Your First AI Employee: What Should It Actually Do?

August 28, 2026•10 min read

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Your First AI Employee: What Should It Actually Do?

If you’ve been hearing nonstop talk about AI and wondering how to use it without losing your voice, your values, or your sanity, you’re not alone. A lot of people are either avoiding AI altogether or using it in the most basic way possible - and missing the real upside. In this post, you’ll learn how to use AI more responsibly and strategically, including how to move from simple prompts to building an AI agent that can actually save you time. We’ll also break down why context matters, how to audit your time before automating anything, and where AI can support your work without replacing the human parts that matter most.

Why “Put AI to Work” Is Bigger Than Just Prompts

A lot of people start with AI the same way they start with any new tool - they ask it for something quick and hope for a good result. That works for a while. But if you stop there, you’re mostly using AI like a search engine with extra steps. The bigger idea is this: AI should help you buy back time, reduce mental clutter, and support your best work. That doesn’t mean it should think for you. It means it should handle repeatable, structured tasks so you can focus on the parts of your work that require discernment, creativity, and human judgment.

That distinction matters. As Lindsay pointed out in this conversation, prompts are useful, but they’re only the beginning. If you just say, “Write me five posts,” or “Build me a marketing strategy,” without context, boundaries, or examples, you’ll usually get average output. Not because AI is bad - but because the input is incomplete. This is where a lot of people get frustrated. They expect a tool to magically produce something excellent without giving it the kind of guidance a real employee would need. If you hired someone brand new and gave them no training, no standards, and no examples, you wouldn’t expect great work on day one. AI is no different. The goal isn’t to use AI for everything. The goal is to use it well.

Start With a Time Audit Before You Build Anything

If you want AI to actually help you, you need to know what you’re trying to change first. That means doing a time audit. This was one of the strongest points in the conversation: too many people assume they need AI for content, when the real time drain might be somewhere else entirely. You may think the obvious answer is to automate your social posts, but once you look closely, you may discover your real bottleneck is task tracking, follow-up, scheduling, or remembering details you’ve already carried in your head all day. That’s why the first question isn’t “What can AI do?” It’s “What is taking the most time, energy, and attention from me right now?”

A good time audit helps you identify:

Repetitive tasks you do every day
Tasks that eat up mental space, even if they don’t take long
Work that doesn’t require your highest level of thinking
Processes that could be standardized

Krystal’s example was especially helpful here. She described building an AI tool for her fitness and nutrition tracking - not to replace her coach, but to reduce the mental burden of logging, checking trends, and wondering whether she was doing it correctly. The AI didn’t do the physical work for her. It helped her track the work, see patterns, and stay focused. That’s the sweet spot. When AI takes over the repetitive parts, you don’t just get time back. You often get brain capacity back too. And that matters more than people realize. If you’re mentally looping on a task all day, you’re not fully present in the work, relationships, or responsibilities in front of you.

The Difference Between a Chat and an AI Agent

One of the most important ideas in the episode was the distinction between a chat window and an agent. A chat is reactive. You ask a question, it answers. You refine it, it responds again. That’s useful, but it still depends on you every step of the way. An agent is different. An agent is more like an AI employee built to perform a specific job over and over within a clear set of boundaries. It has a purpose. It has context. It can be trained to follow a process consistently. That shift changes everything.For example, Lindsay described her “Taski” agent - a tool built to capture tasks, organize follow-ups, and assign action items. Instead of trying to remember everything herself or manually type out the same kinds of instructions over and over, she can talk to the agent, feed it meeting notes, and let it turn those notes into structured next steps. That’s not just convenient. It’s operational leverage.

Here's why this matters:

A chat can help you brainstorm
An agent can help you execute
A chat still requires your attention every time
An agent can be trained to run in the background

The key difference is consistency. If you build a good agent, you stop starting from scratch every time. You’re not just asking for help - you’re creating a repeatable system. That’s why the conversation kept returning to boundaries. An agent should know what it can do, what it can’t do, how you want things phrased, what matters to you, and what should never happen. In other words, it should reflect your values and your standards. That’s especially important if you care about your voice. The point is not to let AI speak for you. The point is to train it to support you without flattening who you are.

How to Build an AI Agent That Actually Helps

If you want to build an AI agent that saves time instead of creating more work, start with a simple framework.

1. Decide what you’re trying to replaceNot everything should be automated. Begin with the repeated task, process, or mental load that’s slowing you down most.This might be:

Managing recurring tasks
Logging health or business data
Organizing meeting notes
Creating first drafts from a clear process
Tracking follow-ups or deadlines

The most effective agent isn’t the one that sounds flashy. It’s the one that solves a real bottleneck.

2. Document the manual process first. Before you build automation, make sure the manual process is clear. This was one of Lindsay’s biggest points: good automation usually starts with a good manual system.If your process is vague in your head, the agent will be vague too.

Write out:

What starts the process
What steps happen next
What inputs the agent needs
What output you want
What should never happen

If you want AI to act like a smart assistant, you have to train it like one.

3. Add context, boundaries, and examples. This is where the quality really improves. AI works better when it understands:

Your voice
Your preferred format
Your “do this, not that” rules
The tone you want
The purpose of the task

Lindsay emphasized that this is what many people miss. They focus on the prompt, but skip the context. Context tells the tool what matters. Boundaries keep it aligned. Examples help it match your standards. If you want a better result, don’t just ask for output. Teach the system what good looks like.

4. Validate the output before you trust it. AI can speed you up, but it should not replace your judgment. That’s why both speakers stressed the importance of reviewing what the tool creates. Krystal’s health example made this especially clear - the tool supports her, but it does not replace her coach. In the same way, AI can help you prepare, organize, or draft, but you still need to approve, correct, and refine the final result. The best workflow is not “hands off.” It’s “human-led, AI-supported.”

Who Should Use AI Agents First

Not every task is the right first project. But for the right person, an AI agent can be a huge win.This approach is especially useful if you work in a role with a lot of repeated processes, high follow-up volume, or constant context switching.Some of the clearest examples discussed in the episode included:

Network marketers - for duplication, daily follow-up, and repeatable communicatio
Bookkeepers - for recurring admin and reconciliation support

Real estate agents - for SOPs, contracts, and team handoff workflows
Contractors and builders - for punch lists, scopes, and job-site task capture
Fitness professionals - for tracking client progress or supporting personalized systems
Content creators - for organizing data, identifying what’s working, and speeding up production.

The common thread is not the industry. It’s the repetition. If you or your team are doing the same kinds of tasks over and over, there’s a good chance an AI agent can help you work faster and more cleanly. But there’s an even deeper reason this matters: AI is quickly becoming part of the modern workplace. That doesn’t mean you need to become a technologist. It does mean you need to become fluent enough to stay relevant, effective, and adaptable. Learning how to use AI well is not just about convenience. It’s about being prepared for how work is changing.

Use AI Responsibly, Not Recklessly

There was a strong thread throughout the conversation about responsibility. That part matters. AI is powerful, but power without discipline creates mess. If you use it carelessly, you waste resources, create low-quality output, and contribute to the very problem you’re trying to solve. If you use it well, you can be more efficient, more thoughtful, and more intentional with the tools available to you. That means:

Don’t generate endless content you won’t use
Don’t ask AI to replace your thinking
Don’t publish output you haven’t checked
Don’t use it in ways that flatten your voice or values

This is where the “UnShakable” mindset comes in. The point isn’t to become dependent on tools. The point is to stay grounded while using them wisely. There are real concerns around AI - security, jobs, energy use, and misuse. Those concerns are valid. But avoiding the tool altogether doesn’t solve those problems. Learning how to use it carefully, efficiently, and ethically is a much better response.

That’s the real takeaway from the conversation: you don’t have to love AI blindly, and you don’t have to fear it blindly either. You can learn to use it with wisdom.

Final Thoughts: Build Systems That Give You Back Time

The biggest win from AI isn’t novelty. It’s margin.When you use AI well, you reduce repetitive work, protect your attention, and create room for the parts of life that matter most. You may not eliminate the work itself, but you can absolutely reduce the mental load around it.That’s what makes AI agents so powerful. They’re not just about speed. They’re about creating a smarter relationship with your time, your energy, and your decision-making. If you take anything from this, let it be this: start with your time audit, build around a real process, add context and boundaries, and keep the human in charge.

That’s how you use AI in a way that actually serves you.

Want to go deeper? Listen to the full conversation or join the challenge if you want hands-on help building your first agent.

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