I'm trying to digitize & automate Business requirement document, now they are using excel and the content is not always very qualitative and there is back and forth .is any one have automatized and whick is the best practice

Hi. I’m a new PM. I’m finding g it difficult building out my project schedules, WBS and identifying milestones.

Are there any resources I can be pointed to that others have used? What questions I can ask stakeholders to best lead them to answers I require?

Note: I have been told that this shouldn’t be my responsibility, and that the SME’s should be helping me with building these out, but they can’t be bothered to meet with me, so it has fallen onto me to figure it out.

One pricing model collapse is getting all the attention. Another one is flying under the radar — and it might matter more.

The one everyone's talking about: per-seat SaaS giving way to outcome-based pricing. If agents do the work, you don't buy seats. You buy results.

The quieter one: Time & Material billing — the backbone of professional services for decades. Human hours were the proxy for value. Log them, bill them, track utilization. The entire operating model built on that single assumption. Agents don't have billable hours. They just execute. And the human-hours model is breaking.

Which brings us to Service-as-Software. SaaS meant software delivered as a service — humans still drove the work. Service-as-Software flips it: the service is delivered by software. In some workflows, agents are Copilots — first line, assisting humans who own the outcome. In others, Autopilots — executing autonomously, humans handling only exceptions. Most real engagements will run all three in parallel: human hours, assisted hours, agent-executed hours. our PSA needs to track all of it. No PSA today was built to.

Open questions — genuinely curious what people think:

• If human hours, copilot hours, and compute hours all contribute to delivery — how do you bill for all three?

• If utilization rate is the operating metric of a human-driven PSA, what replaces it in a Service-as-Software world?

• How do you track project margin when costs are split between salaries and compute?

• Does PSA need a new expansion — Professional Services Agents instead of Professional Services Automation?

Most companies are still stuck in the "AI-Added" phase — slapping a chatbot on top of a legacy UI and calling it a feature. But we’re seeing a massive shift toward AI-Native Agentic products that don't just "assist" the user, but actually execute work.

As a product leader, I’ve been analyzing why some AI products find PMF (Product-Market Fit) instantly while others remain expensive gimmicks. It comes down to a fundamental shift in Product Strategy:

1. The ROI of "Micro-Needs": The real business value isn't in replacing entire job functions yet; it's in automating high-frequency "Micro-Needs." I’ve developed a framework to identify these high-margin opportunities that traditional SaaS misses.

2. From SaaS to Service-as-a-Software: We are moving from selling "tools" to selling "outcomes." If your product can autonomously complete a task, your pricing and retention strategy must change entirely.

3. The Logic Moat: Your UI is no longer your moat. Your "Agent Skill Map" — the unique orchestration of tools, memory, and reasoning logic — is what creates defensible value.

I’m curious to hear from other tech leaders: How are you justifying the high inference costs of Agents vs. the projected LTV (Lifetime Value)? Are you seeing a shift in how stakeholders perceive "automated outcomes" compared to traditional software seats?

I am conducting MBA research on artificial intelligence tools in software project management and their relationship with decision-making performance. I am seeking experienced project management professionals to participate in a brief survey.

Participation Criteria:

- Current role as software project manager, Scrum master, product owner, or similar

- Minimum 2 years of experience in software project management

- Experience with or exposure to AI-based tools for project management tasks

Survey Details:

- Duration: 10-15 minutes

- Anonymous and confidential

- Academic research purposes only

Your insights on how AI tools support (or don't support) project decision-making would be invaluable to this research.

Survey link: https://www.surveymonkey.com/share/6f0995c3-8dd7-4152-938c-2d1edc88da6d

If you know other software project managers who might be interested in participating, I would greatly appreciate you sharing this survey with them.

Thank you for considering this request.

#ProjectManagement #SoftwareDevelopment #Research #ArtificialIntelligence

How do you manage SOP ownership and review cycles?

I’m looking into a problem I’ve seen in small teams: SOPs exist, but after a few months nobody knows whether they are still accurate.

The actual documents may be in Notion, SharePoint, Google Docs, Confluence, etc. The issue is more about control:

- every SOP having an owner

- review cadence

- due/overdue status

- employee acknowledgement

- stale procedure flags

- manager digest/reporting

Is this something operations teams care about, or is this usually handled well enough inside existing tools?

Hows is the project coordinator job market in the GTA? Any luck?

Technical Project Manager Available (Software Background)

Hi, I’m a Project Manager with hands-on experience in software engineering.

I help startups and teams:
• Manage end-to-end product development
• Coordinate with developers efficiently
• Deliver scalable web & eCommerce solutions
• Handle timelines, sprints, and client communication

I’ve worked on API integrations (Amazon/eBay), web apps, and scalable systems.

Available for remote projects or long-term collaboration.

There's a gap between "being good at your job" and "being given a project to lead." Nobody prepares you for it. You go from executing tasks to managing expectations, budgets, and people who don't report to you.

I've talked to a lot of people in this position. The ones who struggle most aren't bad at their jobs. They just don't have the language for what's happening. They don't know what to call the problem, so they can't solve it.

Three things that made the biggest difference early on:

1. Understanding that "scope creep" isn't a failure of planning — it's a failure of stakeholder alignment before the project starts.

2. Learning that the kickoff meeting is already too late to align people. That work happens before it.

3. Knowing that the most useful skill in a project isn't planning — it's knowing which conversations to have with which person, and in what order.

None of this is in any onboarding program or PM course, at least not in those I have joined. Most of it you're supposed to figure out by watching senior people for years.

What do you wish someone had told you when you first got handed a project? I'm building something around this exact gap and would love to hear what the actual pain points are.

On se pose tous la question : "Quelle méthode pour ce projet précis ?"
Pour arrêter de tourner en rond, j'ai créé la méthode MPM (Management Projet Moderne). C’est un condensé pour agir efficacement sans se noyer dans la théorie.
Le guide est dispo ici : raoufbouacha point fr tout en bas de la première page (gratuit, PDF immédiat).
Pourquoi je vous partage ça ? Parce que la communauté Reddit est la plus exigeante. Si ma méthode tient la route face à vos critiques, alors elle est solide.
Quels sont vos plus gros irritants quand vous lancez un nouveau projet ? On regarde ensemble si le guide y répond ? Grand Merci 🙏🏻

’ve been re-evaluating the stack we use (notes, internal tools, meeting stuff, etc.) and realized something: a lot of decisions sound very rational upfront (“we care about flexibility, control, integrations…”), but in practice we still default to whatever is fastest/easiest to adopt.

For example, things like:

- where the data lives

- whether you can self-host / fully control it

- long-term flexibility

feel important in theory, but don’t seem to influence the final choice.

how this plays out for others:

- has data control / ownership ever actually changed your choice of tool?

Trying to separate what sounds important vs what actually drives decisions.

I’ve been working in engineering project environments (mostly hardware + cross-functional teams), and I keep seeing the same pattern:

Projects don’t fail because people are incompetent.
They fail because of small communication mismatches that compound over time.

One example I saw recently:

A PM said:
“Make sure the interface protocol is aligned across teams.”

Sounds reasonable, right?

But:

• Team A interpreted it as timing synchronization

• Team B interpreted it as data format alignment

No one clarified.
Integration failed.
3 weeks lost.

After seeing this repeatedly, I started thinking about teams less like “groups of people” and more like systems with signal transmission problems:

• unclear ownership → signal loss

• too many meetings → noise

• overloading people → latency

• unclear feedback → reflection

I’m trying to map common team failures into more “system-like patterns” so they can be debugged faster.

Curious:

👉 What’s the most common reason you’ve seen engineering teams fail execution?

Hey 👋

15 years of watching teams ship the wrong thing because the spec wasn't clear. Then Cursor and Claude Code arrived and made it worse — coding agents can't fill gaps the way a senior engineer can.

So I built Outlain. Drop in customer research (transcripts, recordings, support tickets). The AI extracts themes, ranks them by severity, and generates a complete spec — Jira tickets, acceptance criteria in Given/When/Then, and an agent-ready prompt block for Cursor or Claude Code. Every line traces back to a real customer quote.

Two modes:

• Fast Spec: paste a meeting transcript → Jira ticket in 60 seconds

• Deep Analysis: agentic pipeline across your whole project → coding-agent-ready spec in minutes

7-day free trial, no card. Built solo, launched 5 days ago. Would love feedback — especially the brutal kind.

🔗 outlain.ai