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Summary
This is part 3 of a 3-part series on prioritization, led by Harry Max, author of Managing Priorities: How to Create Better Plans and Make Smarter Decisions. Part 1 | Part 2 As the hype of Generative AI starts to give way and unprecedented new capabilities go mainstream; prioritization will become both easier and harder. It will become significantly easier because you can converse with a chat agent who can wrangle questions about potential priorities in insanely powerful ways and respond seemingly authoritatively. With access to a vast selection of sorting techniques, frameworks, marketplace simulations, hybrid methods, and other relevant information, AI-enabled solutions will augment our ability to prioritize. But this will put pressure on us as humans to provide the guiding values, ethics, situational awareness, and other information to guide the AI conversation to a productive and sustainable end. The conversation with Former Engineering SVP Mark Interrante will explore the immense power of GenAI to fuel a revolution in prioritization and our ability to create better plans and make smarter decisions.
Key Insights
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Prioritization is a highly complex, political, and context-aware process not easily reducible to algorithmic solutions.
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Generative AI acts effectively as a productivity accelerant by automating lower-level, detail-oriented tasks rather than providing final prioritization decisions.
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AI playbooks codify organizational heuristics and routines, enabling AI agents to assist with pattern matching and diagnostics in prioritization workflows.
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Human judgment remains essential to interpret AI outputs, especially for evaluating outliers and adjusting priorities dynamically.
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Multiple data sources (e.g., customer feedback, sales input, operational data) are best handled separately with their own criteria before integrated prioritization.
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AI can function as a conversational partner or sounding board, helping teams clarify and iterate on prioritization criteria.
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Privacy-sensitive organizations mitigate data risks by deploying open-source generative AI in isolated environments with preprocessing for anonymization.
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Effective prioritization requires stakeholders to align on criteria, a fundamentally political and social step that AI does not currently automate.
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Current AI tools do not fully replace the need for situational awareness, trade-offs, and high-level abstraction in prioritization.
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The analogy of AI as a highly educated intern highlights its role as a smart assistant that must be overseen by experienced humans.
Notable Quotes
"Nobody's gonna listen to you people until you start speaking in the language of business."
"Prioritization work is a political process, and politics are hard to do in technology."
"Generative AI is not the magic eight ball; think of it more as an accelerant for getting tasks done."
"We distinguish between agents doing internal system tasks and assistants interacting directly with users."
"The key is to write down clear criteria and then hand those to the AI to help sort and prioritize."
"It's an iterative process; you check the AI's work and adjust your criteria accordingly."
"We think of AI as a set of interns — smart, well-educated, but still need human guidance."
"Privacy-sensitive companies are deploying hosted open source AI models within their own cloud infrastructure."
"Prioritization tooling is going to follow the path of oversimplification before it adapts to complexity."
"AI can summarize and sift large amounts of customer feedback, helping product managers identify patterns and anomalies quickly."
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