AI Literacy Training
AI literacy training that changes how your team works on Monday
Practical, role specific training built around your actual workflows, so people leave with working prompts and a clear policy rather than a slide deck.
Recognise any of these
What usually brings people here
Half the team quietly uses AI already and nobody knows what data is going into it.
People tried a chat tool, got a mediocre answer, and concluded the whole category is hype.
Leadership wants adoption but there is no policy, so cautious staff stay away entirely.
The training you were quoted is a generic webinar with no connection to your work.
Deliverables
What the engagement produces
Concrete outputs, not activity. Each of these is something you can point at when the work is done.
Role specific sessions
Sales, operations, finance and marketing each get their own session built on their own documents and their own tasks. A generic session teaches nobody anything they will use.
A prompt library your team owns
Twenty to forty tested prompts for the tasks that actually recur in your business, written up with the reasoning so people can adapt them rather than copy them blindly.
A written AI use policy
What may be entered into which tool, what must never be, who approves new tools, and how AI assisted work gets reviewed. Short enough that people read it.
A tool recommendation with reasoning
A specific short list matched to your work and your budget, with the trade offs stated plainly, rather than a list of every product on the market.
Recorded sessions and written notes
Everything is recorded and summarised so new hires can be brought up to the same level without booking another engagement.
A follow up clinic
A session two to four weeks later to work through what people actually hit once they started. This is where most of the durable learning happens.
Process
How the work runs
- 01
Workflow audit
Short interviews across roles to find the repetitive, language heavy tasks where AI genuinely helps, and the ones where it will waste everyone’s time.
- 02
Curriculum build
Sessions are written against the tasks found in the audit, using your documents and your terminology. Nothing generic ships.
- 03
Live sessions
Ninety minutes per role, hands on throughout. People work on their own real tasks during the session rather than watching a demonstration.
- 04
Policy and rollout
The use policy is drafted, reviewed with leadership and circulated with the prompt library, so the rules and the tools arrive together.
- 05
Follow up clinic
Two to four weeks later, an open session on the friction people hit in practice. Common gaps get added to the library.
Why most AI training does not stick
The standard corporate AI session explains what a large language model is, demonstrates a few impressive prompts on invented examples, and finishes with a slide about the future of work. People leave interested and change nothing, because nothing in the session touched a task on their actual list.
The version that works is narrower and less exciting. Find the six tasks a role repeats every week. Build prompts for those six tasks against real documents. Have each person run them on their own work while someone experienced is in the room to fix what goes wrong.
That is a smaller promise and a much larger result. Two weeks later people are still using it, because it was built around what they already do.
Shadow AI is already happening
In most organisations a meaningful share of staff are already pasting work into a consumer AI tool. They are not being reckless. They are trying to get through the day, and no one has told them where the line is.
The risk is not the use, it is the absence of a rule. Client contracts, personal data and unreleased financials end up in a free tier product with unclear retention. A policy nobody wrote cannot be followed.
Every engagement produces a written policy short enough that people actually read it: which tools are approved, what categories of information may go into them, what always requires review, and who to ask when a case is unclear.
What the training does not promise
It will not make anyone an engineer, and it will not make AI do a job that requires judgement your team is paid for.
What it reliably does is compress the language heavy parts of the work: first drafts, summaries, research passes, reformatting, structured extraction, and the endless small rewrites. Across a team that is a real recovery of hours, and the hours come back to the parts of the job that need a person.
Questions
AI Literacy Training questions we get asked
How long does AI literacy training take?
A standard engagement runs about three weeks: audit interviews in week one, live sessions in week two, and the follow up clinic two to four weeks after that. Each live session is ninety minutes per role group. The total time commitment per employee is usually under four hours.
Is this useful for people who are not technical?
Those are the people it helps most. The sessions require no coding and no technical background. They are built around the tools your team already has access to and the documents they already work with.
What size team is this suited to?
It works from roughly five people up to about eighty. Below five, a single focused session plus a prompt library is usually a better use of the budget. Above eighty, the same programme runs but with an internal champion trained per department so it can be repeated without booking new sessions.
Do you help write our AI use policy?
Yes, a written policy is part of every engagement. It is drafted from what the workflow audit found, reviewed with leadership, and kept short deliberately. It is a practical operating document, not a substitute for legal review where your industry requires one.
Can training be delivered remotely?
Every engagement is remote, because the business has no office and does not travel to deliver training. Sessions run live over video with hands on exercises and are recorded for anyone who cannot attend. In practice remote suits this format: people work in their own tools on their own documents, which is exactly what they will be doing the following week.
Often paired with
- AI IntegrationAssistants, document processing and semantic search wired into your real data, with evaluation and guardrails before launch.
- Business AutomationRepetitive work between your systems mapped, automated and monitored, with alerting so a silent failure never goes unnoticed.
- Full Stack DevelopmentCustom web applications and platforms built end to end, from schema design to the interface your customers use every day.