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AI for Data Teams Sprint
How can analysts and developers use AI without turning their workflow into prompt roulette?
A practical, hands-on sprint for using AI deliberately in everyday data work — with verification, context and responsibility built in.
DOES THIS SOUND FAMILIAR?
Start with what is actually happening.
- People use AI individually but practices vary widely.
- Prompts are copied around without understanding the context they require.
- AI-generated code is accepted too quickly or rejected too broadly.
- The team wants practical examples connected to real data work.
WHAT WE NEED TO ANSWER
Good work starts with better questions.
What context does it need to produce useful work?
How do you verify the output?
Where must a human remain responsible?
WHAT WE PRACTISE
The scope follows the problem.
I do not force every engagement through the same checklist. These are the areas that may matter; the agreed scope determines how deep we go.
Understanding unfamiliar code or models
Generating and reviewing DAX or SQL
Refactoring transformations
Creating test cases
Documenting models and decisions
Exploring requirements
Investigating errors
Challenging architecture and design options
WHAT YOU GET
Something you can use after the conversation ends.
The output should help you make a decision, change a practice or move the work forward. It should not exist simply to prove that work happened.
Practical Team Workshop
Hands-on work around realistic data tasks rather than a feature tour.
Reusable Workflow Patterns
Repeatable ways to provide context, ask, inspect and verify.
Realistic Exercises
Tasks that feel like work your analysts and developers actually do.
Verification Checklist
A simple discipline for reviewing AI output before trusting it.
Team AI Working Principles
Shared expectations for responsible day-to-day use.
HOW IT WORKS
Enough structure to make the work clear. Not so much process that the process becomes the work.
Discovery
We clarify the audience, current level, recurring problems and what people should be able to do differently afterwards.
Design
I shape the agenda, examples and exercises around those needs rather than forcing a standard deck onto the team.
Live work
The session combines explanation, demonstration, questions and hands-on practice.
Follow-through
You leave with material, patterns and clear next steps for applying the learning at work.
YOUR TIME COMMITMENT
The main time commitment is the live live workshop / training. A short discovery conversation happens before delivery so the session can be shaped around the team.
A GOOD FIT WHEN
This is likely to help if...
- Your data team already experiments with AI and needs stronger shared practice.
- You want training tied to actual technical workflows.
- You want human verification treated as part of the skill, not a disclaimer.
IT MAY NOT BE THE RIGHT FIT IF
- You only need a high-level AI awareness session for a general audience.
- You are looking for model-development or machine-learning engineering training.
If the problem is real but the format is wrong, that is useful to know early. I would rather redirect the conversation than force the challenge into the wrong service.
AI FOR DATA TEAMS SPRINT
How can analysts and developers use AI without turning their workflow into prompt roulette?
Bring me the problem, not a polished brief. A few sentences about what is happening and what you would like to change are enough to start.
Discuss team trainingPREFER TO WRITE?
Send me a message.
You do not need a polished brief. Whether the topic is Power BI, Fabric, AI, team training, mentoring, coaching or simply a challenge that needs untangling, leave a few details and we can start from there.
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