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Generative AI

AI Training & Capacity Building

From occasional ChatGPT users to a team that uses AI well.

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Having Heard of AI Isn't the Same as Using It

Most teams that say they use AI are experimenting. The occasional email, a document summarised now and then. That is noticing it exists.

The gap is knowledge: what it does reliably, where it fails, how to prompt for useful output, and when to verify.

Skills Tied to the Work Your Team Actually Does

Every example and exercise comes from your business, your tools, and your roles. Generic scenarios do not transfer.

The goal is not enthusiasm. It is specific, repeatable skills your team uses the day after training ends.

What you get

  • A pre-training assessment of current knowledge and role needs
  • A curriculum built on your industry and your actual work
  • Hands-on sessions using real scenarios from your business
  • Role-specific tracks for sales, operations, marketing, and support
  • A prompt library for your most common tasks
  • Usage guidelines covering privacy, verification, and the Data Protection Act

How We Work

  1. 01

    Assess

    We map current knowledge and identify the roles where AI saves the most time.

  2. 02

    Design

    A curriculum built on your tools and your work, not generic examples that do not land.

  3. 03

    Deliver

    In-person, virtual, or train-the-trainer. Hands-on practice throughout, minimal theory.

  4. 04

    Reinforce

    Thirty days of follow-up support, and a session at 60-90 days to review what actually stuck.

What to Expect

Daily use, not occasional
Habits rather than awareness: part of the workflow within weeks.
The first draft stops being the slow part
Time back on writing, research, documentation, and routine communication.
Better output, faster
AI takes the first draft. Your team applies the judgement.
Confident, not intimidated
They know what it does reliably, where it fails, and how to check.

Where This Has Been Needed

What clients came to us with, in their words. Names withheld by agreement.

Healthcare · Nairobi

“Our team spends half the day retyping information off documents.”

If your business runs on invoices, application forms, delivery notes or receipts, the same approach applies. The document changes; the pipeline does not.

Prescriptions and clinical visit notes were arriving as documents and leaving as manual data entry, several hours of it a day. We built a pipeline that reads them, pulls out the fields that matter, and passes them on structured, with review kept in place wherever a mistake would be expensive.

Construction technology · Kenya

“We want to use AI, but we do not know where to start or what could go wrong.”

Building the wrong thing first is the most common way AI budget disappears. A ranked plan costs a fraction of the first mistake it prevents.

A growing startup had more AI ideas than it could fund and no way to rank them. We mapped where AI would return most across client matching, onsite support and back-office work, sequenced it into what to build first, second and third, and set a lightweight risk and governance framework alongside it so the question of what could go wrong was answered before anything was built.

Central banking · Bank of Tanzania

“Our people have heard of AI. They do not actually use it.”

Training that runs on generic examples does not transfer. If it is built on the work your team did last week, it is being used the following morning.

We led the AI upskilling programme for executive and personal assistants at the Bank of Tanzania, built on how adults actually learn rather than on tool demonstrations. It ran on their real work: correspondence, diaries, complex travel, document drafting, and taught a working mental model for what to delegate, how to direct it, and when to check it.

Applied AI · Built on public clinical guidance

“The answer is somewhere in our documents. Nobody can find it.”

Contracts, policy manuals, SOPs, product catalogues. Wherever knowledge is locked in long documents that only two people know how to navigate.

Built against Kenya's national diabetes guidelines: chosen because a wrong answer in a clinical setting is dangerous rather than merely annoying: the assistant answers questions in plain English and attaches a citation that jumps straight back to the source page. It declines the questions it should not answer instead of guessing at them.

Common Questions

That is exactly who this is designed for. If they use WhatsApp and Google Docs, they can use AI productively. No jargon, all hands-on.

We address it directly, with concrete time savings in their own role that they test themselves during the session. Most resistance is fear of replacement, and we are honest about that.

Usually because it was theory on generic examples. We train on real tasks from your business, then follow up at 60-90 days to reinforce it.

Yes, and it is often better. Training leadership first lets early adopters find use cases and demonstrate value before a wider rollout.

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