Applied AI Lab · AIML-972
Fuzzy problem in. Working AI out.
Companies are standing up brand-new roles to put AI to work, and hunting for people who can turn a vague business problem into a product that ships. The Applied AI Lab is where you learn to be that person, by doing it, in ten weeks, for a real company.
- 10
- weeks, scope to production
- 1
- working product per team
- 5
- frameworks you keep
- 0
- slide decks delivered
Why it exists
Every company wants AI in production. Almost none have the people who can get it there.
The gap isn't models, it's translation: turning a fuzzy business problem into something that scopes, builds, evaluates, and ships. That's a new kind of leader, and it's a skill you build by shipping, not by studying.
Small cross-functional teams
MBAs and McCormick engineers, working the way real product teams do.
A real company partner
One real problem, one real organization, one real deadline.
A product, not a deck
Teams deliver a working AI product the partner keeps using after week ten.
The ten-week rhythm
One framework a week. A shipped product by the end.
Each week pairs a 90-minute instructor-led session on one transferable framework with several hours of team-led build and direct time with your client. Learn it Monday, apply it to a live problem by Friday.
Process mapping
Find where the work actually breaks, before you build anything.
Problem quantification
Put a number on the pain, and on the payoff.
Eval-driven development
Define what “good” means before you build, then measure it.
Stakeholder sequencing
Get the right yeses in the right order.
Production handoff
Leave it running inside the org, not sitting in a deck.
You leave with two things.
A portable framework that works on the next problem at any company, and a deployed artifact, a real AI product, live inside a real organization.
Proof
What teams have already shipped
Real products, built for real partners, still in use.
Intro-call researcher
Reps spent ~15 minutes hand-researching every prospect across CRM, LinkedIn, and the web (~34 hours a month). This agent compiles a full pre-call briefing from internal and web sources in about a minute, 93% less prep time and ~32 staff-hours back every month.
Inbound-lead qualifier
Reps hand-qualified every inbound demo request (~33 hours a month). This agent enriches and scores each lead against the ideal-customer profile and posts a decision into Slack, ~27 rep-hours freed every month at 95%+ accuracy.
Legal document drafter
A lean legal team hand-drafted NDAs, addendums, and standard agreements (~5 hours a month on template work). This agent auto-drafts four document types, enriches missing company data, files each in the right place, and routes only true exceptions to legal, under a minute and ~$0.12 a doc, with review cut from ~5 hrs/month to under 30 minutes and 0% misfiled.
Where this leads
The roles this prepares you for
A live look at AI roles companies are hiring for right now.
AI Product Manager
ProductEnterprise SaaS (Series C)
Chicago, ILFull-timeApplied AI Lead
StrategyGlobal CPG
Remote (US)Full-timeGenAI Solutions Engineer
EngineeringHealthcare AI
Boston, MAFull-timeDirector, AI Transformation
OperationsFortune 500 Retail
Minneapolis, MNFull-timeAI Strategy Associate
StrategyManagement consulting
New York, NYFull-timeML Product Management Intern
ProductFintech
San Francisco, CAInternship
Alumni, in their words
Where builders end up
I shipped a real product to a real client in ten weeks. That artifact, and the way I talked about it, got me the offer.
Maya R. · AI Product Manager
an enterprise SaaS company · Kellogg ’25
The frameworks are the part that stuck. I still run process mapping and eval-driven development on every problem we take.
Daniel K. · Co-founder
an AI legal-ops startup · Kellogg ’24
I came in able to talk about AI. I left able to scope it, quantify it, and hand it off to production, that's a different job.
Sofia L. · AI Strategy Lead
a global retailer · Kellogg ’25
The thinking behind it
From Kellogg Insight
The research the frameworks are built on, made legible to managers.
How pricing copilots change the way managers defend decisions
Managers don't just need a price, they need to justify it. AI copilots shift the work from setting numbers to arguing for them.
OperationsAgents in the enterprise: what shipped, and what stuck
Most agent demos die in production. The ones that stick share three traits, narrow scope, the client's own data, and a human in the loop.
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Get involved
Upcoming sessions & events
Faculty roundtable: what agents change for managers
Applied AI Lab Showcase, Q3 cohort demos
Fall Build Sprint: ship an AI agent in a weekend
Synced from the Kellogg AI Club on Campus Groups
Come build the thing you’ll be hired to build.
Students join a team. Companies bring a problem. Both ship something real in ten weeks.