PMBOK 7.0 - Study Group

Many projects don’t actually fail during ex*****on. In many cases, the problems start much earlier — during the planning stage.

I’ve seen projects where the plan looked perfectly structured on paper. Timelines were defined, tasks were listed, and everything seemed organized. But once ex*****on started, gaps slowly began to appear — unclear ownership, hidden dependencies, unrealistic timelines, or stakeholders expecting different outcomes.

Over time, I’ve realized that the difference between a project that runs smoothly and one that constantly struggles often becomes visible before the first task even begins.

That’s why, before finalizing a project plan, it helps to pause and ask a few simple but important questions. Questions that test whether the plan is truly ready for ex*****on — not just well documented.

I’ve put together 12 practical questions that I believe every Project Manager should ask before moving a project into ex*****on. These questions help uncover gaps in scope clarity, stakeholder alignment, resources, risks, and overall readiness.

Because a strong project plan shouldn’t just look complete — it should be realistic, aligned, and executable.

I’m curious — which of these do you think is most often overlooked in projects? Or is there another check you personally apply before approving a project plan?

Project Management is 10% managing tasks and 90% managing the conversations that happen when those tasks go sideways.

We’ve all been there: a project kicks off, and within a week, it feels like everyone is running in ten different directions. The "SIMPLE" task becomes a mountain, and the "CLEAR" goal is suddenly open to interpretation.

In the real world, project management isn't just about moving tickets across a Jira board or checking boxes. It’s about the friction that happens between people, expectations, and deadlines.

The phrases in this infographic aren't just corporate speak—they are your survival kit.
• When a stakeholder asks for a "quick change," saying "Let’s align on the objective" is how you prevent a month of scope creep.
• When a developer looks burnt out, asking "What support do you need?" is how you actually hit your deadline without losing your team.
• When things go wrong (and they will), "Let’s keep communication transparent" is what saves your reputation and builds actual trust.

Real project management happens in the gaps between the meetings. It’s about managing the energy and the conversation as much as the timeline.

Which of these phrases do you find yourself using the most? Or is there a "power phrase" I missed that has saved your project in the past?

Let’s discuss in the comments! 👇

The textbooks make project closure sound like a clean, 10-step checklist. The reality? It’s often the messiest, most challenging phase of the entire lifecycle.

Just when you think you're done, the real obstacles appear.

This infographic breaks down 15 ground-level challenges that can derail even the best-planned project handovers. From "Ghosting Clients" to "Lessons Learned Amnesia," these are the very real pain points project managers face every day.

Here’s how to use this list:
• Audit Your Project: Use this as a checklist for your current and upcoming projects.
• Proactive Planning: Identify which of these are most likely to occur and build a mitigation plan into your final phase.
• Team Discussion: Share this with your team to start a conversation about improving your handover process.

A project isn't truly successful until it’s closed, signed off, and handed over. Don't let these challenges trip you up in the final stretch.

Which of these challenges has been the biggest hurdle in your experience? Drop a comment below!

AI projects rarely fail because of algorithms — they fail because of poor project management.

Unlike traditional software projects, AI initiatives require a structured approach that combines business clarity, data readiness, experimentation, and continuous monitoring.

The CPMAI Framework provides a practical lifecycle for managing AI initiatives effectively:

1️⃣ Business Understanding – Define the real business problem and expected ROI
2️⃣ Data Understanding – Identify and assess the quality of available data
3️⃣ Data Preparation – Clean, transform, and engineer features for modeling
4️⃣ Model Development – Build and train AI/ML models through experimentation
5️⃣ Model Evaluation – Validate performance, accuracy, and bias
6️⃣ Deployment & Monitoring – Deliver the model and continuously improve it in production

In real-world IT projects, skipping even one of these phases can lead to model failures, poor adoption, or inaccurate insights.

Successful AI programs are not just about building models — they are about aligning technology with business outcomes.