Informed i’s Weekly Business Insights
Extractive summaries and key takeaways from the articles carefully curated from TOP TEN BUSINESS MAGAZINES to promote informed business decision-making | Since 2017 | Week 372, October 25-31, 2024 | Archive
Intelligent Choices Reshape Decision-Making and Productivity
By Michael Schrage and David Kiron | MIT Sloan Management Review | October 29, 2024
3 key takeaways from the article
- Profitably thriving through market disruptions demands that executives recognize that better decisions aren’t enough — they need better choices. Choices are the raw material of decision-making; without diverse, detailed, and high-quality options, even the best decision-making processes underperform. Traditional dashboards and scorecards defined by legacy accounting and compliance imperatives reliably measure progress but can’t generate the insights or foresight needed to create superior choices. They weren’t designed for that. Generative AI and predictive systems are.
- Generative AI and predictive systems can surface hidden options, highlight overlooked interdependencies, and suggest novel pathways to success. These intelligent systems and agents don’t just support better decisions — they inspire them. As greater speed to market and adaptability rule, AI-enhanced measurement systems increasingly enable executives to better anticipate, adapt to, and outmaneuver the competition. Predictive and generative AI systems can be trained to provide better choices, not just better decisions.
- Leaders, managers, and associates at all levels can use intelligent systems — rooted in sophisticated data analysis, synthesis, and pattern recognition — to cocreate intelligent choice architectures that prompt better options that in turn lead to better decisions that deliver better outcomes.
(Copyright lies with the publisher)
Topics: Decision-making, Nudging, Generative AI, Technology & Humans
show moreExtractive Summary of the Article | Read | Listen
Profitably thriving through market disruptions demands that executives recognize that better decisions aren’t enough — they need better choices. Choices are the raw material of decision-making; without diverse, detailed, and high-quality options, even the best decision-making processes underperform. Traditional dashboards and scorecards defined by legacy accounting and compliance imperatives reliably measure progress but can’t generate the insights or foresight needed to create superior choices. They weren’t designed for that.
Generative AI and predictive systems are. They can surface hidden options, highlight overlooked interdependencies, and suggest novel pathways to success. These intelligent systems and agents don’t just support better decisions — they inspire them. As greater speed to market and adaptability rule, AI-enhanced measurement systems increasingly enable executives to better anticipate, adapt to, and outmaneuver the competition. The authors’ research offers compelling evidence that predictive and generative AI systems can be trained to provide better choices, not just better decisions.
Leaders, managers, and associates at all levels can use intelligent systems — rooted in sophisticated data analysis, synthesis, and pattern recognition — to cocreate intelligent choice architectures that prompt better options that in turn lead to better decisions that deliver better outcomes. Coined by Nobel Prize-winning economist Richard Thaler and legal scholar Cass Sunstein in their book, Nudge: Improving Decisions About Health, Wealth, and Happiness, the term choice architectures refers to the practice of influencing a choice by intentionally “organizing the context in which people make decisions.”
Translating AI-enabled measurement capabilities into actionable insights, nudges, and options effectively creates dynamic intelligent choice architectures. These structures embed AI insights into decision frameworks and flow; deliver choices that are personalized, predictive, and ethically aware; and help users align decisions with enterprise goals.
When weighing choice architecture options, leaders should explicitly consider how these systems would engage with users and connect them with the job or task for which they are best suited. These are some options for intelligent choice architectures: should nudge algorithms for optimal decisions, can create personalized decision environments, create predictive choice modeling, AI can manage complexity, and can inform decision-making with ethical considerations.
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