Why 40% of Agentic AI Projects Will Fail

Gartner says cost, unclear value, and risk will sink most agentic AI projects. The deeper issue is that leaders can’t see, measure, or translate what their systems are doing.

According to Gartner, more than 40% of agentic AI projects will be canceled by the end of 2027 due to escalating costs, unclear business value, or inadequate risk controls.

That prediction might sound harsh, but it tracks with what we’re seeing across the industry. Agentic AI is the next frontier, but most projects are being driven by hype, not strategy.

“Most agentic AI projects right now are early-stage experiments or proof of concepts that are mostly driven by hype and are often misapplied,” said Anushree Verma, Senior Director Analyst at Gartner. “This can blind organizations to the real cost and complexity of deploying AI agents at scale.”

That’s the heart of the problem. Companies are racing to ship agents without understanding how to measure them, manage them, or translate their performance into business value.

The failures Gartner highlights—cost, value, and risk—are the symptoms. Underneath them are deeper operational issues that explain why these projects stall.

Here’s where most teams go wrong:

1. Misaligned Expectations and Early-Stage Hype

Gartner calls it “agent washing,” vendors rebranding existing tools like chatbots or RPA as agentic AI without meaningful capabilities.

That’s led to inflated expectations. Executives expect autonomous systems that can reason and act on goals, while engineers are still working with probabilistic models that require careful oversight and iteration.

The gap between promise and maturity creates a predictable cycle: excitement, experimentation, and disappointment.

“Many use cases positioned as agentic today don’t require agentic implementations,” Verma explains.

In other words, too many teams are building “agents” where simpler automation would do.

2. Lack of Unified Visibility

Even when agentic projects are built with the right intentions, most organizations can’t see what’s happening once they’re live.

These systems behave in ways that are difficult to explain. Logs are verbose, telemetry is noisy, and outcomes are often different despite the same questions and prompts from customers.

Engineering teams try to fill the gap with observability tooling, but it’s too technical for business teams. That means engineers spend time combing through logs and traces, trying to explain what went wrong, what improved, and why.

Instead of building better systems, they’re translating data. And that slows everyone down.

This means basic questions like “are my customers getting what they want from the agent” aren’t being answered.

3. The Probabilistic Problem

Even when everything else is right, large language models introduce a unique challenge: variability.

LLMs are probabilistic systems. They can produce different answers to the same prompt depending on context, temperature, or small differences in input. That variability is part of their power but it also makes them unpredictable.

If a customer interacts with the same agent twice, they might get two different outcomes. One great, one frustrating.

To manage that, companies need visibility at the customer level. They must be able to monitor every interaction at scale, identify where consistency breaks, and measure whether users are getting value or friction.

Without that layer of observability, companies are managing blind. They can’t tell if the agent is serving customers reliably or simply rolling the dice.

The Real Failure Isn’t Technical

When projects collapse, it’s rarely because the model underperformed. It’s because the organization didn’t have the systems to measure, understand, and communicate what the agent was doing.

AI introduces new behavior that needs new management. Without shared visibility, teams can’t iterate. Without translation, they can’t align.

The result is predictable: budget cuts, “lessons learned,” and another project shelved.

The 40% that fail will make headlines. The 60% that succeed will share a pattern: they’ll treat visibility and interpretation as core infrastructure, not afterthoughts.

Brixo’s View

At Brixo, we see this every day. Teams don’t struggle with capability, they struggle with clarity.

We built Brixo to give that clarity back. Our platform translates complex agent telemetry into business-level metrics such as cost, reliability, sentiment, and performance.

That translation makes AI accountable. Because when everyone can see what’s happening, alignment follows and projects stop failing quietly.

Agentic AI isn’t failing because it’s overhyped. It’s failing because teams are flying blind. The ones that fix that will define the next generation of AI success stories.