30-day playbook for getting small AI wins
Most businesses are curious about AI, but for 30% of UK micro-businesses, AI still feels too complex to act on.
The common advice? Start small.
It is helpful, but also frustratingly vague. What does "starting small" actually look like? What kind of progress can a business make in just 30 days?
That’s exactly what this AI playbook offers: a clear, realistic way to start using AI in a small business setting (one workflow, one project, or one repeated task). Not to overhaul your systems but to help you see what’s possible and what’s worth expanding.
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Your 30-day AI playbook: one small win at a time
This 30-day playbook for AI small wins is broken into four practical phases. Each one covers about a week, but you can adjust your pace to move faster or slower as needed.
Week 1: Choose your focus and set a baseline (Days 1–7)
The first step in any AI playbook is clarity.
Instead of brainstorming 50 possible use cases, zero in on one specific task or workflow. Work with your team to identify one task worth improving and document how it’s currently done.
Here are the common starting points in AI for small businesses:
Drafting templated replies to common emails
Summarising meeting notes or transcripts
Rewriting messy text into a more professional tone
Tagging or sorting support tickets
Cleaning up spreadsheets or removing duplicate data
Once you’ve picked the task, talk about what a “win” would mean. Is it 20 fewer emails to draft? Five hours saved per month? Clearer outputs with fewer edits?
As a bonus step, you can start listing tools the team has already heard of that might help. The goal isn’t to decide yet, but build a shortlist to research next week.
By the end of this week, you’ll have:
One task selected.
Agreement on what “better” looks like.
A rough baseline of how it works now.
Shortlist of tools to explore.
Tip: Avoid starting with customer-facing tasks. Use AI behind the scenes first, where there’s room to learn without pressure.
Week 2: Explore your options and prepare for testing (Days 8–14)
This is your exploration stage. Encourage the team to look at tools aligned with the task, not just the ones making headlines. Think simple, cost-friendly, and fast to try.
By midweek, come together to compare what everyone found:
What looks promising and why?
What have you tried on old or dummy data?
What surprised you, good or bad?
If something feels like a fit, decide together what to test next week. Then set it up:
Make sure anyone testing it has access.
Write a short “how to try this” note with links, prompts, or examples.
By the end of this week, you’ll have a clear test plan and a team prepped to follow it.
Tip: If your team helps shape the setup, they’re more likely to stick with the test and give feedback you can actually use.
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Week 3: Use it on live work + gather feedback (Days 15–21)
Now that the setup’s done, it’s time to test the tool on real work.
Let the person who already owns the task use the AI tool on live work. No need for a complete switch, just run it alongside your usual approach.
Then, hold a 15-minute check-in midweek to gather early feedback. Then review again at the end of the week. You want to know: Is this worth sticking with?
Encourage honest feedback from your team. Ask:
“Would you use this again next week?”
“What part felt helpful? What part didn’t?”
“What would need to improve to make this stick?”
By the end of the week, you'll have real experience using AI for small business, plus early feedback to guide your next steps.
Tip: Don’t judge the tool on perfection. Look at whether it’s consistently useful. That’s the threshold that matters most for adoption.
Week 4: Share what worked and explore what’s next (Days 22–30)
At this point, you’ve tested a real use case and collected practical feedback. Now it’s time to act on it.
Instead of refining the tool in isolation, use this week to bring others into the loop. What did the trial show? And is it worth expanding to other tasks or teams?
Start by wrapping up with the team who ran the test:
Write a quick summary of what was tested and why.
Note the outcome: time saved, better quality, fewer steps.
List a few tips or guardrails for using the tool well.
Then, bring in other teams across the business:
Share the results in your next team huddle or Slack thread.
Ask if anyone else sees a use for it in their day-to-day.
Invite them to flag similar tasks worth testing next.
From there, decide together what to do next. You might formalise the process with a simple SOP, trial it in another part of the business, or pause and choose a different task to test next.
Tip: AI adoption works best when it’s not siloed. Bringing the wider team into the process builds shared understanding and helps make sure your use of AI stays thoughtful, consistent, and trustworthy as it scales.
After 30 days, you’re no longer guessing how to start using AI. You’ve found one place it works, and a way to build from there.
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Common traps to avoid
Even with a clear playbook, it’s easy to drift off-course. Most teams don’t stall because they get sidetracked by assumptions, urgency, or tools that promise too much too soon.
Here are a few things to watch for, and what staying clear of them helps you gain:
Trying to fix a broken process with AI
It’s tempting to throw tech at a frustrating task. But jumping into AI implementation without understanding the process behind the task often leads to messy results.
Spend a little time tidying the workflow first, and you’ll actually see where automation helps, rather than masks problems. That’s what helped one of our past clients, 3V Architectural Hardware, get more out of their tools. We worked with them to clean up the systems behind the scenes, so every improvement actually had room to work.
Assuming AI replaces the person
It’s easy for leaders to view AI as a path to efficiency, and for teams to interpret that as a signal that their roles might shrink. But when AI is framed as a tool that supports good judgment (not one that replaces it), people engage differently. They’re more likely to test, adapt, and improve how it’s used because they still feel essential to the outcome.
Skipping the reflection step
Learning comes from doing, but clarity comes from pausing. If you don’t block time to look back (even 30 minutes at the end of each week), you’ll miss the quiet signs of what’s working and where to steer next.
The teams that benefit most from AI aren’t the ones who adopt the fastest. They’re the ones who stay grounded, reflective, and focused on solving real problems.
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One task at a time wins the race
This 30-day AI playbook is about finding the first small win that makes your team’s work a little faster, clearer, or easier.
You don’t need to be an AI expert to make this work. You just need to:
Pick a task worth improving.
Test one tool, with curiosity and context.
Learn from it and decide what’s next.
And once you’ve got that win? You’ll know where to look for the next one.
If your team’s AI curiosity is growing, but the next step still feels unclear, we can help. Adapt helps teams approach AI implementation one step at a time. No fluff. Just one process, made easier.
