What I Actually Automate (And What I Don’t)
I’ve been using AI integrations in my leadership workflow for a while now. I’d be lying if I said I didn’t go into it kicking and screaming at first. Not because I was opposed to AI integrations, but because in the early phases, systems weren’t particularly sophisticated. The reality was that I was drowning in administrative tasks that were keeping me from actually leading. Yes, some of it was busy work.
At the time, I was running multiple teams, consulting on strategies, and managing a roster of freelance talent across different time zones. I needed systems that could handle the routine stuff so I could focus on the decisions that move the needle. Ultimately, it was ridding myself of the busy work that tipped the scales. The thing with AI integration in leadership is that you have to strip away the buzzwords and “it” solutions and find what works for you.
Understanding AI in Leadership
Most articles about AI in leadership read like they were written by people who have never managed a team. They talk about “AI-powered decision making” and “algorithmic leadership strategies” like AI is going to replace executive judgment. For those of us on the ground, that’s not how it works. Nor should you be leaving decision-making in the hands of AI. For leaders, AI is automating the predictable tasks that eat up your time so you can spend more resources on the unpredictable human problems that require you.
It’s about creating systems that handle routine tasks so you’re fresh for the complex ones.
I use AI to manage information flow, streamline communications, and handle project logistics. I don’t use AI to make strategic decisions, evaluate team performance, or navigate client relationships. Those require human judgment, emotional intelligence, and contextual understanding that AI simply doesn’t have.
AI Integration Tasks That Matter
Here’s what I’ve automated and why:
Email Management and Response Templates: AI sorts my inbox into priority levels and drafts responses for routine inquiries. Client questions about project timelines, team scheduling requests, and status updates all get AI-generated first drafts that I can review and send in seconds instead of minutes.
The time savings here are massive. I’m not writing “Thanks for your email, I’ll review this and get back to you by Friday” fifty times a week anymore.
That said, my PA still checks this and ensures I do, in fact, get back to people by Friday.
Meeting Preparation and Note Summarization: AI reviews previous meeting notes, pulls relevant project updates, and creates agenda outlines based on outstanding action items. After meetings, it generates summary notes and action item lists that I can send to be distributed immediately.
Project Status Tracking and Client Updates: AI pulls data from our project management tools and generates weekly status reports for clients. It tracks deadlines, identifies bottlenecks, and flags projects that need attention.
This is, however, checked by the PO. AI lacks the ability to find nuanced tasks that need to be prioritized.
Research and Market Analysis: When I need background information for strategic decisions, AI handles the initial research phase. It compiles industry trends, competitor analysis, and market data that I can review and build upon.
For client survey insights, AI is an absolute game-changer. Of course, it doesn’t replace strategic thinking, but it eliminates the hours of reading and gathering of basic information before I can start actually analyzing it.
Quality Control for AI Integration in Leadership
AI might be great for automation and insights, but it needs to be quality-controlled. And this is where a PA, your teams, and stand-ups are important. My rules of thumb are:
Check Rules: Review AI-generated content before it goes out. Email responses, project updates, research summaries. Everything needs to get sampled regularly. This catches errors before they become problems and helps me understand where AI performs well versus where it needs more oversight.
Client-Facing Content is NEVER Standard AI: Anything going to clients gets reviewed by either me or my PA before sending. AI can draft it, but human eyes verify accuracy, tone, and appropriateness. Non-negotiable.
Weekly AI Performance Review: Every Friday, I spend two time blocks reviewing AI mistakes from the week. What got flagged? What responses needed significant editing? What research missed important context? This helps me refine prompts and adjust automation rules.
Backup Systems for AI Failures: AI tools go down. Prompts stop working. Systems break. I have manual processes ready for every automated task, so nothing critical gets delayed when technology fails.
How AI Integration Freed My PA for Strategic Work
For me, AI really paid off indirectly. It didn’t save me a significant amount of time for the tasks I needed to do in my work hours. For my PA, though, AI paid off huge. Before AI integration, my PA spent 60% of her time on routine administrative tasks: scheduling, basic email management, status report compilation, and information gathering. She was incredibly efficient at these tasks, but they weren’t using her to free up time for me. I still had strategic thinking tasks that were killing my productivity. Now, while AI picks up the busy work, she can get to strategic coordination, client relationship management, picking up on areas where workflow can be optimized, etc.
The ripple effect has been huge. Now, my PA has become my partner. She is no longer an administrative assistant. She’s taking the workload without burning out and most importantly she is creating exponentially more value than the routine tasks she used to handle.
What I Don’t Automate (And Why)
There are some things I don’t automate. Some for trust reasons and others because I like to be in complete control of the process.
Team Performance Conversations: AI can track metrics and flag performance issues, but discussing them with team members requires me to be present and to understand the issues at hand.
Strategic Decision Making: I still don’t trust AI to make strategic decisions. It simply doesn’t understand market nuances, client relationships, and business context.
Creative Direction and Quality Assessment: AI can help with creative tasks, but evaluating creative work requires understanding brand strategy, audience psychology, and creative intent that AI fundamentally lacks.
Client Relationship Management: AI can draft communications and track interactions, but building and maintaining client relationships requires trust, empathy, and strategic thinking that must remain human.
I think the most overlooked advantage of AI integration is cognitive bandwidth. When AI handles routine decisions, you have more mental energy for complex leadership challenges. I’m not working fewer hours, but I’m spending those hours on higher-value activities. Strategic planning instead of status report compilation. Team development instead of email management. Creative problem-solving instead of information gathering.
AI in leadership means recognizing that it’s a productivity multiplier, not a replacement for leadership skills. The leaders who integrate AI effectively will have a significant advantage because it allows them as leaders to drive results.