Why Most Agentic Pilots Are Failing

Title: Why most agentic pilots are failing

Read time: 2.5 min

Today I want to share something that only became clear after enough reps with clients, specifically, understanding why pilots weren't producing the outcomes we expected. Getting to the pilot stage correctly is hard enough. Conversion from pilot to close should be nearly 100%.

Once I saw what was really getting in the way, I couldn't unsee it.

The majority of our work over the last 18 months has been working with founding teams on deploying AI agents. Some of those deployments have gone really well, others not so much. What I noticed is the difference rarely came down to the quality of the agent itself or how the well the model was trained.

It came down to what was already in place before the agent(s) were ever deployed.

I’m sure a lot of readers have read the MIT article research on this. They basically examined 300 enterprise AI implementations and found that only 5% of pilots deliver measurable business returns. $30 → $40B invested globally, and 95% of orgs are seeing zero return.

I'm not sure how accurate that data is, or the nuance behind it, but I've experienced a version of this play out with teams we work with. Regardless of whether it's accurate or not, seeing it confirmed at that scale made me want to share what we've been learning, because I think there's a better way to tackle this, and we've been fleshing it out in real time.

If A Man Knows Not Which Port He Sails, No Wind Is Favorable.

~Seneca

What I’m SeeIng Consistently

The pattern usually looks something like this. A team gets excited about what agents can do. They find a business case that looks right, run a pilot, and then 30 days later the results aren't where anyone hoped they'd be. The technology gets blamed. Sometimes the internal champion who pushed for it gets blamed.

When we look underneath the hood with these teams, the issue is almost always the same thing: the infrastructure underneath the agent wasn't ready to support the outcome they were going for.

What I mean by infrastructure is clean and accessible data, documented internal workflows, a clear business process the agent is stepping into, and someone internally who has the capacity to own the deployment. When those things are in place, the outcomes we've seen have been genuinely solid. When they're not, it sucks.

This is what the article is pointing to when they say most pilots fail due to brittle workflows and misalignment with daily operations. Think about trying to build a house without the proper foundation in place. Probably not gonna work out well. This is exactly what we’re seeing.

It's not the model. It's the foundation.

How We Started Doing It Differently

Once we saw this pattern clearly enough, we changed how we approach the work with clients, specifically, how we structure the disco conversation before a pilot even begins.

We started what I'd describe as a readiness conversation. Not a sales call, not a scoping call in the traditional sense, more of an honest look at whether the right pieces are in place for an agent to actually do what we're hoping it will do.

We ask things like: Is there a clearly defined workflow this agent will sit inside? Is the data clean and accessible enough to work with? Is there an internal owner who can manage this once it's live, and can we get specific about what success looks like, not in general terms, but in actual measurable outcomes?

What we found is that having this conversation upfront changes the outcomes of POC’s vastly.

Sometimes it means we help a team get their infra sorted before we move forward. Other times it surfaces that the timing just isn't right yet, and we're honest and direct about it. But when we do move into a pilot, the probability of a good outcome is dramatically higher, because we've already done the work to set it up properly.

The Business Case Has to Come First

Another invaluable lesson I've learned from watching these deployments up close is that the business case can't be a nice to have type of thing. Let’s go out and test this in a sandbox environment without clear outcomes you’re benchmarking against will almost 100% of the time fail.

Buyers get the best results by treating AI vendors as business partners, not software suppliers. They want to know exactly what problem is being solved, what the baseline looks like today, and what a successful outcome actually means in numbers they can take to their economic buyer.

As stated in the article is, "We've seen dozens of demos this year. Maybe one or two are genuinely useful. The rest are wrappers or science projects."

What separates the useful ones from the science projects, in my experience, is whether there's a real business case tied to the deployment before it starts. Not after. Not as part of the retrospective. Before.

When we build that with a client upfront, here's the problem, the baseline, what we're targeting, who owns it, the whole engagement runs differently. There's alignment from the start, and the client is invested in the outcome in a way that makes the whole thing more likely to work.

What's Changed For Our Clients

The teams we've seen get the best results share a few things in common. They narrowed the workflow first. Instead of trying to show everything an agent can do across a broad set of use cases, they picked one workflow where the conditions were right and went deep on it. The results from that narrow deployment built the confidence, and the internal credibility to expand from there.

They had an internal owner. Not just a champion who bought into the vision, but someone with the actual technical capacity to manage the deployment day to day.

That person makes an enormous difference.

And they started with a documented business case. Not a vague sense that this would make things faster or easier, but a real number, something they could bring back to leadership and say, here is what we said we would achieve and here is what actually happened.

What I've seen with the deployments that actually deliver, and I'm talking real, measurable impact within the first 6-12 months is they almost always share three things: a narrow business case (wedge), clear internal ownership, and a documented outcome tied to a number someone is accountable for.

What I'd Share With Anyone Going Into This

If I'm being totally candid about what I've learned from going through this with clients, it's that the sequence matters more than anything else. 100% of the time.

Getting the infrastructure right first, even if that takes a few extra weeks, changes the entire trajectory of what comes after. A pilot that's set up properly converts. A pilot that's rushed into before the foundation is ready rarely does, no matter how good the tech is.

Your goal should not be to run more pilots, but to run ones that actually work. You have to educate prospects, be honest and qualify rigorously. Identify whether or not they have the internal infrastructure in place to deploy these agent successfully. If they don’t you need to first help them get that set up first, and then move downstream and deploy.

Most importantly, you must understand sales and how to move the needle with prospects otherwise it’s difficult to even get this far into the conversation. Sales has nothing to do with selling and everything to do with solving. Identify the source of the bleeding, quantify it and drive a wedge to get internal buy in. Without understanding the craft of selling this is very hard to do predictably.

This is where Rampd is incredibly competent.

We made a significant process shift, optimized around it, and it's made a real difference in what our clients experience on the other side.

That’s it for today, peeps.

See you all next week!

Darren




P.S. If you're selling AI and your pilot to close rate isn't where it needs to be, the fix is almost never the product. It's the sales motion underneath it. Let's build it here.

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