Hey everyone, welcome back to The HiveMind Signal. I’m Madison Wade, here with Shane Hermans. Today, we’re digging into a topic that’s honestly everywhere right now—why AI keeps flopping in sales. Shane, you ready to bust some myths?Oh, absolutely. And, you know, I’ve lost count of how many times I’ve heard, “Just plug in AI and watch the pipeline explode.” It’s like, folks think it’s a magic box. But, as we talked about last episode, AI’s not a replacement for broken systems—it just makes whatever you’ve got run faster. Good or bad.Yeah, and I mean, I’ve seen this firsthand. There was this startup in Singapore—super ambitious team, great product. They brought in an AI tool, thinking it’d triple their leads overnight. No changes to their process, no new workflows, nothing. Three months later, they’re like, “Why isn’t this working?” Well, because AI isn’t a miracle worker. It’s an accelerator, not a solution in itself.Exactly. It’s like hiring a genius intern and then just leaving them in the corner with no onboarding. You get what you put in. If your workflows are a mess, AI just amplifies the chaos. And, honestly, that “set it and forget it” mindset is probably the biggest lie in the industry right now.And the expectations are so off. Leaders want AI to “fix” the pipeline, but if you don’t have clear feedback loops or enablement, it just becomes another tool collecting dust. I think the stat is, what, 74% of sales AI tools underperform because of poor adoption or bad data?Yeah, that’s right. And only about 21% of teams even have formal training around the AI tools they buy. So, it’s not surprising that most of these projects just fizzle out. It’s not the tech—it’s the way we use it, or don’t use it, really.So, let’s talk about why these AI projects actually fail. It’s not just about the tech, right? It’s about people, trust, and, honestly, change management—or the lack of it.Yeah, and I’ve seen this play out so many times. Back when I was building fintech platforms at Geniusto, we learned pretty quickly that you can’t just drop new tech on a team and expect magic. You have to contextualize it—make it fit the way people actually work. Otherwise, you get resistance, confusion, or worse, total apathy.Totally. And the best teams don’t just train people once and call it a day. They build real collaboration between humans and machines. Like, they’ll actually explain how the AI is scoring leads, or why it’s recommending certain actions. That transparency builds trust, and suddenly, reps are way more likely to use the tool.Right, and it’s not just about sales, either. High-performing teams are 2.5 times more likely to embed AI into cross-functional workflows. So, marketing, product, sales—they’re all in the loop. That’s when you start seeing real adoption, because everyone understands the “why” behind the tool, not just the “how.”And, honestly, most teams skip the basics. No formal training, no clear use cases, no change management. It’s like, “Here’s your new AI, good luck!” And then they wonder why nobody trusts it or uses it.Yeah, and if you don’t build that trust, AI just becomes shelfware. I mean, I might be wrong, but I think the stat is only about 21% of teams actually feel confident in their AI knowledge. That’s a huge gap.So, let’s get into the nitty-gritty—data and measurement. Because, honestly, it doesn’t matter how many tools you have if your data’s a mess. Garbage in, garbage out, right?Absolutely. And, you know, at HiveMind, we’ve seen this with clients. They had all the latest tools, but their CRM was a disaster. Once we helped them redesign their GTM workflows and clean up their data, suddenly the AI started delivering real results—like, measurable increases in meetings booked and sales velocity. It wasn’t about adding more tech, it was about making the system work as a whole.Yeah, and I think that’s the trap a lot of teams fall into—thinking more tools equals more results. But if you’re not tracking the right metrics, you’re just adding noise. So, Shane, what would you say are the top three metrics leaders should actually care about?Good question. I’d say, first, meetings booked—because that’s a real sign of engagement. Second, sales cycle velocity—how fast deals are moving. And third, win rate shifts. If those aren’t moving, it doesn’t matter how many emails your AI sends. And, uh, I’d add, don’t get caught up in vanity metrics like clicks or email volume. Focus on what actually drives revenue.And watch out for “AI washing,” right? Just slapping AI on a process doesn’t make it better. If you’re not measuring impact, you’re just adding another layer of complexity. It’s like what we talked about in our first episode—simplify, align, and focus on outcomes, not activity.Exactly. And, look, AI can be a game-changer, but only if you treat it as part of a living system—one that needs training, feedback, and constant tuning. Otherwise, you’re just automating chaos. And nobody wants that.Alright, I think that’s a good place to wrap. If you’re listening and you’ve been burned by “set it and forget it” AI, you’re not alone. But there’s a better way—start with your people, your data, and your workflows. Shane, always a pleasure.Likewise, Madison. And thanks to everyone tuning in. We’ll be back soon with more on building resilient, high-performing teams. Until next time, take care.See you next time on The HiveMind Signal. Bye, everyone!

Episodes

Why AI Fails in Sales