The And Life

Cognitive Overload and the Pressure to Keep Up

I was an early adopter of using ChatGPT for content.

At first, it was fun. It was easy to spot gaps, fill holes, create drafts, rewrite ideas, and move faster than I could with a blank page in front of me. It felt like I had found a tool that could finally keep up with the speed of my brain.

Until one day, it stopped feeling like support and started moving faster than my brain could keep up.

And then everything exploded.

New tools started popping up daily. Everyone seemed to be going in a different direction. LinkedIn was full of posts about agents, automation, prompts, workflows, copilots, and the next thing we all apparently needed to learn before lunch. 

Seemingly overnight, AI was embedded into everything and I didn’t understand any of it.

I felt like I had missed the memo. Where had everyone gotten all this information? I had been ahead, and then suddenly I felt behind.

So I panicked.

I started watching endless YouTube videos, opening 18 tutorials at once, saving every promising post, and taking in more and more information without pausing long enough to implement any of it.

The irony was painful.

I was using AI because I wanted to create more capacity, but learning about AI was becoming another source of overload. This cycle was the textbook definition of cognitive overload: when the brain is asked to process more information than it can reasonably hold, sort, or act on at once.

The issue is not only that AI tools exist. It is that we are surrounded by constant information about them.

The Shift

The shift happened when I stopped trying to absorb everything.

I started adding instructions into my own prompts like: only do this step, do not suggest the next step. I closed the videos. I tried the thing in front of me. I searched only when I needed something specific.

Completing a small step and implementing a small process became far more valuable than continuously taking in more information.

That was the lesson.

AI may be able to process an enormous amount of information in seconds, but in the early stages of adoption, we cannot work at the speed of AI.

We have to work at the speed our brains can actually absorb, test, and apply.

As a leader, the biggest shift toward getting your team on a productive path is realizing that information just became cheap.

Stop saving every template. Stop hoarding documents full of “helpful” information. AI can reproduce, reframe, and rebuild much of that in seconds. Just like a cluttered home can suck the life out of you, cluttered digital folders are no longer a badge of preparedness. They are often just another place for overwhelm to hide.

Whiteboard. Brainstorm. Spend time with your team. Look for the parts of the work that are actually slowing people down.

Leave more of the raw information to the machine.

Because when you stop trying to personally hold, save, and update every piece of information, AI finally starts to become useful.

Ambitious Mothers Are Sitting in the Middle

Organizations are investing in AI platforms, productivity tools, automation, copilots, agents, and training. Leaders are being asked to show value, improve efficiency, increase adoption, and prove the investment is changing how work gets done.

At home, parents are trying to understand what AI means for their kids, their learning, their creativity, their privacy, their screen time, and their future.

Ambitious mothers are sitting right in the middle of it.

We are trying to lead change at work while also making sense of what this technology means at home. We are expected to be informed, confident, strategic, careful, curious, and calm while the entire landscape changes every time we open LinkedIn.

It is not just AI adoption.

It is AI overload.

And while everyone is talking about the tool, few are talking about the challenge of leaders to implement the human layer & approach the shift with care, compassion and patience on all fronts.

The Real Problem: AI Can Help, But First You Have to Learn How

Yes, AI can help draft an email.

It can summarize a meeting, rewrite a paragraph, create a checklist, brainstorm ideas, clean up tone, organize information, and help you get past the blank page.

But first, you have to know how to ask.

You have to know which tool to use, what information is safe to enter, whether the output is accurate, how much editing it needs, when it is appropriate to use, and whether it is actually helping you think more clearly.

That is the part people skip when they talk about AI productivity.

Before AI saves time, it often requires time.

Time to test. Time to learn. Time to compare outputs. Time to build trust. Time to make mistakes. Time to create boundaries.

This is especially true when the workflow is more complex than a simple email.

Finance is a good example. Organizing invoices, managing P&Ls, forecasting spend, tracking budgets, and spotting gaps are all areas where AI should be able to help. The potential is obvious.

But it is not always plug-and-play.

The files need to be readable. The data needs to be structured. The tool needs the right access. The prompts need to be specific. The output needs to be reviewed. The workflow needs to be safe.

This is where AI conversations can get too glossy.

Someone says, “AI can help with finance.”

Sure.

But first, you may need to clean the data, define the process, understand the tool, test the output, and make sure you are not creating a beautiful summary from messy inputs.

That does not mean the use case is wrong.

It means adoption requires process thinking, not just enthusiasm.

The Agent Gap

One of the clearest examples of AI overload right now is the conversation around agents.

Everyone is talking about agents.

But every time I go to build one, there is some practical snag. The agent cannot read the file. The integration does not work the way I expected. The permissions are off. The workflow falls apart before it becomes useful.

And somewhere in that process, I find myself wondering: does anyone actually know what they are talking about, or are we all listening to AI-generated human-spoken content about AI-generated workflows that only work perfectly in demos?

That may sound cynical, but it is also honest.

There is a gap between what AI is supposed to do and what it feels like to implement it inside real tools, real systems, real companies, and real lives.

That gap is where leaders need to slow down.

Not stop.

Slow down enough to test what works in reality. While the technology continues to accelerate at full speed around them – and be open to retesting 6 months later, with a different lens, different experiences and likely a much more advanced version of the same technology than 6 months previous.

Leaders Go First, Even When They Are Still Learning

One of the hardest parts of leading through AI change is that your team will often follow your posture before they follow your instructions.

If you are panicked, they will feel the panic. If you dismiss AI completely, they may assume it is not worth learning. If you use it carelessly, they may copy unsafe habits. If you quietly use it but never talk about how, they may miss the chance to learn from you.

This does not mean you need to be an AI expert.

It means you need to model responsible learning.

For years, I have led high-visibility, high-volume teams. In the early days, I taught largely from experience. I openly talked about situations I encountered in my planning days, what went wrong, what I learned, and what I would do differently the next time.

As the team grew, I often hosted quarterly learning roundtables. They were never meant to shame anyone’s mistakes. They were meant to help us learn from each other, because in event planning, the lesson from one mistake can prevent ten more.

I am approaching AI the same way.

I openly talk about what I have tried, what worked, what did not, and what went wildly sideways. Sometimes the hours spent testing a tool, building a workflow, or trying to make an agent work could look wasted from the outside. But they were not wasted. They were hours spent learning how AI behaves in the real world, where the files are messy, permissions are weird, and the demo version rarely matches the Tuesday afternoon version.

Those hours helped me figure out how to lead my team into the future with more honesty. Part of that means sharing what I have already learned. Part of it means creating enough space for them to have their own learning too.

In my team, we learn together. Some people are using AI more than others. Some still say, “It did not give me useful answers.” Often, when we dig into the prompt, the issue is not that AI could not help. The issue is that the prompt was not giving the tool enough basic direction.

That is not failure.

That is the learning curve.

AI fluency is not automatic. People need examples, practice, feedback, and permission to improve. We block time to sit together, in person, and build workflows or agents together. Not because we have every answer, but because the learning has to happen somewhere, and it is far better when it happens out loud.

As a leader, I want my team to copy three things from me: curiosity, better prompting, and verifying outputs.

Not blind trust.

Not avoidance.

Curiosity, clarity, and review.

The Same Questions Show Up at Home

At work, leaders are trying to help teams use AI without becoming overwhelmed, reckless, or dependent.

At home, mothers are trying to help children grow up around AI without fear, shortcuts, or unhealthy boundaries.

The concerns are deeply connected.

• Can we trust this output?
• Will people over-rely on it?
• What information is safe?
• How do we know if it is accurate?
• How do we introduce powerful tools without creating chaos?

And still, there is excitement which holds the same parallels.

AI can also help kids & corporate teams create almost anything they can imagine.

The goal is to help both groups navigate AI enabled tools with creativity, judgment, and boundaries.

A Better Starting Point

One of the fastest ways to stay stuck is trying to understand every AI tool before using any AI tool.

That is where cognitive overload wins.

You do not need to know every platform, read every article, learn every technical term, or chase every feature update.

You need a practical starting point.

Pick one tool. Pick one low-risk use case. Pick one recurring task. Pick one way to reduce friction. Then practise.

Use AI to draft a first version of your weekly team update. Turn meeting notes into action items. Create a checklist from a process you repeat often. Rewrite a long message into a clearer version. Summarize a document, then verify what matters.

Small, repeated use builds confidence faster than endless research.

A good AI use case should make something easier to think through. It should help you see what matters, what is missing, what needs action, what can be simplified, and what should be reviewed.

If AI gives you more options, more drafts, more tools, more tabs, more noise, and more uncertainty, it is not reducing cognitive load.

It is adding to it.

The goal is not more AI.

The goal is more clarity.

A Simple Filter for AI Overload

When you feel overwhelmed by AI, ask:

1. What problem am I trying to solve?
Do not start with the tool. Start with the friction.

2. Is this task safe for AI support?
Avoid confidential, sensitive, legal, financial, employee, customer, or private information unless your organization has approved that use.

3. What role should AI play?
Is it drafting, summarizing, organizing, brainstorming, checking, or explaining?

4. What still needs human judgment?
Context, accuracy, tone, ethics, decisions, and accountability stay with you.

5. How will I know this helped?
Did it save time, improve clarity, reduce back-and-forth, or create a better starting point?

This framework keeps AI from becoming another vague pressure.

It turns it into a leadership tool.

Final Thought

AI is supposed to help us work smarter, but the beginning of adoption can feel like the opposite.

Too many tools. Too much information. Too much pressure. Too many opinions. Too many unknowns.

That feeling is not failure. It is cognitive overload.

The way through is not to learn everything at once.

The way through is to slow the noise, choose one useful starting point, set clear boundaries, and practise in a way your team can learn from.

As leaders and mothers, we do not need to have every answer before we begin.

We need to model how to move through uncertainty with judgment, curiosity, and care.

That is where AI leadership begins.