Define the problem
Turn ambiguous workflows into clear product opportunities
London Business School / Data Science & AI Initiative
Build AI products for LBS
A hands-on lab where participants turn LBS workflows into useful AI products through an 8-week batch model
Spring 2026 Batch
Nine teams are building AI products for LBS workflows through an 8-week cycle of discovery, development, evaluation, and iteration
What participants learn in the Lab
Participants develop the product, technical, and leadership skills needed to build AI products from problem discovery to launch
Understand the user
Run interviews, map needs, and identify where AI can create value
Manage stakeholders
Work with participants, alumni, clubs, staff, and partners to navigate constraints and adoption
Build with AI
Use AI-assisted tools to prototype and ship quickly
Evaluate and iterate
Test quality, learn from feedback, and improve the product
Think toward product-market fit
Move from a promising demo to something people understand, adopt, and return to
Spring 2026 Batch
Nine teams building AI products for LBS workflows
Nine teams joined the first LBS AI Lab batch to build AI products for LBS workflows
Prototype
RecruitSmart LBS
An AI-powered job search co-pilot built for LBS students.
View team PrototypeCafe Smart
Student-powered cafe and restaurant discovery around campus.
View team PrototypeEMS++
A coherent curriculum planner for MBA elective selection and timetable management.
View team PrototypeLBS Compass
A knowledge-base chatbot for incoming students navigating LBS.
View team PrototypeFundEd
A funding discovery and matching platform for prospective LBS students.
View team PrototypeBriefd
One master calendar for LBS events, jobs, and opportunities.
View team PrototypeWayfinder
A mobile guide to rooms and facilities across LBS campuses.
View team PrototypeEventOS
AI-assisted club event planning, venue sizing, and form generation.
View team PrototypeZentra
AI-generated client WIP updates and fee tracking for legal billing.
View teamMentors
Guidance from product and technical mentors
Teams are supported by product and technical mentors through build sessions, product reviews, implementation guidance, and feedback cycles
Academic lead
Academic lead
Kostis Christodoulou
Clinical Professor of Management Science and Operations; Academic Director (MBA, MAM, EMBAG)
Kostis supports the academic direction of the Lab, connecting participant teams with rigorous AI, analytics, decision-making, and product learning
Technical Mentor
Akshay Nagpal
Technical reviews, implementation choices, and AI product shipping.
Profile
Technical Mentor
Ramakrishnan Lokanathan
Agentic engineering, prototype architecture, and build velocity.
Profile
Product Mentor
Zara Mogadasi
Product discovery, user feedback loops, and clearer positioning.
ProfileAutumn 2026 Batch
Join the next batch
Autumn 2026 Batch opens in September 2026