When making data-driven decisions, the input is abstract, but in reality all of the contributors are human, with their own thoughts, needs, and dreams. While HR operates to protect the brand, this is impossible without empathy, sometimes complicating both strategy and execution. This is a catalyst for anxiety, at every level. In other words, even if the tension is more subliminal--rather than overt-- forcing managers to make difficult choices while also recognizing that it may cause harm. When faced with this scenario, I break down what I can do, what I cannot, and what tools I have to mitigate harm and improve overall wellbeing. This can be a balancing act, but this is one of the areas where AI cannot compete.
When people say "Talent Acquisition is becoming Talent Intelligence," I understand what they mean, but from where I sit in the energy sector, it's looking like more of an evolution than a revolution. We've always relied on data to make good hiring decisions; what's changing is the scale and speed of our decisions. Take, for example, mapping candidate networks. In emerging areas like renewables or LNG, talent pools tend to grow slowly and under-the-radar. AI helps us draw connections more easily and reach these people before our competitors. So, no, it doesn't feel like a seismic shift, but rather, a natural next step. And I think it's important to frame it this way. For starters, too much emphasis on the so-called AI revolution has bolstered expectations far over what's truly possible. It's also striking fear in some candidates. But when I talk about the move towards AI and automation as part of an existing trend and an enhancement of what we already do well, people stay optimistic -- and realistic -- about its potential.
At CLDY, I've seen data help us find people who don't look great on paper but are actually fantastic. My old company was the same. The data pointed to this guy with a weird background, we tweaked the interview a bit, and he became one of our best hires. My advice is let the numbers find the person, then trust your gut on their potential. That combo works.
When I'm hiring in behavioral health, our AI dashboards find good candidates, but they aren't everything. I've seen people who look perfect on paper fall flat in the interview. Then there are others whose data might not stand out, but when you talk with them, you just know they get it. The numbers are a starting point, but the conversation is where you find the right person.
At Plasthetix, we've changed how we hire. The data helps us find candidates who actually get cosmetic healthcare marketing, not just generalists. But we still sit down for a real conversation to see how they click with the team. This mix of numbers and real talk gets us people who can do the job and actually fit in here.
At Jacksonville Maids, we started using data to find Gen Z workers who stick around for our busy seasons, and turnover dropped about 50%. But I still call every candidate myself. An algorithm can tell me if someone looks good on paper, but a quick conversation shows their personality and if they'll actually fit with the team. AI handles the first pass, but talking to people is how we find the right hires.
At Superpower, we started using AI tools in our tech recruiting and it's helped us find better engineers and data scientists. These tools spot skills that get missed on resumes, so we're hiring the right people more often and they stick around longer. Still, I always make sure a person has the final say, to confirm someone's not just technically solid but actually believes in what we're doing.
At Magic Hour, when we needed to grow fast, we tried using AI tools to help with hiring. The data was useful for spotting patterns, but our best creative people often came from the stories they told in interviews. So now we use both. The numbers tell us who's qualified, but our gut makes the final call. It feels like we're building a real culture this way, not just an efficient team.
What we've learned so far: when it comes to data-driven talent intelligence, human judgment is not the enemy. At Reclaim247, it guides our recruitment teams at every stage. Algorithms process application trends and identify skill mismatches or long-term role alignment, but in the end, the decision is based on instinct, chemistry and culture. One measure of "post-hire impact" we keep an eye on is how quickly a new hire can add value, not just how long it takes to fill the role. The biggest myth about talent intelligence: it dehumanises hiring. Applied with care and purpose, it gives decision-makers the focus they need to be more human: more empathy, more motivation and more common purpose.
AI tools helped us handle a ton of scheduling and initial ranking, but we learned fast not to let the software make the final call. Even after testing automated resume scoring for Tutorbase, we still interview top candidates ourselves to feel out their drive. The most successful SaaS hiring teams I've seen get this. They use data for scale, but people for nuance. It's how you find hires who actually stick around.