AI

Organizations are adopting AI tools faster than they can tell which of their people will actually use them. This project asks whether structured executive interviews, transcribed and coded, predict which employees go on to adopt AI in their work, and whether leadership experience with transformation, technology, and scaling, observable from career histories alone, bears on an organization’s capacity to direct that adoption. The setting is an AI assessment platform used by private equity firms, in which structured interview transcripts and career records are linked to the organizations those executives lead. The aim is a measurement approach that treats AI adoption as a predictable consequence of what an interview reveals about a person and a team, rather than as a matter of tools or training alone.

Paper available upon request.

In FUND II, teams use AI to build companies. Over ten sessions they choose an idea, map the assumptions that matter most, validate demand with real customers, build a prototype, negotiate founder and financing terms, and pitch the venture, with AI in the loop at every step: ideation, interview preparation, synthetic-persona rehearsal before real customer conversations, prototyping, and pitch refinement. The aim is not to pretend that a validated company can be built in ten sessions but to practice the disciplined behaviors of entrepreneurship, now that the cost of building has collapsed and the scarce skill is knowing what to build and for whom.

We designed the course from scratch for a world in which the tools available to a founding team have changed completely. AI does not replace entrepreneurial judgment, customer evidence, faculty judgment, peer critique, or team accountability; it raises the bar on all of them. A purpose-built course agent, available by voice and text, guides teams through each deliverable by asking them to make decisions, state assumptions, justify evidence, and resolve tradeoffs, gives feedback before submission, and surfaces patterns across the class for reflection sessions built around how the teams actually decided.

FUND I introduces the core logic of entrepreneurial judgment. Its premise is that entrepreneurial management is not only for founders: general managers, investors, joiners, and corporate leaders all have to evaluate uncertain opportunities and mobilize resources before the answer is obvious. The course opens the black box of entrepreneurship. Where do opportunities come from? How does one move from broad market change to a specific customer problem? What makes a business model credible? What does it mean to pivot, or to redeploy resources, when reality does not match the plan?

Students move through opportunity identification, value proposition, business model design, assumption testing, opportunity evaluation, product-market fit, pivoting, and corporate entrepreneurship, building a single opportunity analysis across six short assignments. A course AI agent supports the work with formative feedback before submission. The final assessment is deliberately different: an individual exam, completed without generative AI, in which students evaluate and commercialize a given opportunity. AI improves practice during the course; the exam tests independent judgment.

New Venture Creation is the integrative capstone course in entrepreneurship for the Master in Management. It rests on the insight that entrepreneurial management skills are now essential for general managers and entrepreneurs alike, and it asks students to synthesize what they have learned across the program and apply it to building a new business. The course has two objectives: to help students understand what a business opportunity is and how it fits their own personal and professional situation, and to help them turn an idea into a revenue-generating business, whether in a startup, a corporate setting, or a family firm.

The material follows the entrepreneurial process through cases: identifying opportunities, assembling the team, raising money, testing the business model, and scaling. Teams develop and present a venture concept of their own, and the course includes a startup event that puts students in front of practicing founders. By the end, students should be able to generate and evaluate ideas, design a value-creating business model, prioritize and test the assumptions behind it, anticipate resource constraints, stage the building of a business sensibly, and sell the idea to the people whose support it needs.

Analysis of Business Problems in the MBA and Critical Business Thinking in the Master in Management are the same course: the case-method course at the core of both programs, and for many students their introduction to the case method itself. Its premise is that in an era when AI can produce an instant answer to almost any question, the ability to think critically, exercise judgment, and decide has become more valuable, not less. Most business problems are unstructured: they mix economic, technical, and human issues, and they have no single correct solution. Working through them is the core of what managers actually do.

Each session is a different real problem in a different company and industry, worked through a structured methodology: diagnose the problem, establish the criteria that should govern the decision, generate the real alternatives, analyze, choose, and build an action plan naming the people who will carry it out. The course also trains three complementary perspectives on any situation, economic, organizational, and personal. The final assessment is an individual report on a case the student has never seen, written under time pressure in the last session, because that is the condition under which managers usually have to decide. An AI teaching assistant built around the methodology helps students prepare and improve their own notes without supplying answers.

The company builds AI assessment software for private equity firms. Its product predicts executive performance from structured interview data and guides interviewers toward the follow-up questions that matter. The models sit on a matrix of outcomes and evidence, from career histories through short and long interview transcripts, each with its own validation protocol, and the product is in beta with clients.

I lead all of the modeling work: measurement design, model development, validation, and research and development.

FaceX built face-recognition authentication. Its product was a hashed facial-token sign-in, deployed for ticketless entry at football stadiums and in customer projects across a range of industries. The company was revenue-generating, with a team of ten at its largest.

I co-founded the company, designed and co-developed the facial-token sign-in, led product and engineering, and as interim CEO led the pre-seed fundraising.

Random Ltd was a venture-backed startup building a predictive discovery engine for online content, among the first companies to build recommendation algorithms of the kind that now underlie most content platforms.

I joined as employee number seven, in a product role.