---
title: "84% of companies have AI pilots that never reach deployment. Here's what's keeping them locked in limbo."
description: "84% of companies have at least one AI pilot that never made it to production. Corporate leaders share what keeps pilots stuck in testing and why others succeed. "
image: "https://images.ctfassets.net/lzny33ho1g45/vqzs6KR1yPqVozEvuKEhz/3b1f5d05a4cc798757dae1ea685823dc/stop-hero.jpg"
---

# 84% of companies have AI pilots that never reach deployment. Here's what's keeping them locked in limbo.

84% of companies have at least one AI pilot that never made it to production. Corporate leaders share what keeps pilots stuck in testing and why others succeed. 

Most companies don't have an AI ambition problem. If anything, it's the opposite. Give executives a new AI demo, and they'll find 47 potential use cases before lunch. 

Companies are spinning up pilots by the dozen, and that appetite is only growing. According to [AI spending data](https://zapier.com/blog/ai-spending/), 86% of companies plan to increase their investment over the next 12 months.

But deployment is a different story. More than a quarter of organizations (28%) have run over 100 AI pilots, yet only 13% have broadly deployed those projects across their business. 

This limbo between testing and an actual launch date is where many pilots go to die. To find out why, we surveyed over 800 senior leaders across mid-market and enterprise companies, pinpointing the difference between the teams shipping AI outputs and those stuck in testing purgatory.

**Key findings:**

- [38% of companies say their longest-running AI pilot has been locked in testing for over a year](#longest-running-ai-pilot)
- [41% of stalled pilots are blocked by IT infrastructure, data quality, and system integration issues](#compliance-roadblocks)
- [Budgets of $250K or more significantly increase an organization's AI pilot success rate](#budgets-of-250k)
- [Companies with more successful AI pilots are twice as likely to secure early executive sponsorship](#executive-sponsorship)

## 38% of companies say their longest-running AI pilot has been locked in testing for over a year

AI experimentation is happening at a serious pace and scale. Nearly 60% of organizations have run more than 15 AI pilots to date. 18% have run between 16 and 50, 14% have run 51 to 100, and 28% have crossed the 100-pilot mark. 

The budgets reflect that ambition too. 48% of respondents are allocating $250,000 or more annually to AI pilots and initiatives. 

Running more than 100 pilots sounds impressive until you realize it can also describe a company that has tested everything and shipped nothing—because volume doesn't equal velocity.

Among pilots that did eventually reach production, 39% spent between four and 12-plus months in the evaluation phase before going live. In industries where speed matters, that timeline is hard to justify. AI is supposed to save time, not add months of runway before anything changes.

And that's the data for pilots that succeeded. A separate figure is more telling: **38% of leaders report their longest-running active pilot has been stuck in evaluation for over a year** without reaching production. Once a pilot misses that early deployment window, it appears far more likely to stay in limbo indefinitely than to eventually ship.

Who owns the pilot matters more than most organizations realize. Put a dedicated team on it, and pilots ship in weeks. Hand it to whoever raised their hand in the meeting, and you're looking at months—if it ships at all. 36% of dedicated internal AI teams, the kind championed by forward-thinking organizations that have invested in [AI-specific leadership roles](https://zapier.com/blog/ai-transformation-leader/), get their pilots into production in under a month. When individual business unit leaders run pilots on their own, only 16% deploy within that same timeframe.

One reason pilots stall early is that they're not actually connected to the company's existing tech stack and processes. Every new workflow needs governed access to employees' apps—and building those connections securely, without exposing credentials or rebuilding OAuth for every integration, is slow work that rarely gets prioritized. [Zapier MCP](https://zapier.com/mcp) gives AI assistants like Claude, ChatGPT, and Cursor access to  apps through a single connection, with authentication managed by Zapier—not stored in the model's context or scattered across API keys. Agents get access to the tools they need, but credentials stay out of their hands.

## 41% of stalled pilots are blocked by IT infrastructure, data quality, and system integration issues

A lot of roadblocks to launching AI pilots are human-focused. For example, internal priorities shifted before a scaling decision was made for 28% of organizations. Another 27% couldn't accurately measure or prove ROI to decision-makers, and 27% ran into resistance from employees or stakeholders who weren't ready to adopt the new workflow. 23% simply ran too long without a formal decision point and lost momentum entirely.

But the most common reasons AI pilots stall are more structural. **IT infrastructure, data quality, and system integration issues blocked 41% of stalled pilots**, making it the single biggest obstacle organizations face when trying to move from testing to production. Legal, compliance, and data privacy concerns follow at 29%.

These are fixable problems, but successful integration, strong infrastructure, and secure [governance](https://zapier.com/govern) work best when they're built in from the start, not retrofitted after something goes wrong. Zapier sits between AI tools and the apps they connect to, giving IT control over what each team can reach, which actions are allowed, and visibility into connection activity. That way, companies don't have to choose between letting teams move fast and knowing what's happening.

## Budgets of $250K or more significantly increase an organization's AI pilot success rate

More budget doesn't automatically mean more success, but too little almost guarantees failure.

Among organizations spending under $50,000 on AI pilots annually, only 7% report successfully getting all of their pilots to production. At the other end, organizations with budgets of $250,000 or more are significantly more likely to push most or all of their pilots through.

The sweet spot, though, isn't at the top of the budget range. **Organizations spending between $250,000 and $499,999 show the highest combined success rate of any budget bracket.** 68% report that most or all of their pilots reach production (50% say most, 18% say all). 

That outperforms even the million-dollar budgets, where the combined most-or-all rate drops to 60%. 

The pattern here shows that the more an organization spends, the more of its pilots reach production—but with diminishing returns. Money solves the problem of affording more testing, but it opens a new can of worms: who actually runs this pilot, how is it connected securely to other workflows, and how can you be sure everything is high impact?

## Companies with more successful AI pilots are twice as likely to secure early executive sponsorship

Even with stalled pilots and long evaluation timelines, organizations aren't backing away from AI. **81% of companies say a failed or stalled pilot has minimal to moderate impact on future investment. **The breakdown of that 81%: 29% treat failure as a normal part of the process with no real effect on morale or budget, while the other 52% see stalled pilots as temporary setbacks rather than reasons to change course. Only 2% report severe damage to leadership confidence.

So what separates the organizations that successfully keep deploying AI pilots from those that keep stalling?

When asked to name the single most important factor in getting a pilot to production, leaders pointed to: 

- Having the right data infrastructure from the start (16%)
- Identifying a high-value, easy-to-justify use case (15%)
- Building cross-functional buy-in across IT, legal, and business stakeholders early (14%)

But one factor stands out when you compare organizations by how many pilots they actually deploy. **Organizations that successfully deploy most or all of their pilots are more than twice as likely to have secured executive sponsorship** at the director level or above before launching, compared to companies that only deploy a small number of their pilots (23% vs. 10%). When priorities shift or budgets tighten, an executive sponsor is often what keeps a pilot alive long enough to matter.

## Bring your AI pilots to life with Zapier

Organizations moving AI into production fastest have a few things in common: 

- Dedicated ownership
- Early executive alignment
- The right budget allocation
- Systems that let AI tools actually connect to the apps and data a company already runs

Getting any one of those wrong can keep a pilot stuck in evaluation for months, or indefinitely.

AI workflows are no longer built exclusively by technical teams. They're being spun up inside agent environments, by employees who want to move fast and aren't waiting for IT to catch up. Most already know they want to use AI. The harder question is whether the underlying infrastructure can actually support it.

The data here points to the answer: the top two blockers aren't budget or ambition—they're integration failures and compliance concerns. [Zapier](https://zapier.com/) is built to remove both. Agents connect through OAuth-managed, scoped access, so credentials never touch the model's context. At the same time, IT sees what agents can reach and can revoke access in one place. Every connection runs through the same governed layer, across whatever AI tools, agents, or models the business uses. That's how [enterprise organizations](https://zapier.com/enterprise) move fast without losing control of what their AI is actually doing.

**Related reading:**

- [92% of sales teams drop qualified leads every month—here's why follow-ups are breaking down](https://zapier.com/blog/dropped-leads-survey/)
- [AI workflows: How to actually use AI in your business](https://zapier.com/blog/ai-workflows/)
- [12 AI automation examples from teams doing it right](https://zapier.com/blog/ai-automation-examples/)
- [AI adoption: A practical guide from Zapier](https://zapier.com/blog/ai-adoption/)