Most generative AI pilots fail. Here’s how to join the 5% that deliver

Enterprise teams are racing to integrate generative artificial intelligence (AI). Pilots are launching across business units. Demos are happening weekly. Every platform promises acceleration, transformation, and competitive edge.

But very few of those efforts are delivering results that matter.

According to a recent MIT study, 95% of generative AI pilots never reach meaningful production. Short-term experiments consume budgets, tools proliferate without governance, and business leaders can’t point to a return on investment (ROI).

The problem isn’t imagination or even adoption. The problem is that most organizations treat AI like a technology project instead of a business transformation.

Why most AI projects fail

Misalignment, not capability, is what derails most enterprise AI initiatives.

Pilots get approved without a clear business problem to solve. Ownership falls to IT or innovation teams who aren’t equipped to drive operational change.

The consequences build quickly. One team experiments with pilots. Another signs a new vendor contract. A third creates a custom workflow that never scales. The result is an internal ecosystem of disconnected tools, overlapping efforts, and siloed insight — each advancing in isolation but moving nothing forward.

Meanwhile, shadow AI expands. Employees turn to personal large language models (LLMs) that the organization hasn’t officially enabled to speed up tasks. This year, 59% of U.S. employees admitted to using unapproved AI tools at work, and 75% of these individuals have shared potentially sensitive information with those tools. Meanwhile, security teams scramble to catch up. Leaders lose visibility into who’s doing what, with which tools, and why.

Even when intentions are aligned, the work often lacks staying power. Projects stall because the tools don’t learn, they fail to retain context, and feedback loops are missing. No one’s measuring what’s working, let alone why.

The biggest opportunities — like reducing operating costs, shortening cycle times, and eliminating outsourced spend — never make it past the whiteboard. Too many teams get stuck spinning up demos instead of solving problems that move the business.

Without a model for integration, governance, and measurement, the hype burns out and the budget runs dry.

What success actually takes

The few organizations seeing real results aren’t chasing tools or use cases. They’re aligning AI to strategic business priorities and reshaping how work gets done.

That begins with clarity. The strongest initiatives start with a defined AI vision: a statement of what the business is trying to achieve, where AI fits, and why existing tools can’t close the gap.

From there, execution depends on structure. High-performing teams govern their AI portfolios like product lines. They evaluate vendors based on workflow integration, not just features. They empower champions across departments. They invest in shared infrastructure, not one-off tools. And they standardize how use cases are evaluated, prioritized, and measured.

They also resist the temptation to overreach. Some of the biggest wins come from automating unglamorous work, like document intake, case summarization, standard operating procedure (SOP) retrieval, and customer query handling. The ROI isn’t in the headline. It’s in the time saved, the errors avoided, and the scale achieved.

Turning isolated wins into enterprise value

Yet, organizational fatigue is setting in. According to S&P Global, 42% of companies abandoned most of their AI initiatives in 2025, more than double the number from the year prior. Even the most ambitious programs are stalling out without a model to scale what works.

That’s where SHI’s AI Center of Enablement (AICOE) comes in.

Our approach is anchored around six core capabilities: vision, AI portfolio, people, use cases, governance, and measure. Together, these capabilities form a practical framework that helps teams assess where they are, identify gaps, and build the muscle to scale what works.

Instead of chasing disconnected experiments, AICOE helps teams recognize and replicate patterns. A successful automation in customer support becomes a blueprint for other departments. A workflow that accelerates contract analysis becomes the foundation for broader document intelligence. Repeatability drives the gains.

Each engagement begins with business outcomes. Teams define what success looks like before choosing tools or writing prompts. Vendors are evaluated based on integration with real-world processes, not just feature sets. Governance becomes shared infrastructure, not a blocker. Progress is measured in reduced cost, time saved, and better decisions across the entire organization.

Our experts support the work across AI, infrastructure, and cybersecurity. These are practitioners who’ve supported hundreds of enterprise deployments, helping customers move from experimentation to execution with clarity, speed, and control, and they’ll work with you to accelerate what’s working, fix what isn’t, and avoid the pitfalls that stall momentum.

Creating sustainable AI value

The divide between the 5% who succeed and the 95% who don’t comes down to structure.

Successful organizations don’t treat AI as a collection of tools to manage. They build operating models that connect vision to execution, align governance with business needs, and measure what actually matters. The things that matter are the things that differentiate the business. What are those high-level things that make your business special? The key is identifying the processes that influence what makes your business unique, and targeting AI projects that enhance those processes to unlock real value.

The technology works. The pilots validate capability. What’s missing is the framework to scale them.

NEXT STEPS

Ready to build AI initiatives that deliver? Connect with our experts to assess where you are today and create a roadmap for measurable business impact.