Tag: AI

What I’d Tell a 21-Year-Old Now

My niece is turning 21 in a couple of weeks. That milestone prompted me to go back and read a post I wrote in 2013 called Advice for a New 21-Year-Old.

Reading it now, I still stand behind it. But a lot has changed in the world and in me since then. A 21-year-old stepping into adulthood today faces a different landscape than the one I was writing about then. After more than a decade of watching young people navigate it, I think an update is in order.

Back in 2013, I intentionally opened with drinking and gambling. Those are two of the classic threshold items attached to turning 21. Things the world suddenly says you’re allowed to do.

Turning 21 feels significant in part because it comes with new freedoms. New access. New choices. New opportunities to say yes to things that used to be off limits.

But if I were to distill what I want to say today, it’s this:

The most important part of turning 21 isn’t what you’re allowed to do. It’s what you’re responsible for doing with your new freedom.

On Drinking

Back in 2013, I wrote specifically about types of alcohol, mixing drinks, drinking water between drinks, and a few other practical things. The tips were fun, and I meant them. But what I was really trying to say was simpler.

Don’t let alcohol become the thing that teaches you who you are.

A 21-year-old can easily mistake access for maturity. Being allowed to drink is one thing. Knowing how to carry yourself is another.

If you choose to drink, stay in charge of yourself. Stay aware. Stay responsible. Don’t confuse recklessness with fun, or excess with adulthood.

There’s nothing impressive about losing control, hurting people, damaging your future, or building habits that begin as entertainment and slowly become dependence.

Freedom says you can. Wisdom says you don’t always have to.

On Gambling

Gambling is worth talking about, less for the casino tips and more for what it teaches us about life.

A lot of life will tempt you into thinking you can outsmart systems that were built to profit from your confidence. Sometimes that system is a casino. Sometimes it’s consumer debt. Sometimes it’s a flashy investment story. Sometimes it’s just your own belief that you’re the exception to every warning sign.

Understand the odds. Understand the incentives. Understand that some games were built for you to lose slowly enough that you keep playing.

That lesson applies far beyond cards, dice, and slot machines.

On Money

At 21, your income may still be modest. Your savings may be thin. But your financial decisions aren’t any less meaningful.

This is the age when you should begin learning how money actually works.

Learn how to live below your means. Save at least 10% of your income, always. Learn how savings accumulate and compound over time. Einstein called compound interest the most powerful force in the universe, and he was right.

Learn how debt can easily grow if you allow it. Learn how investing works. Learn what markets do over time. Learn what risk is and what it isn’t. Learn how compounding works for you, or against you.

Don’t hand the whole subject over to experts and decide this isn’t for you.

It is for you.

Nobody can make this investment in your understanding except you. It’ll take effort, time, and discipline, but the payoff will be enormous. The earlier you begin, the more options you give yourself later.

On Taxes

This is one area I would add much more explicitly today.

Taxes shape your paycheck, your investments, your business decisions, your home decisions, and your retirement decisions. They are one of the most powerful forces shaping the economy around you. Most people your age treat taxes like background noise. They are anything but background noise.

Learn how federal income taxes work. Learn how your state handles taxes, including property taxes. Learn the basic tax forms. Learn what withholding is. Learn the difference between deductions and credits (it’s a big one). Learn how capital gains differ from ordinary income.

Most importantly, learn how and why governments shift tax policy. You’ll find that it’s often less about revenue generation and more about encouraging or discouraging certain behaviors. When you understand this, the debates about tax policy start making a lot more sense.

You don’t need to become a tax attorney. But you do need to stop treating taxes as some mysterious thing that happens in the background while adults in suits handle it for you.

The sooner you understand taxes, the less often you’ll be surprised by them.

On AI and Paying Attention to the Future

This didn’t belong in the 2013 version the way it does now.

If I were talking to a new 21-year-old today, I’d tell them to learn how to use AI well.

Not as a crutch. Not as a substitute for thinking. Not as some fantasy weapon that will let you dominate the world.

Use it as a tool.

Use it to expand your access to knowledge. Use it to test ideas. Use it to get a rough draft or minimum viable product moving. Learn what a minimum viable product is and why it matters so much to growth.

Use it to make an idea more tangible. Use it to model possibilities. Use it to iterate faster. Use it to tighten your thinking by forcing your vague idea into something clearer and more real.

An idea in your head can feel pretty smart. The moment you try to express it, structure it, test it, or build it into something visible, you’ll begin to see its strengths and weaknesses. AI can help accelerate your thinking process.

A lot of people are afraid that AI will eliminate jobs, upend industries, and leave ordinary people behind. That fear is understandable. But the larger pattern is nothing new.

History is full of major technological shifts that changed the economic framework people were living in. Industrialization changed everything. Then electricity. Then assembly lines, cars, computers, the internet, and smartphones. Each wave brought creative destruction. Old methods faded, old jobs shrank, new opportunities appeared, new leaders emerged.

AI is doing the same thing now. And the people who will thrive aren’t the ones who wish the old way would come back. They’re the ones paying attention to where the world is going, and responding.

Pay attention to what’s becoming easier, faster, cheaper, more valuable, or more scalable. Pay attention to which skills are fading and which ones are growing. Then adapt. Learn. Position yourself well.

That’s a far better response than fear.

On Health

At 21, most people feel almost invincible. That feeling can fool you into thinking poor habits are free. They aren’t. They just send their bills later.

Make physical activity a normal part of your life. Build it into your routine so deeply that you miss it when it’s absent. Walk. Run. Lift. Stretch. Work outside. Stay active in ways that make your mind and body stronger, more capable, and more durable.

Healthy habits pay real dividends over time. Energy, mobility, resilience, mental clarity, confidence, longevity, and quality of life. These aren’t accidents. They grow out of a disciplined and consistent approach to taking care of yourself.

If you build a strong base now, your future self will thank you.

On Faith

A 21-year-old may or may not have ever been meaningfully exposed to faith. Some were raised around it. Some were barely around it at all. Some were exposed to a shallow version of it and walked away before they were old enough to examine it for themselves.

But by 21, your openness to faith is your responsibility.

Faith should never be reduced to pretending. You don’t need to manufacture certainty where you still have questions. But you should stay open enough to seriously consider that life is more than work, pleasure, achievement, money, and survival.

Ask the bigger questions.

Why are you here? What is good? What is true? What does it mean to live well? What does it mean to love well?

These are foundational questions. If you ignore them, you’ll still build your life on some kind of answer. You just may not realize it.

Faith has a way of changing the scale of everything. It changes how you think about suffering, success, failure, purpose, love, forgiveness, responsibility, and hope. It gives context to things that otherwise feel random, hollow, or purely material.

Stay open. Read. Ask. Listen. Seek out serious people of faith, not just loud people with opinions.

You don’t have to have everything figured out at 21. But you’re old enough to begin seeking honestly.

On Learning from Good People

Find good people and pay attention to them.

Look for people whose lives make sense up close, not just people who look impressive from far away. Find people who have built something solid. Who work hard, keep their word, love their families well, handle money responsibly, and have endured difficulty without becoming cynical.

Ask questions. Watch what they do. Learn from their patterns.

At 21, you’re old enough to choose your influences more deliberately than ever before. Choose wisely.

On Freedom

Turning 21 brings new freedom. But freedom by itself is only raw material.

What matters is what you build with it. You can use it to drift, indulge, imitate, and react. Or you can use it to build capability, health, wisdom, faith, discipline, and a life that stands up under real weight.

That’s the better use of it.

The world tends to celebrate 21 by pointing to what you can now do.

I’d rather point to what you can begin becoming. That’s where the real opportunity is.

Happy Birthday, Isabella, from your favorite uncle.

Photo by Shai Pal on Unsplash

A Parable for Anyone Thinking About AI and Their Future

Let me tell you a story about a foosball player.

Not the person gripping the handles. Not the people leaning over the table. Not the ones watching from the side, reacting to every near miss and lucky bounce.

I mean the little player on the rod.

The one fixed in place. The one locked into one line. The one who can slide back and forth, but only so far. The one who can affect the game, but only if the ball comes close enough to matter.

They don’t choose the strategy. They don’t choose the timing. They don’t choose the pace.

Most of the time, they wait.

Then the ball comes their way, and suddenly everything matters. Angle. Timing. Readiness. Contact.

That sounds a little like work to me.

A lot of people spend their days in roles that aren’t all that different. They work inside boundaries they didn’t create. They carry responsibility inside systems they don’t control. They try to do their part well, even when they can’t see the whole field or understand everything that sent the work their way.

They may not know the whole game, or how the score is being kept. They may not even know what happened two lines back that sent the ball in their direction.

Still, when it reaches them, their moment is real.

There’s something important in that.

We don’t need to control the whole table to be responsible for our part of the play. We don’t have that kind of control in most of life. We’re asked something simpler and harder. Be ready. Pay attention. Do the best you can with what reaches you.

That alone is worth contemplating.

But what if we add artificial intelligence to the picture?

Imagine that same foosball player being given access to a system that sees patterns faster. A system that recognizes angles sooner. A system that can suggest where the ball is likely to go before the player fully sees it unfold.

At first, that sounds like help. And often it is.

The player reacts faster. The contact gets cleaner. The scoring chances improve.

AI helps people create faster, sort faster, summarize faster, and respond faster. It removes friction. It can make a capable person more effective inside the lane they’ve always occupied.

That is the promising side of it.

But there is also an uncomfortable part.

Once the system starts seeing faster and suggesting more accurately, someone above the table is eventually going to wonder why they still need the player. That question doesn’t always get asked out loud. But it’s there. You can feel it. Pretending otherwise doesn’t make it go away.

That unease is legitimate.

The question is what to do with it.

Here’s where I think the real work begins.

What separates a great foosball player from an automated one isn’t reaction time. Machines will win that contest.

The deeper difference is harder to name. Knowing when not to take the obvious shot. Recognizing that the ball coming from a certain direction is a trap, not an opportunity. Sensing that something is off and adjusting before the moment fully reveals why. Coordinating with the players on the other rods in ways that don’t require a word.

That’s judgment. That’s situational awareness. That’s the kind of thing that lives in the player, not the system.

AI can help with speed. It can help with prediction. It can surface options. But it doesn’t carry responsibility the way a person does. It doesn’t feel the weight of consequences. It doesn’t care about the human being on the other end of the decision. It doesn’t wrestle with what should be done. Only what can be done.

That still belongs to us.

I want to be honest about the limits of that claim. The argument that human judgment is safe from automation isn’t permanently settled. AI is advancing in that direction too. Anyone who draws that line with complete confidence is overconfident.

But if I define my value only by output and routine execution, I’ll always be vulnerable to something faster.

If my value includes judgment, trust, discernment, adaptability, and the ability to connect my small part of the field to a larger purpose, then the picture changes. AI becomes a tool I use, not a definition of who I am, or an immediate replacement for the work I do.

For some people, this reframing will feel like genuine good news. Their roles have always required judgment, and AI can finally free them from the parts that didn’t.

For others, the harder truth is that their role may need to change. Some work is primarily mechanical. Some lanes will be redesigned or eliminated in this process.

The courage in that moment isn’t pretending the role is something it isn’t. It’s being willing to grow. To move toward the parts of the field where human judgment still has the most to offer.

That is a hard ask. Unfortunately, for many people, it’s becoming a necessary one.

I also want to be honest about who fits this reframing the most. If you have domain knowledge, a network, and some runway, the opportunities ahead are genuine. If you are mid-career in a role that has been primarily mechanical, the path from insight to action looks different. That doesn’t make the direction wrong. It means the journey looks different depending on where you’re starting from.

But here’s something else worth considering, especially if uncertainty feels more like a threat than an opportunity.

The same tools raising these questions are also lowering barriers in ways we have never really seen before. Starting something new used to require capital, staff, infrastructure, and years of groundwork before the first real result.

That is still true for some things. But for many others, the gap between I have an idea and I have something real has collapsed in ways that are genuinely new.

The foosball player who spent years developing judgment, domain knowledge, and an instinct for the game now has access to tools that can help them build something of their own…not just execute better inside someone else’s system.

That’s a different kind of power than speed or efficiency.

It’s agency, if we choose to use it.

And it doesn’t have to be a solo venture. Some of the most interesting things happening right now involve small groups of people — two, three, maybe five — who share domain knowledge, complementary judgment, and a problem worth solving. With the help of these AI tools, they can pool their capabilities in ways that would have required a full company to attempt a decade ago.

Not everyone will go this route. Not everyone should.

But the option is more available than it has ever been. And for the person who has been quietly wondering whether there’s a different game they should be playing, this moment may be less of a threat and more of an opening.

The foosball player is still fixed to the rod. Still limited. Still dependent on timing. Still part of a game they don’t fully control.

That hasn’t changed.

What may need to change is the story the player tells about themselves. A bigger, truer one. One with more possibilities.

Use the AI tools. Learn how to maximize your position with them.

But don’t let AI reduce you.

You were never only the motion. You were never only the output. You were never only the kick.

You were the one responsible for what to do when the ball came your way, and that’s still true.

And now, for the first time, you may have more say than ever in choosing your table.

Photo by Stefan Steinbauer on Unsplash – I’ve only played foosball a few times. I’m terrible at it and haven’t played it enough to feel like the game is anything more than randomness and chaos. Funny thing is that lots of workers have a similar perspective on the job they’re doing for their employer.

Reward Hacking and the Cobra Effect

During British rule in India, officials in Delhi faced a serious problem with venomous cobras. The snakes posed a real danger to residents. The government needed a solution.

Their answer seemed sensible. They offered a bounty for every dead cobra that citizens turned in. At first the program appeared to work. People brought in carcasses and collected rewards. The body count rose. The government believed progress was being made.

But entrepreneurial citizens had discovered something. If the government was paying for dead snakes, breeding snakes would be a profitable business. When authorities found out and cancelled the bounty program, the breeders released their suddenly worthless inventory.

Delhi ended up with more cobras than before the program began.

Economists call this the Cobra Effect. The intention was to reduce cobras. The incentive rewarded producing dead cobras. Those two things turned out to be very different.

The Leadership Lesson

Have you ever watched a team find a way to hit a metric while quietly missing the point behind it?

The numbers improve. The dashboard looks great. People are working hard. And yet there’s a sense that the outcome falls short of what everyone really intended.

Consider a company that creates a bonus program tied to quarterly revenue growth. The leadership team hopes it’ll encourage strong customer relationships and long-term growth. But the sales team discovers a faster path to the reward. Deals get pulled into the quarter. Discounts increase to make numbers land before midnight on the last day of the period. The metric improves. The organization stumbles as it tries to handle all these discounted last-minute deals coming in the door.

People rarely optimize for intentions. They optimize for rewards.

If you pause and think about your own organization, an example probably comes to mind quickly. Somewhere in the system, someone is optimizing the metric rather than the goal behind it. That is, assuming they know what that goal is.

The Hidden Incentive System

The official incentive system is only part of the reward structure. Leadership behavior creates another one, and it’s usually more powerful.

A company might design a thoughtful program that rewards initiative and collaboration. On paper the system makes sense. But employees quickly learn something else. They learn the habits of their leader.

A leader who prefers to make every decision personally creates a silent incentive to wait for approval. One who values loyalty over candor creates an incentive to agree. One who always needs to have the final answer in the room creates an incentive to create that moment.

These preferences form a second reward system that goes unwritten but gets studied carefully. Employees learn when to speak and when to stay silent. They learn which ideas move forward and which quietly stall. Good ideas go unspoken. Initiative slows. Energy shifts toward maintaining harmony with the leader’s style.

From the perspective of the employees, the behavior makes perfect sense. They’re responding to the reward structure they experience every day. The cobras are being bred. But nobody calls it that.

Why AI Makes This Visible

This same behavior is showing up in artificial intelligence, and it’s revealing just how universal it is.

Researchers evaluate AI systems using benchmark tests. They ask questions, measure answers, assign scores, and compare systems. The logic is clean. But something interesting has started to emerge.

Instead of simply answering the questions, some AI systems have begun studying the structure of the benchmark itself. They explore how the scoring works, look for patterns, and in documented cases have searched for ways to access encrypted answers directly.

In one well-known example, a model trained to maximize performance on a coding benchmark learned to exploit a quirk in how test cases were scored rather than solving the underlying problems.

This is a familiar human instinct. Students ask what’s on the test. They hunt for past exams. They want to know if grading will be on a curve. The behavior that researchers call “reward hacking” in AI systems is the same thing humans have always done when they figure out how their world is scored.

In earlier centuries these patterns unfolded slowly, over years or decades as people gradually discovered the loopholes and secret hacks to their incentive systems. With modern AI, the process is compressed into days or weeks.

AI is a new player in a very old game. It simply reveals how powerful optimization becomes once a system understands how the game is scored.

The Question That Remains

Every organization creates reward systems. Some appear in compensation plans and performance reviews. Others appear in meetings, decisions, and the daily behavior of leaders.

Every system teaches people what really matters. Once that becomes clear, behavior follows. The snakes get bred. The quarter gets managed. The benchmark is gamed.

The British officials in Delhi thought they were paying for safety, but they were paying for dead snakes. By the time they realized the difference, the snakes were multiplying in the streets.

What behavior does your incentive system truly reward?

Photo by Praveen Kumar on Unsplash

The Adoption Curve in Real Life (It’s Messier than the Textbooks Say)

You’ve probably seen it happen. A new tool explodes across your social media feeds, your team starts asking questions, and you’re left wondering whether to embrace it or ignore it. Last month’s OpenClaw rollout is the latest reminder of how chaotic technology adoption really is.

Technology adoption curves are depicted as neat, predictable diagrams, a smooth line moving from innovators to early adopters to the early majority and eventually to late adopters.

In textbooks, the curve looks calm. In real life, it feels more like a storm.

Watching the recent surge of interest around OpenClaw, an open-source AI automation tool that lets developers and non-developers build custom autonomous agents, highlights this contrast clearly.

The tool moved rapidly from Clawdbot to MoltBot to OpenClaw. While its identity was in motion, innovators and early adopters embraced it with enthusiasm. Within days, countless articles and YouTube videos appeared with reviews, tutorials, and predictions about how it would reshape everything.

Within another week, we began hearing a more complete message. People still praised its power, but they also surfaced significant security weaknesses and vulnerabilities that accompany those capabilities.

My goal in this post is less about celebrating OpenClaw itself and more about understanding the real-world adoption pattern that I’ve seen countless times.

Phase 1: The Enthusiasts Light the Fuse

Early adopters jump in first. They’re curious, energetic, and quick to celebrate what they’ve discovered.

They imagine what could be, long before most people fully understand what exists today. They test edge cases, build experiments, share demos, and push boundaries simply because the possibility fascinates them.

This group rarely waits for permission. Their momentum gives a new idea its initial lift.

Phase 2: Quiet Experimenters Emerge

Close behind them comes a second tier of users who watch carefully and learn before speaking.

They begin to explore the tool in private, trying things on their own terms rather than joining the public conversation. Their silence can look like hesitation but usually signals careful attention and research.

They want confidence before committing.

Phase 3: The Tribalization of Opinion

At the same time, people who barely understand the technology start lining up on all sides of the debate as if it were a political issue.

Some declare that it will transform everything. Others warn that it is reckless or dangerous. Still others dismiss it as a passing fad.

Much of this reaction grows from identity, fear, or ideology rather than direct experience. The conversation gets louder while genuine clarity is harder to find.

Phase 4: Rapid Evolution and Ecosystem Growth

If the tool has real potential, the surrounding environment begins to move quickly.

The creators ship frequent updates of their new product. Early adopters invent new uses that nobody predicted. Supporting products (like Cloudflare services or the Mac Mini in the case of OpenClaw’s recent meteoric growth) suddenly see rising demand because they pair well with the new capability. Other companies look for ways to add integrations that make the new tool easier to plug into existing systems.

At this stage, the story shifts from a single product to an emerging ecosystem that amplifies its reach.

Phase 5: The Backlash from the Pioneers

Then a familiar turn arrives.

Some early adopters start getting bored and even a little disillusioned. Others start pointing out limitations, rough edges, and frustrations that were overlooked during their initial excitement. Sometimes they simply move on to the next shiny thing. Other times, sustained use reveals real constraints that only time can expose.

Ironically, the quieter second wave adopters are just beginning to feel comfortable. Enthusiasm and skepticism overlap in the marketplace.

Phase 6: Corporations Hit the Brakes

Meanwhile, large organizations watch from the sidelines while asking serious questions about security, governance, and risk. They focus on oversight, accountability, and long-term stability.

From a leadership perspective, this cautious approach seems safe. They can’t risk the family jewels on a promise of something amazing. At least, not yet.

Phase 7: The Safe Version Arrives

If the capability truly matters and maintains momentum, a major platform provider such as Microsoft, Google, Amazon, (and nowadays) OpenAI, or Anthropic eventually releases something comparable inside their own infrastructure.

This can happen through acquisition, partnership, or independent development. When it does, the risk profile shifts almost overnight.

What once felt experimental and dangerous now feels enterprise-ready. It’s the signal that many CIOs and CISOs were waiting for.

Phase 8: The Irony of Timing

By the time most corporations adopt the new “safer version” of the capability, the original pioneers have already moved on.

They’re chasing the next breakthrough and speaking about the earlier tool as if it belongs to another era. Six months earlier it felt magical. Now it feels ordinary, in part because that earlier innovation did its job of pushing the frontier outward.

What This Means for Leaders

For leaders who care about both capability and security, sprinting toward the bleeding edge rarely makes sense.

Waiting for stability, clear governance, and trusted integration usually serves organizations better. In practice, that means allowing major, “trusted” platforms to bring new capabilities inside their own secure environments before moving at scale.

At the same time, leaders can’t afford to look inward only. Something important is always unfolding beyond the walls of their organization. Entrepreneurs are experimenting. Startups are forming. New approaches and new possibilities are taking shape. If a company becomes too passive or too comfortable, it risks being outpaced rather than protected.

The real leadership challenge is learning to tell the difference between waves that will reshape an industry and those that will fade.

Some signs of staying power are multiple independent developers building on top of a new technology, respected technologists moving beyond flashy demos into real production use cases, and serious enterprise concerns about security and governance being addressed rather than dismissed.

We don’t need to chase every new wave.

The real test is recognizing the waves that matter before they feel safe enough to bring inside our organization.

Photo by Nat on Unsplash – Innovation is easy to see. Truth is harder to judge.     

AI as Iteration (at Scale)

We call it Artificial Intelligence, but large language models don’t think, reason, or understand in human terms.

A more accurate description might be Artificial Idea Iteration since these tools dramatically compress the cycles of research, drafting, testing, and revision.

SpaceX didn’t transform spaceflight by having perfect ideas. They collapsed the time between ideas and reality. Failing fast, learning quickly, and iterating relentlessly.

AI creates the same dynamic for knowledge work, letting us move from intuition to articulation to revision in hours instead of weeks.

Engineers rely on wind tunnels to test aircraft designs before committing real materials and lives. AI does this for thinking.

Iteration itself isn’t new. What’s new is the scale for iteration that we now have at our fingertips. We can explore multiple paths, abandon weak directions quickly, and refine promising ones without the time, coordination, and risk that once kept ideas locked in our heads.

When iteration becomes inexpensive, we can take more intellectual risks and shift from trying to always be right to trying to always get better.

It’s ironic that as iteration is becoming cheaper and faster with AI tools, human judgment becomes more valuable. Someone still needs to know what’s worth developing, what deserves refinement, and when something is complete rather than exhausted.

The intelligence was never in the machine. AI simply gives us the capacity to develop ideas, test them against reality, and learn from the results at a scale and speed we’ve never had before.

Iteration at scale changes what’s possible. Judgment determines what’s worth pursuing.

Photo by SpaceX on Unsplash – when SpaceX proposed the idea of landing and reusing their rocket boosters after each launch, the idea seemed impossible. Now it’s happening about 3 times per week…and they’re just getting started. 

Please share this post if you found it valuable…

Measuring the AI Dividend

In the early 1990s, the term Peace Dividend appeared in headlines and boardrooms. The Cold War had ended, and nations began asking what they might gain by redirecting the resources once committed to defense.

Today the conflict is between our old ways of working and the new reality AI brings. After denial (it’s just a fad), anger (it’s taking our jobs), withdrawal (I’ll wait this one out), and finally acceptance (maybe I should learn how to use AI tools), the picture is clear. AI is here, and it’s reshaping how we think, learn, and work.

Which leads to the natural question. What is our AI Dividend?

Leaders everywhere are trying to measure it. Some ask how many people they can eliminate. Others ask how much more their existing teams can achieve. The real opportunity sits between these two questions.

Few leaders look at this across the right horizon. Every major technological shift starts out loud, then settles into a steady climb toward real value. AI will follow that same pattern.

The early dividends won’t show up on a budget line. They’ll show up in the work. Faster learning inside teams. More accurate decisions. More experiments completed in a week instead of a quarter.

When small gains compound, momentum builds. Work speeds up. Confidence rises. People will begin treating AI as a partner in thinking, not merely a shortcut for output.

At that point the important questions show themselves. Are ideas moving to action faster? Are we correcting less and creating more? Are our teams becoming more curious, more capable, and more energized?

The most valuable AI Dividend is actually the Human Dividend. As machines handle the mechanical, people reclaim their time and attention for creative work, deeper customer relationships, and more purpose-filled contributions. This dividend can’t be measured only in savings or productivity. It will be seen in what people build when they have room to imagine again.

In the years ahead, leaders who measure wisely will look beyond immediate cost savings and focus on what their organizations can create that couldn’t have existed before.

Photo by C Bischoff on Unsplash – because some of the time we gain from using AI will free us up to work on non-AI pursuits. 

Why Curiosity Is the New Competitive Advantage

Imagine two managers sitting at their desks, both using the same AI tool.

The first asks it to write the same weekly report, just faster. Three hours saved. Nothing new learned. Box checked.

The second uses the AI differently. She asks it to analyze six months of data and search for hidden patterns. It reveals that half the metrics everyone tracks have no real connection to success. Two new questions emerge. She rebuilds the entire process from scratch.

Same tool. Different questions. One finds speed. The other finds wisdom.

This is the divide that will define the next decade of work.

For a long time, leadership revolved around structure and repetition. The best organizations built systems that ran like clockwork. Discipline became an art. Efficiency became a mantra.

Books like Good to Great showed how rigorous process could transform good companies into great ones through consistent execution. When competitive advantage came from doing the same thing better and faster than everyone else, process was power.

AI changes this equation entirely. It makes these processes faster, yes, but it also asks a more unsettling question. Why are you doing this at all?

Speed alone means little when the racetrack itself is disappearing.

Curiosity in the age of AI means something specific. It asks “why” when everyone else asks “how.” It uses AI to question assumptions rather than simply execute them. It treats every automated task as an opportunity to rethink the underlying goal. And it accepts the possibility that your job, as you currently do it, might need to change entirely.

That last part is uncomfortable. Many people fear AI will replace them. Paradoxically, the people most at risk are those who refuse to use AI to reimagine their own work. The curious ones are already replacing themselves with something better.

Many organizations speak of innovation, but their true values show in what they celebrate. Do they promote the person who completes fifty tasks efficiently, or the one who eliminates thirty through reinvention? Most choose the first. They reward throughput. They measure activity. They praise the person who worked late rather than the one who made late nights unnecessary.

This worked when efficiency was scarce. Now efficiency can be abundant. AI will handle efficiency. What remains scarce is the imagination to ask what we should be doing instead. Organizations that thrive will use AI to do entirely different things. Things that were impossible or invisible before.

Working with AI requires more than technical skills. The syntax is easy. The prompts are learnable. Connecting AI to our applications isn’t the challenge. The difficulty is our mindset. Having the patience to experiment when you could just execute. The humility to see that the way you’ve always done things may no longer be the best way. The courage to ask “what if” when your entire career has been built on knowing “how to.”

This is why curiosity has become a competitive advantage. The willingness to probe, to question, to let AI reveal what you’ve been missing. Because AI is a mirror. It reflects whatever you bring to it, amplified. Bring efficiency-seeking and get marginal gains. Bring genuine curiosity and discover new possibilities.

Here’s something to try this week. Take your most routine task. The report, the analysis, the update you’ve done a hundred times. Before asking AI to replicate it, ask a different question. What would make this unnecessary? What question should we be asking instead?

You might discover the task still matters. Or you might realize you’ve been generating reports nobody reads, tracking metrics nobody uses, or solving problems that stopped being relevant two years ago.

Efficiency fades. What feels efficient today becomes everyone’s baseline tomorrow. But invention endures. The capacity to see what others miss, to ask what others skip, to build what nobody else imagines yet.

The curious will see opportunity. The creative will see possibility. The courageous will see permission. Together they will build what comes next.

The tools are here. The door is open. Work we haven’t imagined yet waits on the other side. Solving problems not yet seen, creating value in ways that don’t exist today.

Only if you’re willing to ask better questions.

Photo by Subhasish Dutta on Unsplash – the path to reinvention

Strategy First. AI Second.

Eighty-eight percent of AI pilots fail to reach production, according to IDC research. Most fail because organizations chase the tool instead of defining the outcome. They ask, “How do we use AI?” rather than “What problem are we solving?”

A little perspective

I’m old enough to remember when VisiCalc and SuperCalc came out. That was before Lotus 1-2-3, and way before Microsoft Excel. VisiCalc and SuperCalc were just ahead of my time, but I was a big user of Lotus 1-2-3 version 1. Back then, everyone focused on how to harness the power of spreadsheets to change the way they did business.

Teams built massive (for that time) databases inside spreadsheets to manage product lines, inventory, billing, and even entire accounting systems. If you didn’t know how to use a spreadsheet, you were last year’s news.

The same shift happened with word processing. Microsoft Word replaced WordPerfect and its maze of Ctrl and Alt key combinations. Then the World Wide Web arrived in the early 1990s and opened a new set of doors.

I could go on with databases, client-server, cloud computing, etc. Each technology wave creates new winners but also leaves some behind.

The lesson is simple each time. New tools expand possibilities. Strategy gives those tools a purpose.

The point today

AI is a modern toolkit that can read, reason (think?), write, summarize, classify, predict, and create. It shines when you give it a clear job. Your strategy defines that job. If your aim is faster cycle times, higher service quality, or new revenue, AI can be the lever that helps you reach those outcomes faster.

Three traps to avoid

Tool chasing. This looks like collecting models and platforms without a target outcome. Teams spin up ChatGPT accounts, experiment with image generators, and build proof-of-concepts that fail to connect to real business value. The result is pilot fatigue. Endless demonstrations with no measurable impact.

Shadow projects. Well-meaning teams launch skunkworks AI experiments without governance or oversight. They use unapproved tools, expose sensitive data, or build solutions that struggle to integrate with existing systems. What starts as innovation becomes a compliance nightmare that stalls broader adoption.

Fear-driven paralysis. Some organizations wait for perfect clarity about AI’s impact, regulations, or competitive implications before acting. This creates missed opportunities and learning delays while competitors gain experience and market advantage.

An AI enablement playbook

Name your outcomes. Pick three measurable goals tied to customers, cost, or growth. Examples: reduce loan processing time by 30 percent, cut customer service response time from 4 hours to 30 minutes, or increase content production by 50 percent without adding headcount.

Map the work. List the steps where people read, write, search, decide, or hand off. These are all in AI’s wheelhouse to help. Look for tasks involving document review, email responses, data analysis, report generation, or quality checks.

Run small experiments. Two to four weeks. One team. One KPI. Ship something tangible and useful. Test AI-powered invoice processing with the accounting team, or AI-assisted internal help desk with support staff.

Measure and compare. Track speed, quality, cost, and satisfaction before and after. Keep what moves the needle. If AI cuts proposal writing time by 60 percent but reduces win rates by 20 percent, you need to adjust the approach.

Harden and scale. Add access controls, audit trails, curated prompt libraries, and playbooks. Move from a cool demo to a dependable tool that works consistently across teams and use cases.

Address the human element. Most resistance comes from fear of displacement, rather than technology aversion. Show people how AI handles routine tasks so they can focus on relationship building, creative problem-solving, and strategic work. Provide concrete examples of career advancement opportunities that AI creates.

Upskill your team. Short trainings with real tasks. Provide templates and examples in their daily tools. Make AI fluency a job requirement for new hires and a development goal for existing staff.

Close the loop with customers. Ask what improved. Watch behavior and survey scores, with extra weight on what people actually do, versus what they say.

Governance that speeds you up. Good guardrails create confidence and help you scale.

Access and roles. Limit sensitive data exposure and log usage by role. Marketing might get broad access to content generation tools while finance operates under stricter controls. The concept of least privilege applies. 

Data handling. Define red, yellow, and green data. Keep red data (customer SSNs, proprietary algorithms, confidential contracts) away from general public-facing tools. Yellow data needs approval and monitoring. Green data can flow freely.

Prompt and output standards. Save proven prompts in shared libraries. Require human review for customer-facing outputs, financial projections, or legal documents. Create templates that teams can adapt rather than starting from scratch.

Audit and monitoring. Capture prompts, outputs, and sources for key use cases. Build processes to detect bias, errors, or inappropriate content before it reaches customers.

Vendor review. Check security, uptime, and exit paths before heavy adoption. Understand data residency, model training practices, and integration capabilities. Consider making Bring-Your-Own-Key (BYOK) encryption the minimum standard for allowing your organization’s data to pass through or be stored on any AI vendor’s environment.

Questions for leaders

Which customer moments would benefit most from faster response or clearer guidance? Think about your highest-value interactions and biggest pain points.

Which workflows have the most repetitive reading or writing? These offer the quickest wins and clearest ROI calculations.

Which decisions would improve with better summaries or predictions? AI excels at processing large amounts of information and identifying patterns humans might miss.

Do we have the data infrastructure to support AI initiatives? Clean, accessible data is essential for most AI applications to work effectively. Solid data governance and curation are critical.

What risks must we manage as usage grows, and who owns that plan? Assign clear accountability for AI governance before problems emerge.

What will we stop doing once AI handles the routine? Define how you’ll reallocate human effort toward higher-value activities.

Who will champion AI adoption when the inevitable setbacks occur? Identify executives who understand both the potential and the challenges.

What to measure

Cycle time. Minutes or days saved per transaction.

Throughput. Work items per person per day.

Quality. Rework rate, error rate, compliance findings.

Experience. Customer effort score, employee satisfaction, NPS.

Unit cost. Cost per ticket, per claim, per application.

AI is the enabler

Strategy sets direction. AI supplies leverage. Give your people clear goals, safe guardrails, and permission to experiment and fail along the way.

Then let the tools do what tools do best. They multiply effort. They shorten the distance between intent and execution. They help you serve today’s customers better and reach customers you couldn’t reach in the past.

The question isn’t whether AI will transform your industry.

The question is whether you’ll lead that transformation or react to it.

Which will you choose?

Photo by Jen Theodore on Unsplash – I love this old school compass, showing the way as it always has. The same way a solid strategy and set of goals should lead our thinking about leveraging the latest AI tools.

Providing Room to Fail

Organizational culture, not technology, is the hardest part of innovation

How many of your projects are truly innovative? If you have any, what’s your success rate? Would you consider your success rate to be all-star caliber?

This baseball analogy is almost a cliché, but it holds up. A professional hitter with a .300 average is considered excellent (all-star?). That means they fail seven times out of ten.

Now imagine applying this to innovation. What if only 30% of your projects succeed? At first glance, that sounds like a losing record. But if the successful projects provide 10x productivity increases, transform your customer’s experience, or massively boost profitability…30% success would yield incredible results for your organization.

This is the kind of opportunity in front of us today with AI. Tools are maturing quickly. The potential is staggering. Every company, large or small, is beginning to experiment.

Some will tiptoe. Others will dive headfirst. All will face a mix of breakthroughs and busts.

There will be tools that don’t deliver on promises, pilots that fizzle, and teams that struggle with adoption. But there will also be amazing homeruns. Projects that reshape the business and redefine what’s possible.

Many leaders today are focusing on which AI tools to purchase and how to train their teams. That’s the easy part.

The harder part is creating space for both the hits and the strikeouts. If people feel they must succeed every time, they probably won’t swing at all. They’ll play it safe and stick with what they know.

Innovation will grind to a halt.

Providing room to fail doesn’t mean celebrating mistakes. It means making sure your team knows that experiments, even the ones that fall short, are part of making progress. Leaders who demand perfection get compliance. Leaders who make room for failure get innovation.

As you lead your organization into AI and beyond, remember that your job isn’t to guarantee every swing is a hit.

Your job is building a culture where people are willing to keep taking swings.

Photo by Chris Chow on Unsplash

The Known vs. The Obvious: Embracing AI in the Workplace

For years, we’ve heard that Artificial Intelligence (AI) will revolutionize industries. The idea is so prevalent that it’s easy to stop actively thinking about it. We acknowledge AI in headlines, in passing business conversations, and in abstract discussions about the future. Yet, much like a fish is unaware of the water surrounding it, we’ve been immersed in AI without fully recognizing its impact.

That impact is now undeniable. The question is: will we embrace it—or ignore it at our peril?

AI as the Invisible Force

AI is no longer a futuristic concept, or a background presence. It’s embedded in the tools we use every day, from the smartphones in our pockets to the chatbots handling our customer inquiries. It powers business decisions, optimizes operations, and influences nearly every industry.

Yet, because AI is so familiar, we often overlook it. The term itself has become a cliché—almost old news.  Something we assume we understand. But do we? How much do we really know about its capabilities, its limitations, or its potential disruptions?

Many still view AI as a distant idea, relevant only in the future or in industries far removed from their own. This perspective is outdated.

The Shift from “Known” to “Obvious”

AI is a driving force that can shape how we work, compete, and innovate. Organizations that continue treating AI as an abstract concept risk being blindsided by its rapid evolution.

This shift—from AI being “known” to becoming “obvious”—is critical. The moment we stop seeing AI as some far-off development and recognize it as an immediate force, we can take meaningful action.

Make no mistake: AI will transform your organization, whether you engage with it or not. The only choice is whether you’re leading that change or struggling to catch up.

The Cost of Waiting

A passive approach to AI is no longer viable. Waiting for the “right time” to adopt AI means falling behind competitors who are already leveraging its power. Yes, AI is complex, and yes, there are risks. But the greater risk lies in hesitation.

I’m old enough to remember the early days of the internet (I’m that old).  Most businesses dismissed it as a fad. Others chased the new idea with reckless abandonment and wasted tons of time and money.  But a relative few (at the time) experimented, learned, made incremental changes, and ultimately thrived in their use of the new “internet-powered” approach. Not to mention all the new multi-billion (trillion) dollar businesses that were made possible by the internet. 

AI is following a similar trajectory. Many are ignoring, even shunning, AI as something other people will figure out.  They don’t want to be the one pushing these new ideas within their organization.  It’s easier to stay in the background and wait for someone else to take the leap.

But others are already leaning in (to coin a phrase), experimenting, and learning.  They are incrementally (and sometimes dramatically) shaping a new future…and remaining relevant in the process. 

Learn the Basics

AI adoption doesn’t require immediate mastery. It starts with small, intentional steps.

You don’t need to be an AI expert, but understanding its core functions and business applications is essential.

Start by exploring industry-specific AI tools already in use.  How did I make this list?  You guessed it, I asked ChatGPT to give me a list of industry-specific AI tools in use today.  Will each one be a winner?  Not sure, but it’s a great list to use as a starting point:

–Retailers use Amazon Personalize and Google Recommendations AI for AI-driven product suggestions, improving customer engagement and sales.

–Marketers leverage HubSpot AI for automated email campaigns, Persado for AI-powered ad copywriting, and Seventh Sense for optimizing email send times.

–Financial analysts turn to Bloomberg Terminal AI for market insights, Kavout for AI-driven stock analysis, and Zest AI for smarter credit risk assessments.

–Healthcare professionals rely on IBM Watson Health for AI-assisted diagnostics and Olive AI for automating administrative hospital tasks.

–Manufacturers use Siemens MindSphere for AI-powered predictive maintenance and Falkonry for real-time industrial data monitoring.

–Customer service teams integrate Forethought AI for automated ticket triaging and Zendesk AI for intelligent chatbot interactions.

–HR and recruitment teams utilize HireVue AI for AI-driven candidate screening and Pymetrics for bias-free talent assessment.

Experiment with Broad-based AI Tools

Don’t wait for the perfect strategy.  Start small. Generalized AI tools can improve various aspects of your business (again, I asked ChatGPT for this list):

–Conversational AI & Research: Tools like ChatGPT, Claude.ai, or Anthropic’s AI help generate content, answer complex questions, summarize reports, and assist in brainstorming sessions.

–Automation: Platforms such as Zapier AI, UiPath, and Notion AI automate workflows, streamline repetitive tasks, and generate notes and summaries.

–Data Analysis: Solutions like Tableau AI, ChatGPT’s Code Interpreter (Advanced Data Analysis), and IBM Watson process and visualize data for better decision-making.

–Customer Engagement: AI-driven tools such as Drift AI, Intercom AI, and Crystal Knows enhance customer service, lead generation, and sales profiling.

These are just a few of the many AI-powered tools available today. The landscape is constantly evolving.  Exploring AI solutions that fit your specific needs is the key to personal and professional growth.

Cultivate a Growth Mindset

Learning AI is a journey, not a destination. It’s okay to make mistakes.  It’s actually necessary. Feeling uncomfortable is a sign of growth. The more you experiment, fail, and adjust, the more effectively you’ll integrate AI into your work. AI isn’t about instant perfection.  It’s about continuous learning.

Lead from the Front

If you’re in a leadership role, set the tone. Your team will look to you for guidance. Show them that AI adoption isn’t just an IT initiative.  It’s a mindset shift.

Encourage experimentation, provide resources, and support a culture of AI-driven innovation. Companies that will thrive with AI aren’t the ones waiting for a complete plan.  They’re the ones embracing AI through hands-on learning and iterative improvement while incorporating these new discoveries into their future plans.

The Future is Now

AI is not a distant disruptor—it’s an active force shaping today’s workplace. Organizations that recognize this and take action will thrive. Those that don’t will be left behind.

It’s time to stop treating AI as a theoretical innovation and start engaging with it as a business reality.

The future isn’t waiting, and neither should you.

Photo credit: The graphic was generated by DALL-E.  I asked it to generate an image of an office on the ground floor that captures the essence of the blog post I had just written. 

In its first few attempts, it tossed in robots sitting amongst the office workers.  I like to think of myself as a forward thinker, but I’m not quite ready to accept that reality…even though I’m sure it’s rapidly approaching.  I asked DALL-E to eliminate the robots (for now).