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Leaders and employers differ in their approach to AI. Some only allow vetted tools, while others recognize employees will use AI regardless, and seek to leverage it for productivity. Here’s how 20 Fast Company Executive Board members approach supporting AI adoption in their teams.
1. Start with measurable outcomes
Focus on business problems, not the tool. Give guardrails, allow experimentation, and measure results. Sharing small wins helps others adapt AI in their roles. — Chris Erhardt, Chris Erhardt Consulting
2. Let them use the tools they want
Hire strong operators, then let them choose the AI tools that work for them. AI amplifies talent, it doesn’t create it. — Zander Cook, Lease End
3. Model curiosity
Encourage brainstorming about repetitive tasks and celebrate wins. Showcase time-saving AI use cases to reward creativity. — Diana Sabb, Create Something Amazing
4. Let them solve daily frustrations
Make technology easy to use and let employees tackle real problems—like chasing documents—so adoption happens naturally. — Dan Amzallag, Ivalua
5. Integrate into daily workflow
Start with reducing busywork. When AI leads to noticeable improvements, employees see the value in learning it. — Jani Hirvonen, Google
6. Structured enablement
Provide approved tools, guardrails, and short experiments tied to measurable outcomes. This prevents “shadow AI” while turning curiosity into repeatable value. — Nagesh Nama, XLM Continuous Intelligence
7. Encourage experimentation
Encourage curiosity rather than fear. Let employees find personal ways AI can help them. — Barney Robinson, Orchard Creative
8. Continuous research and testing
Host sessions for team members to present AI experiments. Support top-down research and beta testing. — Mack McKelvey, SalientMG and The Credentialed
9. Be transparent
Model your own AI use openly. Connect AI to mentally draining tasks and assure employees it helps, not monitors, them. — Bhavik Sarkhedi, Ohh My Brand and Blushush Technologies
10. Education and peer sharing
Normalize learning and peer exchange. Use short demos, role-based examples, and forums for sharing prompts and workflows. — Britton Bloch, Navy Federal Credit Union
11. Start with tedious tasks
Identify repetitive tasks first; AI provides immediate value without technical expertise. — Frédéric Renken, Lassie
12. Automate internally, not externally
Use AI for internal tasks—summaries, drafts, documentation—while humans manage customer-facing interactions. — Travis Schreiber, Erase.com
13. Treat AI like a teammate
Encourage experimentation in real workflows. AI should remove friction, returning time to employees. — Stephanie Harris, PartnerCentric
14. Define clear business goals
Start AI projects with measurable business objectives, embedding AI in automated processes for better ROI. — Christina Robbins, Digitech Systems, LLC
15. Identify “must-win” workflows
Select key workflows per function, share prompt libraries, and run show-and-tells to demonstrate wins and failures. — Max Azarov, Novakid
16. Start with friction points
Identify bottlenecks like rewrites, handoffs, or updates, then run small AI experiments. Form councils to guide testing. — Debra Andrews, Marketri
17. Normalize the learning process
Acknowledge the discomfort of learning AI. Encourage persistence—grit and repetition lead to intuitive use. — Andrea Lechner-Becker, GNW Consulting
18. Reward measurable success
Pair AI with human review, track time or cost savings, and reward repeatable successes. — Kevin Leyes, LeyesX and Leyes Media
19. Discuss in team meetings
Include AI usage as a recurring agenda topic so employees can share strategies and learn from one another. — Ruchir Nath, Dell Technologies
20. Provide training and follow-up
Train employees, show benefits, provide on-demand resources, and model AI usage from leadership. — Irina Soriano, Seismic