Product Mentor
Zahra Mogadasi
Product discovery, user feedback loops, and clearer positioning.
ProfileCurrent batch · Fall 2026
Fall 2026 projects
From course preparation and club expenses to finding a home in London, this batch is developing tools around the tasks students face every day.
Fall 2026 projects
Selected teams · Projects in development
In development
1MBA
MBA course preparation in one place.
View team In developmentLBSOne
One interface for everyday LBS services.
View team In developmentOff-Campus Compass
LBS guidance for students moving to London.
View team In developmentRA1
Club expenses from submission to reimbursement.
View team In developmentVishmay
Find flatmates and connect with lease handovers.
View team In developmentBreadcrumb
Discover London events and people to go with.
View team In developmentThe Key for Sussex
Keep applications, deadlines and coffee chats together.
View team In developmentRoboTrade
Company research with sources attached.
View team In developmentSimply AI
Find rooms, facilities and step free campus routes.
View teamProgramme
From discovery to a working product
Ten lab weeks, from October 2026 to January 2027, with breaks around the LBS calendar.
Programme outline from the Fall 2026 opening session. Session times and locations will be shared by the programme team.
| Lab week | Dates | Focus and tasks | Workshop / session |
|---|---|---|---|
| 1 | 12–16 Oct 2026 | Discover: problem, users and stakeholders | Teams present their ideas |
| Break | 19–23 Oct | LBS block week | — |
| 2 | 26–30 Oct | Validate: research, evidence and constraints | AI Product Management |
| 3 | 2–6 Nov | Design: solution design and prototyping | Build with AI: big picture & fundamentals |
| 4 | 9–13 Nov | Build: first working product and data access | Build with AI: hands-on |
| 5 | 16–20 Nov | Build: working MVP and midpoint review | Midpoint Session |
| 6 | 23–27 Nov | Test: at least 3 real users and collect test inputs | — |
| Break | 30 Nov–1 Jan | LBS block week, then December break | — |
| 7 | 4–8 Jan 2027 | Evaluate: test set, scores and failure log | AI Evaluation & Testing |
| 8 | 11–15 Jan | Iterate: top fixes, rerun tests and final build | — |
| 9 | 18–22 Jan | Demo: rehearse, gather feedback and fine-tune | Presentation Skills and Showcase rehearsal |
| 10 | 25–29 Jan | Present: finished product and live demo | Final presentation day |
Fall 2026 mentors
Product Mentor
Tiffanie Terry
Tiffanie is a product leader with nearly 20 years in consulting and product strategy. She has built and launched digital products for brands like PayPal and Harley-Davidson, and has led and scaled high-growth product teams. She'll help pressure test your idea to clearly define the problem, users, and strategy to build an MLP (Minimum Lovable Product).
Profile
Technical Mentor
Akshay Nagpal
Akshay leads product at a Series A startup in London, building AI-native consumer products. An Oxford MBA graduate, his background spans product management, data engineering and machine learning, with experience at ING Bank and Nokia. He’ll help teams sharpen their product ideas, design agentic AI workflows and turn early concepts into working products people love.
Profile
Technical Mentor
Alessandro Condorelli
Alessandro is an AI Developer at London Business School, where he builds agentic AI systems and works on applying emerging technologies to real business problems. His background spans AI, analytics, finance, and entrepreneurship, with experience building solutions for organizations including LBS and Harrods. He can help teams turn early ideas into practical solutions, particularly around AI, data, and product development.
Profile
Technical Mentor
Ivan Ante
Former investment manager who led a US$2bn+ multi-asset team and founded two education companies, Flosendo and Eden. Ivan helps teams decide what to build, choose AI tools and test their product.
Profile
Product Mentor
Shreyas Parbat
LBS MBA candidate with around five years in AI products, from data science at Grab to AI automation at an early-stage London startup. Shreyas helps teams develop their AI product, run simple experiments and learn from users.
ProfilePrevious batch
Spring 2026
Explore the first cohort’s projects, team profiles and prototypes.