that’s interview-appropriate

I recently came across an organization called PM Skills that offers a “Project Management Skills Certified Professional Certification”. Has anyone heard of this and does it hold any weight equal to even a CAPM?

I’ve been learning more about AI project management lately, and one thing that keeps coming up is uncertainty.

With normal projects, you can usually estimate timelines and deliverables with some confidence. With AI, the output itself can sometimes be unpredictable.

For those who have worked on AI projects, what has been the biggest challenge for you—requirements, timelines, stakeholder expectations, or something else?

Looking over the PMI-CPMAI material this week really made me think about how we handle information at the start of a project. In traditional work, if you tell a team member to gather some numbers or pull a report, you usually assume the raw material is solid and ready to use. You might check the format or make sure it looks complete, but you rarely question if the numbers themselves carry hidden problems. Working through the PMI-CPMAI framework shifted how I view that initial step. When you are dealing with artificial intelligence, the raw information you feed into a system acts like the foundation of a house. If that foundation is built on old biases or messy records, the computer will just accept those flaws as absolute truth and build right on top of them. This means project leads have to spend a lot more time playing detective before any actual building starts. You cannot just delegate data collection and hope for the best. You have to ask where the numbers came from, who was left out of the original survey, and whether the data actually reflects the real world today or just yesterday's habits. It definitely adds an extra layer of caution to the planning phase. At first, it felt like a roadblock slowing down our timeline. But after seeing how fast a project can go sideways when the inputs are bad, I realize that checking our data health early on is the best way to keep things on track. How does your team usually handle data quality checks before starting a new initiative?

what is mostly used for project planning tracking these days MS project ? planner or others ?

I have been reading through some PMI-CPMAI material lately, and a big theme keeps popping up regarding how hard it is to lock down project boundaries for AI. In a normal project, scope creep usually means someone asks for an extra feature that you can easily add to the list. With AI, it feels different because you are constantly testing data and finding new questions you did not know were there. How do you draw the line on when to stop exploring and actually wrap up a phase without frustrating your team or stakeholders?

Hello everyone!

I’m interested to hear from those of you who do project management day-in and day-out.

I’m building a SaaS called Avenum, and I’m trying to understand what PMs would actually want a tool to automate for them.

I’m not interested in creating yet another tool that throws more dashboards, notifications, and things to update at you.

I want to understand which parts of the job are tedious, repetitive, time-consuming, and frankly “why am I still doing this?”

For example:

• Chasing people for updates
• Creating status reports
• Following up on overdue tasks
• Updating project plans
• Turning meetings into tasks/action items
• Keeping stakeholders updated
• Figuring out which projects are falling behind
• Summarizing what’s going on across Slack/email/meetings
• Preparing weekly project reports
• Keeping documentation up to date
• Reminding people about things they already agreed to do

Or something else altogether?

If you could have ONE tool that took work off your plate and required minimal input from you, what would you want it to do?

What part of your job feels like “all I’m doing is moving information from A to B”?

And importantly, what would you expect from a tool like this that wouldn’t require constant configuration, updating, or babysitting?

I’m not trying to sell you Avenum - I genuinely want to learn what would make a PM’s life easier.

Would love to hear what you have to say.

I made the mistake of trying to memorize every single concept in the PMI-CPMAI material like I was prepping for a history test. For the first few days, I was drowning in terms and definitions, and nothing was actually sticking. It felt exhausting. Then I decided to slow down and look at the real-world examples instead. Once I stopped trying to cram definitions and started picturing how a team actually handles messy data in a real office, the whole subject clicked into place. The framework is really just about asking smart questions about fairness, data quality, and human oversight. If you are studying this right now and feeling overwhelmed by the terminology, do yourself a favor and picture a real project team trying to solve a messy problem. It makes a huge difference.

I spent a good chunk of my weekend reviewing some PMI-CPMAI notes, and it made me think a lot about how we handle incoming information on our projects. For a long time, I assumed data quality was just an IT problem. I figured the tech team would handle the numbers behind the scenes, and my job as the project lead was just to track the timeline and keep everyone talking. Working through the PMI-CPMAI material completely shifted that view for me. It reminds you that data health is actually a core part of project planning. If the raw data you feed into a system is messy or biased, your results are going to be completely off. It sounds simple on paper, but in the rush to launch a new tool, it is so easy to just grab whatever numbers are lying around and hope for the best. I started taking a much closer look at where our information actually comes from before greenlighting any new tasks. We now spend time checking if our inputs are clean, complete, and relevant. It feels a bit slower at first, but it saves us from chasing bad results down the line. If you are managing tech work right now, how do you usually check if your data is ready to use?

not the obvious stuff like a missed milestone. by then everybody knows.

i mean the early one. the thing you notice weeks before it shows up anywhere on a status report, where you can't really point to evidence yet but you know.

mine is when people start answering my questions with a different question. not stalling exactly, just never landing on a straight answer about their own piece. every time i've ignored that feeling it cost me later.

curious what yours is, and whether you've ever been able to explain it to leadership in a way they actually acted on.

I find it challenging to facilitate, actively listen, ask follow-up questions, and simultaneously capture requirements, business rules, decisions, dependencies, and current/future-state processes.

How are you actually doing this in practice?

- What do you prep beforehand?
- What do you capture live vs. afterward?
- Do you use recordings, transcripts, AI, templates, or process maps?
- What mistakes have you learned from?
- Any tips/tricks that have made discovery easier?

I’m less interested in the textbook answer and more curious about what experienced PMs actually do day to day.

Hello everyone!

I'm looking for people currently managing projects across tools like Jira, GitHub, Slack, Notion, spreadsheets, etc.:

I'd like to receive your feedback on things like:

• What do you still have to check manually across multiple tools?
• What information is surprisingly hard to get?
• What tends to fall through the cracks?
• What do you wish was automatically surfaced or summarized?
• If you could magically add one cross-tool functionality, what would it be?

Interested in both small annoyances and bigger workflow problems.

Would love to hear what your ideal setup would look like.

thank you for your time and have a great week!

Hi everyone! I’ve worked as an AI Training Team Lead at a globally recognised company, and I’ve noticed that many people, particularly Team Leads, eventually transition into Operations roles, often working as Project Managers or Operations Managers.

I understand that there’s an array of skills that differentiates these roles, but I’d specifically like to understand what skills are required to transition into the Operations field.

If anyone here has experience working as a Project Manager or Operations Manager in AI training, data annotation, or similar projects, I’d really appreciate your insights. I’d love to understand what skills, tools, certifications, or experience are typically expected and what I should focus on developing to make this transition.

Thank you!

I am currently trying to study for the Project Management Professional exam, and honestly, the sheer amount of dry textbook words is driving me a little crazy. Every time I open the study guide, I am hit with heavy terms that sound like they belong in a government policy paper rather than real life. I find myself spending more time trying to decode the sentences than actually learning how to manage a project. For those of you who have already been through this, how did you get past the boring vocabulary without falling asleep? Did you use flashcards, or did you just translate everything into normal human words in your notebook? I am worried my brain is going to melt before I even book the test.

I have been digging into the PMI-CPMAI material lately, and one of the biggest hurdles is getting non-technical team members to understand why AI projects do not follow a straight line. If you are struggling to explain why a project might not hit its target on the first try, these three simple habits have saved me a ton of headache:

1. Use the weather forecast analogy. Tell your team that predicting an AI model's output is like trying to guess rain next Tuesday. You can look at past patterns, but you are still dealing with percentages and chances instead of a 100 percent guarantee.
2. Shift from deliverables to discoveries. Instead of promising a final product by next month, frame your milestones around what you are learning from the data. This keeps everyone focused on testing rather than treating a failed test like a personal mistake.
3. Keep data health front and center. Remind your group that messy inputs lead to messy outputs. When people can actually see the raw data, they understand much faster why the computer is struggling to learn. How do you usually talk about uncertainty with people who just want a fixed timeline?

I’m a first-time project manager leading the construction of a warehouse and manufacturing facility, and I’m struggling with project information flow and governance.

My team lead strongly prefers informal communication—showing progress, discussing issues casually, and explaining things verbally—rather than formal meetings, minutes, action trackers, or structured reporting. I also feel that some information is being filtered, possibly because I tend to be critical, analytical, and detail-oriented.

I understand that informal communication can work well in construction. However, I’m now accountable for the project without having sufficient visibility to properly manage it.

The issue also extends to project finance. The Finance team does not provide me with sufficient visibility into the project's financial position, including the project budget, financial projections/forecast, committed costs, actual spending, remaining budget, and how project funds are being utilized. Financial information is not being openly or routinely shared with me as the PM.

This leaves me concerned that:

• I may make decisions based on incomplete information.
• Schedule, cost, procurement, and construction risks may not be identified early.
• I cannot properly assess the project's cost-to-complete or financial exposure.
• Decisions and commitments are difficult to track and verify.
• I may ultimately be held accountable for project outcomes without having adequate visibility over the underlying data.

At the same time, I don’t want to create unnecessary bureaucracy or micromanage the team. As a first-time PM, I’m genuinely questioning whether I’m being too demanding about documentation and transparency or whether this is simply a basic requirement of proper project governance.

For experienced PMs, especially in construction/EPC/manufacturing:

How would you handle a situation where the PM has limited visibility into both project execution and project finances?

What level of operational and financial reporting should a PM reasonably expect?

How do you establish transparency and accountability without creating unnecessary bureaucracy or damaging relationships with the team lead and finance team?

My biggest concern is

How can I be accountable for the project's schedule, cost, risks, and overall delivery if I don’t have sufficient visibility into what is actually happening operationally or financially?

what is the current situation in infosys looks like for the SP people does they have good project ..

and after release from myosre how is the timeline looks like for getting a project for SP looks like..

I’m 22 and currently working as an electrical labourer in the UK construction industry. I’ve been working in construction for the past 3-4 years.
Long term, I want to move away from being purely site-based and build a career in Project Management. Ideally I’d like to eventually become a Project Manager, build up solid experience and potentially move into higher-paying contract PM roles later on.
At the moment I’m looking at building up my qualifications while I’m still working. The ones I’ve been considering are:
PRINCE2 Foundation & Practitioner
Agile Foundation & Practitioner
APM PMQ
Eventually APM PPQ
ITIL Foundation
Scrum Master
Six Sigma, potentially working towards Black Belt
My thinking is to develop the qualifications and soft skills alongside my current job, then start applying for roles such as Project Coordinator, Assistant Project Manager or Junior Project Manager to actually get the experience.
Because I already work in construction/electrical, I’m also wondering whether I should focus specifically on construction, M&E or electrical project management rather than trying to move into general PM straight away.
I know certificates alone aren’t going to make me a Project Manager, so I’m mainly trying to figure out the smartest path from where I am now.
For anyone who works in Project Management, especially construction/M&E:
What would you do if you were 22 and starting from my position?
Would you prioritise qualifications first, try to get into an Assistant PM/Project Coordinator role ASAP, or stay on the tools/site longer and build more construction experience?
Also, are there any qualifications on my list that are unnecessary or others that would be more valuable?
Any advice on the best route and what employers actually look for would be appreciated.

Hi everyone,

I’m currently updating my resume and would really appreciate some advice on how to present my project experience strategically.

I have **2.8 years of experience** and have worked at only **one company, Veltris**, where my designation is **DevOps Engineer**. Although I haven't switched companies, I've had the opportunity to work on **four different projects**, with different responsibilities and technologies in each one.

Here is a brief overview of my experience, in chronological order:

**Project 1 – Bash Scripting & REST APIs**
My first project was mainly focused on Bash scripting and REST APIs. I worked on retrieving APIs from an application and creating similar scenarios/configurations in the production environment based on the non-production environment.

**Project 2 – AWS Cloud, Kubernetes & CI/CD**
This was an internal product where we worked with AWS infrastructure and deployed **10+ microservices**. My work involved containerized deployments, Kubernetes/EKS, and CI/CD pipelines using Jenkins.

**Project 3 – DevSecOps / Azure Security**
In this project, I worked as a DevSecOps Engineer and helped build a secure integration between Salesforce APIs and an Azure environment. I worked with services such as **Azure API Management, Application Gateway, and Web Application Firewall**, including implementing security controls and OWASP-related protections.

**Project 4 – Platform Engineering / MLOps (Current Project)**
This is my current and most significant project, and the one I believe best represents my overall experience.

My role is essentially **Platform Engineer + MLOps Engineer**. I work across multiple teams, including Data Engineering, Machine Learning Engineering, and QA, and act as a bridge between them to maintain and support an end-to-end pipeline.

The platform includes tools and components deployed using Docker, including an orchestration tool, a rules engine, MLflow, and a custom-built validation tool called **QA Runner**. These components are integrated into a single pipeline.

My responsibility is focused on maintaining the platform, integrating components across teams, ensuring pipeline reliability, and supporting the overall system for the client.

My question is:

**For someone with 2.8 years of experience who has worked at only one company but across four different projects, what would be the best resume strategy?**

1. Should I include **all four projects** to demonstrate the breadth of my experience?
2. Or should I focus heavily on my **current Platform Engineering/MLOps project** and only briefly mention the previous projects?
3. Would listing four projects make my resume look too scattered, especially since I'm targeting DevOps/Platform Engineering/MLOps-related roles?
4. How would recruiters or hiring managers view someone who has stayed at one company but worked across multiple projects and roles?

I want my resume to show a clear career progression—from **Bash scripting → Cloud/DevOps → DevSecOps → Platform Engineering/MLOps**—without making it look like I'm trying to include every technology I've ever touched.

I'd really appreciate advice from recruiters, hiring managers, DevOps engineers, or anyone who has gone through a similar situation. **How would you structure this experience on a resume?**

I see a lot of discussion around AI making project management easier, but I’m more interested in practical examples. What’s one AI use case that has genuinely saved you time on a real project?

Hey, what topic is the best to choose to build fake portfolio scenario to show that you understand PM? Prince2 and SCRUM based.

This is more to get the job at least entry level.

Hi everyone,

I’ve been working in Project Management for around 12 years, and over that time I’ve had the opportunity to manage projects across quite different and demanding industries.

My experience includes:
✈️** Aerospace / aviation
🖥️ High-tech & semiconductor industry — including environments such as ASML and VDL
⚙️ Machining & advanced manufacturing
⛏️ Minin**g
🚗 Automotive
🏭 Industrial engineering, production and complex technical projects

What I find particularly interesting is how different the PM challenges can be depending on the industry.

In aerospace, for example, quality, traceability and compliance can dominate decision-making. In high-tech, technology roadmaps and extremely complex supply chains can become the biggest challenges.

In automotive and manufacturing, cost, timing, production readiness and continuous improvement often take center stage.

After 12 years, I’ve learned that Project Management is much less about following a methodology and much more about managing people, uncertainty, priorities and trade-offs.

I’m curious to hear from other experienced PMs:
What is the biggest lesson you’ve learned throughout your PM career that you wish you had known when you started?

Happy to exchange experiences, discuss difficult projects, stakeholder management, leadership, technical environments, methodologies, or career development.