Our advice for corporate AI training is to plan it in three parts by role. Leadership gets a half-day on what AI can and cannot do and how to decide where to invest. Business teams get a full day practicing prompting, workflows, and scenarios on the real tasks of their own roles. Engineering teams get one to two days of hands-on RAG and agent practice. The three groups have different audiences, goals, and outputs, so they do not belong in a single session.
What matters more is how the content is organized: by role and domain, not as one general AI literacy course for the whole company. On routine tasks, AI narrows the gap between novices and experts. On complex tasks that need judgment, it amplifies whatever judgment the user already has. What a role gets out of AI depends on which side of that line its core tasks fall, and that can only be worked out inside the role's own tasks.
Why one general course is not enough
We discussed this in a position paper at HHAI 2026; our summary is Equalizer or Amplifier? The Dividing Line Is Task Complexity. The conclusion fits in one sentence: who benefits from AI is decided by domain expertise, not prompt engineering.
Inside a company, this explains a familiar pattern. The same toolset is rolled out company-wide, some teams get real results, and others produce worse work than before. The difference is usually not whether the training was adequate. It is whether the role's core tasks are rule-based checking or weighing judgment calls. A general course can teach how the tools work. It cannot show where that line falls in each role. That is why FIM organizes its training by role and domain.
Leadership, half a day: limits and investment decisions
The leadership session is not about "what can AI do". It is about "where should we invest, and how much first". Half a day is enough for three things:
- Limits: which tasks AI can handle reliably, which outputs always need human review, and what to do when the answer is uncertain.
- Investment decisions: which roles to pilot first, how to judge whether a pilot worked, and when it is worth moving to a formal project.
- Division of labor: hand routine stages to AI to even out efficiency, keep judgment stages with experienced people, and use AI to help them verify faster. The two kinds of stages need to be designed separately.
The output of this session is a judgment, not a purchase list. FIM's training is billed per day with no purchase commitment, so what to do next, and at what scale, stays the company's call.
Business teams, one day: practice on real tasks
For the business day, the material should come from the work the team actually does every day, not from generic examples. The day covers prompting, workflows, and live practice, in roughly this order:
- Split the role's work: which tasks have clear right answers and can be checked against a list, and which need context and trade-offs.
- Put the checking tasks to work first: once AI handles these, less experienced colleagues get closer to the output of senior ones more quickly.
- Practice review on the judgment tasks: the skill being trained is how to verify AI output, ask follow-up questions, and spot the sentence that is wrong, not how to let AI make the call.
Contract review is a good example. Checking whether a clause is present and weighing whether a penalty rate is reasonable are two different kinds of work that need two different approaches. We walk through the split in Contract review: should AI be an equalizer or an amplifier?. Other roles can follow the same idea and separate their own two kinds of work first.
Engineering teams, one to two days: RAG and agent practice
The engineering days are hands-on, focused on RAG and agent practice. The goal is for the team to form a first-hand sense of how much work it takes to build a Q&A tool or a small workflow on their own material, and where it tends to get stuck. Later, when the company evaluates proposals or scopes work with a delivery partner, the engineers can bring their own view instead of relying on demos.
After the training
Training is a starting point, not the destination. As our Solutions page puts it, start with corporate AI training if the team needs judgment before commitment, and move to custom solutions when your own data, existing systems, or compliance requirements are involved. The two can be combined or taken separately. The schedule for custom delivery depends on how much data work and system integration the scope involves, and is set in the proposal.
If you are not sure where to start, a free 30-minute conversation is a good first step to choose the right entry point for your team.