Most enterprise AI pilots fail to scale due to organizational shortcomings
Too many companies are focused on the tech and ignore the structure around it. That’s a mistake. The majority of AI pilot failures, 95% according to MIT, don’t fail because AI isn’t good enough. They fail because businesses aren’t ready for the scale they’re asking for. The expectation is immediate transformation, but the execution is usually fragmented, directionless, or siloed inside IT.
AI isn’t plug-and-play. You can’t expect it to deliver business value without aligning technical efforts with organizational goals. As Bret Greenstein, Chief AI Officer at West Monroe, makes clear, companies typically fall into four failure patterns: they lack a transformation plan, they keep IT isolated from the rest of the organization, they underestimate employee resistance, and they don’t communicate the value the tech is supposed to deliver. That last one, value, is critical. If people don’t get the “why,” they stop listening. And if leadership doesn’t define success clearly, the program won’t be taken seriously.
McKinsey also highlights the disconnect. In their research, 66% of companies haven’t started scaling AI across the business. That’s not a tech barrier. That’s decision paralysis and leadership misalignment. AI systems that have the potential to enhance decision-making, save time, and increase efficiency end up stuck in pilot mode while teams debate next steps. Meanwhile, time is lost, and competitors move.
For executives, the takeaway is simple: Don’t treat AI as just another IT project. Make it a business change initiative from the start. Begin with a solid roadmap that includes input from across the company, not just from engineers. Secure executive buy-in beyond the CIO, and define measurable outcomes that matter to your business.
If the foundation isn’t ready, the pilot won’t fly.
Cross-functional collaboration and a culture of psychological safety are critical for successful AI integration
If AI is only run by tech teams, it will stall. You need cross-functional input, early and often. AI doesn’t just impact systems. It changes how people work. That means every department should have a voice, especially when pilots move into real operations.
Greg Beltzer, Chief Customer Officer for AI and Agentforce at Salesforce, has made this clear: this isn’t a traditional DevOps flow. You don’t just hand it to engineers and wait for delivery. Business teams need to engage during planning, testing, and feedback loops. If they’re left out, you’ll build tools that no one really uses, or worse, tools that create friction.
At Engine, a travel tech company, they’ve done this well. Demetri Salvaggio, VP of Customer Experience and Operations, says they prioritize the “why” behind AI tools when communicating with staff. That approach made a difference. Instead of resistance, they got input and momentum. By being clear about purpose, they stopped fear before it grew, fear that AI might replace people instead of helping them.
There’s more. Joshua Stern, Director of GTM Systems at Engine, highlighted the importance of psychological safety. His team built their customer service agent, Eva, in just 12 days. That speed didn’t come from tech alone. It came from a culture where people could test ideas, fail fast, and share wins across functions. When employees saw their colleagues using AI in useful ways, they followed. Adoption didn’t need to be forced, it spread naturally. But only because the workplace encouraged it.
For executives, this culture piece is not optional. Without it, fear takes over. Teams won’t explore AI; they’ll avoid it. The role of leadership here is to normalize experimentation. Bring in stakeholders outside of IT, encourage pilots that start small, and talk openly about both success and failure.
The companies winning with AI are the ones where leaders champion collaboration and trust, not just innovation. Start there, and the tech will follow.
The challenge of proving AI’s value often stems from inadequate metrics and the adoption of AI atop flawed processes
AI doesn’t create value on its own. You have to measure what matters, and make sure you’re applying the tech to processes that actually work. Many pilots fail because companies focus on deploying the AI but forget to validate the process it’s supporting. Poor inputs give poor results, no matter how advanced the model is.
Greg Beltzer from Salesforce pointed out a common issue: if your process is broken, AI won’t fix it. It doesn’t matter how advanced your agent is, if it’s attached to a clunky workflow or inefficient handoff system, it won’t deliver results. Companies expect AI to transform outcomes, but when the foundation is weak, those expectations collapse.
That’s not the only problem. A lot of companies don’t know how to track success. Time saved sounds like a good metric, like “minutes per employee per day”—but most teams don’t have the tools to monitor that. So they can’t prove impact, even when it exists. Salesforce had to create an observability tool for their Agentforce platform just to close that gap. Demetri Salvaggio and his team at Engine used it to refine Eva, their AI customer agent, based on real-time insights. Without that kind of visibility, optimization is just guesswork.
The data reinforces this. According to MIT, only 5% of generative AI pilots showed rapid revenue acceleration. That’s a value tracking failure on a massive scale. It also shows how easy it is to confuse activity with impact. Just deploying AI isn’t enough, it has to tie into business outcomes that executives, teams, and customers feel.
For leaders, the point is clear: don’t apply AI until you’ve cleaned up the process it touches. And don’t scale until you have the tools to measure performance. AI should improve something that’s already working, not be used to hide the fact that it isn’t. Define success before you start and make sure it can be tracked after launch. That’s how you separate real gains from hype.
Adopting a limited, iterative approach to AI pilots helps resolve dependencies and builds a pathway to scalable deployment
Most AI failures come from trying to do too much, too fast. Enterprises load up dozens of use cases and expect them all to succeed at once. That doesn’t work. It dilutes focus, spreads resources thin, and hides what’s really working and what isn’t. If you can’t isolate impact, you can’t drive improvement. And no team can scale what it hasn’t stabilized.
Bret Greenstein, Chief AI Officer at West Monroe, has seen this firsthand. He worked with one company that wanted to roll out AI across multiple processes simultaneously. His advice was to start with five use cases that shared the same data sources and skills. That way, solving dependencies once would unlock impact across several areas. Once those pilots succeeded, the next phases became easier to execute.
This approach, clear scope and controlled rollout, is what pulled Engine forward. Joshua Stern, Director of GTM Systems, said trying to do everything at once means you end up proving nothing. It slows down progress and makes it nearly impossible to get measurable results. His team focused on one agent, Eva, and built it in 12 days. That wasn’t about speed for the sake of it, it was about focus. They picked the right starting point, learned fast, and laid the groundwork for future expansion.
Demetri Salvaggio, Engine’s VP of Customer Experience and Operations, supports that mindset. His advice is direct: pick a small use case, deliver quick value, and use it to build support. Every success creates momentum. Teams trust the process more when they see results, and that support matters when scaling.
For executives, the lesson is strategic prioritization. Don’t spread your AI initiative across the entire organization on day one. Pick targets that are realistic, measurable, and aligned with existing capabilities. Use these wins to drive internal confidence and reduce resistance. Once the foundation is validated and teams are onboard, expanding becomes faster and less risky.
AI scale isn’t about launching big. It’s about executing small in the right sequence. That’s what gets results.
Key highlights
- Most AI pilots fail due to organizational gaps: Leaders must align technical pilots with company-wide transformation plans, cross-department collaboration, and clear value communication, 95% of generative AI pilots fail largely because these fundamentals are missing.
- Cross-functional trust drives successful adoption: AI implementation needs input and support across teams, not just IT; executives should cultivate a culture of transparency and psychological safety to reduce resistance and accelerate adoption.
- Weak processes and poor metrics undermine value: Deploying AI on broken workflows won’t fix them; leaders should first strengthen existing processes and invest in tools to track clear success metrics like time saved, accuracy improvements, or revenue outcomes.
- Scaling starts with small, focused wins: Instead of spreading initiatives across too many areas, prioritize 3–5 key use cases with shared data and resources; this builds momentum, resolves core dependencies, and lays a stable foundation for scalable AI growth.
