Why 40% of AI projects will be canceled by 2027 (and how to stay in the other 60%)

Most AI projects are failing at infrastructure

Gartner predicts that over 40% of agentic AI projects will be canceled by 2027. The research firm says organizations aren't failing at the AI itself, but at building the enterprise-scale infrastructure needed to make it work.

Successful programs avoid this fate by solving three interconnected crises simultaneously through a unified strategy. Attempting to fix them separately is a recipe for failure.

The three crises of agentic AI

The first crisis is a lack of sustainable velocity. While companies race to deploy, many are forced to pull projects back just as quickly.

S&P Global reports that 42% of companies abandon AI initiatives before production. Examples include McDonald's terminating its AI drive-thru voice ordering after a 100-location rollout and 39% of AI customer service chatbots being pulled back.

The second crisis is a fragmentation tax on finances. AI costs are eroding company margins due to chaotic, untracked spending.

A recent report found 84% of companies see more than 6% gross margin erosion from AI costs. Furthermore, only 15% of companies can forecast these costs within 10% accuracy, leaving most to operate on hope.

The third crisis is the shadow AI security time bomb. Development teams are spinning up connections and moving data without oversight, creating massive vulnerabilities.

86% of organizations have no visibility into their AI data flows, and 20% of security breaches are now classified as Shadow AI incidents. By the time a problem is discovered, the damage is often structural.

Why siloed solutions fail

Most organizations treat speed, cost, and governance as separate problems. They assign them to different teams—development, FinOps, and security—which creates competing silos and makes the overall problem worse.

These elements are fundamentally linked. Governance without speed creates stagnation. Speed without cost visibility burns money. And moving fast without governance just accumulates risk faster.

• Governance enables speed through automated guardrails, not manual reviews.
• Cost visibility enables investment by connecting spending to clear business outcomes.
• Speed enables relevance, as slow deployment paths lead to irrelevance.

The unified platform approach: AI connectivity

The solution is not another point tool, but a new architectural approach called AI connectivity. This is a unified governance and runtime layer that spans the entire data path AI agents traverse.

Agents don't just call LLMs. They connect to APIs, event streams, data sources, and other agents. AI connectivity provides visibility and control across all these connection points.

This approach closes the gap by providing several key functions:

• Unified traffic management across REST, GraphQL, Kafka, WebSocket, and AI-native protocols like MCP.
• Consistent policy enforcement for security, compliance, and cost controls on all traffic types.
• Full data path observability to see what agents are doing, what they're connecting to, and what it costs.
• Built-in monetization infrastructure to meter consumption and enable usage-based pricing.
• Developer self-service that lets teams build and deploy without waiting for manual security reviews.

The race is on

The organizations that will lead the agentic AI era are building these unified platform foundations now. They are establishing the infrastructure to support increasingly sophisticated workloads.

The market leadership window is still open, but it is starting to close. With each quarter, more companies adopt this integrated approach. Once leaders separate from the pack, catching up becomes exponentially harder.

The core question for enterprises is no longer if a unified AI connectivity strategy matters, but whether they are building it or falling behind those who are.