Metrics without the arguments
For your key metrics, we agree on the meaning, calculation, source, exceptions, owner, and the decision each number should support.
For leadership, finance, sales, product, and data teamsA data and AI workshop that ends with an agreement.
I bring together the people who know the problem and someone who can make the call. We work with your metrics, data, or AI plans, then write down what you agreed on and who needs to do what next.
Discuss a workshopA working agreement your team can come back to.
The momentum should not end with the last slide. After the workshop, I write up the conclusions so the team can act on them.
- A shared vocabulary
- The definitions and assumptions everyone agreed on.
- Decisions and open questions
- What we know, what still needs checking, and who will own it.
- The next step
- A short list of priorities, instead of a sprawling transformation roadmap nobody owns.
Each role sees a different part of the problem. A workshop brings those views into one decision.
The most useful sessions bring together people with different responsibilities, each of whom sees part of the picture.
- Owner / CEO Our meetings need to stop relying on three versions of the same number.We put competing definitions on the table and agree on what the company will use to make decisions.
- COO / business owner I need this agreement to change how people work every day.We identify the exceptions, owners, and dependencies that need to work after the workshop ends.
- CTO / data lead I need to turn leadership expectations into a technically sound brief.We separate the business goal from the data, architecture, and security requirements the solution depends on.
We choose the workshop around the decision you need to make.
Every option starts with your examples. Before the session, we clarify who should attend, what to bring, and which question we need to answer.
What your data needs before you give AI access
We explore whether AI will have the right definitions, context, permissions, and human oversight. The output is a set of hypotheses and priorities, not a workshop passed off as a technical audit.
For leadership and owners of AI initiativesFrom ETL to AI agents
Your data team works through what agents mean for the way it builds and operates: metadata, semantics, evaluation, costs, permissions, and human decisions.
For data, analytics, and technology teams
From a public talk
With AI, the quality of the context often matters more than the model.
My work on the shift from ETL to AI agents draws on experience with data warehouses, metrics, and running data teams. The same principle guides the workshop: business meaning first, technology second.
I bring leadership, business stakeholders, and the technical team to the same table.
For more than ten years, I have worked with business leaders, operational teams, and the people who build data systems. I can connect “What do we need to decide?” with the technical reality of data sources, metrics, data models, and AI.
- 10+ years
- in data, BI, automation, and product teams
- Hands-on experience
- workshops, talks, and bringing AI into real workflows
Preparation starts with the question the session needs to settle.
We first clarify the problem and who needs to be involved. I then build the workshop around your examples and send the conclusions afterward.
- 01
Prepare
A short conversation, a question to resolve, and the minimum materials we need.
- 02
Work together
A facilitated session focused on specific situations, decisions, and disagreements.
- 03
Capture the outcome
Agreements, open questions, responsibilities, and a recommended next step.
A good fit if
- The people attending know the problem and can make decisions.
- We can work with specific metrics, processes, or plans.
- You want to reach an agreement and take the next step.
Not a good fit if
- You want a general motivational talk about AI.
- Your goal is to train as many people as possible on a single tool.
- No one attending can take responsibility for what happens next.
What to know before booking.
I adapt the format to the decision and the people in the room. These boundaries stay the same.
What will we agree on before starting?
The goal and scope, the people involved, the materials or access needed, the deliverable, timing, price, and any follow-up work that would be a separate engagement. We start only when we have a shared understanding of all of these.
How will we handle nonpublic data?
Before working with nonpublic data, we will agree in writing on which sources may be used, where they may be processed, who has access, and whether external AI tools are allowed. I do not work with nonpublic data without that agreement.
When should we choose a workshop over training?
A workshop helps when leadership, business, and technical teams need to agree on a specific problem and what to do next. If participants mainly need hands-on practice with ChatGPT Work and Codex on team tasks, the training is a better fit.
Who commissions the workshop, and who attends?
The person commissioning it helps choose the question the session needs to resolve. The people in the room should know the problem, with at least one person able to decide on the next step. I will suggest the right group during preparation; a large audience with no ownership usually does not help.
How long is the workshop, how many people can join, and what does it cost?
I propose the duration, format, group size, and price based on the goal, preparation needed, and people who need to be in the room. We agree on these details before work begins.
Does the workshop include implementation?
Not automatically. The workshop ends with actionable conclusions and a next step. If you need an audit, a workflow sprint, or other help, we agree on that separately.
Does your team keep circling the same issue?
Send me a few lines about the situation and who needs to be in the room. I will suggest whether a workshop, an audit, or another first step makes sense.
Prefer writing from your own inbox? jsem@radekduha.cz