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. Our research offers compelling evidence that predictive and generative AI systems can be trained to provide better choices, not just better decisions.

Machine-designed choices can — and should — empower their human counterparts. As Anjali Bhagra, physician lead and chair of the Automation Hub at Mayo Clinic, explains, “Fundamentally, what we are doing at the core, whether it’s AI, automation, or other innovative technologies, is enabling our teams to solve problems and minimize the friction within health care delivery. Our initiatives are designed by people, for the people.”

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.”1

Drawing from the rich empirical literature of behavioral economics research, Thaler and Sunstein’s work explored how specific factors (such as the number of choices, the inclusion of default choices, and the description of choices) influence decision-making. Integrating AI into the choice architecture design and production process strengthens connections between design and decisions even more. What sets AI-driven choice architectures apart are their abilities to innovatively engage with human decision makers and learn to make better recommendations and choices through feedback loops.

Intelligent choice architectures could design decision environments to nudge managers toward more innovative thinking by injecting and framing “unconventional” options. For example, when brainstorming new product features, an intelligence choice architecture might include wildcard options from unrelated industries, prompting creative idea cross-pollination.

Intelligent choice architectures could also learn to present strategic options in ways that counteract an executive committee’s risk-aversion bias. For example, an intelligent choice architecture might frame potential acquisitions as “growth opportunities” rather than “financial risks,” subtly encouraging bolder decisions.

As strategic investments in AI and human capital further entwine, forward-thinking leaders will recognize that intelligent measurement systems can improve organizational decision-making behaviors by generating novel options, predicting outcomes, and guiding choices. By designing the contexts in which key strategic and operational decisions are made, these structures will shape decision-making’s enterprise future and how leaders choose to pursue strategic aspirations.

Consequently, we argue that intelligent measurement for better choices has enormous implications for operational and strategic decision-making, productivity measurement, and organizational design.