At Pinnacle, we run a monthly HR x AI roundtable for enterprise CHROs and senior people leaders. Most sessions are virtual, with occasional in-person gatherings, and they’ve become a space for leaders from companies like Toshiba, HP, American Airlines, Kelly Services, J&J, Royal Caribbean, Okta, Newscorp, Comcast, HubSpot, and ZoomInfo to compare real-world progress on AI in HR.
Our latest session was one of the most grounded of the year. Leaders came ready to share what’s actually happening inside their organizations as AI adoption accelerates. Leaders came ready to compare what is actually happening inside their organizations as AI adoption accelerates. The signal was clear: we’ve moved beyond theory. The work now is about enablement, culture, and business outcomes.
The macro shift is already here
Entry-level opportunities are collapsing. Entry-level jobs are down 13% since 2022. The share of 21 to 25 year olds at public tech companies has dropped by more than half since 2023, while junior roles overall are down 30%. AI firms now employ more senior individual contributors (up 13%) and fewer junior employees (down 16%).
This isn't happening quietly. 72% of S&P 500 companies now flag AI as a material board risk, and 60% of enterprises have appointed a Chief AI Officer. While three-quarters of leaders report ROI gains from AI, the work still requires human judgment and oversight.
But here's the tension: investment is racing ahead of readiness. 84% of individual contributors and 64% of managers haven't received AI training this year. Only 8% of HR leaders believe managers have adequate AI skills. Just 17% of organizations currently offer AI training, though leading firms like Amazon, TCS, Citi, and Walmart are reinvesting billions to close the gap.
This is a cultural transformation.
The biggest shift underway isn't about deploying technology. It's about helping everyone use it confidently.
One leader noted that their workforce still defaults to waiting for IT or assuming IT is the main group for upskilling around AI. Another pointed out, "this isn't about coders, it's about people learning to think differently." That mindset shift is what will separate organizations that evolve from those that stall.
Formal training is losing. Experimentation is winning.
Across the group, leaders shared that adoption happens when people get to try things, not just hear about them. The most effective learning happens in the flow of work, when employees can safely test ideas, fail fast, and share what they learn.
Nearly every organization represented is now leaning on internal advocates to drive enablement from within. These aren't always technical power users. Often, they're subject matter experts who "get it" and can bring others along.
The challenge: how to structure and deploy champions
There was one point of tension: should champions be technical experts who sit in an AI management office and get deployed across the business? Or should you find the subject matter experts who are naturally AI capable and invest in their technical capacity?
The answer likely depends on your organization's structure and culture. But the broader point holds: enablement scales through advocates, not through top-down mandates.
Build vs. buy is about context, not cost
Most leaders agreed: building sounds tempting, but it's rarely worth it. What looks simple in a demo quickly turns into a maintenance and resourcing challenge.
Off-the-shelf solutions are winning for a reason - and they are, a July MIT study showed that they’re twice as likely to be successful as in-house implementations. They're specialized, scale faster, integrate with existing systems, and let teams focus on adoption instead of upkeep. As one leader put it, "We'll build where our data is the differentiator, but buy for everything else."
The rule of thumb? If it's not your core competency, don't reinvent it. The summer MIT study showed that external pilots are twice as likely to be effective as internal ones.
What's working right now
Give people better options, not more rules. Reduce "Shadow AI" use and increase overall adoption by empowering teams with access to safe, enterprise-grade LLMs instead of banning tools.
Connect AI transformation to the work that matters. The best adoption stories are tied directly to visible, real outcomes.
Judgment, empathy, and creativity remain the top meta-skills. AI's real value is freeing humans to focus on them.
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