After 100 discovery calls in six months, I’ve heard the same frustrations so many times I could recite them in my sleep. But I’ve also learned something fascinating: what companies say they want from agentic AI and what they actually need are often completely different things.
Here’s what really matters to enterprise buyers when it comes to agentic AI—and why most vendors are solving the wrong problems.
The Thing Everyone Says (But Doesn’t Really Mean)
“We want AI that can do everything.”
In 87 out of 100 calls, someone used the phrase “end-to-end automation” or “AI that handles everything.” But when you dig deeper, what they really mean is: “We want AI that can handle the three specific things our team does repeatedly that drive us crazy.”
One VP of Sales told me, “I don’t need AI to write my strategy. I need it to stop me from losing leads because someone filled out a form at 11 PM on Friday and we didn’t respond until Monday.”
The real need: Task-specific intelligence that integrates seamlessly, not a Swiss Army knife that does everything poorly.
What Keeps Enterprise Buyers Up at Night
1. Integration Hell (Mentioned in 73% of calls)
“How long until this actually works with our systems?”
Every single enterprise prospect has been burned by a platform that promised easy integration and delivered six months of consultant fees instead. They don’t want to hear about your 200+ connectors. They want to know: Will this work with our weird SharePoint setup, our custom Salesforce configuration, and that ancient ERP system we can’t replace?
What I learned: Companies would rather have five rock-solid integrations than 50 that sort-of work. They’ve stopped believing in “easy setup” promises and now ask for proof-of-concept timelines upfront.
2. The “Black Box” Problem (Mentioned in 68% of calls)
“How do we know it’s giving us the right answer?”
Here’s what surprised me: It’s not about AI being wrong. It’s about not being able to explain to their boss, their compliance team, or their auditors why the AI made a specific decision.
One legal ops director said, “I don’t care if your AI is 95% accurate. I care that I can show exactly where it found the information for that 5% I need to double-check.”
What I learned: Citation and traceability aren’t nice-to-haves—they’re deal-breakers. Enterprise buyers want AI that shows its work.
3. The Accuracy Crisis (Mentioned in 76% of calls)
“How do we know it’s actually finding the right information?”
This was the big surprise. More than half of prospects had already tried AI search or document tools that gave them fast answers—wrong answers.
One legal director told me, “Our last platform could answer any question in seconds. The problem was, about 30% of the time it was confidently wrong. We spent more time fact-checking the AI than we would have spent finding the information ourselves.”
Another customer shared, “We had an AI system that would give different answers to the same question depending on how you phrased it. Our team lost trust in it completely.”
What I learned: Speed without accuracy is worse than no AI at all. Companies have been burned by “hallucinating” AI that sounds authoritative but can’t be trusted.
4. The Deployment Reality Check (Mentioned in 81% of calls)
“How fast can we see real results?”
Nobody wants to hear about your 18-month enterprise rollout anymore. The most common question wasn’t about features—it was about timeline. Specifically: “How quickly can we deploy this without a PhD in machine learning?”
The companies moving fastest on agentic AI adoption are the ones who’ve learned to avoid vendors that require dedicated data science teams or months of model training.
What I learned: “Time to first value” trumps “comprehensive functionality” every single time.
The Surprising Stuff Nobody Talks About
The “Fast but Wrong” Problem
What they say: “We want agentic AI that can find information quickly.” What they mean: “We want agentic AI that can find the right information quickly.”
This is where I saw the biggest gap between marketing promises and reality. Companies had been sold on agentic AI platforms that could search through millions of documents in milliseconds—but couldn’t reliably tell the difference between a policy that was current versus one that was superseded three years ago.
One compliance manager said, “We need AI that understands context, not just keywords. If I ask about our data retention policy, I don’t want 47 documents that mention data retention. I want the current policy that applies to my specific situation.”
The Hallucination Hangover
More than half of prospects had trust issues with AI because of previous bad experiences. They’d seen AI confidently cite non-existent documents, combine information from multiple sources to create “facts” that never existed, or give different answers to the same question asked on different days.
“We stopped using our last AI tool because it started making up policy numbers,” one HR director told me. “When your AI invents a vacation policy that doesn’t exist, you lose credibility with the entire organization.”
Security Theater vs. Real Security
What they say: “We need SOC 2 compliance.” What they mean: “We need AI that won’t get us fired when something goes wrong.”
The real security conversation isn’t about certifications—it’s about control. Can they decide which data the AI sees? Can they run it on-premises if needed? Can they audit exactly what happened when the AI made a decision?
One CISO told me, “I don’t care if you’re more secure than Fort Knox. If I can’t see what’s happening under the hood, I can’t approve it.”
The “AI Fatigue” Factor
Here’s something I didn’t expect: 43% of prospects mentioned being burned by previous AI vendors. Not because the technology didn’t work, but because it didn’t solve the actual problem they needed solved—or worse, because it gave them wrong information they couldn’t trust.
“Our last AI platform could analyze sentiment in customer emails really well,” one customer success director told me. “But it couldn’t tell me which customer was about to churn and what I should do about it. Plus, when we tried to use it for finding contract information, it kept giving us outdated terms. It was really smart technology that made my job harder and less accurate.”
What I Learned: Companies aren’t looking for impressive AI anymore. They’re looking for agentic AI that they can trust, and makes their Tuesday easier and more reliable.
The Hidden Cost of “Easy”
Most buyers have learned that “easy to use” often means “limited in what it can do.” They’ve been conditioned to expect that user-friendly platforms can’t handle their complex, real-world scenarios.
The question isn’t whether your platform is easy to use—it’s whether it’s easy to use AND powerful enough to handle edge cases without breaking.
What Actually Closes Deals
After watching patterns across 100 conversations, the companies that moved from discovery to pilot to purchase all had three things in common:
- Proof Points, Not Promises
They didn’t want to hear about what your AI could theoretically do. They wanted to see it work on their actual data, with their actual use cases, solving their actual problems—and getting the right answers.
The fastest deals came from prospects who could test-drive the platform with their own documents and see real, accurate results in the first meeting. One prospect told us, “You’re the first vendor whose AI actually found the correct version of our safety protocol. Every other platform kept pulling up the old one.” - Accuracy You Can Trust
Companies had learned to be skeptical of AI accuracy claims. They wanted to see the AI work live, on real documents, with complex scenarios. The question wasn’t “How fast is your AI?” but “How often does it get things right?”
The prospects who moved forward were the ones who tested edge cases—asking about policies that had been updated, procedures that had exceptions, or documents with conflicting information. They needed to see that the AI could handle their messy, real-world data without making dangerous mistakes. - Deployment Honesty
The buyers who converted were the ones who asked the hardest questions about implementation: “What could go wrong? What typically takes longer than expected? When do most companies see their first real ROI?”
They appreciated vendors who admitted upfront that not everything would work perfectly on day one, but who had clear plans for getting there quickly. - Business Impact Focus
The conversations that led to deals weren’t about AI capabilities—they were about business outcomes. How much time would this save? How much revenue could it generate? How much risk could it eliminate?
One operations director summed it up perfectly: “I don’t get promoted for implementing cool AI. I get promoted for making the business run better.”
The Bottom Line
Companies want agentic AI that’s smart enough to understand their business but simple enough that they don’t need a data science team to run it. They want technology that integrates with their existing chaos without adding to it. And most importantly, they want AI they can actually trust to get things right.
After being burned by “fast but wrong” AI platforms, enterprise buyers have learned that accuracy isn’t just a nice-to-have—it’s the foundation that everything else is built on. They’d rather wait an extra few seconds for the right answer than get the wrong answer instantly.
The companies winning in this market aren’t the ones with the fastest algorithms. They’re the ones building AI that combines speed with precision—platforms that feel like magic but work like a utility, delivering reliable, accurate results that people can stake their reputation on.
The real insight from 100 discovery calls: The future of agentic AI belongs to companies that can make powerful technology feel simple, not companies that make simple technology sound powerful.
Want to see how fifthelement approaches these challenges? Book a discovery call and experience the difference when AI actually solves your specific problems.
