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As enterprises accelerate investments in AI, many find themselves locked into rigid architectures, processes, and cost structures that constrain future adaptability, a phenomenon also known as “putty-clay” investment in economics. In this article I explore how the putty-clay dynamic manifests in enterprise AI, why it undermines long-term value creation, and how organizations can design AI strategies that preserve flexibility, resilience, and optionality over time.
Modern enterprises operate in environments where value, risk, and opportunity are expressed not only numerically but also through text, images, audio, video, sensor streams, and complex relational structures. A multimodal AI data strategy recognizes this reality and provides the foundation for building AI systems that reflect how organizations operate, make decisions, and compete.
Enterprises increasingly face complex, dynamic decision environments characterized by strategic interaction, feedback loops, uncertainty, and nonlinear outcomes. Traditional analytics and machine learning approaches, which are primarily optimized for prediction under stable conditions, often struggle to provide actionable guidance in such settings. A powerful alternative emerges from combining three complementary methodologies: Agent-Based Modeling (ABM), Reinforcement Learning (RL), and Causal Modeling.
AI is often framed as a technological breakthrough, but its true significance lies in its economic implications. The business value of AI, therefore, depends less on model sophistication and more on how firms integrate AI into decision-making, organizational design, and capital allocation. This white paper examines the economic channels through which AI creates or fails to deliver business value, emphasizing productivity, scale, competition, and risk.
AI is widely expected to generate significant productivity gains across industries by reducing costs, improving decision-making, and increasing labor efficiency. At the same time, the global economy is wrestling with excess productive capacity, weak demand growth, demographic headwinds, and persistent disinflationary pressures. This raises a critical question: Will AI-driven productivity gains amplify deflationary forces in an already supply-heavy world, or can they catalyze new sources of demand, growth, and price stability?
Artificial intelligence is becoming a critical determinant of economic outcomes, public services, and individual lives, elevating governance as a central challenge for governments and enterprises worldwide. AI governance now sits at the crossroads of innovation policy, economic competitiveness, risk management, ethics, and national security. While jurisdictions differ in their regulatory philosophies and institutional approaches, a common theme has emerged: existing legal, organizational, and oversight frameworks are insufficient to manage the scale, speed, and systemic impact of AI. In this note, I survey the current state of global AI governance, identify key areas of convergence and divergence, and outline likely future directions for policy and enterprise practice.
As artificial intelligence is increasingly relied on to inform high-stakes decisions across business, finance, healthcare, and government, the ability to understand and explain how it operates has become a critical concern. Explainable Artificial Intelligence (XAI) refers to a set of methods, practices, and governance approaches that make AI systems’ outputs interpretable to humans. From an enterprise perspective, explainability is not merely a technical feature or regulatory requirement; it is a foundational capability that enables trust, accountability, risk management, and sustained value creation. This white paper examines what explainable AI is, why it matters, and how it fits into modern enterprise AI strategy.
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