A step-by-step playbook for rolling out AI across your whole team. Real project examples, from first adoption to everyday delivery.
Playbook in the worksFrom one AI power user to a team that delivers
01
Choose a first project
02
Prepare it for AI
03
Get people involved
04
Turn what works into a repeatable team process
Go from one AI power user to a team that delivers
Right now, you might be the person everyone asks for help. The one who writes the prompts, fixes the output, and makes the demo work.
The playbook walks you through choosing a first project, preparing it for AI, getting people involved, and turning what works into a repeatable team process. Real project examples show the starting conditions, tools, team setups, and decisions behind the transition, so you can see what applies to your own team.
Beyond engineering
Bring the people who know the work best.
Developers are an obvious starting point. The opportunity reaches across the team.
Designers
Turn ideas into working prototypes they can test.
Marketers
Build landing pages for campaigns they need to launch.
Managers
Create small tools that take repetitive work off their teams’ plates.
Give each role a practical way to build with AI, with shared standards for what’s ready to use and who looks after it.
What we’re hearing
Teams outside engineering are already building with AI.
Two quotes from public posts, linked to their sources, and one from our team.
Marketing
“We switched from Webflow to Claude. Our marketer became our top committer.”
A blank project makes a great demo. Your team has existing code, established workflows, and deadlines that won’t move.
Learn how to introduce AI into the projects already on your plate. Assess what’s ready, what needs preparation, and where to begin, with real examples of teams that made the transition while continuing to deliver.
Readiness scanExample · checkout-service
01Codebase structureCheckingReady
02Test coverageCheckingNeeds prep
03Docs & context for AICheckingNeeds prep
04CI and deploy pipelineCheckingReady
05First AI task: refund flowCheckingStart here
✓
2 of 5 readyStart here: refund flow
Between the tools
Take back the hours lost between tools.
Someone exports the data. Someone copies it into a spreadsheet. Someone checks it, fixes it, and sends it to someone else.
Each workaround solves a small problem. Together, they create a second job: keeping everything connected. Find the handoffs and repetitive tasks worth tackling first. Give your team a shared direction for what to automate and build next.
Handoff chain · report.csvExample
Time lost per report1 h 30 min
01Exports the data+15 min
02Copies it into a spreadsheet+40 min
03Checks it, fixes it+25 min
04Sends it to someone else+10 min
ENDA second job: keeping everything connected
→
Automated, in one stepSame report, no handoffs
2 min
01 · Assess what’s ready
Readiness scanExample · checkout-service
01Codebase structureCheckingReady
02Test coverageCheckingNeeds prep
03Docs & context for AICheckingNeeds prep
04CI and deploy pipelineCheckingReady
05First AI task: refund flowCheckingStart here
✓
2 of 5 readyStart here: refund flow
Handoff chain · report.csvExample
Time lost per report1 h 30 min
01Exports the data+15 min
02Copies it into a spreadsheet+40 min
03Checks it, fixes it+25 min
04Sends it to someone else+10 min
ENDA second job: keeping everything connected
→
Automated, in one stepSame report, no handoffs
2 min
Join the waitlist
Help your whole team make the leap.
Bring a concrete plan to your next team conversation: where to start, what needs to change, and how you’ll know it’s working.