Future-Proof Mindset

Stay relevant in a changing world.

Tag: Work

  • AI Shouldn’t Replace Your Thinking. It Should Expand It.

    AI Shouldn’t Replace Your Thinking. It Should Expand It.

    Why the future belongs to those who learn to partner with AI rather than simply automate their work. 

    I have always seen Artificial Intelligence as a tool you can partner with to expand your thoughts and abilities. I’ve written about this many times over the past few years, and now it is becoming clear that we are standing at a critical crossroads in how we think about and use AI.

    The first path is clear, and it seems this is the one many are taking without fully considering the impact and consequences.

    Companies are racing to automate as much as they can. The reasoning is straightforward: AI can perform work faster, cheaper, and at greater scale than people. Tasks that once required hours can be completed in minutes. Processes that required entire teams can increasingly be handled by software. Work that once required significant amounts of human effort can now be completed with a fraction of the time and resources.

    They see this as an “easy” way to increase profits.

    But there is a danger in assuming that the ultimate goal of AI is to eliminate as much human effort as possible. Society and work are not simply a collection of tasks that can be broken down into workflows and automated one by one.

    Some of the most valuable things people do cannot be reduced to workflows, diagrams, or automated with a prompt. You, interpret ambiguity. You recognize patterns. You challenge assumptions. You understand context. You make judgments when there isn’t a clear right answer. You imagine what could exist before there is evidence that it should. You connect seemingly unrelated ideas and create something new from them. This is how you have been able to accomplish most of the things you have today and how all of us have been able to build the society we live in.

    The second path is not as clear yet, although it is there, and this is where I see the difference between automation and augmentation.

    Instead of viewing AI primarily as a replacement for human effort, it should be seen as a force multiplier for human thinking. AI has the capability to expand how you think without replacing your thoughts. AI is a tool that can take on the mundane tasks that must be done but drain your energy and mental capacity. It is a tool that you can leverage to brainstorm and iterate on micro proof-of-concepts to improve processes. Ultimately, it is a tool that can minimize your mental workload while enhancing and fostering your creativity, something that has been demonstrated throughout history to lead to greater levels of innovation and advancement.

    Think about what happens when you remove the unnecessary friction from someone’s day. A person who previously spent hours formatting information, searching through documents, cleaning data, writing repetitive communications, or completing other administrative tasks suddenly has that time back. That person now has the opportunity to think about the customer, solve a difficult problem, build something new, or simply explore an idea that they would not have had the time or energy to pursue before.

    This is the path I believe you should be pursuing, and it can also be a dangerous one because it walks a fine line where critical thinking could easily be outsourced if you, as the human users, aren’t careful. Over the past six years, as I’ve been testing the available tools and models, I’ve seen how companies developing these technologies are making it increasingly easy for the AI tool to push its way into becoming the thinker rather than simply the processor.

    The argument they make for this is valid because if the model can take over some of the thinking tasks, it will be more helpful. The more work the tool can complete for you, the more valuable it appears to become. However, the you as the user need to remain alert and intentional so that you don’t simply follow where the AI model is going instead of leading it toward your own unique thoughts, ideas, and perspectives.

    This distinction may seem small, but I believe it will become incredibly important. There is a major difference between asking AI to help you think through a problem and asking AI to think about the problem for you. There is a difference between using AI to challenge your assumptions and using it to make your decisions. There is a difference between using AI to expand your imagination and allowing it to define the boundaries of your imagination.

    I believe this will become one of the defining distinctions between those who merely implement AI and those who adopt it, learn how to work with it effectively, and ultimately thrive because of it.

    Now that I’ve outlined the two paths and how you should be looking at AI adoption and implementation, let’s dive into the framework for how to reach this new level.

    Until recently, there have only been two levels of AI users.

    Level 1: Chat Bot mode

    This is where we all start playing around with these tools as a novelty, testing the waters for what they can do. Many early questions were simple and awkward. Random questions that were meant more as a test rather than an actual inquiry. “Tell me about the solar system?” or “How do you spell strawberry?” While these questions were helpful because it meant you were willing to try out the tool rather than simply ignore it, they barely scratched the surface.

    There was something important about this first stage, though. People were beginning to interact with technology in a completely different way. Instead of learning a specific command or navigating through a series of menus, we could simply ask the computer a question using natural language. For many people, this was the first time technology actually felt like something they could talk to rather than something they had to learn how to operate.

    Level 2: Search Bot mode

    This is where most people currently are and tend to stay because this is how they have always interacted with computers. They use AI models as a search engine: “Find me how to make chocolate chip cookies,” or “What year did NASA retire the space shuttle program?” While this is extremely helpful for quickly getting answers to questions you might have personally, for school, or because you have a bet with a friend, it is still a fraction of what AI can really do.

    The problem with remaining in this stage is that you are still treating AI as an improved version of a tool we have already been using for decades. You are essentially asking a new technology to perform an old task better. There is certainly value in that, but it doesn’t take advantage of what makes these systems fundamentally different.

    The real opportunity begins when we stop simply asking AI for answers and start using it as a partner in the process of thinking.

    Level 3: Brainstorming Partner mode

    This level has recently been unlocked, within the past two years, with the release of newer and more advanced models, and it is a level where I believe you need to make sure you reach. At this level, you will work with AI not simply to review its work and be the “human in the loop,” but rather to have it help you develop your ideas.

    Bring your thoughts to life and show how you can supercharge your uniqueness. This is how you will increase the value you provide to your own business, to your employer, or to your customers.

    The difference here is subtle but extremely important. You are no longer asking AI to give you the answer. You are asking it to help you explore the problem.

    You might bring an idea to the model that is only half formed and ask it to challenge you. You might ask it to identify weaknesses in your argument, suggest perspectives you haven’t considered, or help you develop multiple approaches to a problem. You might use it to create a quick proof of concept that allows you to see whether an idea is even worth pursuing.

    The AI becomes a thinking partner rather than a replacement for thinking.

    An interesting example of this comes from Utah’s Jordan School District. (Full Yahoo article here)

    Over a two-year period, researchers analyzed nearly 14,000 student-to-AI conversations involving 82 teachers. Rather than simply allowing students to use AI as a shortcut for producing answers, the district emphasized intentional use of the technology as a “thought partner.”

    The reported result were striking: students demonstrated a 28% increase in critical-thinking performance, while higher-level reasoning abilities more than doubled across the subjects and grade levels studied.

    The important takeaway isn’t that AI magically made students smarter.

    It is almost the opposite.

    The technology was deliberately designed and used to make students think.

    The AI wasn’t there to remove the cognitive work.

    It was there to stimulate it.

    That distinction should matter far beyond education.

    Because organizations are now facing the same choice.

    Do we use AI to eliminate thinking?

    Or do we use AI to elevate it?

    This two-year experiment across nearly 14,000 students highlights the potential advanced AI models can have for innovation and productivity in the future. However, the key to the success of this program, I believe, was the intentionality of the guardrails the students were given to ensure they were leading the thought process and not simply outsourcing it.

    This is where I believe the future of AI gets really interesting.

    Imagine what happens when every person in an organization has access to a tool that can help them brainstorm, research, challenge assumptions, build prototypes, analyze information, and explore possibilities. Imagine a first-year employee having access to a tool that can help them understand an unfamiliar problem while still requiring them to make the final judgment. Imagine an experienced executive being able to test an idea against dozens of perspectives before bringing it to their leadership team. Imagine a designer, engineer, teacher, writer, analyst, or entrepreneur being able to experiment with ideas at a speed that would have been impossible just a few years ago.

    That is augmentation.

    It isn’t about removing the human from the process. It is about giving the human more capability.

    This is also why I believe you need to be careful about how you measure the success of AI adoption. If the only metric you use is how many employees can be removed from a process, then you are going to miss the much larger opportunity. You should also be asking how many new ideas were created, how many problems were solved, how quickly prototypes were developed, how much time employees gained back, and what people were able to accomplish that previously wasn’t possible.

    If AI gives someone ten additional hours a week, the question shouldn’t simply be, “How much more work can we make them do?”

    The better question is, “What could they create with those ten hours?”

    That is the question that will determine whether you use AI to simply make your existing systems more efficient or whether you use it to fundamentally change what is possible.

    I believe the greatest opportunity in front of you is not to build a world where humans do less. It is to build a world where humans can do more of what actually makes us valuable.

    You should allow AI to take the repetitive work. You should allow it to help you organize information, analyze massive datasets, generate initial concepts, test ideas, and build things faster. But you should remain responsible for deciding what matters, why it matters, and where we want to go.

    The technology should help you get there faster.

    It should not decide where you are going.

    That distinction is going to become increasingly important as AI systems become more capable. The more powerful the tool becomes, the easier it will be to hand over pieces of your thinking without even realizing you are doing it. The temptation will always be there because it is convenient. If a machine can make the decision for you, why spend the time thinking about it yourself?

    Because the act of thinking is part of what makes you human.

    It is how you learn. It is how you develop judgment. It is how you create new ideas. It is how you discover things that nobody has discovered before.

    You should not be afraid of AI becoming more capable. You should be afraid of becoming less capable because you stopped using your own abilities.

    The future I want to see is not one where AI replaces humans.

    It is one where humans become significantly more capable because AI exists.

    That is the difference between automation and augmentation.

    And I believe the people and organizations that understand that difference will be the ones that truly thrive in an AI-driven world.

    If you liked this article check out “The Creative Edge in an AI-Driven World” an article I wrote earlier in the year.

  • Setup for Success Creating Clarity in Business

    Setup for Success Creating Clarity in Business

    As the world moves faster than ever and is becoming more interconnected by the day, founders can no longer afford to operate, with an outdated mindset. To build something lasting, they must learn to focus, lead with intention, and approach their organizations differently from the start.

    The #1 Mistake Founders Make When Building a Team

    One of the most common mistakes I see founders make time and time again is failing to establish a clear vision for the type of organizational structure they want to foster. Too often, leaders bounce from one management style to another because:

    1. They’re chasing what’s currently trending in startup culture.
    2. They haven’t taken the time to define how they want to engage with their employees.
    3. They try to be liked by everyone, employees, customers, investors without realizing that clarity, not popularity, breeds success.

    This lack of clarity leads to inconsistency, confusion, and ultimately, failure. An unclear organizational structure erodes trust, creates misaligned expectations, and makes it nearly impossible to scale sustainably.

    So how do you avoid this trap?

    Step One: Know Your Options

    Start by familiarizing yourself with the different organizational mindsets. Then, map out your own leadership goals. Ask yourself:

    • How do I want to be perceived by my team?
    • What do I want our customers to say about us when we’re not in the room?
    • What values do I want to be at the core of this company today and in the future?

    Once you answer these questions honestly, you can begin to align your internal operations and team structure accordingly.

    While there are countless ways to slice organizational design, I’ve found it helpful to begin with two foundational archetypes. These aren’t mutually exclusive, but understanding them can help bring clarity to your leadership style and company culture.

    Organizational Mindset #1: Mission-Focused (The Believers)

    This is the founder who is on a mission to change the world or at least a piece of it. Your company exists for a larger purpose, and your team is made up of people who genuinely believe in that mission. These aren’t just employees, they’re co-creators.

    In mission-focused organizations:

    • Team members feel a strong sense of ownership and emotional investment.
    • People naturally take the initiative because they care about the impact of their work.
    • There’s a shared understanding that everyone is building something that matters.

    This model tends to work especially well in the early stages of a company, when small, nimble teams need to move fast and think big. It’s not hard to motivate your team when they’re personally connected to the “why” behind what you’re doing.

    However, mission-driven models require strong alignment. If your mission is vague, disconnected from day-to-day work, or inconsistently communicated, it can quickly fall apart.

    Organizational Mindset #2: Customer-Focused (The Service First Team)

    In this model, customer satisfaction is the north star. Every team member, from marketing to product to support, rallies around delivering the best possible experience for the end user.

    What defines this structure is a relentless focus on the customer:

    • Success is measured in smiles, five-star reviews, and repeat business.
    • Employees are driven by the feedback loop of delighting customers.
    • Processes are constantly refined to improve service and simplify the customer journey.

    Customer-focused companies often develop strong reputations in the market and fast. Employees don’t necessarily have to be passionate about the product itself; they’re passionate about solving problems and making the customer feel valued.

    The challenge with this model is internal alignment. If you’re not careful, your team can lose sight of the company’s broader vision, leading to short-term thinking and reactive behavior. Clear communication and strong leadership are essential to maintaining focus and cohesion.

    Why It Matters

    Whether you lean more toward a mission-focused or customer-focused model (or a blend of both), the key is clarity. When your organizational structure aligns with your values, it becomes easier to:

    • Hire people who are the right fit.
    • Make decisions faster and more confidently.
    • Empower employees to take action without second-guessing themselves.
    • Scale culture and operations without losing your identity.

    There’s no one-size-fits-all solution. What matters most is that you are intentional about how you lead and build. Take the time to define your structure early and revisit it often as your company grows.

  • Generative AI Won’t Replace Us, It Will Set Us Free

    Generative AI Won’t Replace Us, It Will Set Us Free

    I’ve been experimenting with generative AI since 2020, and I have to say the progress we’ve seen in just a few short years is amazing. The things this technology can do today were almost unthinkable when I started using those early models.

    I remember trying to get one of the early language models to help me build a simple “to-do list” application. It struggled to say the least. It didn’t really understand what I was aiming for, and to be honest, I had to hold its “hand” through every step of the process. I had to break things down, ask very specific questions, and already have a decent amount of subject knowledge myself. Back then, these models were mostly glorified search engines with a friendlier user interface great for ideas, but not quite partners in development.

    But things began to change and over the years the models advanced, today these tools can really step up as a partner and even co-founders.

    Over time, the interaction became less about guiding the AI and more about collaborating with it. I no longer needed to write most of the code myself or formulate perfectly-worded prompts. Today, I can build a far more advanced version of that original app in just a few hours, something that took weeks in 2020 with the original AI models I began working with. This is the type of leap in productivity that makes headlines scream about the future of jobs being at risk. And to be honest, I get it! I understand why some are sounding the alarms.

    But here’s the thing: if you’re looking at the future of work through the same lens you used five or ten years ago, then yes, it’s going to feel terrifying. Of course, AI seems like a threat. After all, we no longer need interns and junior analyst spending hours manually cleaning datasets or scouring spreadsheets for “leading spaces” in cells (if you’ve ever done this task, you know the soul-sucking pain I’m talking about).

    Let’s break down the problem, opportunities for those willing to learn, review two examples, and talk about what mindset will lead the future.

    The Real Problem How we View Entry-level Work Needs to Change!

    One of the most repeated criticisms I hear is that generative AI will eliminate “rite of passage” tasks the mundane early-career grunt work that, supposedly, teaches many things. But the truth is, of the things AI is replacing we learned very little other than to double-check everything and hit “save” obsessively (especially if you remember the horrors of the blue screen of death). I challenge the idea that mindless mundane tasks are a “rite of passage”. Because while those tasks may have taught us diligence, they didn’t exactly encourage innovation or creative thinking. In fact, they often stopped creativity in its tracks.

    Most of us dreaded the parts of the job where we had our heads buried in spreadsheets, massaging messy data into something usable. We looked forward to the days when we could analyze, problem-solve, and add value. That’s what we were excited about and ironically, that’s the part many never got to, because they burned out in the grind before they had the chance.

    The saddest part? That mind-numbing data cleanup process, done poorly and too quickly created lasting problems. Years later, many companies are sitting on mountains of inconsistent, poorly labeled, and unusable data because junior analysts weren’t incentivized to clean it properly. They wanted to move on to the “fun” stuff too quickly, and who could blame them?

    The Opportunity Ahead

    This is why I’m optimistic about the future with AI. If we use it correctly, we can eliminate meaningless tasks, free up brainpower, and give employees, especially new ones, the chance to start their careers doing meaningful, high-impact work.

    This is why I strongly believe AI shouldn’t replace entry-level employees it should augment them.

    Those junior analysts, associates and interns are still critical. They come in fresh. They have energy, idealism, and a drive to solve the “impossible.” The difference now is, they’ll actually have the bandwidth to try and do it.

    We are at a unique moment in history where everyone from the new hire to the 30-year veteran can and should be given a personal AI assistant. But not just any assistant. These tools need to be tailored to role and context. Some will act as mentors and guides, others as research analysts or organizational experts. Some will summarize meetings. Others will design workflows or analyze code. The possibilities are endless.

    A Tale of Two Companies: The AI Fork in the Road

    Let me walk you through a simplified example I’ve been thinking about lately. It’s not meant to be perfectly realistic, but it illustrates the stakes of leadership decisions in this new world:

    Company A has 100 employees and brings in $100 million in annual revenue, with 50% profit margins. Leadership sees an opportunity to automate low-level tasks and cut 25% of the workforce. Overhead drops, profits rise to $75 million, and shareholders celebrate.

    Sounds smart, right?

    Fast forward five years: the most experienced executives begin to retire. You promote mid-level managers, reorganize a bit, and add a few more automations to keep things running. But eventually, you hit a wall. There’s no bench strength. You’ve hollowed out your talent pipeline by eliminating the very roles that would’ve produced your future leaders.

    Now you’re hiring externally, people who don’t know your culture, your vision, or your values. Innovation slows. Morale suffers. What was once a winning strategy now feels like a short-sighted mistake.

    Company B same starting point takes a different approach.

    Instead of replacing people, leadership introduces AI across the organization with a clear message: This is here to help you, not replace you. Every employee gets access to tools designed to remove drudgery and unlock creativity. You involve the team in the design process. You ask them what they need. You build together.

    Now your 100-person team performs like a 200-person team. Productivity explodes. People are excited, not anxious. You start launching new products, entering new markets, and solving harder problems because your team isn’t burnt out, they’re inspired.

    Which company would you rather be a part of?

    The Infinite Growth Game

    This is what I call the infinite growth game and companies that figure this out will win the next decade. Not just because they use AI, but because they use it intentionally.

    The companies that view generative AI as a tool to eliminate headcount will see short-term gains and long-term decline. The companies that see AI as a lever for human potential will experience exponential growth not just in profit, but in culture, creativity, and resilience.

    Because when you give humans better tools, they don’t become obsolete, they become unleashed.

    What I Hope Every Leader Learns quickly

    I’ve said this before, and I’ll say it again: Generative AI is nothing more than an advanced tool. It reflects the person wielding it. If you use it to replace people, you’re playing a dangerous and unsustainable game. But if you use it to support them to clear the clutter, unlock bandwidth, and give space for innovation you’re giving your team a gift.

    We spent decades dreaming of technology that could help us “get more done.” That dream has now arrived.

    And here’s what we must remember: We didn’t want a tool that would replace us. We wanted a tool that would empower us. One that would help us solve harder problems and create amazing things.

    So let’s stop being afraid of the future.

    Let’s stop measuring productivity in terms of bodies and hours.

    Let’s stop thinking that the best use of AI is replacing entry-level employees with glorified spreadsheets that talk.

    Let’s build organizations where humans and machines work together not in opposition, but in harmony. Where experience is valued, but imagination is celebrated. Where tools make us more human, not less.

    Let’s build that future.