AI training and adoption
For executives, teams and AI leads. Governance sets the rules; training puts them into practice.
Workshops · executives, teams, AI leads
- Format
- Workshops tailored to each audience
- Who is involved
- Executives, the teams that use AI, and your AI leads
- What you leave with
- Teams that know what is allowed, how to go about it and who to ask
- What comes next
- Engineering practices, or production
Who the AI training is for
This training serves organizations that already have AI usage rules, or are about to adopt them, and want those rules to be known and applied.
- Your AI usage rules are adopted, but few people know them or where to find them.
- Practices vary from one team to another, and two people using the same tool follow different instructions.
- Your executives must approve AI projects without reference points on risks, costs and the limits of the tool.
- You have named AI leads, or plan to, and they need tools to fill that role.
One training per audience, in four steps
- Audiences and uses: which roles use AI in your organization, and for which tasks.
- Executives: the stakes, risks and costs, so they can decide on and sponsor projects.
- Teams: exercises on tasks from their own jobs, within the organization's rules.
- AI leads: how to pass the rules on, support colleagues and report use cases and incidents.
AI training built on your own rules
The training draws on what the organization has decided: which data may enter a tool, which tools are approved, who validates what. Without that foundation we would be teaching generalities; with it, each participant leaves knowing what is allowed in their own organization. We begin by listing the roles that use AI and the tasks they use it for, so that each session deals with the participants' actual work.
The three audiences are not trained the same way, because they do not make the same decisions. An executive needs reference points to decide; a team needs exercises on its own tasks; an AI lead needs what it takes to answer colleagues' questions without going back to IT each time. That last role is what keeps the training alive: it passes the rules on and brings use cases and incidents back up.
What you get
Executives able to decide
Reference points on the risks, costs and limits of AI, so executives can fund, defer or stop a project and sponsor others with the facts in hand.
Teams applying the rules
Exercises on tasks from their own jobs, run within your rules, so that everyone knows what is allowed, how to go about it and who to ask.
Equipped AI leads
People prepared to pass the rules on, answer their colleagues' questions and bring use cases and incidents back to those who decide.
After the AI training, equipping the teams
Once the rules are known, development teams and the business lines can use them to work differently. AI engineering practices spell out the tools, access and checkpoints for each track. If an AI service is already in use, production adds monitoring of answer quality, costs and incidents, which your AI leads can help feed.
Frequently asked questions
What is the difference between governance and training?
Governance decides the rules: which data, which tools, which costs. Training makes sure each person knows those rules and applies them in their work.
What is an AI lead?
A person in your organization who acts as the internal contact on AI: they help colleagues, pass on the rules and report use cases and incidents. The training prepares them for that role.
Do we need governance in place before training?
It is preferable, because the training teaches your rules. Without them, the training stays general and teams do not know what is allowed in your organization.
Is the AI training suitable for teams without a technical background?
Yes. The exercises use tasks from each team's own work, within your usage rules, rather than the internal workings of the models. Technical teams follow a separate track, the engineering practices.