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Honestly, I wasn't expecting much when I scheduled the call. It was late, somewhere around 11 PM my time, which meant it was early morning for him. We were looking for someone to lead the integration of our new AI-driven CRM module, and frankly, the pool of candidates who actually understood both the sales process and the underlying machine learning stuff was shallow. You know how it goes. Everyone claims they know AI nowadays. It's on every resume, right next to "leadership" and "synergy."
But then Mark joined the Zoom room.
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He didn't have a fancy virtual background. Just a plain wall, slightly out of focus, and a mug that looked like it had seen better days. He looked tired, but the kind of tired that comes from actually doing work, not just pretending to be busy. We skipped the small talk. I asked him straight up: "What's the biggest lie people tell themselves about AI in CRM?"
He didn't hesitate. "That it fixes bad data," he said. "Everyone thinks you can just plug in a smart algorithm and it'll magically clean up years of messy entry logs. It won't. It'll just learn the mess faster."
That hit home. Hard.
We spent the next forty minutes digging into the guts of our project. Most candidates talk about efficiency gains, percentage boosts in conversion, all that shiny stuff. Mark talked about friction. He talked about how sales reps hate typing things into systems they don't trust. He mentioned a project he worked on two years ago where the AI suggested follow-up emails that were technically perfect but sounded completely robotic. The clients noticed. The reps stopped using the tool.
"It's not about the model accuracy," he told me, leaning into the camera. "It's about trust. If a salesperson doesn't trust the lead score the AI gives them, they'll ignore it. And if they ignore it, the data stops flowing. If the data stops flowing, the model dies. It's a feedback loop, but not the good kind."
I found myself nodding more than I usually do in interviews. Usually, I'm checking boxes. Does he know Python? Check. Has he managed a team? Check. But this felt different. We were talking about the human side of the tech stack. He asked me questions too, which is always a good sign. He wanted to know about our data governance. Not the policy documents, but the reality. Who actually owns the customer data? Is it Marketing? Sales? The IT guy who's been there for ten years and hates change?
He guessed it was the IT guy. I laughed. He was right.
There was this one moment where the connection lagged, and his audio cut out for a few seconds. In that silence, I looked at my notes. I had written down "Risk: Adoption" in big letters. We've spent so much budget on the software license, on the customization, on the training sessions. But we hadn't really spent enough time thinking about whether people would actually want to use it. Mark seemed to get that instinctively. He didn't talk about forcing adoption through mandates. He talked about finding the champions. The one or two sales reps who are early adopters, getting them on board, letting them show the others that it doesn't suck.
"You don't sell the tool," he said. "You sell the time they get back. If the AI writes the summary call notes, that's ten minutes they get to breathe. That's the pitch."
It's funny how simple that sounds, but in the corporate world, we often overcomplicate things. We buy enterprise solutions that require a PhD to operate, then wonder why the team reverts to Excel spreadsheets. Mark's approach was grounded. Practical. Maybe a bit cynical, but in a way that felt safe. He wasn't selling me a dream where robots take over the sales floor. He was talking about augmentation. Helping humans be less bored so they can be better at being human.
Towards the end, I asked him about the failures. Everyone talks about wins. I wanted to know what broke. He told me about a churn prediction model that was too accurate. It flagged clients who were likely to leave, but because the model was so aggressive, the account managers started treating those clients differently, almost defensively. That behavior actually caused the clients to leave. The model created the reality it predicted.
"That was a hard lesson," he said, rubbing his eyes. "We had to dial back the confidence scores. Sometimes knowing too much too soon is worse than knowing nothing."
We wrapped up the call around midnight. I didn't say "we'll be in touch." I just said, "Thanks for being real." He smiled, a quick, genuine thing, and logged off.
Now I'm sitting here writing this, looking at the feedback form. The standard questions feel inadequate. "Rate communication skills 1-5." "Technical proficiency." It doesn't capture the vibe. It doesn't capture the fact that for the first time in months, I feel like this project might actually work. Not because the tech is superior, but because the person behind it understands that technology is just a tool people have to pick up and use every day.
If we hire him, the real work begins. It's not coding. It's convincing Sarah in accounting that the new data fields are necessary. It's helping Mike in sales understand that the AI isn't there to replace his commission. It's the messy, unglamorous work of change management.
I look out the window. The rain has stopped. The street is quiet. Most people are asleep. But tomorrow, the emails will start flooding in again. The stakeholders will want timelines. The board will want ROI projections. But for now, I've got a name on a shortlist that feels right.
In this industry, we chase the next big thing constantly. AI this, blockchain that. But at the end of the day, it's still just people talking to people, with some software in the middle trying to keep score. If you forget the people part, the software doesn't matter. Mark knew that. And honestly, that's worth more than knowing how to tune a hyperparameter.
I'll probably recommend him to the hiring committee tomorrow. There's risk, sure. There's always risk. But compared to the risk of building something nobody uses? It feels like a bet worth taking. I close my laptop. The screen goes black, reflecting my own tired face back at me. Time to sleep. Tomorrow's going to be a long day, regardless of who we hire. But at least now, it feels like we might have a shot at getting it right.
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