Project Management Tips, Trends, and New Tools Podcast Por Andres Diaz arte de portada

Project Management Tips, Trends, and New Tools
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This is the podcast where Project Managers get updated. Every week we explore the latest trends, tools, methodologies, and news from the world of project management. If you lead teams, handle impossible deadlines, or simply love frameworks like Scrum, Kanban, or PMI, this is your space. Chats with experts, software analysis, productivity hacks, artificial intelligence applied to projects, and everything you need to stay one step ahead. Listen, learn, and improve your way of managing. Because in the world of projects, staying up-to-date changes everythingCopyright 2025 Andres Diaz Economía Gestión Gestión y Liderazgo
Episodios
  • Prioritization in Kanban with AI: what comes first

    Prioritization in Kanban with AI: what comes first
    Oct 8 2025
    - 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 andresdiaz@bestmanagement.org
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    6 m
  • Smart meeting minutes: decisions in minutes

    Smart meeting minutes: decisions in minutes
    Oct 1 2025
    Smart Meeting Minutes summary - Definition: Smart Meeting Minutes are minutes that record decisions with clear owners, deadlines, and success criteria, turning discussions into actionable tasks aligned with business objectives. - Key elements: Decision (what was agreed), owner (who executes), deadline (when it should be closed). Often paired with an executive summary and an actions section. - Benefits: Faster decision-making, greater accountability, better traceability and transparency, and built-in continuity between meetings. They can also integrate with project management tools to auto-create tasks. - How they work in practice: During the meeting, the minutes taker captures decisions with the three elements, a formal minutes document is generated and shared, and automated tools can extract tasks and detect dependencies. In follow-ups, action statuses are reviewed and updated. - Practical template: 1) Heading (date, time, duration, participants, absences) 2) Meeting objective 3) Decisions made 4) Actions (task, owner, due date, priority) 5) Follow-up and success criteria 6) Attachments or context - Implementation steps: 1) Use a single adaptable template for all meetings 2) Define roles (leader, minutes writer, decision validator) 3) Integrate with project/task management systems 4) Send minutes after meetings and solicit questions 5) Review progress at the start of each session - What you need to start: a smart minutes template, a system for owners and deadlines, automation/AI for summaries and task extraction (if available), and a follow-up policy. - Useful phrases: examples for decisions, assignments, due dates, and follow-up. - Common mistakes to avoid: not recording decisions leads to improvisation; no assigned owners leads to inaction; no deadlines causes endless tasks. - Audience questions: challenges in clarity, responsibility, or dates; what to automate; useful template types. - Episode goals: show how smart minutes convert debates into executable decisions, provide a step-by-step process, and offer practical tools teams can apply immediately. - Closing: invitation to subscribe, give feedback, and share. Remeber you can contact me at andresdiaz@bestmanagement.org
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    6 m
  • AI-based effort estimates for agile iterations.

    AI-based effort estimates for agile iterations.
    Sep 24 2025
    Summary: The episode explores using AI to assist with effort estimation in agile sprints. AI can turn each user story into observable signals (relative size, technical complexity, uncertainty, dependencies, risks) to predict effort in hours, days, or story points, aiming to reduce bias, speed up planning, and enable data-driven improvement. It stresses starting from existing data (delivered stories, past estimates, task durations, defects/rework) and keeping data clean and consistent. Three practical AI approaches are discussed: AI as a planning assistant, a learning model with human calibration, and a hybrid method that decomposes stories into tasks for consolidated estimates. A five-step action plan is provided: prepare the backlog and units, collect/clean data, define features, run a short pilot, and measure/calibrate. The text emphasizes data governance, privacy, and a culture of continuous improvement, noting that breaking stories into tasks helps AI capture micro-efforts and reveal hidden dependencies. Realistic expectations are that AI reduces variability and fosters informed discussion rather than delivering perfect predictions. Practical examples include decomposing a product page story into subtasks and using lightweight AI-guided planning during sprint meetings. The closing encourages experimentation, thoughtful review in retrospectives, and avoiding reliance on a single estimate. Key takeaways: - AI-assisted estimation uses signals like size, complexity, uncertainty, dependencies, and risk to predict effort. - Start with clean, historical data and track metrics such as average error, bias, and cross-team variability. - Approaches include AI as an assistant, a learn-from-history model with human calibration, or a hybrid that decomposes stories into tasks. - A practical 5-step plan: prepare backlog, clean data, define features, run a 3–5 story pilot, measure and adjust. - Success relies on data governance, privacy, human judgment, and a culture of continuous improvement; breaking down work helps AI capture micro-efforts and hidden dependencies. - Realistic use includes reduced planning variability and better sprint commitment; monitor for over-optimism and adjust accordingly. - AI-guided planning during sprint meetings can provide context-rich estimates that teams validate. Remeber you can contact me at andresdiaz@bestmanagement.org
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    7 m
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