Many leaders believe they are data-driven but are merely 'data-inspired,' using data to confirm pre-existing beliefs. True data-driven decision-making, or Decision Intelligence, requires pre-committing to a decision framework and metrics before the data is even analyzed.
There is a significant gap between the perceived value of generative AI by individual users and the ability of organizations to measure its ROI at scale. To bridge this, leaders must define what 'good' looks like and establish clear metrics for success before deploying AI tools.
AI is a powerful tool for generating options and automating tasks, but it is fundamentally a probability engine, not an objective thinker. Humans must remain in control, setting the goals, defining the values, and making the final choices, using AI as a partner rather than an oracle.
AI and automation will increasingly take over repetitive, procedural work (coined 'thunking'). The resulting challenge for management will be to redesign work to effectively cultivate and measure the creative, engaged, and innovative work ('thinking') that remains.
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