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03 · AI FLUENCY

Your organisation has AI. The clarity to use it well is the part nobody sold you.

The tools arrived fast. The understanding did not. We close that gap, directly and in plain terms, for the people who use AI and the teams who build with it.

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WHY ORGANISATIONS COME TO THIS

The licences are bought. The clarity is missing.

Most organisations did not decide to adopt AI. It arrived. A licence here, a pilot there, a team experimenting on its own. What is missing is the part that makes any of it worth the spend: a clear, shared, honest understanding of what AI can do for this organisation, and what it cannot.

People arrive at this page for four reasons. Usually more than one at once.

  • TRIGGER 01

    Spend, no change

    AI tools were bought. Months later, almost nothing about how the organisation works has actually changed.

  • TRIGGER 02

    Adoption that stalled

    A team tried AI for a few weeks, hit friction, and quietly drifted back to the old way of doing things.

  • TRIGGER 03

    The board wants a straight answer

    Leadership knows the organisation should have a position on AI. Nobody can yet say plainly what it is.

  • TRIGGER 04

    Article 4

    Since February 2025, the EU AI Act has required staff who deal with AI systems to have a sufficient level of AI literacy.

Different reasons. The same underlying problem. The people expected to use AI, or to decide about it, do not yet have the clarity to do either well. That is the gap this engagement closes.

HOW WE CLOSE IT

Two groups. Two kinds of confusion. One practical answer.

The confusion is not the same for everyone. The person using AI in their daily work is stuck on a different question than the team building with it. So the work is shaped in two tracks.

USES AI

"When is this worth using, and can I trust what it gives me?"

BUILDS WITH AI

"Which architecture is right, and what holds in production?"

SAME NEED · CLARITY · TWO DIFFERENT STARTING POINTS

Both groups need clarity they can act on. They need it delivered differently. So we built two tracks.

FOR NON-TECHNICAL STAFF

Fluency for the people who use AI, not the people who build it.

A structured programme that takes non-technical staff from uncertainty to genuine working confidence with AI. It is built on the 4D Framework developed by Anthropic. A model designed for exactly this. Not how AI works under the hood. How to work with it well.

Four capabilities. Four habits of mind your people leave with.

  • Delegation

    Knowing which tasks to hand to AI, which to keep, and how to tell the difference. The instinct for what AI is genuinely good for.

  • Description

    Getting a useful result by being clear with the model about what is actually needed. The difference between a vague answer and a sharp one.

  • Discernment

    Judging the output. Knowing when it is right, when it is plausible but wrong, and how to tell the two apart.

  • Diligence

    Using AI responsibly. What is safe to put in, what is not, and where human judgment stays firmly in charge.

The programme is consistent and proven, because these capabilities do not change much from one organisation to the next. What does not change either is who delivers it. This is run by the people who build and govern AI systems for a living, both of whom have completed Anthropic's Certificate of Completion in Teaching the AI Fluency Framework.

Staff leave able to use AI well in their actual roles. And the organisation has a workforce that meets the AI literacy expectation of Article 4 as a matter of course, rather than as a separate exercise.

FOR TECHNICAL TEAMS

Technical teams do not need a course. They need senior people who have already made the decisions they are facing. So this track is an interactive workshop, built around the team's own architecture, their own stack, and the AI questions actually in front of them.

A demo is easy. AI that runs in regulated production, and still holds when someone inspects it, is not. The gap between the two is everything that surrounds the model: how it fits the architecture, what it is built with, where it runs, what constrains it, and what an auditor can be shown afterwards. That gap is what the workshop works through.

FIG · CAPABILITY MAP
AI IN PRODUCTION 01 · Architecture 02 · Data and retrieval 03 · AI or code 04 · Model selection 05 · Deployment tier 06 · Guardrails 07 · Logging and traceability 08 · Human oversight 09 · Evaluation and documentation

Nine facets of one engineering problem. The workshop works through the ones your build actually turns on.

  1. 01 · Architecture

    How AI fits the systems you already run. What it connects to, what it must not, and where it sits in the stack.

  2. 02 · Data and retrieval

    Preparing the data a model learns from, and grounding it in your knowledge through RAG and vector search. The unglamorous layer that decides whether the output is any good.

  3. 03 · AI or code

    The honest call. Some problems want a model. Many are better, cheaper and more testable as plain deterministic code. Knowing which is which is the engineering.

  4. 04 · Model selection

    Which model for which job. Capability, cost, language and licensing, mapped to the task actually in front of the team.

  5. 05 · Deployment tier

    Hosted API, enterprise cloud, or infrastructure the client controls entirely. Grounded in our three-tier AI model.

  6. 06 · Guardrails

    What the system is allowed to do, what it must refuse, and how those limits are enforced rather than hoped for.

  7. 07 · Logging and traceability

    What every call records. So that months later, the question "why did it do that" has an answer.

  8. 08 · Human oversight

    Where a person stays in the loop, by design, and how that gate holds under load.

  9. 09 · Evaluation and documentation

    How the team knows the system still behaves after a model update or a prompt change, and the record that makes the deployment defensible to an auditor.

The workshop draws on close to a decade of running AI in regulated production. Not generative AI alone. The full arc: preparing the data that machine learning models train on, deploying those models in live systems, the MLOps to keep them running, and now agentic architectures built under real governance constraints.

  • ~10 years AI IN PRODUCTION
  • Classification · extraction · RAG · agents BUILT AND SHIPPED
  • Regulated sectors WHERE GETTING IT WRONG HAS CONSEQUENCES

The team works through their genuine questions with people who have answered the same ones, in environments where the answer had to hold. They leave clearer on the decisions in front of them, and better equipped to make the next ones without us.

THE TWO TRACKS, SIDE BY SIDE

One engagement. Shaped to who is in the room.

  Non-technical staff Technical teams
The question How do I use this well in my work? Which architecture, and what holds in production?
Format Structured programme Interactive workshop
Built on The 4D Framework Your real systems, from architecture to production
Shape Consistent and proven Scoped to the team and its stack
They leave with Working confidence with AI Clarity on the decisions in front of them

WHY THIS IS AN IMAGEPLUS ENGAGEMENT

We are genuinely glad to share what we know.

We are at the edge of this field, and we like it there. We also think understanding AI should not be rationed.
IMAGEPLUS

When an organisation comes out of this clearer, more confident, and more in control of its own AI, that is the outcome we wanted. That is the spirit of the work. It is also why the clarity holds: it stays with your people, not with us.

COMMON QUESTIONS

What organisations ask before they start.

  • Is this only for organisations that need to comply with the EU AI Act?

    No. Article 4 is one reason organisations come to this, and a real one. But most arrive simply because they have AI and want their people to use it well. The fluency is the point. Meeting the Article 4 literacy expectation follows from it.

  • Do we have to run both tracks?

    No. Some organisations need only the staff programme, some only the technical workshop, many both. The right combination is part of the first conversation, not a fixed package.

  • What does the 4D Framework actually cover?

    Delegation, Description, Discernment, and Diligence. Four practical capabilities for working well with AI. It was developed by Anthropic and is designed for people who use AI, not for people who build it.

  • Is this generic training, or specific to us?

    The staff programme is consistent by design, because the core capabilities are the same across organisations. The technical workshop is the opposite: it is built around your team’s own architecture and questions. Both are delivered by the people who build and govern AI systems at Imageplus, never outsourced.

  • Who delivers the work?

    The same people who would run any Imageplus engagement. Senior practitioners, present in the room. There is no separate training department and no handoff.

ENGAGEMENT SHAPE

We select the right track: user fluency, technical workshop, or both.

NEXT STEP

Ask us anything about AI. We mean it.

The first conversation is not a pitch. It is the part of the work we are happy to give away: a straight, useful answer about where your organisation stands with AI and what would move it forward.

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