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Daily standups did not fail because teams stopped caring. They failed because software delivery outgrew a coordination model built for visible, physical work. Self-reported updates cannot surface slow drift early enough to protect roadmap confidence.

Developer productivity isn't a coding problem. It's a coordination problem. This piece breaks down where engineering time actually goes and what a proactive delivery system looks like in practice.

The PM doesn't spend the morning doing reconnaissance. They start the day knowing where friction exists and what needs a decision. Standups become decision forums instead of status updates. Roadmap confidence increases because problems surface earlier. The PM's time shifts from coordination to judgment.

AI tools for product and project managers have matured, but most still stop at visibility rather than execution. Traditional PM platforms use AI to speed up documentation and planning, while analytics tools surface delivery metrics and trends. A new category of AI-native tools is emerging to actively detect delivery issues, coordinate action, and reduce operational overhead allowing PMs to focus on strategy, prioritization, and stakeholder alignment instead of chasing work across systems.

DORA metrics are four research-backed measures that show how well software delivery actually works. Instead of tracking activity like story points or tickets closed, they focus on real outcomes: how often teams deploy, how long changes take to reach production, how frequently releases cause issues, and how quickly teams recover when things break. Together, these metrics balance speed and stability, helping engineering leaders spot bottlenecks, reduce risk, and improve delivery predictability without burning out their teams.

There are over 200 companies providing engineering intelligence tools - tools that analyze developer performance metrics in detail. The critical challenge? None of these tools actually Do any work - they provide great analytics, but they don't communicate with at-risk developers, they don't assign tasks, and they don't set up meetings. Diving deeper into a real life scenario highlights the difference.

There are many great engineering intelligence and analytics tools out there - but most suffer from the same problem. They are Reactive, not Proactive. An AI Project Manager should be proactive, jumping in to DO the work of asking for status, scheduling, and followups. This has only recently become possible as LLMs have progressed to reasoning models that can act at a higher success rate.

Traditional project management tools are fundamentally reactive, not proactive. As a result they've failed to solve a wide range of software team pain points, including context switching, estimation, communication, and more. Let's dive into the details on the pain points, why the status quo isn't working, and what is missing from the capabilities that exist today.

70% of projects still fail to meet their goals, even as PMs spend 60% of their time on coordination overhead - chasing updates, running meetings, and manually following up. This post explores the background of software project management, how it's failed, and how AI project management asks a different question: "Can we automate the project management coordination work itself?"

A candid look back at the “8-week sprints” at AT&T that exposed the illusion of productivity—and how DevHawk was born from the need to track real work, not just time.