Communitizing Your AI Efforts

seanpmcclean · ‎07-29-2026

Welcome to the page - adoption and AI success will come from . . . YOU

Everyone talks about AI. But adoption is another thing entirely. And helping real teams use it well is that step to value that’s often missing. In these sessions, I'll be joined by Dawn Porter, and we’ll explore how to embed AI adoption, and grow Communities of Practice and Champions. We’ll hear real world use-cases and lessons learned from leaders.

But this isn't a just a session recap page. It's a living space with notes and resources from our time together in these sessions. AI adoption fails when it stays invisible - and that happens when people are quietly struggling to figure things out alone in silo's.

So let's figure it out together!

My Commitment to you:

Refreshing this page monthly with new resources and responses
Hosting a quarterly community AI session (stay tuned here for details)

If you got something out of the session, please pay it forward. Drop a note here when you try something communitizingly AI-y. Ask a question when you're stuck. Chances are someone else in this community is dealing with the same thing, and they'd probably love to know they're not alone.

My ask of you:

Share! Whats the first AI Champions or Community challenge you're facing in your organization right now? Drop your answer below.

Mike Scully · ‎08-04-2026

For all the new AI features and capabilities within the Australia Release
https://www.servicenow.com/community/upgrades-and-patching-blogs/kickstart-your-servicenow-australia...

Beth_478 · ‎08-04-2026

AI Champions have been created in my Org as the "smartest people or the ones at this level" this group has also become a group in a glut of non constructive and non inclusive meetings.

seanpmcclean · ‎08-04-2026

oh wow that's a bit of a "yikes" - particularly because they were already named as such, it kind of becomes a bit difficult to "remove". . . Here's a thought (or a 'pivot'?) that might be worth trying?

- Maybe something as simple as a "rename" (i.e. instead of "champions", something like "seekers" or "pioneers" building and documenting their very technical things for others to explore. . . )
- Then building a new group of "communicators" (or some other term) with some of the kind of careful selection (credibility, communication finesse, network), and resources to grow them into that evengelizing role

I'll admit this tactic is one I tried from a previous community that got very quickly "over everyone's head" in technicality - worked well in that case, and is still growing today - with the technical "pioneers" doing some great things still but the communicators making the bigger people-channel difference. . .

seanpmcclean · ‎08-04-2026

OK folks thank you for participating in our session! As promised, below is a worksheet with notes, resources and relateds for communitizing. Dawn Porter will be posting the on Champions later this evening or tomorrow:

Building & Growing AI Communities of Practice

The SCALE Framework for AI Adoption

From: Communitizing Your AI Efforts | ServiceNow University AI Learning Days | Sean McClean

Why Communities of Practice Matter: AI adoption stalls not from weak tools, but because learning stays invisible. People quietly redesign their own workflows alone rather than out loud. A Community of Practice makes that learning visible again, and directly addresses the barriers driven by siloed AI work.

Source: Harvard Business Review, "Peer Influence Can Make or Break Your AI Rollout" (Mar 2026)

S - Start Small, Start Real

How CoPs enable it: Identify high-pain, low-complexity use cases together that are real problems and not just demo problems.

Source: Wenger-Trayner, Cultivating Communities of Practice: A Guide to Managing Knowledge

Our starting use case: _________________________________________________________________

C - Cultivate Champions

How CoPs enable it: Find and grow champions who are visible, trusted peers not the most technical person in the room.

Source: OpenAI Academy — The AI Champion Role

Our champion-champions: _________________________________________________________________

A - Address Fears and Friction

How CoPs enable it: Create a safe space to ask 'dumb questions,' share uncertainty, and experiment without turning early AI learning into a performance test. Psychological safety is not a soft add-on here; it is what lets people surface risks, confusion, and better practices early enough for the community to learn from them.

Source: Google re:Work, Project Aristotle — Understanding Team Effectiveness

Psychological safety actions we'll take: _________________________________________________________________

L - Link to Value

How CoPs enable it: Shared metrics and lessons-learned libraries turn individual wins into organizational proof.

Source: McKinsey, The State of AI 2025

Value metrics we'll track: _________________________________________________________________

E - Evolve Through Learning

How CoPs enable it: Built-in feedback loops and retrospectives. Psychological safety is a requirement for teams under pressure, not a luxury for calm times.

Source: Amy Edmondson, HBR — "In Tough Times, Psychological Safety Is a Requirement, Not a Luxury" (Nov 2025...

Learning cadence: _________________________________________________________________

Within a Community: Makeup and Tactics

The 90-9-1 Rule ("Participation Inequality"): 90% are lurkers, 9% are contributors, 1% are creators. This is normal. Lurking isn't laziness, it's newcomers absorbing the group's culture and vocabulary before they feel safe enough to post.

Source: Jakob Nielsen, Nielsen Norman Group — "Participation Inequality"

Activating lurkers through Legitimate Peripheral Participation:

☐ Start with the smallest possible contribution ("on a scale of 1–5...")

☐ Cultivate — recognize and build on the tiniest contribution publicly

☐ Address & Link — connect every contribution back to something the poster cares about

☐ Evolve — revisit and resurface old contributions so lurkers see the thread is alive

Critical Success Factors — Why Programs Fail (Avoid These)

No CoP sustainability — who owns this ongoing?: _________________________________________________________________

Lack of sponsorship — which executive removes barriers?: _________________________________________________________________

Treated as a 'launch,' not a 90-day activation window — we'll sustain for ____ months: _________________________________________________________________

Operating Norms

Meeting cadence: _________________________________________________________________

Who facilitates: _________________________________________________________________

How we share learnings: _________________________________________________________________

References & Further Reading

Post your SCALE worksheet — even half-filled-out — in the AI Learning Days community thread. The community grows the same way the framework says it should: small, real contributions, cultivated in public.

dawnp · ‎08-04-2026

Let the AI CoP building begin! Below you'll find five simple tactics you can use right away to start building a community in your organization. Can't wait to hear from you in the comments below.

AI Champions & Evangelists

5 Counterintuitive Tactics to Build and Keep Growing Them

From: Communitizing Your AI Efforts | ServiceNow University AI Learning Days | Sean McClean

This worksheet is built to leave the room with you. Fill it out during the session or in the days right after. Post your version in the community thread so others can build on it.

Pick for Credibility, Not Skill

Why: The effective champion profile is a mid-level employee with real peer trust, not the strongest AI user in the room. Enthusiasm and peer trust predict champion effectiveness better than technical background does. Pick the person everyone already to go for help.

Source: OpenAI Academy - The AI Champion Role

Champion name: ______________________________________________________________________
Title / team: ______________________________________________________________________
Why they're a trusted peer (not just skilled): ______________________________________________________________________
Owner: ____________________________	Target date: ________________________

Champions ≠ Strong Prompters

Why: There's a real, active distinction being taught in the field right now between 'people who are great at AI' and 'people who make other people great at AI.' Champions should demo their own actual work not canned examples because authentic, relevant demos are what colleagues respond to.

Real work we'll demo (not a vendor example): ______________________________________________________________________
Owner: ____________________________	Target date: ________________________

Generic Training: 23% Sustained Adoption. Role-Specific: 67%

Why: That's roughly a 3x gap and it's not about training quality, it's about whether the training addresses the champion's actual job function. Champions are the mechanism that delivers role-specific training at scale, but only if their own training goes beyond tool proficiency.

Source: Hartz AI - 2026 AI Champion Programme Guide

Champion	Role / Function	Customized Training Focus

Real Work Beats Vendor Scenarios

Why: Peer influence is the strongest driver of sustained AI usage in enterprise settings, but only when what's demonstrated is authentic. A champion showing how they drafted supplier criteria in 20 minutes lands harder than a generic 'here's what AI can do' demo. Concrete before abstract.

Our champions' real-work demo topics: ______________________________________________________________________
Owner: ____________________________	Target date: ________________________

Executive Sponsor: Remove Barriers, Not Cheerlead

Why: Champions programs that lack visible executive support stall — not from lack of enthusiasm, but because middle managers deprioritize champion activities when they compete with operational demands. The measurable version of 'get exec sponsorship': track whether the sponsor removed at least 2 organizational barriers champions surfaced in the first quarter.

Source: Deloitte - State of AI in the Enterprise 2026

Barrier 1 our sponsor will remove this quarter: ______________________________________________________________________
Barrier 2 our sponsor will remove this quarter: ______________________________________________________________________
Owner: ____________________________	Target date: ________________________

Implementation Worksheet

Champion ratio — 1 per 15–20 people (early phase: 1 per 25–30). We need ____ champions: ______________________________________________________________________

Time commitment — 3–4 hrs/week per champion. Flag anything over 5 hrs/week as burnout risk: ______________________________________________________________________

Cadence — weekly demos for first 90 days, then biweekly once adoption stabilizes: ______________________________________________________________________

90-day target — 47%+ active weekly usage in champion-supported teams: ______________________________________________________________________

Distribution check — what % of candidates are outside IT/Finance? Aim for spread across business units: ______________________________________________________________________

References & Further Reading

Post your filled-out worksheet (or just one tactic you tried) in the AI Learning Days community thread — this page only gets more useful the more of us add to it.