Concepts
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Predictive models are often rejected not because they underperform, but because leaders mistake understanding for control. The fear of a “black box” isn’t really about opacity—it’s about hesitation to trust a system when outcomes feel uncertain. Demanding full transparency can create the illusion of influence while slowing decisions and diluting accountability. What executives actually need…
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Analytics should not ask executives to “choose their own adventure.” While exploratory analysis is valuable, open-ended insights without judgment create ambiguity, not clarity. Leaders don’t need more scenarios—they need guidance on which path to take and why. Decision-driven analytics reframes analysis around the decision at hand, explicitly weighing trade-offs, risks, and expected impact. The result…
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Strategy maps and balanced scorecards are powerful tools—but they’re not causal models. They encode assumptions about how value is created, yet most organizations stop at measurement instead of validation. In this post, I explore why decision-driven analytics is the missing link between strategy frameworks and real business impact—and how it helps insights finally get past…
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This post explores how Expected Pipeline Value—a probability-driven, risk-adjusted view of the pipeline—transforms B2B sales pipeline management by applying Decision Science principles to evaluate both deal probability and deal value. By moving beyond simple deal counts and nominal (face) value, this approach delivers a clearer view of pipeline quality, improves resource allocation, strengthens forecast credibility,…
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AI has leveled the playing field of analysis. Every enterprise can now generate models, dashboards, and insights at scale. But when analytic power becomes universal, advantage shifts elsewhere—to how well organizations decide with it. In the GenAI era, success will hinge less on analytic sophistication and more on decision capacity: the ability to frame options,…
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AI doesn’t erase human judgment—it exposes its importance. As AI streamlines analysis, advantage shifts upstream to decision capacity—how well organizations make choices. Decision Scientists connect AI-powered insights to strategy by clarifying options, quantifying trade-offs, and closing the loop with evidence and feedback. When sophisticated insights are widely available, the winners are those who decide confidently…
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Every organization faces hidden bias in its decision-making—from confirmation bias and groupthink to sunk-cost fallacy. This post includes a practical table of the most common cognitive biases and how to counter them, briefly outlining how a Decision Science approach builds stronger processes that lead to clearer, smarter, and more reliable outcomes.
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Executive Takeaway: Markov models matter because they don’t just show where customers or deals are today—they reveal how groups are likely to move tomorrow—toward growth, retention, or churn. They reveal not just the what (likelihood of churn, revenue forecast) but the pathways—how customers and deals actually flow over time. That creates runway for action, giving…
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The way we do data science has changed forever. GenAI makes coding frictionless—but without structure it gets messy and invites slippage down rabbit holes. I’ve been refining CRISP-AI, a lightweight process (inspired by CRISP-DM) to work smarter with AI.
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My latest post discusses optimizing Marketing spend using reinforcement learning (RL) and an LLM as a meta-controller—an approach we term Agentic-AI. It details creating a custom RL environment for managing multiple Marketing channels and describes how the LLM meta-controller orchestrates the overall learning process. Rather than making spend decisions directly, the LLM guides iterative training,…