Navigating the “Nervous Laughter” Phase of GenAI Adoption Inside Your Organization
The recent NYT piece - “C.E.O.s Want Their Companies to Adopt A.I. But Do They Get It Themselves?” struck me for how succinctly it captured pervasive issues across the C-suite in too many companies and organizations. It’s encouraging to see executives rolling up their sleeves with GenAI tools – but the real breakthroughs happen when leaders model curiosity and the people closest to the work have a sense of ownership over how AI is used.
Let’s go through what a Be Kind AI Advisors approach looks like here, focusing on encouragement, transparency, inclusion, knowledge-sharing, and a little humility. Think: learning in public, celebrating early attempts, and making space for every role to contribute – safely.
Name the real blocker: fear, not FOMO
“They all say A.I. is the future, use A.I. to do stuff. And then they don’t make any decisions or choices.” - Ethan Mollick on executives talking about - but not using - AI.
Businesses and talking heads like to call it “FOMO,” when it comes to GenAI, but what I hear at every level is fear:
Individual contributor: “If I get 3x faster with AI, do I just get 3x more work?”
Manager: “If I use AI, will people think I’m cheating – or replaceable?”
Executive: “How do I steer this when I’m learning myself, and there’s no rulebook?”
Everyone: “Is it OK to use AI on this?”
You can’t move through fear you won’t name. Psychological safety precedes experimentation. A simple leader script helps:
“We’re learning this together. Using AI isn’t cheating; it’s a skill. We’ll reward responsible use and sharing ideas/solutions that work. No one is judged or graded for trying. Let’s see what we can accomplish as a team…”
Closest to the work = closest to the value
“This is not a tool you can delegate down the hall to the chief information officer. They need to be hands-on in both where the technology is going and how they can apply it today.”
Central teams (IT, data science) can’t guess the realities of every function and department. A sales coordinator, finance manager, communications lead, or operations director knows exactly where the friction is in their work – and what “good” looks like when they use AI.
Empower the people doing the work to identify opportunities to apply AI in their day-to-day. Executives need to provide their employees with the latest tools, build awareness, enable training, inspire creativity, and encourage ownership – not distribute a generic how-to-guide slide deck and a one-size-fits-all list of “approved use cases.”
Value emerges where the work actually happens.
The operating model: top-down vs. bottom-up
Spoiler alert: you need both.
Leaders set direction, fund sandboxes, remove blockers, and – this matters – use the tools themselves in public.
Teams propose and execute on real tasks, run time-boxed experiments, document wins and risks, and measure the impacts.
One simple ritual that I loved in the NYT article: invite junior employees to leadership meetings to demo real workflows they’ve improved with AI. Pair it with a short segment where an exec shares their workflow or prompt, what worked, and what fell flat. Curiosity goes viral when it’s modeled on both sides.
Make it a habit: put practice on the calendar
A model becomes culture only when it shows up on calendars. Replace one “status/approval” slot each week with a 30-minute test and learn:
Comms/Marketing: refine a message, test tone with a few “audience personas,” and cut jargon.
Product/Ops: turn raw requirements into a first draft spec or checklist.
HR/Legal: distill a policy draft, then fact-check and cite sources.
Sustainability/GR: scan partners/stakeholders and summarize positions before a meeting.
Quick win exercises using AI beat policy docs or lectures about AI. Doing reduces fear.
Culture by design: incentives, norms, language
As I’ve shared, we’re in the “nervous laughter” phase – people hide both their struggles and their wins. Normalize use and make it safe to share:
Make “Did you test it with ChatGPT/AI?” a gentle checkpoint, not a gotcha.
Track and celebrate quick wins (time saved, quality lift).
Create champion networks across functions; give them assets to share.
Use language that clarifies accountability: “Co-created with AI, human-approved.”
New habits stick when they feel safe, seen, and celebrated.
What to measure (so trust compounds)
If you only track licenses and dollars spent, you’ll get shelf-ware for your shiny AI tools. Measure what matters:
Time saved and cycle time reduced on real tasks.
Quality lift (clarity, tone, accuracy, fewer revisions).
Confidence scores (self-reported) by team/role.
Wins documented and shared per quarter.
% of leaders who demo usage monthly.
Coverage (teams with a named champion and sandbox access).
Progress becomes contagious when it’s visible and specific.
A realistic 90-day runway
For anyone looking to enable the use of GenAI tools in their organization, here’s a sample of what a three month AI adoption roadmap looks like.
Days 1–30
Pick 2–3 priority teams; set simple guardrails; run weekly prompt sprints (bring two real tasks, iterate, compare outputs, reflect).
Capture 10 quick wins and share them org-wide.
Days 31–60
Expand champions; launch weekly office hours; roll out role-based prompt guidance (first prompts, QA checklist, “when not to use”).
Leaders demo publicly twice.
Days 61–90
Add an “AI check” to common workflows (e.g., drafts, briefs, recaps).
Publish v1 playbook and a simple KPI dashboard.
Decide where to scale and where to pause.
Avoid these failure modes:
Top-down only: policy first, practice later → shelf-ware.
Tool tourism: hopping platforms without habits → no muscle built.
Perfection paralysis: waiting for certainty vs. running safe pilots.
Individual agency: the safest investment you can make
There are no 10-year GenAI veterans. No official playbook. Everyone from intern to executive is learning in real time. That’s good news: you can start today.
Pick one task. Spend five minutes with an AI assist. Keep a simple win log. Share one tip a week with your team. That’s how culture shifts – from the inside out.
Be Kind, applied to adopting AI
Kindness inside any business or organization isn’t soft; it’s how you scale trust. Kindness here is practical: make space for people to try, tell the truth about risks, and learn in public. When leaders model curiosity and the people closest to the work have actual ownership of how AI is used, breakthroughs stop being slogans and start becoming habits. If you want help making this real:
Lunch and learns to explore what is possible with GenAI
Executive working labs (hands-on, no slide marathons)
Department use case sprints + role-based guidance
90-day adoption kickstart
DM me or schedule time directly for a 30 minute discovery call with Be Kind AI Advisors. Let’s turn nervous laughter into real wins – together.