Category: Strategy

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

The Mirage of Strategic Clarity

Strategic Planning That Can Survive Reality

It was the second day of a two-day strategic planning retreat. Revenue projections stretched across the screen. The CFO walked through all the assumptions in his spreadsheet. Customer acquisition costs will flatten, churn will improve by two points, and the new product will capture eight percent market share within six months.

Everyone nodded along, acting as if these forecasts represented knowledge rather than elaborate guesses built on dozens of assumptions, any one of which could be wrong.

Three months later, a competitor launched an unexpected feature. Customer behavior shifted. The CFO’s projections became relics of a reality that never existed. The entire strategic planning process had been built on an illusion.

What we pretend to know

In his 2022 memo The Illusion of Knowledge, Howard Marks explored how investors mistake confidence for clarity. He began with a line from historian Daniel Boorstin:

“The greatest enemy of knowledge is not ignorance, it is the illusion of knowledge.”

Leaders face a brutal paradox. Boards expect forecasts. Teams want confidence. Investors demand projections. The machinery of leadership demands certainty.

So, we build elaborate forecasts and make decisions based on assumptions we know to be fragile. We treat detailed guesses as facts.

Physicist Richard Feynman once said, “Imagine how much harder physics would be if electrons had feelings.” Electrons follow discrete laws, unlike people. People innovate, resist, panic, and occasionally do something amazing nobody saw coming. Competitors behave differently than our models assume. Markets shift for reasons we never thought possible.

Marks describes forecasting as a chain of predictions. “I predict the economy will do A. If A happens, interest rates should do B. With interest rates of B, the stock market should do C.” Even if you’re right two-thirds of the time at each step, your chance of getting all three predictions correct at once is only about thirty percent.

Leadership forecasts work in a similar way. We predict customer adoption rates. If adoption hits those numbers, we’ll need a certain operational capacity. With that capacity, we can achieve specific margins. Those margins will attract investment.

Each assumption depends on the previous one. The chain is only as strong as its weakest link.

The tools we trust

Walk into any strategic planning session and you’ll likely encounter two frameworks treated as gospel:

-SWOT analysis (strengths, weaknesses, opportunities, and threats)

-SMART goals (specific, measurable, achievable, relevant, and time-bound).

Business schools teach them. Consultants recommend them. Leaders deploy them with confidence. Each relies on assumed knowledge that may not exist.

A SWOT analysis claims to know which possible developments count as opportunities versus threats. It’s a snapshot of assumptions masquerading as strategic insight. An opportunity exists only if you can identify it, execute against it, and do so before circumstances change. The framework provides no way of acknowledging uncertainty.

SMART goals often confuse precision with accuracy. “Increase market share” becomes “increase market share in the Northeast region from 12% to 15% by Q4 2026.” It sounds specific, and therefore rigorous. It’s easy to be precise about something unpredictable.

And how do we know a goal is achievable? We make assumptions about resources, market conditions, and competitor behavior, then write a goal that treats our assumptions as facts.

Both frameworks serve a valuable purpose. They force structured thinking. But they also seduce leaders into believing they know more than they do.

What should we do instead?

To be clear, this isn’t an argument for abandoning planning. Organizations need direction, priorities, and coordinated action. The question is how to plan in ways that acknowledge what we can’t know while still making decisive progress.

A better path involves changing how we plan and how we talk about the future.

Distinguish between direction and destination. Amazon knew it wanted to be “Earth’s most customer-centric company” without knowing exactly what that would look like in year ten. “We’re moving toward increased automation” carries more truth than “we’ll reduce costs by seventeen percent by Q3 2026.” The first creates direction. The second creates false precision.

Separate what you know from what you assume. Customer complaints increased forty percent this quarter. That’s knowledge. Saying the trend will continue is extrapolation. Predicting that fixing the issue will increase retention by five points is speculation. Present plans that show what you know, what you’re inferring, what you’re assuming, and what you’ll do if you’re wrong.

Build optionality into everything. Create strategies that work across multiple futures. Hire people who can do, or think about, more than one thing. Build modular systems with flexibility in mind. Create decision points where you can change course.

Use familiar tools differently. Run a SWOT analysis, then list three ways each opportunity might fail to materialize. Write SMART goals, then document the assumptions those goals depend on and how you’ll adapt if they prove incorrect.

Here’s a concrete example. You’re deciding whether to build a new product line. The traditional approach creates a detailed business case with market projections and revenue forecasts. You present it. People debate assumptions. A decision gets made.

An alternative approach defines what success means, then identifies what must be true to achieve it. You sort those conditions into things you can validate quickly, things you can validate over time, and things you can validate only much later. Stage investments to match the timing of the validations, rather than an arbitrary quarterly schedule.

The difference in these approaches is critical. In the first, the business case pretends to represent knowledge. In the second, it becomes a set of hypotheses to test over time.

The harder path

Amos Tversky observed, “It’s frightening to think that you might lack knowledge about something, but more frightening to think that, by and large, the world is run by people who have faith that they know exactly what’s going on.”

We select leaders for their ability to project confidence about an unknowable future. We reward decisiveness over doubt. Then we wonder why strategies fail when reality diverges from our projections.

Most of us live in this system. We’ve built organizations that demand the illusion of knowledge.

Real leadership creates organizations resilient enough to find answers as circumstances unfold. It builds teams that can adapt rather than simply execute a plan written many months ago.

When did you last change a forecast because reality diverged from your assumptions?

When did you last reward someone for identifying that a plan was failing?

Start small. Pick one decision where you can be explicit about uncertainty. Structure one investment to test assumptions instead of betting on a forecast. Have one conversation where you separate what you know from what you’re guessing.

Plan in ways that acknowledge uncertainty and position your organization to learn. Lead with confidence about principles while staying adaptable around specifics. Build organizations that can adapt when reality diverges from the plan.

Because it will. The measure of leadership lies in how well your culture can face that truth.

The CFO’s spreadsheet was never the problem.

The illusion that it represented knowledge was.

Photo by Michael Shannon on Unsplash

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.

What Worked Yesterday Isn’t Enough – Rethinking Customer Expectations and Continuous Improvement

I heard a quote recently from Tony Xu, the CEO of DoorDash:

“What we’ve delivered for a customer yesterday probably isn’t good enough for what we will deliver for them today.”

It’s not about failure. Xu isn’t saying we got it wrong. He’s pointing to something more subtle that applies not only to tech companies like DoorDash, but to every business in every industry. Regional banks. Manufacturers. Educators. Consultants. Entrepreneurs. Even nonprofit leaders. No one is exempt.

It’s tempting to believe that what worked before will keep working. After all, if it’s not broken, why fix it? That quiet assumption that if we keep doing what we’ve always done, success will follow.

But that mindset is quietly dangerous.  The world isn’t that simple.

Customers don’t live in yesterday. They live in the now. They’re comparing their experience with us not just to our competitors, but to the best parts of every interaction they’ve had today.

They’re comparing our website to their grocery buying app. Our onboarding process to a streaming service subscription they love. Our customer service calls to the help they received (or didn’t) from their cell phone company.

We’re not being compared to the bank down the road or the business across the street. We’re being measured against the most seamless, most helpful, most human-centered experience our customers have ever had.

That’s a very high bar. It’s unfair…and they don’t care.

It’s easy to forget their perspective from inside our organizations. We become focused on the big system conversion we’re managing, the vendor issue we’re troubleshooting, the reorganization plans we’re working on this quarter, or the new regulatory review that’s keeping us up at night.

These are real and important things. But the customer doesn’t see them, nor should they.

They’re living in their own world, with their own challenges and needs. They’re asking, quietly and constantly, “Are you making this easier, or harder, for me?”

They’re rightfully selfish in that way.

Some important questions to consider:

–What are my customers or team members quietly expecting that I haven’t noticed yet?

–What have I continued doing because it worked before, even though the market has changed?

–What future am I preparing for? The one I’ve known in the past, or the one that’s unfolding in a new direction?

–Am I making excuses that only make sense inside our organization?

I don’t think leadership is about chasing every trend. But I do believe it’s about staying awake. Staying open. Listening for what’s emerging and not just reacting to what someone else has made clear.

The fact that something worked yesterday doesn’t make it sacred. It makes it a foundation. And foundations are meant to be built upon…not celebrated as finished.

If we truly care about the people we serve, we’ll stay curious about how to serve them better. Because they’re not standing still. Their lives are shifting. Our job isn’t to cling (desperately) to relevance. It’s to keep earning it.

So, we never stop building. We keep asking the hard questions. We stay close to our customers so we can hear what they’re not saying yet. And we must choose to meet tomorrow’s expectations before they arrive at our doorstep. 

Yesterday’s work mattered. It carried us here. But it’s today’s effort—and our willingness to keep stretching—that will decide if we’re still invited to serve tomorrow.

As Shunryu Suzuki once said, “In the beginner’s mind there are many possibilities. In the expert’s mind, there are few.”

It’s great to be an expert in our field. But sometimes, a beginner’s mindset is exactly what we need to see things from the most important perspective. Our current and future customers’ perspective.

Photo by Bayu Syaits on Unsplash – I love the imagery of these two climbers at the top of a mountain.  They may take a short rest to celebrate their achievement, but that next peak is already in their sights.