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I run go-to-market for a startup in one of the fastest-growing markets. Needless to say, it feels like I’m always moving at a million miles an hour, and it’s necessary most of the time. We have big goals and plenty of competition. Time is of the essence.

The only way this pace works is because we took the time internally to design a cross-functional operating model, rhythm, and motion aligned with the outcomes we are striving to achieve as an organization. Every team, sales, marketing, product, people, operations, is aware of the opportunity in front of us and committed to getting after it. This is the commitment we know we need to get the results we want.

In any organization and in any industry, alignment is critical to change management. Without it, there’s no clear ownership, essential considerations get lost, and the initiative never makes it beyond the test phase. This is the challenge many enterprises are facing today with AI deployment.

Gartner predicts that 40% of agentic AI deployments will be canceled by the end of next year, citing unclear business value as a key reason. This is the result of poor alignment up front, and why I believe internal alignment is one of the most underrated factors in moving AI initiatives from experimentation into full-scale production.

The misdiagnosed problem

When an AI project stalls, the easiest thing to blame is the technology. This can be true, but more often, that problem shows up when moving from production to full-scale deployment.

The reason for stalling before getting out of pilot mode goes back to not aligning internally on what the definition of a successful pilot is. A CX leader may care about containment rate, while the operations team is focused on average handle time, and the CFO is tracking cost-per-contact. If no one agrees on what success looks like for these metrics prior to launch, there’s no shared verdict at the end of a pilot. With no verdict, there’s no production.

That’s why I believe AI pilots need to be reframed as proof of value. Because whereas a pilot can easily become an open-ended experiment with no clear end goal, a proof of value requires internal alignment on what that value is. If proof of value works, you already have everything you need to make a production commitment. But proof of value requires an alignment conversation to happen before the work starts.

Five questions to ask before you start your pilot

Aligning on these topics up front will ensure you’re able to draw a clear verdict at the end of the pilot:

1. What’s your AI maturity level?

Every AI pilot looks different depending on where an organization is in its overall AI journey. That’s why you need a readiness conversation before a pilot conversation. Gather your stakeholders and ask these questions:

• Do you know which LLM providers you’re aligned to?
• Do you have a framework for guardrails?
• Is your legacy infrastructure compatible with MCP?
• Are your data and systems in an AI-ready accessible state?

These questions will cover the basics to make sure you’re asking the right questions of your vendor and building the right foundation.

2. How do you define success?

Get every internal team that will be involved in a successful deployment into the same room. Discuss what metrics matter most to them, and align on what kinds of tests must be run in order to determine value. Knowing what people are looking for before defining the scope of the project will help prevent trying to fit a square into a round hole later on.

3. What’s the deadline?

Determine a definitive deadline for when you want the pilot to be complete. This will not only prevent the pilot from fading into the background as another thing to eventually check on, but it will also hold all stakeholders, vendor included, accountable in delivering on the expectation.

4. Which use case do we start with?

A pilot that succeeds in its simplest form proves almost nothing about how it will work in production. Work with your stakeholders to identify a use case that’s high enough volume to generate meaningful signal, narrow enough to scope tightly, and requires connecting to systems that are ready for integration. That way, you’re set up for every use case that could follow in production.

5. Who owns the production phase?

If the pilot works, then what? Who owns the agent in production? It could be the internal team, a partner, or the vendor. Figure out what ongoing management looks like before you start the pilot, so the product doesn’t get ignored once it goes live.

Alignment is the prerequisite

At Parloa, we’re able to move fast because we’re aligned cross functionally and around the globe. The same principle applies for AI deployment. Get your teams speaking the same language, committed to the same definition of success, and be clear on who owns what when it works. From there, teams are able to act autonomously, move fast, and scale to transformative outcomes. Ignore any of the pre-work, and the technology doesn’t matter. You’ll still face extra expenses with little ROI to show for it.

Read more about how to move from pilot to production in Parloa’s Guide to crossing the AI divide.