Note on AI-Augmented Value Creation in Emerging-Market Venture Capital

By Romi Bhatia, Steve Ciesinski
2026 | Case No. E969 | Length 14 pgs.
Venture capital (VC) is entering a new operating era. Silicon Valley remains at the forefront of artificial intelligence (AI) experimentation, but the use of generative and agentic AI is expanding rapidly across emerging markets and developing economies (EMDEs). This Note examines how AI is reshaping venture capital’s operating model in EMDEs and offers insights as to how VCs can design an AI-augmented value-creation system that meets the needs of tech founders in the AI era. It examines the transition from partner-dependent assistance to systemized, AI-augmented post-investment value creation. Drawing on Global Ventures, a UAE-based VC firm, and survey and interview insights from 25 emerging-market venture investors, it explores how AI can expand support capacity, change engagement cadence, and enable more proactive assistance. It considers the trade-offs between scalable, personalized support and trust, confidentiality, founder autonomy, and human judgment, while asking whether AI democratizes VC capabilities or creates new sources of differentiation. While the pace and maturity of adoption vary considerably across regions, the direction is increasingly clear: AI is changing both what VC firms can offer founders and how that support can be delivered.

Learning Objective

This note equips students and practitioners with an analytical toolkit to explain AI’s impact on post-investment value creation in emerging markets; assess trade-offs between scale, trust, and human judgment. It further challenges students to evaluate whether AI democratizes VC capabilities or strengthens differentiation. A practical checklist helps readers assess workflows, governance, data handling, founder consent, and accountability in designing an AI-augmented value creation support system. Designed to stimulate classroom discussion, the Teaching Note frames AI adoption as an operating-model design challenge: deciding what to automate, what to augment, and what to keep human-led to meet founders’ evolving needs.
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