Deloitte's 2026 AI Report Validates the Problem webAI Was Built to Solve

The hardest part of enterprise AI isn't building models. It's making them work in the real world.

Deloitte just released their annual State of AI in the Enterprise report, surveying over 3,200 leaders across 24 countries. Their data points to a conclusion we've been building toward since day one: enterprise AI adoption and ROI improve when AI is treated as operational infrastructure—embedded in real workflows and designed for deployment—not as isolated pilots or standalone tools.

In the report, Deloitte correctly articulates the symptoms of stalled enterprise AI adoption. Their data, however, points to a deeper, unifying cause.

Two findings in particular illustrate this:

Finding #1: The pilot-to-production gap. Only 25% of companies have moved 40% or more of their AI experiments into production. The rest are stuck in what Deloitte calls the "proof-of-concept trap"—pilots that succeed in controlled conditions but stall when they hit infrastructure requirements, integration complexity, and security reviews.

Finding #2: The access-to-activation gap. Worker access to AI tools expanded 50% in just one year. But among workers who have access, fewer than 60% actually use AI in their daily workflows. That pattern, Deloitte notes, "remains largely unchanged from last year."

More experiments. Broader access. And yet: pilots aren't scaling, and workers aren't adopting.

The report frames these as separate challenges requiring different solutions. We see them as two symptoms of the same underlying problem: the gap between AI as an idea and AI as an operation.

Why More Pilots and More Access Aren't Working

More pilots haven't closed the pilot-to-production gap. Broader access hasn't closed the access-to-activation gap. That's because these efforts address availability, not the structural barriers that prevent AI from becoming part of daily operations.

The real blockers are baked into how AI gets built and deployed:

• Infrastructure that isn't ready for production from day one. Every deployment triggers security reviews, compliance checks, and integration work that should have been solved upstream.
• AI tools that sit outside existing workflows. Workers are less likely to adopt tools that require context-switching, no matter how capable.
• Latency and friction that make AI feel like extra work. If it takes seconds instead of milliseconds, people often find workarounds.
• Security and compliance reviews that stall every deployment. Cloud dependencies mean every new use case restarts the approval process.

These aren't problems you can train your way out of. They're built into the system. Which raises the question: what does it look like when you solve for them from the start?

What Closing Both Gaps Actually Looks Like

We saw this play out recently with Springshot, an aviation operations platform that orchestrates aircraft turnarounds for major airlines.

Springshot needed AI that could analyze safety compliance photos in real-time, flag issues instantly, and do it without disrupting the workflow workers already knew. The result: a webAI computer vision model that went from zero to 100% adoption across Spirit Airlines' entire network in one hour.

The pilot-to-production gap? Solved. The model went live across 500+ daily flights because the infrastructure was already in place. No lengthy security reviews for cloud data transfers. No integration complexity. No unpredictable costs at scale.

The access-to-activation gap? Solved. Workers didn't need training or a new app. They just saw an overlay guiding them to take better photos. The AI was embedded in the workflow they were already using and ran fast enough that it felt like part of the process.

These two outcomes—scaling to production and driving adoption—aren't separate victories. They're the result of the same underlying philosophy: an architecture designed for deployment from the start.

The Root Cause Is Architectural

Deloitte recommends "hands-on, role-specific training and visible executive advocacy" to drive AI adoption. That's reasonable advice, and it will help at the margins. But it's treating the symptom, not the cause.

The root cause of stalled AI adoption and unrealized ROI is architectural: the foundational decisions about where inference happens, how models integrate into workflows, and whether the deployment environment is owned or rented. The real strategic question for enterprises isn’t whether to adopt more AI tools, but whether they’re making architectural choices that let AI operate inside daily workflows rather than something employees have to work around.

Until those fundamentals change, the gaps Deloitte documented will persist. The data shows where the industry is stuck. The path forward is building AI that's designed to operate, not just to demo.