It’s August, it’s hot, and everywhere you look, kids are heading back to school. Families are adjusting schedules, stores are filled with notebooks and backpacks, and there is a familiar sense that summer is winding down and it is time to get serious again.
This year, I have been thinking about back-to-school season a little differently because, in many ways, I am going back to school too.
Over the past several months, I have been investing a significant amount of time in learning more about artificial intelligence and, more importantly, learning how I want to use it in my business. I am not trying to become an AI expert, and I am not interested in adding technology simply because everyone is talking about it. What I want is a better understanding of where AI can improve the way I work, where it can make my business more efficient, and where it can help me create more time for the work that requires my experience and attention.
What has surprised me most is that learning AI has made me think less about technology and more about the way my business actually operates.
Investing in yourself looks different as your career evolves.
Early in our careers, investing in ourselves is usually expected. We pursue degrees, certifications, training, mentors, and new experiences because we are still building our knowledge and credibility. Learning is part of the job.
As we become more experienced, that relationship with learning can change. We become the person others come to for answers. We build expertise, develop reputations, lead teams, make decisions, and become increasingly comfortable in the areas where we know what we are doing.
That experience is valuable, but it can also make becoming a beginner again surprisingly uncomfortable.
I have found that AI is forcing me back into that beginner mindset. There are tools I do not understand yet, processes I am still figuring out, and questions I could not have asked a year ago because I did not know enough to ask them.
There is something healthy about that.
I wrote about a related idea in Reinventing Your Career: Why Executives Must Evolve Before the Market Forces Them. Career reinvention does not always mean making a dramatic move or abandoning what you have built. Sometimes it means recognizing that the world around you is changing and choosing to evolve before circumstances make that decision for you.
That is how I am approaching AI.
I am learning from people who understand how the pieces fit together.
One of the smartest decisions I have made in this process has been recognizing that I do not need to figure everything out on my own.
I have taken online classes with Maria Elena Duron, found of Smart Brand System, who has helped me understand the Google Workspace universe differently. One of the most useful lessons I have learned from her is that AI becomes much more valuable when you stop thinking about each tool separately and start understanding how the technology you already use can work together.
That has changed the way I look at my own operations. Instead of asking what a particular AI tool can do, I am looking at how information moves through my business, where I am repeating work, where I am wasting time searching for information, and where a better connection between systems could make the entire process easier.
I have also signed on to work with Austin Belcak, who created the job seeker platform Cultivated Culture, to build what I have started calling my “business brain.”
That work is teaching me another important lesson: AI is only as useful as the context you give it. If the knowledge inside a business is scattered across documents, emails, folders, systems, notes, and the owner’s head, there is a limit to how effectively AI can use that information.
The same is true inside larger organizations. Institutional knowledge often lives in places that are difficult to access, and sometimes it lives almost entirely with particular people. Before asking what AI can automate, leaders may need to ask a more basic question about how knowledge is captured, organized, and shared.
Before you choose an AI tool, look carefully at the work.
One of the easiest mistakes to make with AI is to begin with the technology instead of the problem.
People ask which tool they should be using, which platform is best, or what everyone else is trying. Those questions may eventually matter, but they are not the best place to start.
A more useful exercise is to spend a week paying attention to how you actually work.
Look at the tasks you repeat, the information you constantly search for, the processes that take more steps than they should, and the work that routinely gets delayed because someone has to find something or wait for an answer.
Then separate that work into a few broad categories.
Some work is repetitive and procedural. Some involves searching for information that already exists somewhere. Some requires analysis, judgment, and decision-making. Some depends heavily on trust, relationships, influence, or understanding another person.
Once you see those distinctions, the role of AI becomes much easier to evaluate.
You may find tasks that can be automated almost completely. You may identify work where AI can provide a useful first pass, but a person still needs to review the result. You may also recognize areas where technology can support the work but should never be responsible for the outcome.
That is a much more thoughtful way to approach AI than simply adding another tool to your technology stack.
Efficiency matters only if you know what you will do with the time.
One of the reasons I am investing in AI is that I want to make my business more efficient, but efficiency by itself is not the goal.
If technology saves me two hours of administrative work every week, the real value depends on what I do with those two hours. If I fill the space with more email, more meetings, and more busywork, then I have not gained very much.
If I use that time to work more closely with clients, think more deeply about their positioning, improve a process, develop a stronger strategy, or build a better business, then the efficiency becomes meaningful.
Executives should be asking the same question.
If AI gives a team more capacity, where should that capacity go? Should it support strategy, innovation, customer relationships, employee development, better decision-making, or work that has been consistently pushed aside because there was never enough time?
Efficiency without intention can simply create room for more work. The value comes from deciding what deserves the time you are creating.
Learning AI has made me value human expertise even more.
There are parts of my work where AI can be extremely helpful. It can organize information, compare documents, identify patterns, help me think through a process, and reduce some of the repetitive work involved in running a business.
There are other parts of my work where the human element is the entire point.
When an executive tells me about something they accomplished, they often describe it as simply doing their job. They might tell me they led an initiative, managed a team, implemented a system, or handled a difficult transition.
The real value often does not appear until we start talking about what actually happened.
What problem were they solving? What was at risk? Who did they have to influence? What resistance did they encounter? What changed because they were involved? How did that change affect the business?
What conversation is where an ordinary responsibility becomes a meaningful accomplishment.
AI can help me organize the information after we uncover it, but it does not replace the experience required to know which question to ask next or the judgment required to understand why a particular detail matters.
That connects closely to something I wrote in How to Tell Your Story So People Remember You: A Guide for Executive Leaders. A strong professional story is not simply a list of facts. It requires context, meaning, and an understanding of what another person needs to recognize about your value.
As AI becomes more common, I think that distinction becomes even more important.
Executives should ask better questions about AI before making bigger decisions.
If you are trying to determine where AI belongs in your career, your team, or your organization, I would start with three practical questions.
The first is: What are we actually trying to improve?
Maybe your team spends too much time searching for information. Perhaps reporting takes hours every week. Maybe customer information lives in too many places, or key processes depend on one person knowing how everything works.
Identify the problem before you decide what technology belongs in the solution.
The second question is: What should technology assist with, and what should people continue to own?
Not every task that can be automated should be automated. Judgment, confidentiality, relationships, risk, and consequences still matter.
AI may help someone prepare for a difficult conversation, but that does not mean AI should conduct the conversation. It may summarize information before a decision, but that does not mean the system should make the decision.
The third question is: What will we do with the capacity we create?
If the answer is simply “more work,” then you may be missing the larger opportunity.
Investing in yourself does not mean learning everything.
There is no realistic way to keep up with every AI platform, every new feature, and every new prediction about how work is changing. Trying to do that would probably become a second job.
Investing in yourself requires more focus than that.
Ask what you need to understand for the work you do today and the work you want to be doing in the next few years. Think about what is changing in your profession, where your knowledge may need updating, and which new capability would make you more effective.
Then decide what learning actually deserves your time and money.
For me, that has meant taking classes and hiring people who know more than I do. It has also meant spending time experimenting, making mistakes, asking questions, and discovering where I have gaps in my own understanding.
That is part of the investment.
Professional development is not only about adding another credential to your LinkedIn profile, though I did just get the Certified Executive Resume Master (CERM) certification from CDI. Sometimes it is about improving the way you think, learning to ask better questions, or discovering a better way to do something you have been doing for years.
Your experience does not disappear when you learn something new.
One reason experienced professionals sometimes hesitate to step into unfamiliar territory is that being new at something can temporarily make us feel less capable.
That feeling can be misleading.
Learning AI has not erased anything I already know about career strategy, executive positioning, business, or working with people. I bring all of that experience into what I am learning now.
Experienced leaders do the same thing.
You are not approaching a new technology with the perspective of someone who has never led a team, managed risk, made difficult decisions, navigated organizational politics, or dealt with the consequences of getting something wrong.
Your experience comes with you.
I explored a similar idea in Career Identity After a Defining Role Ends: How Senior Leaders Rebuild Confidence Without Shrinking Themselves. A role may end and a title may change, but your judgment, pattern recognition, operating maturity, relationships, and leadership experience do not simply disappear.
Learning something new does not mean starting over. It means adding another layer to what you already know.
Back-to-school season may have something to teach the rest of us.
I do not know exactly where my AI learning will take my business yet, and I am comfortable with that.
What I do know is that I am already thinking differently about how the business works. I am questioning processes that I had stopped questioning, thinking more intentionally about how information is organized, and looking for ways technology can support the client experience without getting between me and my clients.
I am also becoming clearer about which parts of my work actually require me.
That has already made the investment worthwhile.
For executives and professionals, I think there is a broader lesson in that experience. The return on investing in yourself is not always another degree, promotion, or immediate financial payoff. Sometimes the return is greater capacity, sharper judgment, a new perspective, or a better way of doing work that has become unnecessarily complicated.
The important part is continuing to learn before the world around you makes that choice for you.
As the kids head back to school this year, it may be worth asking what you should be learning too.
What is one thing you could invest in learning this year that would make you better at the work that matters most?
Choose one thing that is relevant enough to deserve your time, and then make a real investment in learning it.
Where could AI give you back time for the work that actually requires your experience, judgment, or leadership?
And maybe the bigger question for those of us who have spent years becoming experts at what we do is this: What are you willing to become a beginner at again?
I’d love to hear what you are learning, experimenting with, or still trying to understand about AI. Share your thoughts in the comments.👇
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The tools will continue to change. Your ability to learn, adapt, exercise judgment, and clearly communicate the value you bring will continue to matter.
📣 Know an executive or professional who is trying to figure out where AI fits into their work? Share this newsletter with them. Apparently, back-to-school season isn’t just for the kids.