How to Write Non-Functional Requirements for Your Project

PM Pain Point: How to beat decision latency (the 20-minute fix) Decision OS: For every critical item, capture Owner, Options, Impact, Deadline on a single line. No doc? No decision. 48-hour SLA: Publish it. If the owner doesn’t decide by T+48, you escalate automatically. Scope Gate: Any “quick tweak” must show impact to timeline/cost/risk before entering the plan. Daily 10 @ 10: A ten-minute standup strictly for decisions needed today (not status theater). Asynchronous by default: Loom + a one-pager beats herding calendars. Executive “approve/decline” happens in the ticket. Result: Fewer meetings, faster UAT burndown, and a go-live that’s gloriously boring (the good kind). What’s the last decision that sat too long in your program—and what did it cost? Drop it below. I’ll share a one-page Decision OS template you can use tomorrow Joe

📑 FRD vs BRD – Why Both Matter in Projects Most people confuse BRDs and FRDs. Here’s the difference I learned: BRD = What the business needs (goals, KPIs, stakeholders) FRD = How the system should behave (fields, workflows, validations) Example: In LC automation, BRD captured the goal (reduce TAT), FRD defined system rules (mandatory fields, approval paths, audit logs). 👉 Question: Have you worked on both BRD & FRD? Which one do you find harder to get sign-off on? #BRD #FRD #BusinessAnalysis #ITProjects #SwitchToTech

Maintaining Service Level Agreements (SLAs) Challenge 2: Lack of Visibility into SLA Performance Case Study: An IT service provider struggled to track SLA compliance because reports were manual and inconsistent, making it hard to identify bottlenecks. Solution: - Designed a custom JIRA dashboard displaying real-time SLA performance, breach trends, and ticket resolution times. - Set up automated reports to alert managers when SLA thresholds were at risk. - Integrated JIRA with Confluence to create a self-updating SLA performance wiki for stakeholders. Outcome: The team gained real-time SLA tracking, enabling proactive issue resolution and reducing SLA breaches by 25%. How are you tracking SLA performance in your team?

Throughput (SDLC metrics) are generally a terrible way to see the impact of GH Copilot (or any SDLC improvement).. ... a simple simulation shows why sources of variability, like hours worked or time spent on inefficiencies, end up overwhelming any improvement in the throughput metric trend line.

PSCU’s journey shows that automating unit test creation not only improves code quality but also accelerates time-to-market. Their case study reveals how reducing manual workload led to a more agile development process and increased overall test coverage. Ready to elevate your testing strategy? Discover how they did it https://hubs.la/Q03dbYvB0

PSCU’s journey shows that automating unit test creation not only improves code quality but also accelerates time-to-market. Their case study reveals how reducing manual workload led to a more agile development process and increased overall test coverage. Ready to elevate your testing strategy? Discover how they did it https://hubs.la/Q03dbYvB0

PSCU’s journey shows that automating unit test creation not only improves code quality but also accelerates time-to-market. Their case study reveals how reducing manual workload led to a more agile development process and increased overall test coverage. Ready to elevate your testing strategy? Discover how they did it https://hubs.la/Q03dbYvB0

PSCU’s journey shows that automating unit test creation not only improves code quality but also accelerates time-to-market. Their case study reveals how reducing manual workload led to a more agile development process and increased overall test coverage. Ready to elevate your testing strategy? Discover how they did it https://hubs.la/Q03dbYvB0

Recently I was chatting with someone who had a downtime on production due to, guess what! "a feature flag toggle". Short story below. The feature flag was always in off state. Product team toggled the flag to make the feature live after 4 months. This resulted in an incident and nobody could figure out why/how for more than 30 mins. Lot of teams use feature flags as convenience to the reduce deployment risk. But this doesn't really _reduce_ the risk. The risk just moves from "deploy time" to "runtime". Now, instead of "will this deployment break?" you end up asking "which combination of 15 feature flags will break?". One advice I have is: Don't keep feature flags for months and months. Prune them and reduce the cyclomatic complexity in the codebase. Without regular care, feature flags pile up and the code becomes unmanageable. Feature flags are great for gradual rollouts. They're technical debt when they become permanent configuration. Every flag you add is a code path you need to test and maintain.

🚧 A few years back, we were gearing up for a release. My team had everything lined up—except one critical piece: a vendor API. The delay wasn’t ours, but soon the pressure shifted onto us. Questions about our capability started surfacing, and I could see the morale dipping. Instead of letting the blame snowball, I took a step back. I went into Jira, tagged every blocked story as “waiting on vendor,” and built a simple impact chart. It showed exactly how many points were stalled beyond our control. At the Sprint Review, I presented this—not as finger-pointing, but as transparency. The effect was immediate. Leadership finally saw where the bottleneck was and redirected their energy to resolving the vendor issue. The result? ✨ The team’s credibility was restored. ✨ Stakeholders gained clarity instead of frustration. ✨ We built trust by focusing on facts, not blame. Sometimes, protecting your team isn’t about defending—it’s about making the invisible visible