Hands-On Training Is the Missing Link to Real AI Value at Work

AI's value starts once employees get hands-on: A manager's playbook

Boston Consulting Group's Global Chief AI Ethics Officer, Steven Mills, puts it plainly: the biggest drag on AI ROI is a lack of hands-on training. Employees want about five hours of guided practice, but only a third get it.

BCG's recent research reports that only 5% of companies are seeing meaningful value from AI, while 60% report minimal gains despite major spend. The fix isn't more tools-it's rethinking processes and giving people real reps.

What this means for leaders

• Treat AI as a capability, not a license. Budget for training, coaching, and change management-not just tools.
• Start small to unlock momentum. Let teams use AI on simple tasks (email edits, draft cleanups). Quick wins trigger a virtuous cycle.
• Redesign workflows. Don't bolt AI onto old processes. Ask: "If we started fresh with AI, how would we run this?"
• Measure value in operations, not demos. Time saved, cycle time, quality, cost-to-serve, and customer outcomes.

The 5-hour training model employees actually want

• 60 min: Tool access, account setup, basic prompts, safe data use.
• 90 min (hands-on labs): Drafting, summarizing, rewriting, and structured extraction on real work docs.
• 60 min (team use-case workshop): Map 3 tasks per role; define prompts, inputs, and outputs.
• 60 min (SOP creation): Convert best prompts into checklists; define when human review is required.
• 30 min (guardrails): Data classification, privacy, citation, and acceptable use.
• 30 min (metrics & next steps): Set weekly targets and a feedback loop.

30-60-90 day rollout

• Days 0-30: Pick 3-5 high-volume use cases. Stand up access, basic policy, and a pilot cohort (10-20% of a function). Establish a value baseline.
• Days 31-60: Expand to full team. Create prompt libraries, SOPs, and "office hours." Publish a weekly scoreboard.
• Days 61-90: Redesign 1-2 end-to-end processes with AI in the loop. Automate handoffs. Lock in reporting and budget for scale.

High-ROI use cases to start with

• Sales: Email personalization, call summaries, QBR prep, proposal drafting.
• Service/Ops: Ticket triage, response drafting, incident summaries, knowledge base updates.
• Finance: Variance analysis drafts, board-pack narratives, policy Q&A.
• HR: Job descriptions, interview guides, policy clarification.
• Legal/Procurement: Clause extraction, redline suggestions, RFP/RFI drafts.
• IT/Engineering: Code review notes, test case generation, documentation.

Metrics that prove value

• Adoption: % of team using AI weekly; prompts per user.
• Time: Average minutes saved per task; cycle time reduction.
• Quality: Error rate, approval rate, SLA adherence.
• Financial: Cost-to-serve, pipeline velocity, margin impact.
• Risk: Policy violations, data leakage incidents (target: zero).

Guardrails without slowdown

• Data rules: Clear do/don't for PII, customer data, and confidential docs.
• Human-in-the-loop: Define review steps by risk level (public, internal, confidential).
• Logging: Keep prompt/output logs for audit and coaching.
• Model choice: Use safer models for sensitive work; upgrade when needed.
• Reference frameworks: See NIST's AI Risk Management Framework here.

Public sector is moving fast

Mills notes that agencies are getting access to AI agents from major providers at low cost. Expect adoption to spike as employees want the same tools at work that they use at home.

For context on federal direction, review OMB's government-wide AI policy briefing.

Common pitfalls to avoid

• Buying tools without funding training and coaching.
• "Training theater" with slide decks and no hands-on work.
• Vanity metrics (accounts created) instead of operational outcomes.
• Plugging AI into old processes instead of redesigning them.
• Over-centralized approvals that stall momentum.

Next steps

• Pick one function and one process this quarter. Fund five hours of hands-on training.
• Stand up a prompt library, SOPs, and weekly office hours. Name champions.
• Publish a scoreboard. Reward teams that turn time saved into better customer and financial results.

Helpful resources

• AI courses by job role for building role-specific capability.
• Popular AI certifications to standardize skill levels across teams.

The message is clear: give people real reps and rebuild the workflows. That's how you move from experiments to measurable value.