The Power User Gap for technology and telecoms
AI training for non-engineering teams in tech companies.
In technology and telecoms, engineering got the training and everyone else got the licences. Sales, support, operations, HR and legal are where the gap is widest, and where the hours are. I look at how those teams work, find where AI can do real work in it, and teach them to use it there.
For whoever is responsible for getting non-technical people using AI. Nothing to code.
74 talks and 78 workshops in 36 countries since 2014. Google Developer Expert, Agentic AI Foundation ambassador.
Same task, same tool, two different jobs.
What most people send
Write a summary of this report for the leadership team.
What your top 5% send
Here is the report, and the three slides the board saw last quarter.
Summarise it for our leadership team, who already know the background and want what changed.
One page. Lead with anything that moves a number. Flag anything you're unsure about rather than smoothing it over.
Nobody taught anyone the second version. The people who write it worked it out on your documents and your approvals, which is why no vendor course covers it. I teach it on your own work.
Where the gap shows in technology and telecoms.
The Power User Gap for technology and telecoms teaches the sales, support, operations, HR and legal teams in a technology or telecoms company to use ChatGPT, Claude or Gemini on their own work.
These are the kinds of work I'd look at first. Your team leads and process documents decide which ones we actually start with.
Support ticket summaries
A long ticket thread turned into what the customer asked, what was tried and what is still open, ready for the handover or the escalation.
Sales call preparation
The account notes, the last few emails and what the prospect has published, pulled into a short brief and the questions worth asking on the call.
Internal policy questions
HR and operations answer the same questions about leave, expenses and process again and again. The tool drafts the answer from the policy document and quotes the line it relied on.
First reads of contracts
A first pass for legal and commercial teams: the clauses that differ from your standard terms, the dates and obligations, and what deserves a close look. A lawyer still reads it.
Operations updates
Exports, status notes and chat threads turned into the summary the team sends round, with the numbers taken from the source rather than retyped.
What the teaching looks like there
These teams sit next to engineers who got the training first, so the baseline often shows a wide spread: a few people with habits of their own, and many who stopped at asking questions. They practise on their own tickets, accounts, policies and contracts, with the tools the company already pays for, and nothing in it involves code. Afterwards the same people say what they now hand to AI and where they still don't, which is the change against the baseline.
You see what changed, team by team.
The people attending say how they use AI before anything is taught, and what they hand to it after. It works with whatever tools you've already bought. Remote or on-site, so teams in different countries get the same teaching without anyone flying anywhere. No pre-work for attendees.
A 30-minute call
Where your rollout is, and which teams or workflows you'd like to start with.
I look at how your teams work
I talk to the team leads and go through the process documents for the workflows you've chosen, and suggest others where I see a better place to start. How long it takes depends on the scope we agree.
Before
A baseline before anything is taught
The people attending say how they use AI on those workflows today and how that work gets done. That is the mark everything after is measured against.
I teach your people to use AI there
Remote or on-site, for the people who haven't worked it out yet. They learn to use AI on those workflows, so they do that work more efficiently and AI becomes the first thing they reach for. Your power users are welcome to join.
After
What changed
After the teaching, the same people say what they now hand to AI and where they still don't. You see the change against the baseline, team by team, and we decide where the next cohort starts.
For when someone asks what the licences bought.
You hear from the people attending how they use AI before anything is taught, and from the same people afterwards what they now hand to AI and where they still don't. That's the before and after, team by team, to take to whoever approved the spend.
The teaching comes as named courses, one per group of teams, and the call picks which.
Why the gap is still there.
You have probably tried some of these already. This is what each one leaves you with.
The vendor's own course
Teaches the product: the features, the modes, what the assistant can do. Your power users didn't learn there. They worked it out on your documents and your approvals, which no vendor has seen.
An e-learning platform
Produces completion rates. Everyone finishes the module, and on Monday the same one-line prompts go out as before.
Your own power users
They can show what they do, and they often will. They can't see where their habits came from, and they have their own jobs, so the rest of the team gets a lunchtime demo and a link.
A generic AI trainer
Runs the same day for every organisation, on prepared examples. Here the teaching starts from your workflows, so what people practise is the work they go back to.
Who you'd be working with
I've spent twenty-five years teaching engineers and their organisations whatever technology had just arrived, and the last few building with these tools.
- 74
- Talks
- 78
- Workshops
- 36
- Countries
- 2014
- On stage since
Google Developer Expert in Web Technologies, Agentic AI Foundation ambassador, MBA. Your engineering team is different work: the AI Engineering Workshop teaches them to work with coding agents and to build agents, by the same person, so one conversation can cover both.
More about meBefore you book.
- Our engineers already had AI training. Is this the same thing?
- No. This is for the teams outside engineering, on their own work with ChatGPT, Claude or Gemini, and nothing in it involves code. Training for your engineering team is the AI Engineering Workshop, taught by the same person, so one conversation can cover both.
- Does it work within our company's AI policy?
- It works with the tools and the policy you already have, and the teaching follows the policy on each workflow. Writing it stays with you.
- We already have power users in every team. Why bring someone in?
- Your power users are welcome to join, and they usually do. They have their own jobs, and the habits they built are hard to see from the inside. The teaching takes what works on your own workflows to the people who haven't got there yet.
- Which teams should go first?
- We agree that on the call, and the workflow review can change it. A team with a lot of repeated written work, such as support or sales, is a good place to look first.
- What does it cost?
- It is quoted against the scope we agree on the call. Nothing is priced on this site because no two organisations need the same amount of work.
- What do attendees need to prepare?
- Nothing. There is no pre-work. The scoping happens between us before the teaching.
Questions about the tools, who attends and recording are answered on the Power User Gap page.
The same teaching, for other kinds of work: professional services, financial services and insurance, legal and luxury and heritage.
Start with a 30-minute call.
Tell me when the tools went out and which teams you'd start with. It's one quote, for the workflow review and the teaching of the teams we agree on the call, so the figure you forward covers all of it.