first operating model, meaning most enterprises are applying AI tactically rather than redesigning how the business runs.

The measurement gap compounds the problem. Thirty-five percent of organizations do not collect quantified AI metrics at all, and 10% track no AI outcomes in any form, according to the study. Without that data, operations and IT leaders have no reliable way to decide which pilots deserve investment for scale and which should be wound down.

Structural blockers stall the pilot-to-platform move

When respondents described what prevents AI from moving out of proof-of-concept, two factors dominated: integration complexity, named by 41%, and data silos, cited by 38%. These are not new IT challenges, but they take on sharper consequences at AI scale, where models depend on consistent, well-governed data flows across systems that were never designed to share them.

Only 39% of enterprises report meaningful progress aligning AI strategy, governance, and operating models. The Forrester study frames this as a structural imbalance: AI ambition is outpacing organizational readiness, and without closing that gap, even well-funded initiatives tend to stall in controlled pilots rather than reach production at enterprise scope.

What enterprises want from AI partners

The study asked respondents what capabilities matter most when selecting an AI implementation partner. The top answers cluster around the same lifecycle and governance themes: the ability to engineer, deploy, and operate AI systems at full scale across the entire lifecycle was cited by 48%, tied with strong governance and security capabilities. Close behind, 47% prioritized seamless integration with existing systems.

Regional patterns add nuance. Demand for full lifecycle capability is highest in North America at 59% and EMEA at 54%. Organizations in Asia Pacific and Japan lean more heavily toward end-to-end strategic and execution support, signaling different maturity curves and partnership expectations across geographies.

FPT's response: platform and methodology

FPT published the study alongside two internal offerings. The first is FleziPT, the company's AI platform, backed by a global engineering workforce exceeding 30,000 AI-augmented engineers and AI Factories in Vietnam and Japan. FPT reports that FleziPT-enabled delivery can cut development time by up to 60%, reduce rework by more than 50%, and lift developer productivity by 30%.

The second is FPT CASAN, a five-stage AI transformation framework, with levels labeled Curious, Augmented, Standard, Automatic, and Native. The methodology is designed to give enterprise teams a structured way to assess AI readiness, tighten governance, and push AI into core business functions rather than leaving it confined to skunkworks projects.

What this means for your team

• Audit your AI metrics program before expanding pilots: the study shows 35% of enterprises have no quantified AI outcomes data, making scaling decisions effectively blind.
• Treat integration architecture as a pre-condition, not an afterthought: 41% of respondents named integration complexity as their top barrier, meaning infrastructure readiness should precede use-case expansion.
• Evaluate vendor partners against full-lifecycle criteria: governance, security, and existing-system integration each matter to roughly half of enterprise buyers, so narrow point-solution vendors carry structural risk at scale.
• Assess organizational alignment, not just technology readiness: only 39% of enterprises report progress aligning strategy, governance, and operating models, and that gap, not the AI tools themselves, is what most commonly keeps deployments stuck in pilot.