Why AI Tools Fail Without Active Management

The uncomfortable truth about extracting value from AI vendors in 2024

I recently had a candid conversation with a friend about our experience with AI tools at work. My initial reaction was, frankly, a bit salty: “a lot of smoke and mirrors and not much value.” But after reflecting on it, I don’t think it’s fair to simply blame the vendors. They’re genuinely trying. The problem is more structural than that.

The real insight I walked away with is this: without active management, companies are not going to extract meaningful value from AI tools. And that’s fundamentally different from how we’ve bought and deployed software for the past decade.

The Established SaaS Playbook Doesn’t Work Here

Think about mature SaaS categories like Salesloft, ZoomInfo, or even Salesforce. These products have years—sometimes decades—of usage patterns that have been refined through millions of customer interactions. The playbooks are clear:

• Sales leaders know exactly how to run a cadence
• Ops teams know how to structure their data enrichment workflows
• Best practices are documented, shared at conferences, and embedded into vendor onboarding

When you buy Salesloft, you’re not pioneering new territory. You’re adopting proven workflows that your vendor, your peers, and the broader market have validated. The learning curve is a well-worn path.

AI tools have none of this. The category is so nascent that use cases and workflows aren’t engrained. They’re not easily adopted. They’re often not even well-defined by the vendors themselves.

The FTE Problem

Here’s the uncomfortable reality I’ve experienced: if you don’t have dedicated headcount to manage your AI tools, you’re probably not going to see meaningful ROI.

Why? Because someone needs to be thoughtful about:

• Which use cases actually fit your business (not just what the vendor demo’d)
• How to integrate AI into existing workflows (not create parallel processes people ignore)
• Ongoing iteration and adoption (because v1 is rarely v-final)
• Measuring actual impact (beyond “we use it”)

Most companies don’t have an “AI Tools Manager” role. They tack AI procurement onto existing ops or IT functions that are already stretched thin. The result? Tools get bought, pilots get run, but sustained value never materializes.

The Vendor Dilemma: Reactive vs. Proactive

Many AI vendors are great at being reactive. File a ticket, hop on a call, they’ll help you troubleshoot. But what companies actually need is proactive guidance—vendors who will tell you:

• “Here’s what our successful customers are doing that you’re not”
• “Here’s the internal champion profile that drives adoption”
• “Here’s the specific workflow that generates the most value in your industry”

The challenge is that AI vendors themselves are still figuring this out. They may not have enough successful deployments to have pattern-matched their way to repeatable playbooks. So even if they wanted to be proactive, they may not have the answers.

This creates a chicken-and-egg problem: customers need guidance to succeed, but vendors need successful customers to develop guidance.

What This Means If You’re Buying AI Tools

1. Budget for dedicated management. Treat AI tools like a capability that needs to be built, not a product that just needs to be deployed. That might mean a fractional role, a dedicated project, or explicit ownership on someone’s goals.

2. Ask vendors the hard question: “What are your successful customers doing that generates the most value?” If they can’t answer clearly and specifically, that’s a signal about the maturity of their playbook.

3. Start narrow, go deep. Rather than rolling out broadly with light adoption, pick one team, one use case, and obsess over making it work. Learn the lessons there before expanding.

4. Accept iteration as the norm. Unlike buying a mature tool where the path is clear, AI adoption is inherently experimental. Your first implementation probably won’t be your best.

What This Means If You’re Building AI Tools

1. Invest in customer success as R&D. Every successful deployment is data for your playbook. Treat CS not as support cost but as product development input.

2. Be proactive, not just reactive. Your customers don’t know what they don’t know. Push insights to them; don’t wait for them to ask.

3. Document and share the patterns. The vendors who win this market will be the ones who can tell customers “here’s exactly how to get value” instead of “here are features—good luck.”

4. Acknowledge the FTE problem. Consider how your product can either reduce the management overhead or make the case for dedicated resources clearer.

The Bottom Line

We’re in an awkward adolescent phase for enterprise AI tools. The technology is real, the potential is massive, but the usage patterns and best practices haven’t crystallized yet.

That means companies buying these tools need to accept more ownership of their own success than they’re used to with mature SaaS. And it means vendors need to invest heavily in turning early wins into repeatable, teachable playbooks.

The AI tools that will dominate the next decade won’t just be the ones with the best models or features. They’ll be the ones that crack the “active management” problem—either by reducing the need for it or by being so good at enabling it that their customers succeed despite the inherent challenges of the nascent category.

Until then, plan on rolling up your sleeves. The magic isn’t in the tool; it’s in the work you put into making it work.