What’s Really Holding Back Your Agentic AI Pilot—and How to Fix It

The pressure to immediately integrate AI into your legacy systems can be overwhelming. And if you thought the consistent C-suite requests to add agentic AI into your processes reached a fever pitch in 2025, be prepared for even greater urgency in 2026.

With this push to add agentic AI as soon as possible, it’s only natural to rush into a new pilot without truly understanding the full requirements of such an undertaking. That’s why the vast majority of enterprises are currently pursuing AI pilots, but only a small portion of those pilots ever deploy.

Nearly 100 percent of AI pilots fail. That’s not an exaggeration.

Why the urgency? It’s because agentic AI can truly transform your business. In the case of customer service, AI pilots show companies increased efficiency, turning support into a strategic asset instead of a cost center. While AI handles repetitive queries, agents can give their full attention to tricky, complex support problems. AI not only assists support agents and helps them achieve greater customer happiness, but it also allows companies to redirect resources to revenue-generating activities.

Lay a solid foundation for integration

Let’s think about an agentic AI pilot like building a house. Instead of simply starting construction on what’s already there, you do some clearing and pour a strong foundation. You can’t throw AI bricks atop a legacy system constructed with outdated tech stacks and poor data sources. To function properly, AI needs clean integrations, accessible data, and modern APIs. AI pilots expose where modernization is desperately needed, and they either accelerate spending, justifying an increase in the building blocks needed around the systems AI agents need to access, or they simply fail.

There’s a vast chasm between failed pilots and successful deployments. It’s crucial that your solution integrates with existing workflows and tools without disruption. That’s because agentic AI isn’t simply another tool; if integrated correctly, AI touches all parts of your company. Why? Let’s go back to the customer service example: AI needs to learn in real-time to match a company’s voice while prioritizing privacy, especially in industries like healthcare or finance, where trust is critical. Moving from chatbots to agentic AI, your support team is shifting from a passive, request-and-wait model to an interactive, autonomous service.

Identify potential data, context, and workflow challenges

Agentic AI runs off data, and a large challenge enterprises face stems from data immaturity: sensitive information, the lifeblood of any company, simply isn’t ready for AI. This information can either be low quality or hard to access; internal systems might have poor governance oversight, leading to the exposure of private, sensitive data.

Two additional challenges have to do with context and workflow clarity. In this instance, context is simply what information you allow the AI to access. To function effectively, your agentic process needs to consume much more than a small sampling of your data; it needs it all. For many organizations, that creates a trust issue. The solution? Bring your AI in-house to keep sensitive data on-prem.

Embedding AI in known workflows is a very low-impact way to get the ROI of AI. While the biggest leverage from AI comes when it handles new flows that weren’t possible before AI, such as offering contextual support inside a product, the fastest way to integrate AI into an enterprise is to embed it in existing tools and workflows, and let it connect the dots behind the scenes.  It’s also extremely important that the true value of your AI pilot is understood within your workflow. Everyone from the C-suite on down should understand the benefits and uses of AI, where it can and can not add value to their daily lives. AI can’t be a black box that nobody knows … they need to understand it.

Finally, agentic AI software requires more stringent security architecture simply because these systems dig into user behavior, continually learning from that information and actually taking action based on learnings. The best AI customer service support truly examines the issue, considering all available data, to develop lasting solutions that are resolution-based.

Information needs to be protected but not walled off. As long as companies impede the information flow to AIs, they’ll keep underachieving and seeing failed pilots. Yet security teams simply will not allow some data and APIs to be shared with cloud vendors, because it expands the security perimeter of the company to a vendor where they have no control.

Throw out the old change management playbook

Agentic AI is continually changing, constantly transforming your business. That means traditional change management, which comes with an endpoint, needs to be updated. With continuous model updates, agentic AI forces enterprises to break out of the tired post-implementation maintenance cycle. Flexibility and the ability to quickly adapt to new updates are key.

With a constantly updating process, security needs to be solid. While you addressed infrastructure earlier in the pilot, you need to constantly update users about new models to make sure everything is used appropriately.

This means it’s crucial to shorten the integration window to make sure users have ample time to learn new processes and train appropriately. Find an AI pilot that doesn’t require significant engineering work to connect every data source. With a zero-integration design, some pilots could be deployed not in months, but literally in hours. This gives enterprises a much more achievable ROI.

And remember, agentic AI only works when it can take action: querying databases, triggering workflows, and accessing customer records. That requires deep integration with sensitive systems, which can be an anxiety-inducing prospect. Security is just one reason a cloud-first AI approach won’t scale. This year, expect more enterprises to look into self-hosted and private-cloud deployments, which will soon become the default for any enterprise serious about autonomous operations. It’s a solid foundation and the key to building a structure that lasts.