The workshops worked. People built their first AI teammates and workflows, saw what AI could do with their actual work, and left excited to keep building.
But few teams have a plan for what comes after. The experiments stay scattered. The early excitement doesn’t connect into a sustained operating model. The hardest parts aren’t technical.
This is Part 2 of a two-part series. Part 1 covered four dimensions for seeing where your team is with AI: Q&A machine to thought partner, tool to workflow, faster to different, and org chart to work chart. You don’t need it to get value from this one, but it’s a useful companion.
Quick Take
AI transformation is human change management. Every phase exists to serve the people going through it.
What I’m sharing here comes from working with teams through the full journey. There aren’t best practices yet. We’re all figuring this out. But I’ve seen enough patterns to know what tends to work, what tends to stall, and where teams get stuck. This is a starting point, not a playbook to follow step by step. Adapt everything to your goals, culture, and pace.
Key takeaways:
- Mindset shifts happen through demos of what’s possible with applied AI use cases and actually building for their own work
- A hackathon gives a team the moment to stop experimenting in pockets and start building together
- Workshops start the change. Weekly office hours, show-and-tells, and a safe space for failure are what keep it going.
- The goal of every phase is to make the team capable of evolving on their own
AI Video Explainer and AI Podcast Versions of This Newsletter
To support different learning styles and time constraints, this newsletter is available as an 5-min AI video explainer and a 20-min AI podcast with two AI hosts. If you haven’t seen these AIs in action, they’re worth a view. I used Google’s NotebookLM to create these and personally reviewed them for accuracy and responsible AI use.
The Full Path
You can run a workshop in an afternoon. Taking a team from experimentation to a sustained operating model takes months.
Every team adapts this differently. Some compress phases, some skip Phase 0 because they’ve already done the groundwork, some run all the workshops at once instead of sequencing them. The value of seeing the full path is knowing what’s involved so you can plan for it.
Phase 0: Discovery and Readiness
Before any workshops, understand where people are. Some teams already know this from conversations and observation. Others benefit from a structured approach, like an employee AI sentiment survey, to surface concerns that don’t come up in meetings.
A communication plan is one of the most underestimated steps. And I think the honest version works better than the safe one. People come in with different concerns: some feel overwhelmed, some are skeptical that AI applies to their work, some worry about looking like beginners. Acknowledging those concerns directly builds more trust than ignoring them.
Regardless of how anyone feels about AI, being AI literate is baseline. When people are literate, they make better decisions for themselves, their families, their teams, and their organizations.
Upskilling is a gift to your team. It helps the business, and it helps them compete in their careers beyond your company. That message, when it’s genuine, builds trust in ways that corporate talking points can’t.
Identify your trailblazers early: the people already experimenting on their own. They become peer mentors, early testers, and proof to the rest of the team that this is doable. They pull people forward in ways that leadership support alone can’t.
Teams that skip the communication work tend to spend months fighting resistance that could have been addressed early.
Phase 1: Foundation
This phase grounds everyone in why AI adoption connects to customers and business outcomes, and gives people hands-on experience building.
In larger organizations or teams where alignment isn’t strong yet, leadership often goes first so they can champion what comes next. In smaller or more aligned teams, you might bring everyone in together.
The four mindset shifts from Part 1 develop when people see what’s possible through applied use case demos and build something real with their own work and see it produce results.
A workshop like this typically covers five areas:
Hands-on building workshops follow, organized by function so people build with use cases relevant to their role before connecting to broader workflows. I’ve seen these work well virtually and in person.
I recently ran a workshop with the marketing team at Applied Systems. Their CMO, Courtney Rains, described the shift she saw:
“When the demos started, you could see it click. People watched use cases they do every day, competitive intel, campaign planning, customer content, get built live in front of them. Light bulbs were going off around the room.
During the hands-on session, people progressed at different speeds, and that’s completely fine. Some were still exploring prompts. Others built full workflows. Everyone left with a clearer picture of what’s possible.”
Whether you run workshops yourself or bring in outside help, the structure works because it moves from why to what to how. People leave with something they built and a different sense of what AI can do for their work.
Need a starting point for what to build? This interactive tool helps teams explore AI teammate and workflow ideas.
There’s also a separate workshop for what I call enablers: the people who will eventually form the enablement team. This covers cross-system automation, agent orchestration, and workflow design into business systems, because these folks need different skills than the rest of the team. Not everyone should have read/write access into every business system. This team helps with those connections, data, security, compliance, governance, and cost management.
Phase 2: Build and Prepare
Sometimes it’s a cross-functional team and sometimes it’s a select operations group like Marketing Ops and Rev Ops that has the skillset needed and that works with Legal, IT, and Finance. Responsible AI guidelines get developed collaboratively. A tracker shows what’s being built across the org.
This phase gets messy. People build teammates that overlap with what someone else built. Some experiments don’t get used. That’s learning by doing. The tracker helps make the work visible so the mess can eventually become structure.
What keeps the momentum going between the workshop and the hackathon is the habits and routines you put in place:
- Reprioritizing people’s work to give them the space and time to learn AI
- Weekly office hours for support, inspiration, learning, and troubleshooting
- Show-and-tells where teams share what’s working, what’s not, and what they’ve learned
- Failure sharing to normalize experimentation. One CMO I work with runs “Thriving Thursdays” and “Failing Fridays,” giving equal time to both
- A Slack or Teams channel for ongoing peer sharing and learning
- Trailblazer spotlights at all-hands to recognize early adopters
Sarah Weddle, VP of Brand Marketing at Harbor Capital Advisors, Inc., described what weekly sessions did for her team:
“AI can answer a lot of questions we have about how to use it, but what actually moved our team forward was the live sessions. Having a space to troubleshoot real challenges, see what others were building, and learn in real time made it practical.
At Harbor, we talk a lot about the value of access, and having direct access to that kind of expertise made all the difference. You could see the shift—team members became more creative and intentional in how they leveraged AI as a thought partner. That change in mindset alone was a game changer.”
The safe space for failure is where I see teams struggle most. Some leaders say they want experimentation but shut it down the first time something doesn’t work. When people can share what didn’t work without judgment, experimentation stays alive. When it gets shut down or ignored, people stop trying new things.
For a deeper look at how this works in practice, see How a Lean Team Transformed into a Human+AI Powerhouse.
Somewhere in this phase, you announce a hackathon. Two signals tell you the team is ready. People start building workflows that cross functions on their own, like a marketing and sales team connecting an SDR research workflow without being asked. And your AI teammate tracker starts showing overlaps, duplicates, and clear gaps, which means enough people are building that coordination is needed.
Phase 3: Hackathon
I’ve started calling this the turning point. A team goes from building in pockets to building together.
In the teams I’ve worked with, a hackathon is a multi-week build window where people refine what they’ve already started, followed by a showcase day where teams present their work. It’s not a weekend sprint or a competition for bragging rights.
When responsible AI adoption enables strategic initiatives, the C-suite typically gets involved. They champion it and even get their hands dirty building teammates and workflows themselves. This is a cultural shift.
A judging panel reviews submissions against criteria like business impact, scalability, and readiness for responsible use. The payoff goes beyond the judging. For the first time, the full team sees everything that’s been built. The team decides together what’s worth scaling.
Rhiannon Naslund, CMO of Origami Risk, used a leadership workshop to prepare her marketing team for a companywide hackathon.
When the company held a hackathon, our team was ready. We had seven submissions, the most of any team, and we’re one of the smallest in the org. One of our submissions was a campaign analyzer that turned a weeks-long quarterly review into a one- day process. The hackathon got people collaborating on ideas and most have continued building since.
I think it’s important to mandate experimentation, not simply roll out tools. The workshop we did before the hackathon was a really important step to help my team develop new skills and be successful.
Phase 4: Scale and Sustain
The operating model is democratized building, centralized enablement. Everyone closest to the work keeps building. The enablement team makes it safe and scalable. When a new workflow needs to connect to business systems, the enablement team triages it: who builds it, what systems it touches, what reviews it needs.
To see what this model looks like in practice, see AI Moved Fast: What Changed and What to Do Next.
You don’t need dozens of workflows to prove this works. Think big, start small, move fast. Pick a handful, make them work well, and add on. Some will crop up organically from trailblazers already building. Others come from leaders who prioritize workflows tied to strategic initiatives, address the biggest pain points, or reimagine processes that were built for a world that no longer exists.
The AI growth dashboard from Part 1 helps leaders track progression across the four dimensions and see where people need support, including where someone’s confidence has grown and where they might be stuck.
Governance is where teams often get stuck. Too little and you get risk. Too much and you slow everyone down.
One framework worth considering: Brice Challamel, who was VP of Products & Innovation at Moderna managing 1,800+ GPTs (now Head of AI Strategy & Adoption at OpenAI), uses a two-axis matrix to rate every AI teammate by scope of impact (individual, team, company) and severity if it goes wrong (mild disturbance, really annoying, catastrophic).
The combination gives you a criticality rating that determines how much oversight each teammate needs. (He describes this framework in Beyond the Prompt podcast.)
To see what Phase 4 looks like at scale, see How a Leading Cybersecurity Company Redesigned Marketing Workflows to Scale Business Impact with AI.
The goal of this entire phase is to make the team capable of evolving on their own. Here’s what the full arc looks like when you put it all together.
Phase 0 readiness is easy to skip and expensive to skip. Ongoing habits need to continue after any formal initiative ends. Define what success looks like before you start. Teams that wait end up scrambling for metrics after the fact to prove the work was worth it.
No two teams run this the same way. But seeing what’s involved helps leaders plan.
The Bottom Line
AI transformation is human change management. We need to meet people where they are and bring them along with compassion and grace.
If you’re not sure where to start, pick one workflow that crosses two teams and look at what breaks at the handoff. That’s where an AI teammate can do real work, and it’s the fastest way to show your team what’s possible.
This is doable. Teams are doing it right now, across industries, at different sizes, at different paces. Some run the full path on their own. Others bring in someone who has been through it before to move faster. Either way, the playbook is the same: invest in your people, create space for applied learning, and build the structure so new ways of working stick.
If any of this resonated and you want to compare notes, I’d welcome the conversation.
The Practical AI in Go-to-Market newsletter shares learnings and insights in using AI responsibly. Subscribe today and let’s learn together on this AI journey.
For applied learning: Explore our applied AI workshops, offering both strategic sessions (use cases and roadmaps) or hands-on building (create AI teammates during the workshop). You’ll leave with either a clear plan or working solutions. Office hours provide follow-on support to keep momentum going.
For ongoing advisory: I work with leaders as a strategic advisor through their AI transformation, providing guidance, frameworks, and a sounding board as they build. If that’s what you need, let’s talk.
For team transformation: I partner with GTM leaders to move from scattered AI adoption to an operating model where human and AI teammates work together across workflows. See real examples: a lean GTM team’s step-by-step playbook and a global cybersecurity leader scaling to 150+ marketers with 57 AI teammates integrated into daily workflows.
For speaking: Here are virtual and in-person events where I’ve covered a variety of AI topics. I’ve also keynoted at many organization and corporate-wide events.
If any of this resonated, I’d love to hear from you. Reply to this email, find me on LinkedIn, or come say hi at an event.