40% of Agentic AI Projects Will Fail by 2027. Here's How to Be in the 60%

Gartner just dropped a sobering prediction: over 40% of agentic AI projects will be canceled by the end of 2027. The reasons? Escalating costs, unclear business value, and inadequate risk controls.

If you're an enterprise leader evaluating AI agents, this isn't a reason to slow down. It's a reason to get smarter about how you deploy them.

The companies that end up in the successful 60% won't be the ones who moved fastest. They'll be the ones who moved with discipline—choosing the right use cases, building the right infrastructure, and measuring the right outcomes.

Here's how to be one of them.

Why 40% Will Fail

Gartner's prediction isn't pessimism—it's pattern recognition. After surveying over 3,400 enterprise leaders, three failure modes emerged:

1. Escalating Costs Nobody Budgeted For

Most organizations underestimate what it takes to move AI agents from pilot to production. Integration with legacy systems creates compatibility issues and data silos. Process redesign inflates timelines. What started as a quick win becomes a multi-year transformation project.

The result: projects stall before reaching production, burning budget without delivering value.

2. Unclear Business Value

Here is an uncomfortable truth: most agentic AI propositions lack significant ROI. Current models do not have the maturity to autonomously achieve complex business goals or follow nuanced instructions over time.

When leadership asks what is the return on this and the answer is vague, projects get cut.

3. Inadequate Risk Controls

Unsecured AI agents can access sensitive customer data, make decisions on behalf of employees, and take actions across your entire tech stack with little oversight. Without strong identity and access controls, you are one misconfigured permission away from a data leak or compliance failure.

According to PwC's AI Agent Survey, trust varies dramatically by use case. Only 20% of leaders trust AI agents for financial transactions, and just 22% for autonomous employee interactions. The bar for mission-critical processes is high.

The Agent Washing Problem

Before blaming the technology, consider who is selling it to you.

Gartner estimates only about 130 of the thousands of vendors claiming agentic AI capabilities are actually delivering genuine agentic solutions. The rest are engaging in agent washing, rebranding existing chatbots, RPA tools, and AI assistants without substantial new capabilities.

The tell? If your agent can only respond to queries but cannot autonomously plan, execute, and adapt across multiple steps, it is not an agent. It is a chatbot with better marketing.

This matters because agent-washed solutions create the appearance of innovation while delivering chatbot-level results. When those results disappoint, executives blame agentic AI rather than the vendor who oversold them.

What the 60% Will Do Differently

The companies that succeed with agentic AI share five characteristics. None of them are about having the biggest budget or the most advanced model.

1. They Choose the Right Use Cases

Not every process needs an agent. The best candidates share specific characteristics:

Complex, dynamic environments: Supply chain optimization, cybersecurity threat response, dynamic credit approvals

Multi-step, multi-agent collaboration: End-to-end procurement orchestration, cross-functional incident response

High-value, low-frequency decisions: Warranty claims processing, hospital discharge coordination

Generic agents fail in high-accuracy fields like accounting and finance, where nuanced domain knowledge is required. The winners pick use cases where agentic AI unique capabilities create measurable business value.

2. They Focus on Enterprise Productivity, Not Individual Tasks

Here is where most organizations go wrong: they deploy agents to augment individual workers rather than orchestrate actions across business units.

A customer service agent that answers questions faster is nice. A system that autonomously coordinates between support, billing, and fulfillment to resolve issues end-to-end is transformative.

Gartner advice: Use AI agents when decisions are needed, automation for routine workflows, and assistants for simple retrieval. The distinction matters.

3. They Build Guardrails Before They Scale

The trust gap is real. According to Workato HBR research, 86% of organizations plan to increase agentic AI investment but only 6% trust AI agents to autonomously handle core end-to-end business processes.

The difference between a chatbot error and an agent error? Chatbot mistakes are immediately visible. Agent mistakes can cascade through business processes before anyone notices.

Successful deployments implement:

• Human-in-the-loop checkpoints for high-stakes decisions

• Comprehensive audit trails for every agent action

• Clear escalation paths when agents hit edge cases

• Role-based access controls that limit agent permissions

4. They Embrace Composite AI

Pure agentic AI is not always the answer. The most successful implementations combine multiple approaches:

• Machine learning for pattern recognition

• Symbolic reasoning for explainable decisions

• Traditional automation for routine workflows

• Agents for complex, multi-step orchestration

This composite AI approach matches the right technique to each part of the problem rather than forcing everything through an agentic paradigm.

5. They Measure What Matters

Many failed projects are judged against narrow cost-savings metrics instead of measuring what agents actually deliver: long-term productivity, accuracy improvements, and compliance benefits.

One European logistics platform reduced customer support response time from 2 hours to under 90 seconds using agentic AI. That is not a cost-savings metric. It is a competitive advantage.

Successful organizations track:

• Time-to-resolution (not just cost-per-ticket)

• Decision accuracy over time

• Process completion rates

• Exception handling efficiency

The Bottom Line

Venture capital investment in agentic AI surged 265% between Q4 2024 and Q1 2025. By 2028, Gartner expects at least 15% of day-to-day work decisions will be made autonomously through agentic AI, up from virtually none in 2024.

The opportunity is real. So is the risk of joining the 40% who will not make it.

The path to the successful 60% is not about moving faster. It is about moving smarter: choosing the right use cases, building guardrails before you scale, and measuring outcomes that matter.

Agentic AI is not a technology you install. It is a capability you build. The enterprises that understand this distinction will be the ones still running their agents in 2028.