Enterprise AI Analytics for Data Leaders
Hai Guan
Co-founder @ AI Analyst Lab
Shane Butler
Co-founder @ AI Analyst Lab
Don't let AI analytics turn your data team into the QA department
Most AI analytics rollouts start the same way. Someone connects an agent to the warehouse and announces self-serve. Week one is exciting. By week three, five teams have five retention numbers, every answer gets forwarded to the data team with "is this right?", and your analysts spend their week checking work they didn't do.
The model was fine. What was missing was everything around it: modeled tables, written definitions, tests with known answers, and people who own them. In one vendor benchmark, the same model went from 54% to 100% accuracy once the definitions and business rules were written down.
That work is organizational, so it lands on you. Who owns the definition of an active user? Who approves a KPI change? What do you tell legal, security, and finance? What do you promise your exec at day 30, and what would make you stop?
This workshop is three hours on those questions. No code, no setup. We cover what enterprise AI analytics takes, the myths that sink it, how to govern metrics as a standing process, how to judge the tools you already own, and how to set a strategy with milestones your leadership will believe.
You leave with a 90/180-day plan you started in the room.
What you’ll learn
Go from scattered AI experiments to a written plan, a governance process, and answers for every team that will push back.
See the four parts behind every "ask your data" product: definitions, an agent, access control, and tests
Spot the six myths that sink rollouts, starting with "connect it to the warehouse and you're done"
Set strict and flexible tiers so only the numbers that matter go through approval
Leave with a RACI for who defines, approves, and implements metric and KPI changes
Design a simple way for anyone to flag a wrong answer and get the context fixed
Place your first domain on a four-rung ladder, from raw tables up to a semantic layer with tests
Score your current stack on control of evals, retrieval, and access before you buy anything new
Fill in a one-page strategy: why, where first, who, what you fund, how you'll know, and your stop rule
Set expectations with three honest statements before anyone sees a demo
Set exit criteria for each phase, so a milestone is a result and not just a date
Map what runs in parallel and what has to wait, including the security review most teams start too late
Name the failures that would hurt most, how you'd notice each one, and what you'd do
Rehearse the questions legal, security, finance, and skeptical execs will ask, with answers
Pick your champion teams and the order to win people over
Workshop agenda
Where are you
Place your company on two lines: from "yolo mode" to "lockdown," and from nobody using AI to a company platform. Then the last thing your CEO said about AI.
What enterprise AI analytics actually is
Anyone asks a data question where they work and gets a vetted, governed answer. The four parts behind every product, and why one analyst and a whole company need different things.
The big myth, and what a failed rollout looks like
A live demo of the same question answered on raw tables and on written definitions. Then the six myths that sink rollouts, and how the data team ends up doing QA.
Context and evals, and why now is the time to write things down
Context is what the agent knows; evals tell you if it works. Why documentation finally pays off, and how it turns into people and process work. Short break after.
Governing metrics and context
Strict and flexible tiers, the roles, and a RACI for who defines, approves, and implements changes. Plus a simple flow for anyone to flag a wrong answer and get it fixed.
Buy vs build, starting from what you own
Most stacks already ship an agent. A checklist for whether yours gives you control of evals, retrieval, and access, and when it's time to buy something new.
Strategy, expectations, and your roadmap
Write a one-page strategy, check your data foundations, set expectations, and build milestones with exit criteria. Then map dependencies and name the failures that would hurt most.
The pushback playbook
The questions legal, security, finance, and skeptical execs will ask, and how to answer them. Who to win over first, and how to pick champion teams.
Your first 30 days
Type your first 30-day commitment and your stop rule. Then what happens next, including office hours two weeks later.
Learn directly from Hai & Shane
Hai Guan
Co-founder @ AI Analyst Lab | Head of Data | Ex-LinkedIn, Pinterest, Meta
Shane Butler
Co-founder at AI Analyst Lab | Ex-Stripe, Nextdoor, PwC, Appfolio
Who this workshop is for
Heads of data asked to "do something with AI." You own the numbers, so you'll own the wrong answers. You want a plan before a pilot.
Analytics managers whose team already checks AI answers. You need definitions, owners, and a process to stop the "is this right?" pings.
CTOs and engineering leaders weighing AI data tools. You want to know what to control, what must exist first, and when to buy new.
What's included
Live sessions
Learn directly from Hai Guan & Shane Butler in a real-time, interactive format.
Lifetime access
Go back to course content and recordings whenever you need to.
Community of peers
Stay accountable and share insights with like-minded professionals.
Certificate of completion
Share your new skills with your employer or on LinkedIn.
Live 3-hour workshop, plus the recording
Teaching, live examples, and five prompts where you work on your own company. Everyone who enrolls gets the recording.
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