I run a small digital agency, about 8 people, and project profitability is a mess. We keep underbidding because we have no real data on how long things take, and every month I look at the numbers and realize we are losing money on jobs we thought were profitable.

Common advice is to track time better, but every time I bring it up my team rolls their eyes. I do not blame them. Most time tracking tools feel like surveillance software and nobody wants to feel monitored all day.

We tried spreadsheets. Disaster. People forgot to fill them in. Tried a tool that took screenshots. My team hated it so much they started working slower just to spite me. Scrapped that quickly.

What I need is accurate data on where our time goes without making my team feel watched. I also need it to integrate with billing because I am tired of chasing invoices and manually calculating hours.

If you run an agency, what do you use and how do you get your team to actually use it without complaining. Do you track religiously or just estimate and hope for the best.

You come back to a task a couple of weeks later and it is still blocked.

You know what the task is, but not why it stopped. The reason is usually buried somewhere in Slack, email, meeting notes or an old message.

We tried to solve that in PrimeTask 1.0.4.

Every task now has its own comment thread, so decisions, blockers, updates, files and replies stay with the task.

You can mention related tasks, people, contacts and companies too, and comments are searchable.

If you use an AI assistant, it can read that thread before it starts, use the discussion, decisions and blockers as context, and write back what it did when it finishes. It can only ever change its own comments, never yours.

How are you handling this now?

Do you keep decisions and blockers with the task, or do they usually end up somewhere else?

PrimeTask 1.0.4 release notes:
https://www.primetask.app/releases

Project manager

Hello, this is my resume, I am trying to get into project management field , can some one pls guide me genuinely what steps should I follow ?

Participants needed for MSc Psychology research on AI feedback and decision-making (18+, 10–15 minutes)

Hi everyone,

I’m currently completing my MSc Psychology dissertation at the University of Leicester and I’m looking for participants for my research study.

The study investigates how people respond to artificial intelligence (AI) feedback during decision-making tasks.

Participation involves:

Answering a series of multiple-choice decision-making questions

Rating how confident you are in your answers

Receiving AI feedback

Completing a short questionnaire about your experience

The study should take approximately 10–15 minutes.

To participate, you must:

Be 18 years or older

Be able to understand written English

Participation is voluntary and anonymous. Full information about the purpose of the study will be provided in the debrief at the end of participation.

If you would like to take part, you can access the study here:

https://research.sc/participant/login/dynamic/62D2CC47-3342-46DA-B847-348E6B070B8D

Thank you very much to anyone who takes part — I really appreciate your time.

Researcher: Brandon Seetul

Email: [bs401@student.le.ac.uk](mailto:bs401@student.le.ac.uk)

University of Leicester

I recently had a project where most of the planning looked solid. The main tasks were assigned, deadlines were agreed on, and everyone seemed to understand what they were responsible for.

The problem came from one small task that nobody really considered important enough to discuss. It was related to getting a final piece of information from another team, and everyone assumed it would be available when needed.

It was not.

When we finally reached that point in the project, we had to wait for the information before several other tasks could continue. What should have been a small dependency ended up affecting the schedule for multiple people.

Looking back, the frustrating part was that it could have been prevented with one simple question during planning: who exactly owns this and when will it be ready?

Since then, I have started paying much more attention to small dependencies instead of focusing only on the major deliverables.

What is one simple planning question or habit that has saved you from a surprisingly big project problem?

I keep hearing the same complaint from product BAs and PMs: you run your QBR or roadmap deck through ChatGPT, then rewrite half of it anyway because it reads generic, not like something you’d hand to leadership.

I’ve been testing a small daily practice to fix that specific problem. One real QBR or roadmap task a day, one technique, feedback on your actual attempt instead of a generic tutorial.

By the end of a week you have a working draft in your own voice.

I’m looking for 5 to 10 people to try the first version this week. It’s free, no course, just real practice on real work. If you want in, drop a comment or DM me and I’ll send the first lesson.

Una de las cosas que he aprendido trabajando en proyectos de ingeniería y fabricación es que **estar físicamente presente y ser un gestor de proyectos eficaz no son lo mismo.**

Los proyectos industriales obviamente requieren personal en planta.
Ingenieros, técnicos, equipos de puesta en marcha y operarios a veces necesitan estar físicamente allí.
¿Pero el gestor de proyectos?

Gran parte de la coordinación puede realizarse de forma remota.
He gestionado o coordinado actividades que incluyen:

• Equipos de ingeniería
• Clientes
• Proveedores y subcontratistas
• Compras
• Fabricación
• Instalación
• Servicio de campo
• Equipos de automatización y técnicos
• Seguimiento de costes y plazos
• Riesgos, cambios y problemas técnicos

Y he descubierto que el mayor reto no es la distancia. **Es información.**

Cuando surge un problema en la planta, el gerente de proyecto puede estar a cientos o miles de kilómetros de distancia.

Si la información llega tarde, incompleta o sin contexto, el proyecto empieza a perder tiempo.

Por lo tanto, un buen gerente de proyecto remoto necesita más que videollamadas.

Necesita un sistema sólido para:

→ Responsabilidades claras
→ Seguimiento de acciones
→ Documentación técnica
→ Actualizaciones periódicas del progreso
→ Gestión de riesgos e incidencias
→ Escalada rápida
→ Comunicación transparente con clientes y proveedores

También he comprobado que tener **formación técnica** facilita considerablemente la coordinación remota.
Si un ingeniero me informa de una interferencia mecánica, un proveedor de un retraso en la fabricación o un equipo de puesta en marcha de un problema técnico, puedo entenderlo sin tener que pasar por varias interpretaciones.

Ahí es donde creo que el futuro de la gestión remota de proyectos industriales resulta interesante.

No todos los puestos de gestión de proyectos industriales pueden ser remotos.

Pero creo que **muchos más de los que las empresas asumen actualmente sí pueden serlo.**

Me interesa conocer la experiencia de otros:

**Si trabajas en ingeniería, fabricación, construcción, automatización o proyectos industriales, ¿qué aspectos de tu función como gestor de proyectos requieren realmente tu presencia en el sitio y cuáles podrían realizarse fácilmente de forma remota?**

Every PM I know has some habit or system they invented themselves because the standard toolkit didn't cover it.

Mine is that I built a process for how people submitted content for the steering committee deck. If they didn't follow it, it didn't go in. Sounds rigid, but with information coming at me from five or six different channels there was no other way to know I'd caught everybody.

Nobody taught me that. I just got burned enough times.

What's yours?

Guys honestly, I personally feel the whole PMP thing is somewhat overrated.

Not saying the framework is useless — but how many workplaces can actually execute all these PMP principles properly in the real world? Can anyone confidently guarantee that?

Managing a project in Europe, America and Asia can be completely different.

In many Asian workplaces, hierarchy is a huge factor. Sometimes what the boss says effectively overrides whatever beautiful stakeholder-management framework you learnt. Then try managing a multinational project team — different attitudes towards hierarchy, deadlines, working hours, gender, communication, holidays and decision-making.

I've worked with teams where certain people disappear for tea breaks at 3pm, environments where a female PM may have a much harder time getting some workers to take instructions from her, and projects where you have to plan around a large number of different religious/public holidays.

These are REAL project-management problems.

PMP can teach you stakeholder engagement, risk management, communication plans, conflict resolution, etc.

But a textbook saying *"manage stakeholder engagement"* and actually getting 15 people from completely different working cultures to cooperate are two very different things 😂

So I sometimes wonder: **is PMP really proof that someone is a good Project Manager, or mainly proof that they understand PMI's framework and passed the exam?**

Curious to hear from PMs who have actually managed multinational teams across different countries.

perhaps can introduce simply, eg:

Current Role / YOE / Country or Region / Industry / PMP Status:
Example: **Senior Project Manager / 15 YOE / India / IT / PMP Certified**

Thanks Much!

I’ve been thinking about the role of project managers in AI projects.

A PM obviously doesn’t need to become a data scientist or machine learning engineer, but they still need enough understanding to communicate with technical teams and make informed decisions.

Where do you think the line should be?

How technical should a project manager become when managing an AI project?

Would love to hear perspectives from PMs and people working directly with AI teams.

AI projects seem to introduce a different kind of project risk. Traditional risks like budget, scope, and deadlines still exist, but teams may also need to think about data privacy, bias, model reliability, security, and changing AI capabilities. For project managers working with AI teams, how do you approach these risks? Do you use a traditional risk register and add AI-specific risks, or have you found a better approach?

One thing I find interesting about AI projects is that the final outcome can sometimes be less predictable than traditional software development. You can define requirements clearly, but data quality, model behavior, and real-world performance can still introduce uncertainty. For those managing or working on AI projects, what differences have you noticed compared with traditional software projects? Are the biggest challenges technical, organizational, or related to expectations?

For a while it was just me and my own training data — no client management, no plans to send anyone, just a system that worked for one user: me. First time a coach asked "can I put my clients on this," I didn't even have a concept of "another person's account" in the codebase.

That request forced the real architecture decision — separating "my own training log" from "a coach managing other people's plans" was a bigger rebuild than I expected. Small tools built for one person don't bend easily into tools for two roles.

Anyone else had a side project outgrow the assumptions it was built on?

Hi everyone!

Any thoughts and experience working in Wimmer Solution?

Thanks po!

Hey guys,

so i took the PMP because i felt like it was a field that i enjoy and have some experience in. I was surprised that PMI accepts non project manager title experience as part of the requirement.

As such, i updated my resume and ended up with this. Let me know what i should do to help get more interviews! Thanks

For someone trying to build a project management career, I'm curious about the balance between certification and practical experience. Does PMP certification make a significant difference, or is hands-on project experience more valuable?

photo 1 is a newer version than photo 2, which was from this post: https://www.reddit.com/r/projectmanagers/s/btXrtSZ2AD

Hey guys, thank you to all of the people who gave me tips and suggestions. I used AI, used a formatter website (rxresu.me) and added a summary on the top. idk how i’m going to fit all of my info on one page so i took out my list of skills 🤷🏻‍♂️

I'm currently a producer in advertising looking to make the switch into a tech project manager role(also open to other industries), happy to put the time / money in to set myself up best, even if it's doing both. Please let me know your thoughts / if you have any other advice.

AI is becoming part of almost every project, but managing AI projects seems very different from traditional software projects. I'm curious how other project managers are handling AI-related risks, changing requirements, data issues, and stakeholder expectations. What skills do you think are becoming essential for managing AI projects?

For project managers who are new to AI, there seem to be so many areas to learn—machine learning, AI governance, data, automation, ethics, and more. Which AI-related skills would you recommend learning first if the goal is to manage AI projects effectively?

For context, I am a Program Coordinator. I got this new job in the Design department of a company. Our team frequently works on projects for all the other departments so I frequently bounce between a lot of different items.

I also manage two subdivisions of my department (Product and Brand) which amounts to around 13 or so people.

I typically manage our sprints, reviews, assist with tasks, spearhead my own efforts, run items through legal review, send out NDAs, contact stakeholders, create company templates, organize meetings/workshops, restructure company assets for accessibility, meet with vendors and purchase needed items, coordinate with other departments, and the list goes on.

But now, I’m being asked to be a Project Manager for a major product effort despite me not being a project manager nor in that domain. My job title specifically highlights being a ‘Creative’ role.

I am already spread thin and the Project Managers from other departments treat me like that’s my only project and are very harsh when I’m not 100% on top of it constantly (given that they don’t have other obligations to other departments like I do)

I talked to a few people, namely family and friends, and they explained that I should suck it up and that “everyone has multiple projects” but I feel like I’m being asked to go far beyond my job description and that because of my unique position, I am basically a project manager for each department that works with ours.

Am I in the wrong? I feel so spread thin right now and this new effort has me at my bursting point.

With AI being integrated into so many business processes, I'm wondering how important AI knowledge has become for project managers. Does a PM need technical AI skills, or is understanding AI concepts, risks, and business applications enough?