Artificial intelligence (AI) is undermining many of our industrial age ideas – including the best ways to develop business models, investment strategies, and critical assets.  The result is that many companies are playing with an outdated rulebook, and they are being punished by capital markets for it.

The industrial age was, and remains, the foundation of business thinking for most organizations and their leaders, including Michael Porter’s 5 P’s and even some of the most notable Nobel Prize in Economic Sciences winners. The basics of how businesses and countries are managed and measured no longer bring the admiration of investors. One problematic management approach is accounting systems based on Generally Accepted Accounting Principles (GAAP), which classifies plant and property as assets, and people and intangible assets as expenses. The capital asset pricing model (CAPM) also contributes to the problem, because it defines what a capital asset is and isn’t, and assumes that investor behaviors don’t influence market prices. Companies can’t change these measurement systems by themselves, but they don’t have to use them as their only models. Read More

#ai-first, #strategy

This article provides a brief survey of the platform strategy literature and is organized around launch strategies, governance, including private ordering, and competition. A platform strategy is the mobilization of a networked business platform to expand into and operatein a given market. A business platform, in turn, is a nexus of rules and infrastructure that facilitate interactions among network users. A platform may also be viewed as a published standard,together with a governance model, that facilitates third party participation. Platforms provide building blocks that serve as the foundation for complementary products and services. They also match buyers with suppliers, who transact directly with each other using system resources and are generally subject to network effects. Examples include operating systems, game consoles, payment systems, ride sharing platforms, smart grids, healthcare networks, and social networks. Read More

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How can firms profitably give away free products? This paper provides a novel answer and articulates trade-offs in a space of information product design. We introduce a formal model of two-sided network externalities based in textbook economics—a mix of Katz and Shapiro network effects, price discrimination,and product differentiation. Externality-based complements, however, exploit a different mechanism than either tying or lock-in even as they help to explain many recent strategies such as those of firms selling operating systems, Internet browsers, games, music, and video.The model presented here argues for three simple but useful results. First, even in the absence of competition,a firm can rationally invest in a product it intends to give away into perpetuity. Second, we identify distinct markets for content providers and end consumers and show that either can be a candidate for a free good.Third, product coupling across markets can increase consumer welfare even as it increases firm profits.The model also generates testable hypotheses on the size and direction of network effects while offering insights to regulators seeking to apply antitrust law to network markets. Read More

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A new business model strategy based around intermediary platforms powered by AI/ML is promising the most direct path to fastest growth, profitability, and competitive success.  Adopting this new approach requires a deep change in mindset and is quite different from just adopting AI/ML to optimize your current operations.

As a data scientist you may be wondering why you need to be concerned about strategy and business models.  It’s simple.  Different types of AI/ML are most appropriate for different business objectives.  So whether you’re a data scientist being asked to plan and present the most appropriate portfolio of projects, or a CXO looking to support your new digital business model, you need to understand the relationship between data science and strategy. Read More

#ai-first, #strategy