Your office team is probably already using AI. Someone tidies an awkward email in ChatGPT. Someone else pastes a meeting transcript into Copilot and asks for the action points. Chances are nobody showed them how, or agreed what's safe to paste in.
In Great Britain, 55% of working people told the Office for National Statistics in mid-2026 that they use AI for work or study. In a separate ONS business survey, only 11% of UK firms with 10 or more staff said more than half their workforce had any AI training. In BCG's June 2026 survey of almost 12,000 workers, from frontline staff to leaders, only 36% felt properly trained.
This is a plan an office manager or practice manager can run without buying a course: one session, then four weeks of short follow-ups. It's built on well-tested ideas about how people learn a new skill, and on what I saw when I helped a team at an architecture practice start using new AI tools for email and meeting minutes.
What your team is actually worried about
Before planning anything, I had an AI assistant go through months of forum threads (mostly Reddit, mostly US) where office staff, executive assistants, paralegals and accountants talk about AI at work, and I read what it found. Five worries came up again and again.
- “Use AI”, with no direction. Goals like “find five ways to use AI”, and little help with how.
- “Will this replace me?” Especially when the person asking them to use it decides their job.
- “Checking it takes longer than doing it myself.” Invented details, misheard minutes, formatting to fix by hand.
- “Is it safe to paste this in?” Owners and IT people raise this most. Staff ask it more quietly.
- Training that misses the point. One assistant described 20 minutes of watching AI write an email she could write in 90 seconds. People want help with their tasks.
Why banning it isn't enough
In KPMG and the University of Melbourne's 2025 study of more than 32,000 employees, almost half admitted uploading sensitive company information to public AI tools, and only two in five said their workplace had a policy. A short written rule and one approved tool go further than a ban. If you don't have the rule yet, my AI policy template works for any office, not only law firms.
Before the session: about an hour of your time
First, check that training is what's missing. Ask the owner five questions (and whoever runs your IT the first one). If the answer to any of the first four is no, fix that first, because training won't. If the owner isn't ready to commit, ask for one task and one month as a trial, and show them the week-4 numbers.
- Setup: which tool, and can everyone log in with the right mailboxes and files connected? On Microsoft 365, ask whoever manages your accounts which Copilot you have: Copilot Chat with your work sign-in comes at no extra cost, and Microsoft says it doesn't use your prompts to train its models (look for the green shield at the top), while Copilot inside Outlook and Word is a paid add-on.
- Rules: is there a written rule on what information can go into which tool?
- Permission: has the owner said, in so many words, that people are expected to use it for these tasks?
- Time: will people get 20 to 30 minutes in the following week to use it on real work?
- Skill: are people unsure how to do the task well with AI? This is the part training fixes.
At the architecture practice, a group we onboarded weren't using the tool a few days later, and the main reason didn't look like skill. The shared project mailboxes they send most of their email from hadn't been connected yet.
Write the week-4 goal first. For example: “Each person drafts client email replies with AI on at least three days a week, checks them, and follows the data rule.”
Pick three real tasks. AI tends to help with these:
- Task A: replying to a client email thread (taught in the session).
- Task B: minutes and an action list from meeting notes or a transcript (week 2).
- Task C: turning a long document into a short summary or checklist (week 3).
Leave scheduling out for now. In the UK Department for Business and Trade's Copilot trial it was one of the tasks that took longer with AI.
Time task A once, with a timer. One real example per person, the week before. That's your baseline.
Send a short form: their main tasks, how they use AI now, what they need, what worries them, and one non-confidential task to bring. Step-by-step guidance that helps a beginner can get in the way of someone already confident, so it's worth knowing who's who.
Get the room and the tech ready. About 6 to 12 people, in the room if you can. With fewer than six, do the pairs stage as one group. If people do different jobs (reception, bookkeeping, assisting a partner), let each person pick the kind of email they answer most as their task A. Each person tests their own login the day before (at the architecture practice, one session was called off because users hadn't been added). If you're new to AI yourself, rehearse the demo once and save a good and a weak draft as a fallback.
Make the planted-mistake draft. Take a sample reply and break it four ways: change a date, delete the answer to one question, add a client detail that shouldn't be there, and make one line curt.
The first session, minute by minute
The shape is what education researchers call gradual release of responsibility: you show, you do it together, then they do it alone. Pearson and Gallagher, who described it in 1983 for children's reading, called the middle stage, guided practice, the critical one, so it gets the biggest block of time.
| Minutes | What happens |
|---|---|
| 0–5 | Open. What we'll cover, and the week-4 goal. Everyone's goals from the form on one slide: “anything to add?” Then name the “will this replace me?” question and ask what's on people's minds. |
| 5–9 | The data rule and the four checks. Public, Internal, Confidential, with one example of each from your own office; Confidential means stop and ask. The four checks before anything goes out: facts (dates, names, promises), complete (every question answered), tone (sounds like you, follows your rules), data (right tool for the data level). |
| 9–17 | You show task A. Type a weak request and ask the group what's wrong with it. Then a better one. Then fix the draft in the same chat instead of starting again. Check it out loud. |
| 17–28 | Together, in pairs. On a sample email. Pair a confident person with a less confident one, who types. Beginners start from your better request; confident pairs get the task only. Odd number? One group of three. |
| 28–34 | Spot the mistake. Hand out the planted-mistake draft. Count how many errors each pair finds. |
| 34–48 | On their own. Each person works on the real task they brought. |
| 48–52 | Share and recall. One or two people show what they made: what did it get right, what did it miss? Then everyone writes the four checks from memory. |
| 52–56 | Close. Everyone saves one request that worked, names one task they'll use it on this week, and fills in their line of the training record. |
| 56–60 | Spare. There's usually a slow login or a good question. |
A weak and a better request for task A, to show at 9–17:
- Weak: “Reply to this email.”
- Better: “Draft a reply to the email thread below from [client]. Answer both of their questions. The deadline moved to Friday the 14th after my call with them today. Keep it under 120 words, friendly and plain, no dashes, sign off as Sam.”
The better one gives the AI what it can't know (the call), says what the reply must cover, and sets the house rules.
Why it's built this way. Succeeding at something yourself builds more confidence than watching or being told you can, and early failures are especially discouraging (Bandura), so the first solo task is real and set up to work. Recalling something from memory, spread over weeks, beats rereading it (Dunlosky and colleagues), so people write the four checks from memory and come back to them each week.
How to answer “will this replace me?” Honestly, and without promises you can't keep. Something like: “I can't speak for every decision this firm makes. What today is about is the copying, pasting and retyping part of your week, and you staying the person who checks and decides what goes out. What's on your mind about it?” Then listen.
What I learned helping a real team
With my delivery partner Raghu Ramdas (Integrow AI), I helped a team at an architecture practice I work with start using two new AI tools, one for email drafts and one for meeting minutes. A few moments changed how I plan these sessions.
- The invisible thumbs-up. In an online session with about a dozen people and cameras off, each “any questions?” met silence. It could mean “got it” or “not in front of everyone”. What got the group moving was one volunteer sharing their screen while the others did the same clicks on their own laptops. Next time I'll ask by name about something specific, or ask everyone to type one word in the chat.
- “Can we just do three?” Asked for ten example emails to teach the tool their writing style, someone asked whether three would do. For now, yes. What you ask people to prepare has to match what they have on hand that day.
- The dashes. Early in the session, someone asked why the drafts were full of dashes. We fixed it on the spot with a standing rule (an instruction saved in the tool so it applies to each new draft). Small, visible fixes like that seemed to build more trust than the demo.
- Drafts on request. One tester found that most automatic drafts were for emails nobody needed to answer. We switched to drafts on request: a person decides when AI writes, and reads it before it goes.
- The habit wins on Monday morning. Asked if they were using it daily, the first testers said they still opened Outlook first. Setting up the tool was the easy part. That's what the next four weeks are for.
That rollout is still going and we haven't measured usage yet, so these are moments that shaped the plan, not proof that it works.
Weeks 1 to 4: where it sticks or doesn't
A lot of what decides whether training gets used happens after the session. In their review of training transfer research, Grossman and Salas found support at work has some of the strongest evidence behind it, and lack of opportunity to use the skill is a major barrier.
- Day 3: a one-line message to each person: “Used it yet? Stuck anywhere?” Keep it to one question. At the architecture practice, messages with several asks got mixed up.
- End of each week: a 15-minute check-in. The four checks from memory, one person shows a real win, you fix one problem together. Week 2 practices task B, week 3 task C.
- Week 2: the manager asks each person to show one real output, not to grade it, but so people see it matters.
- A card at the desk: their saved request, the four checks, the data rule, and who to ask.
- Champions: two if you can, one keen and one who does the task most. At the architecture practice, the clearest handover was a tester showing colleagues in her own words.
The skeptic. Some people will object on principle: accuracy, ethics, losing skills they've built. Those are fair points. Ask them to be the one who checks drafts for mistakes in the week-2 check-in. A careful skeptic is good at that.
Measuring it honestly
At week 4, look at three things:
- Time: re-time task A with a timer, the same way as before.
- Use: how many days a week each person used it on the task (0 to 5), from a simple tick sheet. The manager's look at one real output per person is a better check than asking people how they think it went.
- Quality: a new draft with four planted mistakes. How many does each person catch now?
Be realistic about what you'll see. The UK's cross-government Copilot experiment reported about 26 minutes saved a day, but that was self-reported. The Department for Business and Trade's smaller evaluation, which had a comparison group, found people saved time on drafting and summarizing, but no evidence the team got more done overall. Your own before-and-after numbers will tell you more than a headline figure.
Keep a one-page training record
Write down who attended, what was covered, the data rule version, and the follow-up dates. It takes five minutes, and it lines up with what some rules and guidance ask for:
- UK (any firm): UK GDPR applies to personal data you paste into any tool, so a written data rule and a record of who was trained on it help show how you handle it. Your professional body may have its own guidance.
- EU: since a July 2026 amendment, the AI Act asks organizations using AI in the EU to take measures to support staff AI literacy, rather than guarantee a level. The Commission's Q&A says no certificate is needed and an internal record is an option.
- England and Wales (law firms): the SRA Code of Conduct for Firms (paragraph 4.3) asks firms to keep their managers and employees competent and their skills up to date.
- Australia: the government's voluntary Guidance for AI Adoption asks organizations to train the people accountable for AI.
In Singapore, if you'd rather pay for an outside course, the SkillsFuture Enterprise Credit can help: the current credit covers eligible courses completed by 30 November 2026, and a redesigned credit starts on 1 December 2026 (one condition is at least three resident employees).
This isn't legal advice. Rules change, so check the current version of each source below for your own situation.
The template
Copy it, fill in the brackets, and run it. A 90-minute version of the session, the form, a scoring sheet and more planted-mistake exercises are in the longer version I use with clients. Email me if you'd like it.
Your one thing this week: ask the owner the five setup questions. If any answer is no, that's the first fix.
Questions people ask
Do we need a certificate to show our staff are trained in AI?
Not under the EU AI Act. The European Commission's Q&A on its AI-literacy rule says there is no need for a certificate, and organizations can keep an internal record. A one-page record per session (who attended, what was covered, what each person will use it for, follow-up dates) is a sensible habit wherever you are.
How long should AI training for admin staff take?
One live session of 60 to 90 minutes, then four short check-ins of about 15 minutes over the next four weeks. The follow-up matters as much as the session: people need a reason and the time to use it on real work that same week.
Which AI tool should admin staff use?
The one your firm has approved and is paying for, on a business plan where your data isn't used to train the model. For many offices on Microsoft 365 that's Copilot Chat, signed in with your work account. Pick one, write down which information can go into it, and train on that tool only.
Is free online AI training any good for office staff?
Free courses can cover the basics, but most use generic examples. What changes day-to-day work is practicing on your own emails, meeting notes and documents, with someone checking the result. You can do that in-house with the plan in this article.
How this was written
This article was drafted with Claude, an AI assistant. It did the research, including reading the forum threads, and the first draft, building on my training method and my notes from the architecture practice. A separate AI review re-checked the sources. I read it, edited it, and the views in it are mine. The forum examples are paraphrased, with no names.
Sources
Checked on 20 October 2026.
- Office for National Statistics, Artificial intelligence in UK businesses: 2023 to 2026 (20 July 2026). The 55% is self-reported use for work or education in Great Britain (Opinions and Lifestyle Survey); the 11% is from the Business Insights and Conditions Survey. ONS advises against comparing the two directly.
- BCG, AI at Work (June 2026; 11,749 respondents)
- KPMG and the University of Melbourne, Trust, attitudes and use of AI: A global study 2025 (May 2025; fieldwork November 2024 to January 2025)
- Department for Business and Trade, Microsoft 365 Copilot pilot: evaluation report (August 2025; trial October to December 2024; self-reported diary data)
- Government Digital Service, Microsoft 365 Copilot experiment: cross-government findings report (June 2025; trial September to December 2024; self-reported)
- Regulation (EU) 2026/1744 amending the AI Act, Official Journal (24 July 2026), and European Commission, AI Literacy: Questions & Answers
- ICO, Guidance on AI and data protection (UK)
- Microsoft, Copilot Chat privacy and protections (updated 24 August 2026)
- SRA, Code of Conduct for Firms, paragraph 4.3
- Australian Government, Guidance for AI Adoption: Foundations (October 2025), practice 1.4
- SkillsFuture Singapore, Information Memorandum EED/2026/1 (17 April 2026), and Ministry of Manpower, Enterprise Workforce Transformation Package factsheet (3 March 2026)
- Pearson, P. D., & Gallagher, M. C. (1983). The instruction of reading comprehension. Technical Report No. 297, University of Illinois.
- Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215.
- Dunlosky, J., Rawson, K. A., Marsh, E. J., Nathan, M. J., & Willingham, D. T. (2013). Improving students' learning with effective learning techniques. Psychological Science in the Public Interest, 14(1), 4–58. Mostly studies of students; testing effects are also reported for middle-aged and older adults.
- Grossman, R., & Salas, E. (2011). The transfer of training: What really matters. International Journal of Training and Development, 15(2), 103–120.
- Kalyuga, S., Ayres, P., Chandler, P., & Sweller, J. (2003). The expertise reversal effect. Educational Psychologist, 38(1), 23–31.
- Forum threads on Reddit (r/ExecutiveAssistants, r/LawFirm, r/Paralegal, r/Accounting, r/msp, r/smallbusiness), mostly 2025 to 2026, paraphrased.