Gartner predicts 40% of agentic AI projects will be cancelled by 2027: How to avoid become a statistic

In a recent Gartner press release, the firm made a bold prediction: Over 40% of agentic AI projects will be canceled by the end of 2027. That’s nearly half of all efforts to embrace one of the most hyped innovations in enterprise AI. And yet, this shouldn’t come as a surprise.

Agentic AI—AI that can reason, act autonomously, and complete multi-step objectives—has captured the imagination of business leaders and IT teams alike. From internal copilots to autonomous customer service agents, the potential to increase productivity, reduce repetitive tasks, and accelerate decisions is massive. With the growing availability of foundation models and new frameworks like Anthropic’s Model Context Protocol (MCP), companies are racing to spin up pilots and proof of concepts. But according to Gartner, the majority of those will hit a wall.

So, what’s going wrong—and more importantly, how do you avoid becoming part of the 40%?

Why Agentic AI Projects Fail

Gartner outlined three core reasons these projects may stall or be cancelled. Each one maps to a real operational or strategic gap we’ve seen across early adopters:

1. High Cost, Low Ownership

Agentic AI isn’t just another SaaS tool you spin up with a credit card. These projects require infrastructure decisions, model evaluations, orchestration logic, access to internal tools and APIs, and continuous oversight. They often fall into a gray area between data science, engineering, and line-of-business owners—meaning no one is clearly accountable for driving results. Meanwhile, IT and security teams are left holding the bag for implementation, integrations, and risk mitigation.

2. Unclear ROI

Most companies are still in the experimentation phase. Maybe your agent helps write better emails, summarizes a few support tickets, or fills out a CRM field. Nice? Sure. But is it transformational? Not yet. Without a clear path to value, it’s hard to justify the cost and effort of building, managing, and securing an agentic AI system at scale. Executives are starting to ask the hard questions: where’s the impact?

3. Security Risks Are Ignored

Let’s not sugarcoat it: unsecured agentic AI is a ticking time bomb. These systems can take actions across your tech stack, access sensitive customer and financial data, and make decisions on behalf of employees—all with little to no oversight. Without strong identity and access controls in place, it’s only a matter of time before a rogue request or misconfigured permission leads to a data leak, an inappropriate action, or a compliance failure.

When you combine all three—high cost and complexity, limited or unclear value, and risk—it’s no wonder many projects fizzle, often before ever making it to production.

How to Beat the Odds (and the Statistic)

The good news: this 40% failure rate isn’t inevitable. Companies that are succeeding with agentic AI are doing a few things differently:

• They’re treating AI agents like first-class citizens in their infrastructure, with proper identity, access, and lifecycle management.

• They’re focusing on high-impact, connected use cases—not just isolated prompts or copywriting assistance.

• They’re building on secure, scalable foundations that make it easy to launch and govern agents without overburdening IT.

And that’s where a hosted MCP platform like Natoma comes in.

Natoma: The Fast Lane to Secure, Scalable Agentic AI

Natoma helps enterprises adopt agentic AI without the chaos. It’s a hosted platform built with Model Context Protocol, which means you can give AI agents access to the tools and data they need to be useful—without compromising on control or visibility. In short, it helps make sure you’re on the right side of the 60/40 split.

Here’s how Natoma helps companies avoid the top reasons agentic AI projects fail:

✅ Faster Time to Value

Natoma makes it easy to connect your LLMs and agents to internal systems via a growing library of secure integrations. You can go from idea to execution in minutes—not months. That means your agents aren’t just sending better emails—they’re resolving tickets, enriching customer profiles, reconciling transactions, and completing real workflows that move the needle.

✅ Built-in Security & Governance

With Natoma, every action an agent takes is tied to a secure, non-human identity. You get fine-grained authorization, audit logs, and enforcement of least-privilege access—so agents only do what they’re allowed to, on behalf of users who actually have the right to request it. No more silent errors. No more over-permissioned copilots.

✅ Shared Ownership, Simplified

Because Natoma is a hosted platform, your AI, security, and engineering teams can share a single foundation. Agents are governed through centralized policies, and access is controlled through modern identity protocols. That means less IT lift, clearer ownership, and easier scaling.

Don’t Be the Statistic

Agentic AI has the potential to reshape how businesses operate—but only if it’s done right. The companies that succeed won’t be the ones chasing the latest model or spinning up rogue copilots. They’ll be the ones that treat agentic AI as a secure, governed, and integrated part of their business operations.

The 40% failure rate Gartner predicted is a warning—but it’s also an opportunity. With the right platform and the right foundation, you can turn experimentation into execution, and ensure your AI projects don’t just survive—they thrive.

Start fast. Make an impact. Stay secure.
With Natoma, you don’t have to choose between innovation and safety.

Ready to beat the odds? Let’s build something that lasts.