Hands-on, tool-agnostic AI training for leadership teams and their organizations. Not a slideshow about artificial intelligence — a working day built on your own e-mails, documents, and decisions, followed by thirty days of measured adoption.
Training that ends when the room empties doesn't change how anyone works. Five stages — before, during, and after the workshop — turn a day of practice into a lasting operating habit.
A pre-training survey maps each participant's experience level, weekly workload, and the documents they actually work with. Scenarios are written from your real work — not generic demos.
A 45-minute online session one week before the workshop: accounts, interface, first contact. The in-person day is never spent on mechanics.
A full-day, hands-on workshop. More than 60% of the day is spent on participants' own e-mails, documents, and tables — everyone leaves with a working routine, not notes.
A management note built on what actually surfaced during the day: use-case hypotheses, prioritization, and a pilot design — presented to leadership in a separate session.
Usage and outcome measurement at day 30. Without follow-up, adoption fades within weeks — so we measure, reinforce, and report instead of declaring victory on day one.
At this level, the number of teachable concepts is small — and that is a feature. One mental leap carries the whole day: from a place you ask questions to a place you assign work. Four rules serve that leap, repeated across real tasks until they become reflexes.
Generative AI is not a search engine. Write a brief: context, role, source, format, constraints. Then delegate the task like you would to a capable assistant.
The model doesn't know your world. Attach the document, the table, the regulation — and constrain the answer to that source only.
The real value shows up in the second and third message. Push back, correct, sharpen — most users quit exactly one message too early.
A fluent answer is not a correct answer. Verify the critical lines yourself before anything carries your signature.
Each scenario is tied to a measurable indicator — baseline at calibration, re-measured at day 30. We don't promise percentages; we propose to measure. The party offering measurement is more credible than the party promising numbers.
A one-page, decision-oriented summary from a 100+ page report, contract, or regulation.
Two contracts, two proposals, two versions of the same regulation — what changed, and what it means.
"What is NOT in this document?" — the question executives value far more than summarization.
Devil's advocate, pre-mortem analysis, stakeholder simulation, and "the 10 hardest questions I will be asked".
Briefing notes, presentation skeletons, decision minutes — drafted in your corporate language.
The work most agendas never get to. The gain here isn't time saved — it's a task that finally becomes feasible.
Instead of sending an analysis request and waiting days, executives read their own tables at their own desk — with a verification habit built in.
E-mail drafts, action lists from meeting notes, follow-up sequences — repeated weekly, measured monthly.
What an executive should take from this program is not tool proficiency — it's decision discipline. These five outlast any specific product or model generation.
Which work is fully delegable, which is done together, which must never leave your desk. Most executives think about this explicitly for the first time.
"Where did you get this?" becomes automatic — asked of AI first, then of people. This is where institutional decision quality actually improves.
Deciding whose data goes where, under which contract, to which country — no longer an IT topic, but a board topic.
Not usage counts — cycle time from start to approval. Most AI initiatives are declared successful on the wrong metric, then quietly die.
Not a grand transformation program: a narrow pilot, honest measurement, then a deliberate scale decision.
The calibration survey decides which intensity applies. In mixed groups, experienced and first-time participants are deliberately paired — the worst seating plan is putting equals side by side.
Success first, concept second, caution last. The day opens with a real task completed in the first 40 minutes — no theory. A starter prompt set instead of an overwhelming library, one working routine instead of three, and the option to attend with an assistant so no one freezes at the keyboard. No jargon: no APIs, no tokens, no model architecture.
Bad habits get corrected, not built. Delegation mapping across the real weekly workload, brief-writing with live feedback rounds, decision-support techniques — devil's advocate, pre-mortem, stakeholder simulation — and data & table analysis with a mandatory manual-verification step.
The data-classification module sorts your organization's real document types into three classes — in the room, debated openly. Each class maps to an access model. Cloud and air-gapped are not rivals; they are layers that serve different data classes side by side.
Commercial-terms plans where your inputs are contractually excluded from model training. Suitable for public and internal-use data classes.
Region-constrained deployments for data-residency requirements — inference and storage kept within a chosen geography.
Fully isolated deployments where no data ever leaves your perimeter — the only complete answer for your most sensitive document classes.
Every engagement is scoped to your organization — participant count, experience level, customization depth, and follow-up needs. Pricing is proposal-based.
A compact awareness format: the four rules, two hands-on blocks, and a condensed data-classification module. For teams that need a fast, credible first contact.
Calibration survey, online setup session, full-day hands-on workshop, complete material set, and the management note with pilot design.
Everything in the Full Program, plus reinforcement sessions, an on-site office hour, and a day-30 usage measurement report. One day builds skill; thirty days build habit.
Program structure and language are aligned with national AI competency and literacy frameworks: in-person workshops, before-and-after measurement, safe use, and data awareness.
Tell us about your team and how they work today. We'll come back with a calibrated program design and a proposal.