95% of AI pilots fail. Here’s a 3-stage path forward

The headlines are everywhere: 95 percent of generative AI pilots are failing, according to MIT. Another recent study predicts that in two years’ time, 40% of agentic AI projects will be canceled. A growing consensus is that AI is overhyped. We think that is the wrong takeaway.

AI itself is not failing. The pace and immediacy of its advancement are creating an overstimulated environment where leaders feel pressure to jump in. The temptation is to chase grand visions without the tight scoping and deliberate sequencing that real transformation requires.

Compounding the issue are familiar challenges: poor integration with existing workflows, unclear ROI, weak change management, and slow adoption. Too many pilots remain side experiments that never become business-critical tools. 

Enterprises are treating AI as a quick proof of concept rather than as a staged journey. They are chasing broad demonstrations instead of solving specific problems. This is not a fault of ambition, but a byproduct of AI’s extraordinary capabilities. It looks so powerful in task-level use cases that it is easy to overestimate how quickly those same gains will scale in enterprise settings.

That is why pilots collapse.

The hype trap, redux

This is not the first time businesses have been swept up by a technology wave. Think back to the early days of the internet. In the late 1990s, companies rushed to “be online” with splashy websites. Many delivered little value. The real transformation came later, when e-commerce platforms, payments systems, and logistics integrations made digital a core business channel.

Generative AI is at a similar moment. Consumer adoption is everywhere, but business transformation requires context, integration, and credibility inside workflows. Until those are in place, many pilots will stay trapped in hype.

The forces driving ‘pilot purgatory’

Executives launch AI pilots because they want to show momentum. But even when framed against important business goals, the expectations are often too lofty for a pilot to deliver on technical capability alone. 

AI is powerful and full of potential, yet real change comes only when the technology is embedded into workflows, supported by training, and reinforced through new ways of working. This disconnect shows up in three ways:

• Pilots start small—and get stuck there. Narrow scoping is useful to prove early value, but without the right foundations and a plan to expand, many pilots never grow beyond isolated wins.

• Pilots underestimate operational lift. Integrations, change management, and training are skipped or rushed, which makes outcomes fragile and difficult to sustain.

• Pilots fail to build credibility with end users. Without clear wins in daily tasks, agents and employees do not change behavior, so adoption lags.

The result is predictable: excitement at kickoff, disappointment at the end.

What success really requires

The 5% of pilots that succeed, according to the MIT study, are rooted in a sharper question. Not “how do we test AI,” but “which goals matter most, and how can AI augment the processes that move the needle.”

For a CIO, that might mean reducing ticket backlog to 30% or cutting mean time to resolution in half. For a CHRO, it might mean improving employee experience by accelerating service request response time from days to hours. For a CFO, it might mean reducing cost-to-serve by 20% while preserving satisfaction.

Once those goals are clear, the scope of AI becomes obvious. And from there, the product experience must serve two audiences at once:

• Leaders, who need proof of value through clean metrics tied to their business outcomes, and

• End users, who need credibility through quick wins and meaningful outcomes that feel undeniable in daily work. 

When both audiences are served, trust builds, sponsorship strengthens, and adoption expands.

The staged path out of purgatory

At Freshworks, we have seen that transformation happens in stages, not in a single leap. That is why we frame AI adoption through a three-step AI readiness model.

Stage 1: Readiness

This is where organizations prepare the ground. Leadership alignment and clear success definitions are essential, but so is the operational work. Workflows need to be audited, data hygiene addressed, and KPIs defined. Champions and test groups are identified so there are owners who can carry the change forward. Employees also need to be introduced to how AI will support them, with training that creates confidence rather than fear. Readiness is about ensuring that when AI is turned on, it lands in a place prepared to capture its value.

Stage 2: Activation

In this stage, AI is embedded into select high-friction workflows where its impact can be quickly seen. That could mean deploying a support copilot into ticket triage, FAQ responses, or knowledge surfacing. Change management is critical during activation. Employees need training, clear guidance on how to use AI, and reassurance that the system can be trusted. Feedback loops from users and usage data help refine the experience and keep teams engaged. When people feel supported and confident, adoption grows. That is how activation is achieved: early wins that prove credibility and build momentum.

Stage 3: Expansion

With early wins in place, the focus shifts to scale. New workflows are added, such as provisioning requests, employee service management, or more complex case resolution. Advanced capabilities like AI-powered agents or insights are introduced. Change management continues, with training tailored to different levels of user comfort and reinforcement of new behaviors. Champions are empowered to lead adoption within their teams, creating a flywheel of credibility and curiosity. Expansion is where AI stops being a tool in pilots and becomes part of the operating fabric of the organization.

This staged approach is not about slowing down. It is about sequencing adoption in a way that compounds value, ensures users are equipped to succeed, and transforms pilots into enterprise impact.

The leadership imperative

The MIT study is not proof that AI is doomed. It is proof that organizations are trying to shortcut the hard work. Instead of readiness and wins, they want to leapfrog and scale.

Here, executives have an important role to play. They must insist on clear scoping, staged adoption, and alignment to outcomes. They must support their teams with the playbook that builds readiness, delivers activation wins, and then scales to expansion.

The best AI pilots do not start broad. They start focused. They target the overlooked workflows where efficiency, accuracy, and satisfaction gains are easiest to measure. These early, pragmatic wins build the credibility and curiosity that allow AI to spread.

History shows the arc. The internet, ERP, and workflow automation all moved through hype, shallow pilots, and eventually structured integration that reshaped industries. AI is on the same trajectory.

The question for today’s leaders is whether they will chase broad proofs of concept that fade out, or whether they will build pragmatic momentum by aligning AI to goals, workflows, and people.

That is how enterprises move from the 95 percent who fail to the 5 percent who lead.