- Summary: - The text presents IA-powered Kanban prioritization as a way to decide what work to tackle first by focusing on delivering continuous value. - Prioritization should be data-driven, using a board with statuses (Pending, In Progress, Done) and scoring tasks on impact, cost of delay, dependencies, and size to avoid low-value work. - AI is framed as a co-pilot that suggests a ranking based on expected value, urgency, risk, and other criteria, while humans validate and adjust. Daily IA-driven recommendations can guide queue-review discussions, increasing clarity while preserving human decision-making. - Before adopting IA, teams should assess their data readiness and maturity to determine how well IA can be integrated. - How IA works: define criteria (business value, customer impact, learning potential, dependencies, task size) and metrics (cost of delay, strategic alignment, delivery capacity); then apply a scoring model to produce a priority index for each task. - A practical, step-by-step starter guide: 1) Define clear, measurable success criteria and risk reduction. 2) Clean the backlog: map dependencies, remove duplicates, estimate size. 3) Create a prioritization score (e.g., value 40%, customer impact 30%, cost of delay 20%, dependency 10%). 4) Feed IA with project data and start with a two-week pilot. 5) Add an AI Priority row on the board and maintain daily ordering. 6) Conduct a short daily stand-up to validate rankings and move tasks In Progress as needed. 7) Measure results (delivery times, rework, customer satisfaction) and adjust weights/rules accordingly. - Fun fact: coupling value signals with human review yields sustained gains in speed and quality; IA speeds up conversations but humans provide clarity and context. - Concrete example: IA accounts for cost of delay and dependency, potentially elevating a high-value-large, dependent task over a seemingly simpler, independent task, leading to greater clarity and deliberate prioritization. - Practical setup suggestion: add three columns—“AI Priority,” “In Review,” and “In Progress.” Include each task’s expected value, cost of delay, size, dependencies, and target date; IA ranks, team validates, and daily decisions move tasks forward. - Audience prompts: consider whether you have a backlog ready for scoring; what would be lost by not prioritizing with IA? What value criteria and data do you currently have? - Goals: establish a clear method to start IA-powered prioritization, reduce waste, shorten delivery times, and enhance decision quality. - Closing: invites subscription and feedback for the podcast episode. - Remeber you can contact me at [email protected]