The AI Trust Problem in HR: Why Generalists Are on Shaky Ground

HR’s trust curve is tanking. And the folks holding the bag? Often, it’s the generalists.

Let’s be real for a second.

I watched an HR team that was talented, well-intentioned, and truly trying to do the right thing roll out a new AI hiring tool. It promised everything: faster screening, reduced bias, more time for humans to focus on the human stuff. You know the pitch.

But within weeks, things started to smell off. The candidate pool got weird. Hiring managers were side-slacking things like, "Are we sure this is working?" And then came the Glassdoor review:

“Not even a human said no, just silence after I uploaded my resume. AI ghosted me.”

And poof. The trust was gone.

It didn’t matter how fancy the tool was, how many explainability graphs the vendor showed in the pitch. Once trust is broken, it’s hard to rebuild. The recruiters went back to manual screening. Leaders hit pause on further rollout. And the team? They were smart people without the tools or vocabulary to defend what they’d just deployed.

If that hits a little too close to home, you’re not alone.

Because the truth is, HR isn’t just navigating AI. It’s drowning in it. And the generalists? They’re getting hit the hardest.

💥 The Trust Curve Is Breaking

In theory, AI should be HR’s best upgrade in decades. Quicker decisions. Better equity. Less soul-crushing admin.

In practice?

• We’re trusting tools we don’t fully understand

• We’re buying from vendors who promise “bias-free” but can’t prove it

• We’re deploying black boxes into deeply human processes

And the thing is, this isn’t just about flawed tech. It’s about eroding confidence in the tools, in the data, in ourselves.

I touched on this in my LinkedIn newsletter, but let’s peel back a few more layers.

Because what’s really at stake here isn’t just bad AI.

It’s HR’s credibility.

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🧠 Why Generalists Can’t Stay General

The old-school HR generalist had range: recruiting one minute, coaching the next, compliance the day after that. That model isn’t irrelevant. But it’s no longer sufficient.

Today, HR generalists don’t just need to coordinate. They need to question.

They need to ask what’s under the hood of that AI model. Where the training data came from. Whether the outcomes are actually helping, or just making bad decisions faster.

But here’s the catch: most haven’t been trained to do that. Most weren’t hired for it. And yet, they’re still the ones expected to explain it when things go sideways.

Gartner found that only 4% of HR teams have truly optimized their use of AI, and more than half don’t feel confident in the tools they’ve launched. That’s not just a skill gap. That’s a credibility cliff.

When trust in AI breaks down, the trust in HR often goes with it.

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⚠️ Where HR Is Fragile (And Why It Matters)

Let’s talk about the cracks forming under the surface. These aren’t always loud, but they’re dangerous:

1. We’re over-trusting vendors
   Flashy demos. Friendly reps. “Ethical by design” slide decks. But ask them how their model handles regional bias or job title variance, and things get fuzzy fast. If your team isn’t pushing for clarity, you’re flying blind.

2. We’re scaling bad data
   If your internal data is messy or worse, biased, your AI tool will turn that into high-speed dysfunction. It’s not magic. It’s an amplifier. And what it amplifies depends entirely on what you feed it.

3. We’re getting paralyzed
   When teams feel underprepared, they freeze. They wait for legal. Or IT. Or divine intervention. And in that paralysis, harmful or useless systems get launched anyway.

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🧭 What Needs to Change (And Who Needs to Change It)

No, HR doesn’t need to become a hive of Python coders. But it does need to become a whole lot savvier. The best teams I’ve seen in 2025? They’re doing three things differently:

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1. They’re Getting Tech Fluent Just Enough

Not trying to be data scientists. But they know how to:

• Spot sketchy data inputs

• Ask vendors the hard questions

• Read a model explanation and understand when to be concerned

💡 Try this: Run a bias check on your hiring funnel. Slice it by job family, source, and geography. Odds are, you’ll uncover trends that would’ve gone unnoticed for years.

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2. They’re Treating AI Like a Product, Not a Policy

Great HR teams are starting to behave more like product managers. That means:

• Piloting new tools in safe spaces

• Gathering real-time user and candidate feedback

• Tweaking constantly instead of “set it and forget it”

One large org I know of built A/B testing into every AI tool launch. Not just measuring time-to-fill, but measuring how fair candidates felt the process was. That’s design thinking, applied to trust.

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3. They’re Centering Ethics from Day One

Ethics isn’t an afterthought. It’s the design brief.

The teams doing it right aren’t waiting for regulators. They’re:

• Demanding transparency in vendor sourcing

• Documenting how humans stay in the loop

• Giving users (and candidates!) visibility into what’s driving decisions

Need inspiration? Peek at the OECD AI Principles or AI Now’s frameworks. Then go build your own checklist that fits your org.

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Coming Up in Part 2: Rebuilding Trust and Reimagining HR

Next time, we’ll tackle the rebuild:

• What it actually looks like to design a trust-first HR system

• How to evolve your team without turning everyone into tech bros

• Practical rituals, roles, and tools for the AI-era people function

And no, this isn’t about replacing HR. It’s about finally building the kind of HR function people trust again.

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The views expressed in this post are my own and do not represent the opinions of my employer or any affiliated organizations. References to companies, tools, or statistics are included for commentary and informational purposes only. This is not legal, compliance, or technical advice, just one outspoken practitioner’s take on what’s happening at the messy, fascinating intersection of AI and HR.