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 everything
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Prioritization in Kanban with AI: what comes first
Wednesday, October 8, 2025 • Duration 05:31
- 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
Smart meeting minutes: decisions in minutes
Wednesday, October 1, 2025 • Duration 05:56
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
AI-based effort estimates for agile iterations.
Wednesday, September 24, 2025 • Duration 06:47
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
OKRs for projects: align teams and results
Wednesday, September 17, 2025 • Duration 07:37
Summary:
- The episode explains OKRs (Objectives and Key Results) as a tool to align teams and outcomes in projects, emphasizing clarity and measurable progress over simply tracking tasks.
- OKRs act as both a compass (objective) and a dashboard (key results). A good OKR set uses 3–4 well-chosen key results that are specific, measurable, and verifiable.
- Core steps:
1) Define a concrete, inspiring objective for the next 12 weeks.
2) Design 3–5 key results that prove progress toward that objective.
3) Assign owners and cadence, with short weekly check-ins to discuss progress and blockers.
4) Align with the team and stakeholders using a visible board and regular explanations of how each KR drives the objective.
5) Measure, learn, and adjust with weekly reviews and a formal quarterly review; adapt plans or goals as needed.
- The text provides practical examples, tips for maintaining focus, and exercises to apply in meetings, along with a call to share examples and feedback.
- It emphasizes that when tasks are linked to a shared objective, motivation increases and unnecessary meetings decrease, supporting continuous improvement.
Remeber you can contact me at
andresdiaz@bestmanagement.org
How artificial intelligence facilitates risk management in projects
Wednesday, July 16, 2025 • Duration 05:48
This episode discusses how artificial intelligence (AI) is transforming risk management in projects by enabling proactive problem prediction and decision-making. Andrés Díaz explains that project risk management involves identifying, analyzing, and responding to potential issues like delays or budget overruns, a process traditionally challenging in complex projects. AI, with its ability to learn from data, recognize patterns, and predict risks more accurately, serves as a smart assistant that automates risk detection, reduces errors, and accelerates responses.
Practical steps for integrating AI include gathering data, exploring AI tools with features like predictive analysis and anomaly detection, training teams to interpret results, and continuously monitoring risks. Companies investing in AI have seen significant benefits, such as up to a 30% reduction in losses, turning chaos into control. However, successful implementation requires investment and a blend of AI with human judgment to avoid errors.
The episode encourages listeners to adopt AI technologies to stay competitive, improve project safety, and turn risks into opportunities. It also suggests reviewing past projects for potential early risk detection with AI and learning from successful case studies. Ultimately, AI empowers proactive management, transforming traditional risk handling into a smarter, more efficient process.
Remember you can contact me at andresdiaz@bestmanagement.org
Artificial intelligence tools for automating reports
Wednesday, June 18, 2025 • Duration 05:31
This episode, hosted by Andrés Díaz, explores how AI tools are transforming report automation in project management. These tools leverage machine learning and natural language processing to analyze data and generate clear, accurate reports quickly, saving time and reducing manual effort. They can integrate with platforms like Jira, Trello, or Asana, offering real-time dashboards and customizable reports tailored to different stakeholders. Implementing AI for report automation enhances decision-making by identifying patterns, predicting risks, and providing up-to-date insights, which can improve organizational efficiency by up to 40%. To effectively adopt these tools, it's important to identify key reporting tasks, select compatible solutions, train teams properly, and start with small-scale implementations. While AI can't replace human intuition, it serves as a valuable assistant for making proactive decisions and maintaining competitiveness. Ensuring high-quality data and evaluating costs are crucial steps. Overall, AI-driven report automation offers significant benefits, including time savings, improved accuracy, and better strategic insights, making it an essential tool for modern project management.
Remember you can contact me at
andresdiaz@bestmanagement.org
How is artificial intelligence transforming the planning of your projects?
Friday, June 6, 2025 • Duration 05:45
This episode, hosted by Andrés Díaz, explores how artificial intelligence (AI) is transforming project planning and management. AI is already revolutionizing the field by enhancing efficiency, accuracy, and speed through early risk detection, predictive analysis, and resource optimization. Díaz explains that AI involves machines learning and reasoning like humans and emphasizes its accessibility to everyone with basic knowledge.
AI's relevance lies in its ability to accurately forecast project timelines and costs by analyzing data and trends, allowing managers to address issues proactively. To get started, he recommends using AI-enabled project management platforms like Monday, Asana, or Trello, and suggests beginning with simple tools such as virtual assistants. Creating a roadmap for integrating AI involves identifying key processes, researching suitable platforms, and training teams.
Díaz highlights AI's capacity to learn and improve over time, aiding in sentiment analysis, obstacle detection, and decision-making support. He clarifies that AI is designed to complement human judgment, not replace project managers, and that companies using AI see significant efficiency gains. Additional tools like Power BI and chatbots can further enhance project outcomes.
He encourages listeners to leverage AI's potential by taking small, experimental steps, emphasizing that failures are part of innovation. Embracing AI can lead to more predictable, cost-effective, and high-quality projects, giving users a competitive edge. Díaz concludes by urging everyone to start integrating AI into their workflows now, as the technology is here to stay.
Remember you can contact me at andresdiaz@bestmanagement.org
