Education and visibility: the two levers HR needs to drive AI adoption
Employees want to use AI, but they're hesitant. More than half haven't adopted the tools yet, citing ethics, oversight, and job security. At the same time, 51% want better training and upskilling, and 86% say they're willing to reskill if their jobs change. The gap is clear: people need skill and signal.
They also expect HR to lead. Around 68% of employees believe HR should guide AI adoption, yet only 26% of HR leaders are highly involved. That's a missed opportunity. The fix is straightforward: build real education, make AI use visible, and address fears directly.
What employees are telling us
Data from SHRM shows strong demand for practical training, with many workers still unsure how AI fits their day-to-day. Younger employees report the highest concern about job impact. As Jim Link, SHRM CHRO, puts it: building a strong learning culture improves engagement and productivity and supports the entire employee experience.
Andy Biladeau, SHRM's chief transformation officer, adds a simple mindset shift: "Embedding a growth mindset shifts the emphasis from 'learning in the flow of work' to 'work in the flow of learning.'" In other words, make real work the classroom.
Make learning the default (and useful)
- Set role-based outcomes: what should recruiters, HRBPs, L&D, and HR Ops do faster or better with AI in 30-60 days?
- Keep it hands-on: short exercises tied to actual tasks (draft a job post, summarize a policy, QA a candidate screen, build a learning outline).
- Pair policy with practice: data privacy, bias checks, human review steps, and clear approval criteria for sensitive work.
- Stand up "AI office hours" and internal champions for quick help and pattern sharing.
- Collect feedback in the flow: quick forms after each use case; ship updates based on what people struggle with.
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Make AI use visible (social proof matters)
A report from Irrational Labs found that "behavioral contagion" drives adoption. Employees are more likely to use AI if they know someone who does, and 79% say they'll use it when managers endorse it. Visibility isn't fluff - it's fuel.
- Manager endorsements: open team meetings with one concrete AI use from last week and one to try this week.
- Show the work: short demos of prompts, context, and final outputs - include what didn't work.
- Share wins: weekly thread highlighting time saved, quality gains, and lessons learned.
- Make it normal: dedicated channels for prompts/templates; lightweight leaderboards for contributions (not usage hours).
Address the fears directly
- State the intent: "AI assists people; it doesn't make final decisions on hiring, performance, or pay." Put that in writing.
- Publish approved use cases and red lines (e.g., no personally identifiable info in tools without approval).
- Clarify review steps for sensitive tasks and who signs off.
- Explain how accuracy, bias, and data risks are checked - and how employees can flag issues safely.
Governance that enables, not blocks
- Approved tools list with simple risk tiers and guidance per tier.
- Human-in-the-loop checkpoints for recruiting, ER, compliance, and policy work.
- Prompt/output logs for sensitive workflows; periodic audits and red-team tests.
- Access tied to training completion; refreshers every 6-12 months as tools change.
What to measure (and share)
- Adoption by team and role; training completion rates.
- Time saved per use case; quality/accuracy deltas versus baseline.
- Employee sentiment on trust and job security, tracked quarterly.
- Issue reports resolved and policy updates shipped.
HR starter use cases
- Talent acquisition: draft job descriptions, personalize outreach, generate interview guides, summarize screenings (with human review).
- L&D: create micro-lessons, build role-based learning paths, convert SOPs into checklists and quizzes.
- HR Ops: summarize policy changes, draft macros for FAQs, build templates for manager communications.
- Employee relations and compliance: policy comparisons, case summary drafts, meeting notes clean-up (keep originals attached).
- People analytics: explain dashboards in plain language, outline hypotheses for further analysis.
A simple 30-60-90 for HR
- Days 0-30: pick 5 approved use cases, publish policy guardrails, train managers first, open office hours.
- Days 31-60: expand to 3 more teams, launch weekly demos and wins thread, start basic metrics.
- Days 61-90: audit outcomes, refine training, adjust policies, scale champions, and set next-quarter goals.
The path is practical: teach people how to use AI on real work, make usage visible so it spreads, and protect them with clear rules. As one SHRM insight puts it, people thrive when work becomes the learning ground. Do that well, and adoption will take care of itself.
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