When AI Becomes Your Partner, Managing It Becomes Your Job
Six stories of shared experience in stepping into AI management.
Last month, Claude misunderstood my project brief and delivered something completely off-target. I found myself staring at the output, wondering:
Should I start over, or take a few minutes to coach it toward what I actually needed?
That moment of choice reveals how work has quietly changed.
We've become responsible for directing systems that can process information faster than we ever could, but still need our guidance to understand what matters.
When it works, we feel brilliant.
When it fails, we wonder if we're the problem.
Most of us stumbled into this role over the past couple years, moving from simple requests to designing workflows and making daily decisions about what to delegate.
The surprising part? Success has less to do with technical expertise and more to do with the softer skills of collaboration — patience, clarity, and knowing when to step back.
In the work that follows, six of us AI enthusiasts — myself,
, , , , and — gathered to share our stories about learning to manage AI effectively. The experiences you're about to read tell there's no single blueprint for AI management, but there are shared patterns in how the best relationships develop.Before we dive in, let’s meet the other five AI managers; if one or all of these resonate, please subscribe:
Claudia - AI Weekender: Data scientist turned AI builder who spent six years building machine learning models in FinTech startups before discovering how fundamentally different modern AI management is from traditional model development.
Daria - AI Blew My Mind: Turn AI into your unfair advantage with prompts, tools, and workflows that upgrade how you think and work.
Joel - Leadership in Change: helps you lead with sustainable impact and conviction, turning AI into your biggest advantage in business, solopreneurship, and mission-driven work.
Wyndo - AI Maker: Shares AI workflow and thinking framework to build smarter, work faster, and live better—with AI.
Zain - StrAItegy Hub: Your insider guide for the AI era. We're the colleague who translates the hype into how-tos and helps you turn shiny tools into trusted systems.
The stories start here.
1. Zain / The Great Re-Allocation
Working at a company the size of General Motors, I get a clear view of the real, tectonic shifts happening under the surface of corporate life. While most of the conversation is about new tools, the most profound change isn’t about technology at all. It’s about what it means to be valuable. For me, managing AI is about navigating this personal and professional transition.
For the last two decades, a successful corporate career was built on being the expert “Doer”—the person who held the knowledge and could execute tasks with speed and precision. That era is over. AI now handles execution, research, and first-draft analysis better and faster than any human can. The skills that once built careers are now a commodity.
This is what I call the Great Re-Allocation of professional value. Your worth is no longer in doing the work, but in your ability to direct it. This is the biggest promotion opportunity of our careers: the shift from Doer to Director. The Doer was valued for spending weeks creating an exhaustive competitive analysis deck. The Director is valued for tasking an AI with synthesizing a competitor's last three earnings calls, recent patent filings, and customer forum sentiment into a single strategic brief—and then using their expertise to decide what it means for our product roadmap. The work is no longer in the complexity of the artifact, but in the wisdom of the prompt and the courage of the decision.
So, to answer the question: what does managing AI mean to me? It means consciously managing my own evolution. It’s about re-allocating my focus from providing the answers to framing the right questions. It’s about trading the comfort of execution for the responsibility of judgment. Execution has become a commodity. Your strategic judgment is now the scarce asset.
2. Claudia / From Training Models to Coaching Conversations: A Data Scientist’s Journey
I spent six years at FinTech startups building ML systems for credit scoring and fraud detection. Then I built something that made me realize how fundamentally everything has changed: an AI assistant that can answer questions about my newsletter content.
In the old world of information retrieval, everything was based on keywords. You know that frustrating feeling when you're searching for something that you know exists, but you can't find the magic combination of words? Traditional search systems, from Google Search to enterprise tools like Elasticsearch, are essentially sophisticated word-matching games. They’re effective at what they do, but they’re still fundamentally limited by whether your search terms happen to match the exact words in the content.
My AI assistant works completely differently. Instead of keyword matching, it understands meaning and context through what's called semantic search. For example, when someone asks, “how do you transition from analytics to working with AI?”, it doesn’t hunt for those exact words. It captures the question’s intent and surfaces relevant content, such as my posts about "moving from analyst to data scientist" or "the work projects that helped me break into data science.”
This semantic understanding is a game-changer. Instead of spending months training an AI from scratch on my content, I connected an existing AI technology to my content library. The AI retrieves relevant pieces based on meaning, then crafts personalized answers that combine my content with its broader knowledge, and it sounds like me!
But here's what surprised me most: although it's technically a Python web app, the important work feels less like traditional programming and more like coaching. Crafting the instructions for how the AI should respond feels more like wordsmithing and experimentation than coding.
Eight months into this transition, I've realized that being an "AI manager" in my field means we're moving from a world where we had to speak the machine's language using mathematical transformations to one where machines can process our natural language.
3. Daria / Becoming Your Own AI Manager
When I first heard the term “AI Manager”, I pictured a boss in a big office, a technical leader responsible for deploying AI technology across an entire company.
But the more I use these tools, the more I realize the most important AI Manager isn't a job title. It’s me. I'm the one making dozens of small choices every day about how I use AI: if it should help me, add to my work, or do a task for me. The question is whether I'm doing it intentionally.
Every day I have to choose: do I let an AI brainstorm for me, or do I think on my own first? Do I accept and implement a solution it proposed that I didn't even think through? Do I use it to confirm my own thoughts or to challenge them? Do I accept a suggestion that flattens my own writing voice?
When we just react without thinking, we risk losing our own point of view. Our critical thinking muscles get weaker, and our own way of solving problems gets pushed aside by the AI’s first, easiest suggestion.
There’s a second problem, too: being a bad manager. At first, like most people, I treated AI like a magic box and gave it lazy instructions. Naturally, I got generic, useless results back. As I learned more, I understood the problem wasn't the AI. The problem was me. My frustration was a mirror of my own bad habits and how poorly I was managing it.
So, being an effective AI Manager has become a three-part role. First, I manage myself by consciously deciding which jobs to keep, which to delegate, and which to collaborate on. Second, I manage my AIs like a team, giving them clear instructions and coaching them with feedback. And third, I act as an orchestrator, picking the right tool for the right job. This way, the whole system becomes an extension of my abilities, making me more effective.
In the end, being my own 'AI Manager' has less to do with mastering technology and more to do with self-management. It all comes down to this single question: Am I being intentional with how I use these tools, or just reacting? Answering that question, every day, feels like the real work.
4. Jenny / From Problem-Solver to System Builder
My first AI management was asking ChatGPT to write better emails or getting Claude's help debugging code. Simple requests, immediate outputs. How hard could that be, right?
After building dozens of AI-powered tools and automating workflows, I realized something unexpected:
Managing AI as a solo builder isn't about delegating tasks, it's about designing collaborative infrastructure.
Traditional management is about coordinating people and processes. But as a solo AI manager, I'm designing systems that handle the entire pipeline of execution. When I outsource my web search to AI, I didn’t expect all LLM deliver the same outcome, I assigned different roles based on the capabilities of each. When I built a second brain that analyzes my writing patterns and surfaces insights, I wasn't just optimizing workflows, I was selecting specific AI tools to work for different scenarios.
The management challenge becomes fundamentally different. Instead of setting guidelines for team members, I'm architecting how my AI components interact with each other and participate in my working process. Rather than status meetings and feedback loops, I'm designing data flows and decision trees that let me operate like a one-person agency.
To me, managing AI as a solo builder means creating infrastructure for rapid idea validation and execution. You become less of a traditional individual contributor and more of an ecosystem manager, where success is measured by how effectively your AI systems amplify your ability to turn curiosity into working solutions.
5. Joel / Marketing & Business AI Manager
For me, managing AI didn’t start with ChatGPT.
For those of us in business and marketing, it began years ago with Google Ads, testing variants and using what is working best. I wasn't coding AI, but I was already managing outcomes, quietly guiding tools to make smart decisions on my behalf.
That early experience revealed something crucial: working with AI isn't about mastering technology. It's about developing intuition for when and how to guide intelligent systems toward better results.
That foundation evolved into something more intentional. Today, I lead what I call my Awesome but Invisible team ;), a collection of AI-powered products that expand my capacity without hiring anyone new.
A designer through Napkin.io
A coder via Claude.ai
A researcher with Perplexity.ai
A creative creator with my custom GPT (JoelGPT)
Automations that turn scattered ideas into headlines in my Notion.com dashboard and iPhone shortcuts
Together, we function like a five-person creative team, but the dynamic is different. Instead of email chains and status meetings, I prompt, review, refine, and repeat. The feedback loops are immediate. The iterations are rapid.
To me, this approach reveals a fundamental truth:
AI isn't about replacing human judgment; it's about amplifying your capacity to think, create, and execute at scale. With this workflow, I have so much more time to focus on the final 10-20% of finetuning my work while the first 80% is handled by my AI team with some strategic prompting and guiding.
I believe that shift demands a new kind of leadership, one that's less about traditional delegation and more about collaboration with intelligent systems. You don't need to master every AI tool or understand the underlying algorithms. You need to discover a workflow and collaboration structure that fits your role and reinforces your unique voice.
The magic happens when you stop trying to control AI and start learning to create along with it.
6. Wyndo / How I Manage My AIs in My Workspace
Managing AI used to mean crafting the perfect prompt and hoping for good output. But after six months of experimenting with AI that can actually access my work environment, I've learned that managing AI feels more like managing a remote team member who's incredibly capable but needs clear direction and boundaries.
My breakthrough came when I realized my AI wasn't just giving me advice anymore—it was taking actions across my entire digital workspace. When I tell Claude to analyze a client meeting and create a project plan, it's reading actual transcripts from Fathom, creating real databases in Notion, and updating my actual task list in Todoist. Suddenly I'm managing an AI that's actively participating in my work rather than just observing from the sidelines.
This changes the management dynamic completely. I find myself setting boundaries for what my AI can and cannot do autonomously, much like establishing permissions for a new team member. I've learned to give my AI clear project ownership while maintaining oversight on execution quality. When it creates a project timeline, I review and adjust. When it drafts follow-up emails, I edit tone and add personal touches. The AI handles the coordination and initial execution; I handle strategy and final refinement.
The most interesting part is learning to delegate to something that never gets tired, never misunderstands context (because it can see everything), but also never questions whether a task makes strategic sense. Managing AI that lives in your workspace means becoming comfortable with rapid execution while staying sharp on direction and quality control. You're managing an incredibly efficient team member that can work across every system you use, which requires a completely different skillset than managing traditional AI conversations.
How We’re All Learning to Lead AI
We all arrived at the same truth through different experience: the real skill in AI isn't technical mastery, it's learning to orchestrate intelligence. Our journeys revealed something unexpected:
Zain identified the Great Re-Allocation happening beneath corporate surface changes: professional value shifting from execution speed to strategic judgment.
Claudia transitioned from building statistical models to crafting conversational coaching, discovering that semantic understanding eliminates the language barrier between humans and machines.
Daria realized that the most critical AI manager isn't a job title—it's the daily choice to intentionally direct these tools rather than drift into dependency.
Joel assembled his "Awesome but Invisible team," proving that AI management is really about designing collaborative workflows that multiply creative capacity.
Wyndo learned that effective AI management mirrors leading high-performing remote teams: clear boundaries, strategic oversight, and trusting capable systems to execute.
I, Jenny, evolved from reactive problem-solving to proactive system architecture, building AI infrastructure that continuously improves how she works and creates.
What we discovered: managing AI successfully has less to do with prompt engineering and more to do with workflow design, strategic thinking, and knowing when to step in versus when to step back.
The revolution isn't in the technology, it's in how we're learning to lead it.
Have you found yourself stepping into this new kind of management role in your daily life or career?
What does managing AI mean to you?
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