I’ve managed teams for 20 years. The work keeps changing, which means my Manager Operating System must evolve too. Treating it like a living system helps me lead with clarity and speed in an AI-driven environment.

I realized I was running on instinct. It worked, but it wasn’t intentional. I started treating my manager operating system like a product. Minor releases, weekly reviews, keep what works.

I’m testing AI tools to remove friction, not replace judgment. Meeting summaries, pattern spotting, and lighter status updates. The goal is simple: more time for decisions, coaching, and strategy, less time in repeat work.

Here’s what I’m trying, what helped, and what didn’t.

Why I Rebuilt My Manager Operating System for Scale

Three months ago, I took on a second organization while running my existing team. Twelve direct reports, two functions, calendar chaos.

I had solid management operations built over two decades. Weekly one-on-ones, monthly team health checks, quarterly strategy reviews. But the system that worked for six reports broke at twelve.

I was spending 12 hours a week in status meetings and another 8 hours compiling updates. My calendar had zero white space for strategic thinking.

The breaking point came when my CEO asked about a cross-team initiative. I gave a ten-minute answer covering everything except what he asked. He said, “You’re working incredibly hard. But I can’t tell what you’re actually working on anymore.“

That hurt. But the CEO was right.

I needed to rebuild my operating system for this new scale. Don’t throw everything out; remove friction and add leverage where it matters most.

What Managers Get Wrong About Effectiveness

Most managers confuse motion with progress. Even an experienced leader like myself can fall into this trap from time-to-time.

More emails sent? Must be productive. More meetings attended? Clearly engaged. More decisions made? Obviously adding value.

Wrong. At the executive level, your job isn’t making more decisions. It’s creating clarity so your team makes better decisions without you.

Adaptive leaders are not unsystematic. Structure creates capacity. When you systematize repeat decisions and updates, you have the attention to see small signals: burnout risk, duplicate work, and drift on priorities. A clear manager operating system gives you that space.

What I’m still figuring out: The balance between staying close enough to sense issues and far enough to let leaders lead.

The Manager Operating System I’m Testing with AI

After six months of experiments, I’ve landed on three core pillars. They create the capacity for the adaptive leadership that moves teams forward. At least for now. It’s a moving target, but I digress.

Pillar 1: Sensing Systems That Keep You Attuned

The best leaders sense where their team is. Energy shifts, emerging conflicts, changing needs. But sensing requires bandwidth. If operational details bury you, you lose the capacity to read the room.

Recent research supports this link between adaptive performance and structured goal clarity. The agile way of working and team adaptive performance study found that agile practices and well-defined goals significantly improve team adaptability and outcomes.

Weekly outcome setting. Every Monday, I spend 15 minutes defining what “winning the week” looks like. Not tasks, outcomes.

Last week: “New product lead confident in their first 90 days,” “Engineering and Sales aligned on Q4 capacity,” “Board materials reflect our strategic pivot.” Research shows that companies that review goals quarterly generate 31% better returns than those that review them annually. But the real value is the forcing function to assess where your team is and what they need from you right now.

High-leverage time blocking. I protect specific blocks for work that requires full attention: coaching conversations, strategic thinking, and sensing check-ins with no agenda.

I use Notion AI to categorize my commitments by strategic value each week. If I spent 12 hours in status meetings and 2 hours coaching leaders, that’s a signal to rebalance. The tool doesn’t decide for me; it surfaces patterns I miss when moving fast.

Reflection as a sensing tool. Every Friday, 30 minutes. What went well? Where did things feel off? What signals did I pick up about team dynamics?

Experiment: Claude AI as a thinking partner for reflection.

Result: Last month, it connected three conversations where different people mentioned unclear priorities. I hadn’t noticed the pattern. Fixed it in one all-hands, immediate clarity improvement.

Failed experiment: Tried using AI to analyze team sentiment from Slack messages.

Result: Complete miss. The AI couldn’t accurately read tone or context, creating false signals that wasted time investigating non-issues. Killed it after one week.

What I’m still figuring out: How much AI synthesis is helpful versus how much pulls me away from direct observation.

Pillar 2: Building Leadership While Moving Fast

My job is to develop my team’s leadership and decision-making ability. If I’m still making all the calls after 20 years, I’m failing. But delegation without context doesn’t work. You dump problems on people, then criticize their decisions.

The key is scaffolded autonomy: increasing decision authority with the right frameworks and support.

Recent research reinforces this approach. Team Learning in the Field identifies goal clarity, coaching-oriented leadership, and team composition as structural drivers of higher performance. These findings align closely with the idea of scaffolded autonomy. Leaders who create structure without micromanaging see faster learning and better outcomes.

Decision frameworks that transfer judgment. For repeatable decisions, I create explicit frameworks that codify my thinking. Not rigid rules, clear principles and criteria.

Our product prioritization framework: strategic alignment, customer impact, execution confidence. My product lead makes prioritization calls independently now because they understand the framework I’d use.

Using the Decision Velocity Framework, I categorize decisions by reversibility and impact. High-impact irreversible decisions stay with me.

Low-impact reversible decisions get delegated with clear frameworks. Everything in between gets collaborative decision-making, where I coach thinking rather than make the call.

Experiment: AI to pressure-test frameworks with edge cases.

Result: Identified three scenarios my hiring framework didn’t cover. Added criteria before we actually hit those situations. Faster hiring decisions with fewer escalations, reduced time-to-offer by roughly 5 days.

Pattern recognition across teams. Individual team leads see their domain, but they might not notice three teams hitting the same obstacle.

I use Notion AI to consolidate themes from one-on-ones and team meetings. The AI automatically tags topics such as capacity constraints, skill gaps, cross-team dependencies, and strategic confusion.

Experiment: Monthly pattern review across all teams.

Result: Flagged that four leaders struggled with stakeholder prioritization. That’s a systemic clarity problem, not individual gaps. One facilitated session on stakeholder management helped everyone get better at strategically saying no.

Failed experiment: Tried having AI draft delegation checklists based on past successful handoffs.

Result: The AI created generic checklists that missed team-specific context. Each delegation still needed custom thinking about what context required a specific person. Wasted 2 hours before I realized the pattern.

What I’m still figuring out: when to step in and when to let leaders figure it out themselves.

Pillar 3: Strategic Efficiency Without Losing Connection

The balance I’m always striking: efficient enough to avoid operational drowning, but not so removed I lose touch with what’s happening or lose the human connection that builds trust.

Automate the routine, protect the relationship. Status updates, meeting prep briefs, and weekly summaries follow patterns and can be systematized.

Experiment: Notion automation for executive summaries based on project status.

Result: Cut prep time by 3 hours per week. Reduced status meetings from 12 hours to 4 hours per week. Reinvested that in unstructured team conversations where I listen and sense.

My rule: If it follows a template, template it. If it requires reading emotional subtext or building trust, protect it as human time.

Stay close without getting lost in the details. The sweet spot is understanding enough to ask good questions without having to review every decision.

Experiment: Claude to summarize the technical context in 3-4 sentences before project discussions. This approach mirrors the framework in Hybrid AI Agent Systems: The Leadership Edge in Automation, where AI operates as a fast task runner paired with human reasoning for context and judgment.

Result: Get informed conversation context without reading 40 pages of documentation. Stay helpful without losing hours in details that my team leads should own.

Continuous experimentation. Every quarter, one new test tool, workflow, or approach.

Experiment: AI to draft strategic communication to teams.

Result: Mixed. AI captured key points but missed the tone and personal connection needed to rally people. Learned AI is great for structure, not emotional engagement.

Failed experiment: Automated weekly one-on-one agenda generation based on recent work activity.

Result: Agendas felt impersonal and missed what people actually wanted to discuss. One direct report said it made them feel like I wasn’t paying attention. Immediate kill, back to collaborative agenda-setting.

What I’m still figuring out: Which communication should be AI-assisted versus fully human-crafted.

The 7 Habits of Highly Effective People, Updated for AI Workflows

Stephen Covey’s principles from The 7 Habits of Highly Effective People still work. Applying them through AI productivity tools changes how managers operate at scale.

I use these as weekly prompts, not rules.

Be Proactive. Run a weekly automation audit. One meeting, one report, one approval. Remove or simplify one.

Begin with the End in Mind. Write weekly outcomes before you schedule tasks. Let outcomes drive the calendar.

Put First Things First. Protect two 60-minute focus blocks. Move status to async notes.

Think Win-Win. Use delegation checklists. Give context, criteria, and a clear decision owner.

Seek First to Understand. Review AI summaries, then validate with one live question. Summaries are cues, not conclusions.

Synergize. Document one shared framework per month: prioritization, hiring, or incident response. Teams move faster when the criteria are clear.

Sharpen the Saw. Friday reflection. What moved, what stalled, what to test next week. Please keep it to ten minutes.

Pick one habit to test this week.

Experiments, Results, and Misses

Here’s what I tested over three months as I scaled from six to twelve direct reports.

Week 1: Decision audit and delegation

Mapped every decision type I was making. Asked which actually required 20 years of experience versus habit.

I personally approved every customer communication and product roadmap change. Why? “I have the context and relationships.” But my team leads had that context too. They just lacked frameworks.

Experiment: Two simple frameworks. Customer communication (brand guidelines + approval thresholds by customer size). Roadmap changes (strategic alignment criteria + customer impact assessment).

Result: Delegated both to team leads. Reclaimed 8 hours per week, zero quality issues after one month.

Week 2-3: Information flow redesign

With twelve reports, I drowned in status updates and meeting notes. Couldn’t track what each team worked on or spot cross-team patterns.

Experiment: Notion workspace where each lead maintains weekly outcomes and decisions. Notion AI generates a consolidated Friday summary.

Result: The First summary immediately showed three teams waiting on the same legal review. Fixed with one conversation. Previously, I wouldn’t have noticed for weeks. Cut the status meeting time from 12 hours to 4 hours per week.

Week 3-4: Relationship rhythms

Couldn’t maintain weekly one-on-ones with everyone without losing strategic thinking time. But didn’t want to become distant.

Experiment: Flexible cadence based on where each leader is. Newer leaders get weekly 45-minute coaching sessions. Experienced leaders crushing it get bi-weekly 30-minute check-ins. Everyone gets monthly no-agenda conversations.

Result: Team engagement scores went up 23% because people felt more empowered. I reclaimed 10 hours per week for strategic work.

Failed experiment: AI-drafted recognition messages

Tried using AI to draft personalized recognition for team members.

Result: Messages were technically correct but felt robotic. One team member said, “I could tell you didn’t actually write it.” Lesson: Automate prep, not humanity.

Quick Wins to Strengthen Your Manager Operating System This Week

You don’t need to overhaul everything. Start with one change.

Map your week to outcomes, not tasks. Five lines, ten minutes. What needs to be true by Friday for the week to matter?

Delegate one decision type. Pick one category you decide repeatedly. Spend 30 minutes creating a simple framework with criteria, and test it with one leader this week.

Move one status meeting to async. Keep one live sensing check-in where you read the room and connect with people.

Automate one weekly summary. Use Notion AI, ChatGPT, or a template for routine updates. Add your strategic context and judgment on top.

Run a two-week test on one workflow. Pick one information bottleneck, test an AI tool to organize it. Evaluate based on time saved and clarity gained. Keep or kill.

Research (MIT Sloan Management Review) shows frequent goal discussions drive better execution. Build that loop into your operating system.

• Use weekly check-ins to review priorities and blockers.
• Hold a monthly metrics review to inspect outcomes, not effort.
• Reset goals quarterly to keep focus and alignment high.

Feedback loops like these are core to adaptive leadership practices I explored in The Truth About Leadership Decision Loops in 2025.

FAQ: Manager Operating System and AI

How a Manager Operating System Helps You Scale Impact

The best leaders build systems that amplify judgment, develop team capabilities, and create space for adaptive leadership.

Your manager’s operating system should help you better sense where your team is. It should free you from operational details, allowing you to focus on coaching and strategic thinking. It should build your team’s leadership abilities without creating bottlenecks.

AI tools are powerful amplifiers, but they’re amplifiers, not replacements.

What is the judgment about where your team is at? Still you. What is the decision about what your org needs right now? Still you. The coaching conversation that develops capabilities? Definitely still you.

What AI handles: routine information synthesis, pattern spotting, and documentation that used to eat hours of your week. That gives back mental bandwidth for leadership work that only you can do.

I’m constantly refining my operating system because the work keeps changing. The team that needed one thing last quarter now needs something different. But deliberate systems and continuous improvement mean I adapt faster.

The goal isn’t working less. It’s ensuring every hour creates maximum value. Stay attuned to human elements that matter while being efficient about everything else. Build team capabilities while moving fast on strategic priorities.

That’s the operating system worth building.

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