Decision Intelligence: Get alignment on outcomes, build simple models, and get value from the process

2023-05-09 · Source: Mike Talks AI · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Corporate Strategy & Leadership · Depth: Intermediate, quick

Summary

The "Decision Intelligence Lab" podcast, in its 19th episode, featured Dr. Lorien Pratt, co-founder of Quantellia and author of "The Decision Intelligence Handbook," who defined decision intelligence. Pratt's framework emphasizes solving larger problems by first aligning on desired outcomes, then mapping the specific actions or decisions required to achieve them. A key principle highlighted is the effectiveness of simple models; often, a rapid, less accurate model (e.g., 60% accuracy in one week) can provide significant value compared to lengthy efforts for higher accuracy (e.g., 98% accuracy in six months). Furthermore, the process of decision intelligence itself, particularly aligning on outcomes and actions, frequently yields substantial value, sometimes even before sophisticated models or software are implemented, as seen in half of Pratt's projects.

Key takeaway

For Product Managers evaluating new initiatives, prioritize aligning your team on clear, measurable outcomes before committing to extensive data infrastructure or complex modeling. Your focus should be on identifying the critical decisions and actions that directly impact these outcomes. Starting with simpler models can quickly reveal significant value, allowing you to iterate and refine rather than over-engineer from the outset, thus accelerating time-to-value for your product.

Key insights

Aligning on outcomes and using simple models drives significant value in decision intelligence.

Principles

• Prioritize outcome alignment for problem-solving.
• Simple models often suffice for early value.
• The decision process itself holds inherent value.

Method

Focus on desired outcomes, then map actions. Prioritize simple models for rapid insights, avoiding over-engineering with extensive data collection or complex IT projects.

In practice

• Align stakeholders on desired outcomes first.
• Develop 60% accurate models in one week.
• Frame problems with objectives, variables, constraints.

Topics

• Decision Intelligence
• Outcome Alignment
• Simple Models
• Decision-Making Process

Best for: Product Manager, Entrepreneur, Data Scientist, AI Product Manager, Consultant

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• Whose Alignment? Comparing LLM Process Alignment Across Diverse Organizational Decision Contexts — Process alignment measures how LLMs weight information like organizations, revealing policy adherence beyond mere output accuracy.
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• If you had 30 minutes with a CEO, decision factories, decision managers, KISS 2.0, and being nice — Organizations should evolve into "Decision Factories" by prioritizing actionable decisions over just models and dashboards.
• Decision-Aligned Evaluation of Uncertainty Quantification — Decision-alignment is crucial for evaluating uncertainty quantification metrics to ensure utility in downstream tasks.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Mike Talks AI.