AI isn't the problem...

Imagine this: Your HR team spends months selecting the perfect AI tool. Demo after demo, pilot after pilot, the technology checks every box. Yet, six months later, the adoption rate is abysmal. The tool sits unused, conversations swirl with skepticism, and your digital transformation stalls. If this feels familiar, you’re not alone.

Despite years of hyped predictions, HR’s pace of AI adoption has been slow. Why? The real friction point is human.

After two decades working across nearly every facet of HR, and hundreds of conversations with leaders for the Human Capitalist Podcast, one thing has become crystal clear: The roadblocks are not found in your codebase or your procurement process. They live in your mindset, execution, and planning.

Let’s unpack why people—not algorithms—are the most significant drivers and blockers of progress in HR’s AI revolution. (If you prefer watching versus reading, I got you👇)

Watch Here: https://youtu.be/2X-K7KhmJoY

Current State of HR and AI Adoption

The narrative around AI in HR is often one of urgency—every conference, webinar, and white paper forecasting seismic change. The numbers, however, paint a more nuanced picture.

• About 27% of organizations have achieved organization-wide AI adoption in HR by 2025.
• 78% leverage AI in at least one HR function—from recruiting automation to engagement analytics.
• Fewer than half have moved past the pilot phase, citing compliance, data quality, and… yes, internal resistance as key culprits.

Why the caution, especially in HR? The answer is twofold: the sensitivity of employee data and the deeply human, unpredictable variable which is our capacity for trust and change.

As one of my recent podcast guests said, “AI’s not the tough part. Getting humans on board is where it gets interesting.” This dichotomy of tech potential and human hesitation is at the core of every successful (and failed) digital transformation the HR sector has witnessed. It's just with AI, these are happening faster.

What are the three obstacles to AI adoption?

Let’s get specific. Through both research and direct experience, three common people-centered themes emerge as the main blockers:

1. The Skills Gap: Literacy, Not Coding

When HR leaders talk about an AI skills gap, most jump straight to technical jargon. But as Andrea, an HR transformation consultant and podcast guest, observed:

“Most HR professionals aren’t stuck because they can’t code. They’re stuck because they lack operational AI confidence—they haven’t been trained how to use AI-generated insights in everyday decisions.”

SHRM’s 2025 Talent Trends report puts it starkly: More than 60% of HR professionals cite a lack of AI understanding or confidence as their top barrier, not a shortage of software or technical resources.

But here’s the bigger problem: HR is home to two very different personas when it comes to AI according to Andrea Derler, Ph.D. :

• The Curious: These are your early adopters. They see AI as an opportunity, build strong business partnerships, and leverage data for real impact. Give them freedom, tools, and advanced training—and they’ll take your organization further than any outside consultant.
• The Anxious: These folks emphasize risks, compliance, and question every new platform. Sometimes even data feels threatening, not just AI. For them, the focus needs to be psychological safety, peer learning, and sharing small wins.

Bridging this divide requires HR leaders to identify both personas, tailor communications, and design learning journeys that meet each group where they are.

Upskilling That Works

What actually narrows the gap? If you have followed my content during 2025, you will know the answer: upskilling and reskilling.

• On-demand, micro-learning programs (offered by AIHR, Coursera, and SHRM)
• Cross-training via internal hackathons or working groups
• Bringing in nontraditional HR hires from compliance, engineering, or education who bring fresh perspectives and digital fluency

Organizations cultivating a “learning as culture” mindset (not a checkbox) are 92% more likely to innovate and sustain digital change.

2. Data Privacy and Ethics: Building Trust Is Everything

Ask any HR leader what keeps them up at night about AI, and “privacy” comes up every time.

HR manages deeply sensitive data—compensation, performance, demographics, even health and social security numbers. Add AI analytics or generative tools that could perpetuate bias, and trust can evaporate in an instant.

Guru Sethupathy , CEO of an AI ethics company, breaks it down into four pillars for building trust:

1. Strategy: Start with the problem, not the tool. Ask: What challenges do we really need to solve? Automate where it saves time, but don’t chase buzzwords.
2. Data & Tech: Do you have the data quality necessary for accurate models? Are your chosen vendors transparent about methodology and outcomes?
3. Governance: Have you set ethical guardrails? What are your red lines? Which processes must remain human-led? Bring legal and compliance into the process from day one.
4. Change Management: How will AI alter roles, responsibilities, and workflows? Define these clearly—ambiguity breeds mistrust.

A recent study found 56% of executives identify ethical AI use as their top concern in HR. Less than one-third of firms have clear AI governance, even as the demand for explainability and transparency rises.

Without trust, even the best technology will be underused or outright rejected.

3. Resistance to Change: The Elephant, the Rider, and the Path

Perhaps the truest barrier? Fear of the unknown.

There’s a wonderful metaphor from the Heath Brothers’ book Switch that breaks down how we process change:

• The Rider: Represents logic—the rational, planning mind.
• The Elephant: Represents emotion—the much stronger (and less predictable) part of our brain.
• The Path: Represents the situation, structure, and messaging for change.

A recent study stated 70% of HR resistance isn’t about the technology, but about how change is messaged, supported, and experienced emotionally.

If all your change management efforts speak only to the Rider (logic) like ROI, efficiency, cost savings, your Elephant (emotion) will eventually take the reins, driven by fear, job insecurity, or overload.

Practical ways to calm the Elephant could include:

• Early involvement: Give your teams ownership over pilot programs and feedback loops.
• Transparent communication: Explain not just what is changing, but why—and what “good” looks like.
• Reframing the narrative: Present AI as augmentation, not annihilation. It’s a co-pilot, not a competitor.

McKinsey reports that 375 million workers may need to reskill or change roles by 2030 due to AI’s impact. This isn't just in HR, but across the global workforce. Panic hijacks logic. The job of HR is to keep both the Rider and the Elephant engaged and supported.

And I will acknowledge this is probably the most challenging mountain we have had to climb as a practice.

How do you build AI-Ready Organizations: Some tactical steps to consider

So what can HR leaders do right now to make real progress? Move from theory to actionable transformation with these proven strategies.

Host an HR AI Hackathon

If you think hackathons are only for engineering, think again. HR teams across leading organizations are leveraging them to demystify AI, build confidence, and spark innovation. Here’s how:

• Form cross-functional teams (mixing HR, IT, compliance, and line managers)
• Define 3-5 real business challenges (e.g., speeding up onboarding, improving candidate screening, automating FAQ responses)
• Equip with AI tools and support (Workday, Eightfold, generative AI, automation platforms)
• Run a 48-hour challenge to design, prototype, and demo working solutions
• Recognize and showcase wins—then implement the most promising ideas

Companies running regular hackathons report adoption rates 3x higher and rapid progress toward broader AI literacy.

Build a Culture of Continuous Learning

• Curate micro-learning resources on AI fundamentals, ethics, and tool-specific training
• Incentivize curiosity—reward those who try, even if they fail at first
• Empower the “curious” to coach the “anxious”
• Partner with external education providers (SHRM AI+HI Specialty Credential, AIHR, Coursera)

Continuous learning cultures are 92% more likely to see sustained innovation.

Establish Radical Transparency in Governance

• Publish your AI strategy internally
• Create FAQ documents and videos explaining why, how, and where AI will be applied
• Hold “ask me anything” sessions with legal, tech, and leadership teams
• Regularly update stakeholders on project milestones and governance reviews

Transparent communication is the bedrock of trust—and trust is the currency that makes AI adoption stick.

Calm the Elephant of Resistance

• Frame AI as augmentation, not a replacement
• Share success stories and mini case studies from teams that have benefited
• Provide psychological safety nets: anonymous Q&A, counseling resources during job redesign
• Leverage the “path”—make the environment for change easy and rewarding

Conclusion?

The obstacles to AI adoption in HR aren’t hiding in your code or procurement processes—they’re present in your culture. Skills gaps, data ethics and privacy, and resistance to change remain the biggest hurdles. But these aren’t insurmountable.

By investing in continuous learning, building trust through radical transparency and governance, and addressing the emotional side of change, any HR function can move from tech-phobic to tech-forward.

Your Action Plan

• Identify your team’s dominant persona. Are they curious, anxious, or a blend? Design learning accordingly.
• Host your first HR AI Hackathon. Use the how-to above to drive practical upskilling and real results.
• Download our AI Readiness Self-Assessment to benchmark your progress (resource below).
• Subscribe to the Human Capitalist Podcast for deeper dives with the innovators and pioneers leading the way.