A hands-on workshop built around the tools, documents and processes your team actually uses — so people leave with uses they run the next morning, not a list of tools they forget by Friday.
In short
A BrAIght Wave AI workshop for employees is hands-on training built around the tools and workflows a team already uses. It runs in Hebrew or English as a half day, full day, two-day intensive, or a multi-cohort series for larger organizations, and ends with every participant holding concrete uses built on their own tasks — including clear boundaries for anything involving company information.
We don't run one generic session for a whole company. The group is defined by the kind of work it does, because day-to-day tasks are what matter — not the tool.
Finance, HR, procurement, service and operations — people working with documents, forms, reports and correspondence. The gain is in preparation, summarising and drafting, and in cutting repetitive work nobody enjoys.
Managers need both to use the tools themselves and to decide what their team is allowed to do. The workshop covers both: personal use, and how to spot a task that should — or shouldn't — be handed to AI.
Research, specs, drafting, content and feedback analysis, worked on your real material, including what must never be pasted into an external tool.
Developers, QA and data people working with coding assistants and agents. The focus is the workflow itself — where an assistant saves time, where it creates rework, and how to review its output.
The structure is fixed; the content isn't. This is the full-day skeleton — shorter formats compress it, the intensive expands every step.
A short, accurate grounding — why a language model gets things wrong, why it sounds confident while doing so, and what that implies about which tasks to hand it. No history of AI, no architecture diagrams.
Every participant brings a task from last week. We work it in the room, against the tool, until the output is usable. This is the part that turns a demo into something that sticks.
Moving from a one-off prompt to something saved, shared and re-run whenever the task recurs — including how to notice quality dropping and what to fix when it does.
Uploading documents, working on existing data, and using the tools you already license — instead of introducing five new ones nobody will buy.
Personal data, customer data, code and commercial material. We go through what your licensed tools permit, what they don't, and which outputs need human review before leaving the company.
Everyone writes down two or three concrete uses they're taking back, and we collect them into one team list. That's what lets a manager check a month later what survived.
Four formats we run regularly, in Hebrew or English, on-site or remote.
Most of what decides whether a workshop holds up happens in preparation. This is what we do before the date.
One call with the sponsor and two or three with representative participants, so we know how the work actually looks rather than how it's described in a deck.
We ask participants for tasks from their own work, and those become the exercises. No invented examples.
We work with what you're actually permitted to use, so nobody leaves dependent on a tool they can't access.
If there's an internal AI policy, the workshop teaches it. If there isn't, we mark exactly which questions need deciding — and who in the organization has to decide them.
We'd rather say this before a date is booked.
The move from an impressive pilot to something running in production, inside your existing systems, with an owner in the organization able to maintain it. This is where most AI projects stop.
A clear decision about what to do with AI, what not to do yet, and in what order — built around how your organization actually works, not around a list of tools.
Tell us who the group is and what kind of work it does, and we'll come back with a proposed format and content — not a brochure.
Let's talk