An in-house programme for the people within your organisation who are responsible for actually rolling out AI. No demos and no introductions to chatbots: we cover use-case selection, vendor choice, AI Act classification, pilot design and the transition to production. Designed for CIOs, CTOs, product and transformation leads.
AI implementation training is not about understanding AI or using ChatGPT on the shop floor. It focuses on genuinely embedding AI systems into your business processes. Participants learn how to select use cases, compare vendors, classify risks under the AI Act, set up pilots with clear success criteria, and how to take a working prototype into a controlled production environment.
The difference from general AI literacy training lies in scope. Literacy targets the whole organisation and covers foundational knowledge, prompting and risks for everyday use. Implementation training is for the small group of people who make the decisions on architecture, vendors, budget and compliance, and who are accountable for whether an AI initiative either delivers returns or is neatly stopped. That calls for a different language, a different pace and a different kind of assignment.
We deliver the programme in-house at your organisation, using cases from your own domain, and, if you wish, involve your IT architect, legal counsel and business owner in the same sessions, so alignment starts during the training itself. What in many classroom courses only lands weeks later in a working group happens with us at the same table: the tech lead who says where model routing will get tricky, the privacy officer who immediately flags which processing they cannot sign off, the product owner who hears why the business case comes down to tokens. The training thus doubles as a moment of alignment.
We base the programme on what we see working at clients who roll out an AI project successfully, and on what goes wrong at clients who have to start again. The content is deliberately vendor-neutral: we cover Anthropic, OpenAI and Google alongside open source, and we do not sell licences. Should you choose, after the training, to follow up with our AI agent or platform development, that is a separate decision and not part of the training agreement.
You are responsible for an organisation-wide AI programme and want to get a grip on the choices your teams make before investments start to build up.
You are building a product with an AI component and want to know which architectural choices will remain scalable now and which you would have to expensively redo in a year's time.
You are technically grounded but new to LLM architecture, RAG, evaluation suites and model routing. You want a framework to justify build-versus-buy-versus-partner decisions.
You advise boards or translate strategy into execution. You need a mental model that goes beyond vendor pitches and marketing material.
You lead a digital transformation in which AI is a component. You want to know which parts are technical, which are process-related, and where the real adoption risks lie.
You translate the AI Act, GDPR and sector regulation into workable controls. You want to understand the technology well enough to ask the right questions, not to build it yourself. In mixed sessions you notice more quickly where your own risk appetite and technical reality diverge.
Participants leave the programme with a prioritised list of AI use cases, scored on ROI, risk and feasibility. This replaces the usual "twenty ideas on a Miro board" that nobody takes ownership of.
A practical set of criteria your teams can use to compare vendors (Anthropic, OpenAI, Google, open source) and make architecture choices for LLM gateway, RAG, evals and model routing, without assuming vendor lock-in from the start.
A draft compliance roadmap: which use cases fall under which AI Act category, which DPIA processes are ready, how DORA fits in, and who in your organisation makes which decision. Not a policy document nobody reads, but a working overview you can actually steer by.
The programme is modular. We adjust the order and emphasis to your sector and your ongoing initiatives, but this is what it covers.
Use case selection, ROI modelling and pilot design as one coherent decision.
LLM gateway, RAG, eval suite and model routing: what each is, and when you need it.
Anthropic, OpenAI, Google or open source: a framework for choosing models on their merits.
AI Act classification, GDPR overlap and concrete hallucination mitigation for each type of use case.
When to build in-house, when to adopt a SaaS product, and when to work with a development partner.
How to get the board, legal, IT and the business pulling in the same direction, and how to stop a single party from blocking progress.
Clear success criteria, an honest failure protocol and a built-in kill switch before you start.
From a working prototype to an AI platform engineering setup you can still maintain two years from now.
Employee adoption as the critical path, not a chapter in a launch deck.
KPIs, tokens, latency and quality: what you measure during the pilot, and what you measure in production.
AI Act, DPIA and DORA in one line, with attention to who delivers what, and when.
An implementation pathway with our team afterwards, if you want one. No obligation.
There are three reasons we have been delivering this training more and more often over the past year. First, much of the AI Act has been enforceable since 2026. Organisations that still don't know which of their ongoing AI applications fall under which risk category are not only running a compliance risk; they are also missing the chance to adjust their architecture to that classification before production pressure makes it difficult.
Second, we see that many organisations have now completed a first pilot. That pilot was often a ChatGPT integration or an internal assistant. The real work starts afterwards: what we do with customer data, how we handle agentic workflows, which RAG approach scales to hundreds of documents, and how we arrange evals so we know what the model does in production. Those questions call for decision-makers who understand the trade-offs, not external vendors who dictate them.
Third, the vendor landscape is changing quickly. What was the logical choice a year ago is often no longer so. Models have become more capable, cheaper and faster; new players have emerged; open source has gained ground. An organisation that doesn't systematically upskill its people on vendor selection risks signing the wrong two-year contract or, conversely, missing a better option because nobody had the time to look. The training gives your people the framework and the language to make those choices consistently, even as the market keeps moving.
For the first session, we map which AI initiatives are already underway, which teams are involved and which compliance requirements apply. We tailor the modules to your context and use your own cases as the common thread.
We work in compact sessions with a mixed group of participants, typically tech, product, legal and business sitting together at the table. Pre-work between sessions is limited; there is no homework culture, but there are concrete assignments that feed into your regular team meetings.
Afterwards, you have a supported pilot portfolio and a governance roadmap. Optionally, we can take the follow-on project forward with our own interim AI tech lead and build team, or you can carry it on yourselves. There is no vendor lock-in for the training itself.
A thirty-minute introductory call in which we go through which AI initiatives are under way, which roles within your organisation are involved, and which modules are most relevant to your situation. No obligation; after the conversation we send a proposal with the approach and scope.
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