Beyond the Hype: Real-World AI Examples Saving HR Teams Hours.
Practical implementation stories from organizations that made AI work
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Happy Wednesday,
It's been an absolute whirlwind of meetings these past few weeks!
This spring, I'm leading several upskilling programs, and I'm consistently blown away by the feedback, energy, and curiosity from participants.
HR people are diving deep, asking insightful questions, and immediately connecting concepts to their daily challenges.
If there was ever any doubt about HR's ability to adapt and thrive amid technological change – these sessions have completely erased it for me!
We have so much potential in our field, and seeing this engagement firsthand makes me confident: we will not just survive this transformation, we'll lead it.
Anyhow, in today's piece, I'm sharing some of the most impressive real-world AI implementations I've encountered.
Let's get into it.
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One of the absolute best things about working with organisations and teams around the world is getting to peek behind the curtain and see what they’re REALLY doing with AI.
To go beyond the hype, the hopes and dreams and build stuff together with other curious people. They're using AI in creative, meaningful, and smart ways.
Being a bit biased here are a couple of examples of what we’ve been doing together.
First out, hiring. When opening a position, it’s usually a never-ending back-and-forth between the recruiter and the hiring manager to get the job req right and the job ad to stand out. One team I worked with tossed that old-school way out the window and built their own AI bot. Now, instead of taking a week to figure out job requirements, hiring managers just tell the AI what they need directly. They went from this taking a couple of days due to the scheduling logistics of getting the meeting to happen, down to minutes. Simple, slick, and incredibly effective. (And it’s saving a ton of money!)
Then there's my beloved CustomGPT. It's become like the Swiss Army knife of AI. Sure it has it’s limitations but it’s also solving real issues for teams that I’ve worked with. Everything from answering questions on complex collective bargaining agreements to policies nobody really wants to read, and voilà—the AI spits back instant, precise answers. Some teams start out super lean (20 bucks for one user of ChatGPT and a bit of scheduled curiosity), while others go big with enterprise-level solutions. Either way, everyone gets exactly what they need without wasting time digging through documents.
Copilot Studio is another staple solution that's saved countless hours.
Those endless repeat questions like “How much vacation do I have left?” or “What’s our parental leave policy?”? For some of the friends in the organisations I’ve worked with Copilot Studio now handles 60-90% of those routine queries. The best part with this is, in many cases, utilising a technology that people already have and are paying for. Takes a bit of patience to setup yet as said, cost-effective in the end.
Then we have one of my absolute favorite AI use cases involves capturing "silent knowledge." This was done in a team of experienced blue-collar workers who’ve spent years mastering their craft, but all that knowledge sits mostly in their heads (or hands!). We wanted to capture that knowledge since they also had an aging workforce. So first, we proved our concept by using a CustomGPT. Then once proven, we built on that, creating a custom app where employees could verbally share their daily insights and tricks. Now, instead of losing that valuable knowledge, the company has built a constantly growing library accessible of “how-to”. The biggest benefit here is that the app also helps with feedback back to the customer that the worker visited, they get an explanation and insights to what was done at their home/office etc.
Lastly, performance management is beginning to find it’s use cases as well.
Take an organisation in the automotive industry for example —they completely transformed their entire performance evaluation process. AI helps managers gathers performance data, analyse 360-degree feedback, and then delivers personalized development insights. What used to be a stressful, drawn-out process has become fast, meaningful, and may I dare to say useful process. (I was about to say delightful but that might be a stretch since the jury is still out here as they are yet to complete their full first performance review cycle with this AI-powered solution.)
These are snapshots of how organisations I’ve worked with are working with AI and these are obviously picked because they are good examples. I could also list where I’ve talked and helped organisations and more or less nothing has happened.
From my experience, what truly sets apart AI projects that delivers results from those that never get off the runway comes down to two essentials:
First, having a crystal-clear problem statement—knowing exactly what challenge you're tackling and why it matters. Without this clarity, AI becomes just another flashy tech solution wandering around looking for a purpose.
Second, and equally important, is mindset. You need that "split-vision"—the ability to clearly see the problems facing your organization today while also keeping an eye on the exciting possibilities AI offers tomorrow. Think of it as holding today's reality in one hand and tomorrow's potential in the other. This is hard though and that’s usually why they bring in someone like me - to look into the future. Because for most of us, we can’t just wake up with this vision; it requires nurturing, active curiosity, and ongoing learning - and that requires time (and education and that's why successful organizations invest continuously in educating their teams about AI.).