After being away for a bit, this week’s AI update shifted away from headlines and toward something more practical: how we actually start using AI agents in our work and daily routines.

The conversation focused on moving past AI as a chatbot and toward AI as a worker that can research, build, analyze, and execute tasks. The real takeaway is that AI is not just a faster way to ask questions. It is becoming a way to get real work done.

Here’s what stood out.

1. AI is moving through three levels of capability

The first level is chat. This is what most people use today: asking questions, writing emails, or generating ideas.

The second level is automation. This is where AI is inserted into workflows using tools like Zapier or Make.

The third level is agents. This is where AI can plan a task, execute it, revise it, and continue working with less handholding.

Most people are still operating at level one. The real leverage is starting to explore level three.

Why it matters:
If you only use AI like a search engine, you’ll see small improvements. If you use it to complete entire tasks, the gains are much bigger.

2. The mindset shift is becoming a director instead of a doer

One of the strongest ideas in the discussion came from Dan Martell’s concept of becoming a director.

Instead of trying to do every step yourself, you define the outcome, give clear instructions, and review the results. AI does the heavy lifting while you guide the direction.

The key steps are simple:

• Define the outcome
• Provide clear instructions or examples
• Review and refine the result

You do not need perfect prompts. You need a clear goal.

Why it matters:
The biggest mistake people make is trying to control every step. AI works better when you focus on the outcome and guide the process.

3. AI doesn’t always reduce work. Sometimes it expands it.

A Harvard Business Review study found that AI tools often increase workloads rather than reduce them. That sounds negative, but it’s actually a sign of increased capability.

When you can do things faster or better, you start doing more of them. You refine more. You improve outputs. You offer more value.

A simple example is writing emails. Before, you wrote and sent it. Now you might draft it, run it through AI, improve the clarity, and add better incentives.

The result is a better output, but it may not save time.

Why it matters:
AI often raises the standard of work rather than eliminating work.

4. The best way to start is simple: one tool, one task

There are too many AI tools to try everything. The better strategy is to pick one tool and use it deeply.

Then choose one task you do every week that takes time or mental energy and let AI help handle it.

This could be research, competitive analysis, content creation, coding, or administrative work.

Why it matters:
Depth beats dabbling. Learning one tool well is far more valuable than experimenting with twenty.

5. Practical uses often start small

One of the best examples this week was a simple one.

Daphne built a small phone app using Claude to track her dogs’ vitamin schedule. The schedule changes weekly and is easy to forget. Claude generated the code and showed how to deploy it as a local app on her phone.

It solved a real problem without needing traditional development.

Why it matters:
AI allows people to create small, custom tools for everyday problems that previously required a developer.

6. Privacy concerns are real, but they shouldn’t stop progress

Agentic systems become more powerful when they connect to tools like Slack, email, and other systems. That naturally raises privacy questions.

The key is balance. Protect sensitive information, but don’t let fear prevent you from experimenting.

In many cases the value of using these tools outweighs the risk.

Why it matters:
Over-protecting yourself can lead to missing the opportunity entirely.

7. AI can increase the value you deliver to clients

One example discussed was using AI to generate competitive analysis reports for clients.

AI could research competitors, analyze pricing and positioning, pull reviews, and summarize insights. That type of report can help clients see their market more clearly.

It may take very little time with AI, but it creates significant value.

Why it matters:
AI isn’t just about saving time. It’s about delivering better insights and stronger services.

8. AI often makes people more capable, not less busy

By the end of the conversation, one thing became clear. AI hasn’t made us less busy. If anything, it’s made us more active.

But the difference is capability.

Daphne can now handle CSS tasks that previously required help. She can test ideas and learn faster. That may take time in the moment, but it builds skill and independence.

Why it matters:
AI doesn’t just automate work. It expands what individuals are capable of doing.

Final Summary

The big lesson this week is simple: AI is shifting from a tool you talk to into a system that can actually perform work.

The people who benefit most will not be the ones chasing every new tool. They will be the ones who:

• Define clear outcomes
• Use AI to execute tasks
• Focus on one tool at a time
• Experiment with real problems
• Use AI to create more value for others

AI may not reduce your workload. In many cases it increases what you are able to do.

But that is where the opportunity is.