The MMC Ventures Artificial Intelligence Investment Framework

MMC is pleased to announce the release of our Investment Framework for investing in early-stage artificial intelligence, with a focus on companies using machine learning. Written by MMC head of research David Kelnar and published on the MMC Writes blog, the framework captures the specific considerations the MMC team makes when evaluating investment opportunities in this space. 

“Artificial intelligence?—?specifically, machine learning (ML)?—?is a powerful ‘enabling technology’ that represents a paradigm shift in software capability. But how do we, as investors, evaluate early stage software companies that put ML at the heart of their value proposition? Below, we introduce our ML Investment Framework.

Our Framework captures 17 success factors for early stage ML companies. Because sizeable returns stem from a company’s potential for value creation, effective value realisation, and defensibility, we group the success factors into these three categories. Using an alternative lens, the 17 factors span six competencies: strategy, technology, data, people, execution and capital. Informing, but not dictating, our discussions with ML companies, our Framework also provides a blueprint for supporting the ML companies in which we invest.”

In addition to the usual factors we consider when meeting a startup, when evaluating ML companies there are additional factors we consider, and some traditional considerations on which we place more emphasis. Additional factors, such as the suitability of ML to solve a problem, and the scope for network effects through data, reflect particular characteristics of ML. Traditional points on which we place greater emphasis, such as quantifiability of ROI and commerciality of management teams, reflect the dynamics of the ML market we have observed in meetings with 90 UK ML startups. No company will be strong in all areas, and success factors differ in their relative importance.

Click here to read the full framework 

machine learning framework

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