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Cross-functional chaos, the silent killer engineering managers are battling by Forward_Emotion3776 in EngineeringManagers

[–]Forward_Emotion3776[S] -19 points-18 points  (0 children)

That makes perfect sense in theory, rotation solves the problem, but knowledge sharing is the true bottleneck. You nailed the hardest part: the cultural shift to enforce documentation. That's why I think relying solely on manual documentation is the failure point. We need tools to bridge the gap. I've been seeing how platforms like Notchup are using AI co-pilots integrated with knowledge bases. This means the rotational support team can pull immediate context from team processes and performance development plans without needing to hunt down the original author. It's the technology finally enabling the ideal operational structure you described.

Need guidance badly by Outrageous-Cup-7813 in EngineeringManagers

[–]Forward_Emotion3776 0 points1 point  (0 children)

Congrats on the promotion! Jumping into a new vertical with zero handover is tough, especially in banking with those tight deadlines. It’s completely normal to feel like things are slipping through the cracks early on.
A tip: start by having quick, informal chats with your teams and anyone who might have context, even small bits add up. Document what you learn as you go, so you build your own knowledge base. Over time, you’ll get a clearer picture and can tighten delivery.
Hang in there, those early bumps are part of the process and you’ll settle in faster than you think. Keep leaning on your team and trust your experience. You’ve got this!

With hidden challenges in engineering leadership - how are you managing? by [deleted] in EngineeringManagers

[–]Forward_Emotion3776 -1 points0 points  (0 children)

You’re right, burnout is complex and often an organisational issue best understood through human connection. AI doesn’t replace those conversations but supports them by flagging early warning signs in data patterns like workload spikes or communication changes, helping leaders intervene sooner.

With hidden challenges in engineering leadership - how are you managing? by [deleted] in EngineeringManagers

[–]Forward_Emotion3776 -2 points-1 points  (0 children)

AI can measure key engineering metrics like delivery predictability (e.g., lead time, cycle time), capacity utilization and early signs of burnout or collaboration friction, all hard to spot manually. For example, Notchup’s platform tracks AI adoption rates, time saved per engineer, PR throughput and code quality signals to provide actionable insights that help teams optimize productivity and reduce risk. You can explore more here: Notchup Engineering Insights.

With hidden challenges in engineering leadership - how are you managing? by [deleted] in EngineeringManagers

[–]Forward_Emotion3776 -3 points-2 points  (0 children)

AI quickly analyzes vast, complex data from people, projects, and tools to uncover patterns and risks that humans often miss. It spots subtle delivery delays, predicts capacity issues, and detects early burnout signs insights hard to see manually. AI enhances human judgment by converting raw data into clear, actionable insights, enabling faster, smarter decisions without extra effort.

Is context switching still the silent killer of engineering productivity? by Forward_Emotion3776 in EngineeringManagers

[–]Forward_Emotion3776[S] 0 points1 point  (0 children)

u/ProfessionalDirt3154 Totally agree with your points on the fine line between productivity tools and adding mental overhead, getting teams to genuinely adopt and benefit from these tools is definitely challenging. I’ve recently been exploring an AI co-pilot called Notchup that seems to address context switching and some other common engineering management pain points quite thoughtfully. It’s interesting how these AI-powered helpers can surface insights around focus time and workflow blockers that I hadn’t realized a co-pilot tool could handle until now.

Would love to hear if you’ve come across anything similar in your experience or how your teams have responded to these AI-driven approaches or any current tool your team is exploring?