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The AI Leadership Popularity Contest

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The POINT: Welcome to the AI Leadership Popularity Contest — where every leader must decide: do you want to be popular or respected? In the algorithm-driven workplace, your likability might earn quick applause, but only respect earns you the trust that sustains influence. The contest is on — and the stakes are higher than ever!

Welcome to the AI Leadership Popularity Contest!

Every leader eventually faces the same question: Would you rather be popular or respected?

In the past, this was a philosophical debate. Today, it’s a contest with very real stakes. Artificial intelligence (AI) has turned every hiring decision, pricing model, and customer interaction into a popularity vote on your leadership.

Being popular may earn applause.
Being respected earns trust.
And in the age of AI, trust is the only way to win.

Welcome to the AI Leadership Popularity Contest!

The Only Way to Win the Contest: Trust

The modern workforce doesn’t just follow leaders — they scrutinize them. Add AI into the mix, and every decision comes under a brighter spotlight. Employees and customers don’t only want to know what the algorithm said; they want to know why you chose to act on it.

A liked leader hides behind AI: “That’s just what the system recommended.”
A respected leader steps forward: “Here’s why we designed the model this way, and here’s how I’m accountable for its outcomes.”

Trust is the deciding factor. Without it, the popularity contest is over before it begins.

Why Likability Gets Votes, But Respect Wins Elections

Think of likability as the campaign trail — handshakes, smiles, and soundbites. In leadership, that’s being approachable, pleasant, and easy to work with. It’s valuable, but fleeting.

Respect, on the other hand, is what wins the election. It’s built on competence, consistency, and character. It’s the infrastructure behind the campaign: reliable systems, ethical decisions, and results that last.

In AI leadership, likability is the friendly chatbot. Respect is the secure, bias-audited, well-governed system behind it. One might charm you in the short term. The other sustains your credibility long after the contest ends.

The Perils of Leading for Applause

Leaders who chase likability often avoid the uncomfortable. They’ll fast-track AI pilots without governance, roll out shiny automation tools without transparency, or dodge hard conversations about bias and job displacement.

These moves may win quick cheers — like handing out candy at a campaign rally. But when the first scandal hits, applause turns into scrutiny. Employees and customers remember whether you built your campaign on charisma or character.

Respect means making the hard calls, even when they cost you short-term popularity.

Respect Anchors Balance When the Stakes Are Higher

The best leaders know balance matters: you need approachability and accountability. But AI tilts the contest.

Why? Because AI amplifies consequences. A bad hiring decision made by a human affects one role. A biased AI hiring model poisons the entire talent pipeline. A flawed algorithm in lending, insurance, or healthcare can damage thousands of lives — and reputations.

That’s why respect isn’t optional anymore. Employees will forgive a leader who isn’t everyone’s friend. They won’t forgive one who hides behind machines or fails to safeguard them. Respect anchors the balance when the spotlight is on.

Consistency: The Winning Campaign Strategy

Campaign promises are meaningless if they don’t match actions. Leadership works the same way.

Consistency is how respect compounds. If you say you’ll be transparent about AI governance, then show your work. If you say you’ll let data drive decisions, don’t override models when they’re inconvenient. If you say innovation matters, don’t block automation out of fear.

Inconsistent leaders lose elections — and teams. Consistent leaders build trust that no algorithm can shake.

Respect Is the Only Title That Lasts

Technology moves fast. Today’s AI is tomorrow’s legacy code. But respect outlasts the hype cycle.

A leader who earns respect through clarity, accountability, and integrity creates a permanent leadership asset. Employees follow you not because the system told them to, but because they believe in your judgment. Customers stay loyal not because of your latest app, but because they know your values don’t change with the algorithm.

Likability fades with trends. Respect wins the office — and keeps it.

The AI Leadership Imperative

The AI Leadership Popularity Contest is here, whether you like it or not! Every decision you make — human or machine-assisted — casts a vote in your favor or against you.

If you want to thrive in this new era, remember:

  • Likability gets you applause.
  • Respect earns you trust.
  • Trust wins the contest.

Leadership isn’t a Popularity Contest (but it isn’t an Unpopularity Contest either!)

Sam Palazzolo

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Sam Palazzolo - The AI Leadership Popularity Contest

The AI-First Organization: Redefining Workflows, Talent, and Leadership for the Next Era

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Generative AI has moved beyond the experimentation phase. It is no longer a side project or an optional pilot—it is a defining capability that is reshaping the way organizations operate. Yet while adoption is accelerating, impact has lagged. Many leaders are asking: how do we translate widespread AI use into sustainable, measurable transformation?

The answer lies in building what I call the AI-First Organization. Rather than bolting AI onto existing processes, an AI-First Organization integrates generative AI into the core of strategy, workflows, and decision-making. It means asking at every turn: “How can AI inform, accelerate, or transform this work?”

This article explores how to build an AI-First Organization—one that redefines workflows, elevates talent, builds trust through governance, and positions itself for lasting advantage.

Sam Palazzolo The AI-First Organization

From Adoption to Advantage

Today, two-thirds of companies report using generative AI, but very few have unlocked its true potential. Adoption without integration rarely produces results. Consider two contrasting examples:

  • A global bank rolled out AI copilots across its back office. Initial productivity surged, but gains quickly eroded because workflows stayed rooted in old processes.
  • A brokerage firm, by contrast, embedded generative AI into its advisor workflows with rigorous oversight. The result: 98% adoption and meaningful gains in efficiency and client service.

The distinction highlights a key truth: the AI-First Organization doesn’t “add tools”—it reimagines how work gets done.

Employees Are Already AI-First—Leaders Must Catch Up

One of the most surprising dynamics is that employees are often ahead of their leaders. Studies show they are using AI tools three times more than executives realize. This shadow adoption represents both a risk and an opportunity.

Left unmanaged, it can expose organizations to compliance and security vulnerabilities. But when harnessed, it can transform employees into co-creators of new workflows and innovations. In fact, companies that engage at least 7% of their workforce in transformation initiatives are twice as likely to outperform their peers on shareholder returns.

In an AI-First Organization, employees are not passive users. They are active participants—building agents, piloting workflows, and sharing learnings across teams.

The North Star of an AI-First Organization

Every transformation needs a North Star. For an AI-First Organization, that vision must be anchored in outcomes, not tools. The critical question is not “Where can we add AI?” but “What outcomes do we want AI to unlock?”

Examples include:

  • In healthcare, AI-driven patient engagement reducing readmissions by 15%.
  • In retail, AI-powered merchandising cutting inventory holding costs by 20%.
  • In professional services, AI knowledge copilots halving the onboarding time for new hires.

This shift in framing—from tool deployment to outcome orientation—helps align leadership, technology, and talent toward the same strategic direction.

Trust as the Foundation of AI-First Work

The AI-First Organization is built on trust. Employees must trust the outputs, customers must trust the service, and regulators must trust the governance. Without it, adoption stalls.

Core Components of Trust-Centric AI

  • Human-in-the-loop oversight for critical workflows.
  • Bias and hallucination controls to safeguard accuracy.
  • Cross-functional governance bodies ensuring ethical use.
  • Transparent evaluation and communication so employees understand how models are validated.

Morgan Stanley’s success underscores the point: trust, not technology alone, drives enterprise-wide adoption.

Redesigning Workflows: From Copilots to MVOs

Becoming AI-First means rebuilding workflows from the ground up. The path typically unfolds in three stages:

Phased Workflow Evolution

  • Assisted Workflows: Humans supported by AI copilots (e.g., marketers drafting campaigns with AI).
  • Agent Groups: Semi-autonomous clusters of agents coordinating tasks (e.g., finance teams running forecasting scenarios).
  • Minimum Viable Organizations (MVOs): Fully autonomous agent-driven units running lean business functions (e.g., customer service handled primarily by AI, with human escalation only when necessary).

This staged progression ensures organizations scale AI integration responsibly while capturing increasing levels of efficiency.

Redefining Talent for the AI-First Organization

The organizational model must also evolve. Some business units will become lean AI-led MVOs. Others—particularly those requiring human judgment and empathy—will remain human-led but AI-augmented. This requires a fresh talent strategy.

Talent Roles of the Future

  • AI Workflow Optimizers: Professionals redesigning processes around AI capabilities.
  • Automation Product Owners: Leaders accountable for scaling AI-enabled systems.
  • Reskilled Frontline Talent: Employees shifted toward creativity, relationship-building, and problem-solving—skills AI cannot replicate.

An AI-First Organization invests as much in reskilling talent as it does in new technology.

Employees as the Catalyst of Change

Top-down mandates alone will not build an AI-First Organization. Transformation accelerates when employees are empowered as change agents.

McKinsey’s own internal “Lilli” platform illustrates this: 17,000 employees created their own AI agents, leading to 92% usage. Similar results are possible when employees are encouraged to experiment, build, and share.

The principle is simple: participation drives adoption, and adoption drives results.

Closing Thoughts: Leading the AI-First Future

Generative AI is not just another tool—it is a structural shift in how organizations configure work, allocate talent, and generate value. Building an AI-First Organization requires:

  • Reimagining workflows, not just deploying tools.
  • Redefining talent strategies for AI-enabled roles.
  • Building trust through governance and transparency.
  • Empowering employees as co-creators of change.

Leaders who embrace this mindset will scale smarter, faster, and more resiliently than those who remain in the experimentation stage. The AI-First Organization is not the future—it is already here.

For executives committed to scaling in this new era, now is the time to act. Define your North Star, empower your people, and build trust into the system. That is how organizations turn generative AI into lasting competitive advantage.

Sam Palazzolo

If you want practical insights on scaling strategies and leadership in the AI era, I invite you to sign up for my Business Scaling newsletter at sampalazzolo.com

Customer Funding: Venture Funding’s Overlooked Option

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For decades, the default growth story has been simple:
Raise more money. Venture capitalists back the big idea. Banks extend credit. Balance sheets swell with other people’s capital.

But this binary view—equity or debt—comes at a cost. It assumes that outside capital is the only fuel for growth. For many companies, especially those looking to scale beyond the early stage, the result is dilution, debt, and distraction.

There is a third way forward. Customer funding—still underutilized even among experienced leaders—is emerging as a viable, and often faster, growth engine.

The Problem with the Two-Legged Stool

Research from McKinsey & Company (Strategy: Beyond the Hockey Stick) underscores how difficult it is to achieve breakout growth: fewer than 1 in 12 companies move up a performance tier in a decade. One contributing factor is that the pursuit of external funding becomes an end in itself.

Time and energy that could be spent deepening relationships with customers instead gets poured into pitch decks, investor roadshows, and loan negotiations. Equity can provide time and capital for bold moves, but at the cost of ownership. Debt can amplify returns, but adds pressure and risk. Both create dependencies on decision-makers outside the company’s walls.

It leaves too many leaders sitting on a two-legged stool—unstable, waiting for someone else to believe in them enough to fund their next move.

The Third Leg

Customer funding adds the missing third leg.
Instead of relying solely on outsiders, you let customers fund growth directly. They do this when they pre-pay, subscribe, commit early, or purchase services that finance the creation of future products.

John Mullins, a professor at London Business School and author of The Customer-Funded Business, has documented how companies as different as Airbnb, Dell, and countless service firms have built expansion on customer cash rather than investor dollars. This approach doesn’t remove the need for outside funding. It simply changes the order of operations: customers first, capital later.

How Customer Funding Works

Customer funding isn’t a single tactic. It’s a mindset—one that expresses itself in several proven models.

Platforms like Airbnb and Uber show the matchmaker model: connecting buyers and sellers, taking a fee, and scaling without ever owning inventory.
Companies like Dell demonstrate pay-in-advance models, securing revenue before building through pre-orders or direct-to-consumer commitments.

Many organizations opt for a subscription model, transforming one-off sales into recurring revenue streams that create predictable cash flow. Software-as-a-service businesses are the classic example here.

Another path begins with a service-to-product approach. High-value, bespoke services generate income that funds the development of standardized, scalable products. Consulting firms that evolve into software companies follow this trajectory.

Finally, time-and-materials contracts allow agencies, consultancies, and specialized manufacturers to fund their capability building and growth as client work progresses.

The unifying principle is simple: the customer relationship becomes the funding source.

Why This Approach Matters Now

Conditions for growth have shifted. Capital is more expensive. Investors are slower to commit, valuations are lower, and lenders are scrutinizing cash flows more than ever. Meanwhile, competitive pressures have only intensified.

In this environment, customer funding delivers two powerful advantages:

  • Speed: Pre-orders, subscriptions, and bundled service contracts create immediate access to cash, allowing companies to act faster than those waiting for a round to close.
  • Focus: When customers pay upfront, what gets built aligns tightly with market demand. It is real-time validation that external funding alone can’t replicate.

Integrating Customer Funding into a Broader Strategy

Customer funding is not an argument against external capital. The best operators use all three levers—equity, debt, and customer funding—to complement each other. Starting with customers, however, de-risks the business.

When you arrive at the table with proof of demand and revenue in hand, you negotiate from a position of strength. Investors and lenders respond differently when the market has already validated your path.

For leaders exploring this approach, ask yourself:

  • Can our best customers be persuaded to commit earlier, even pre-paying?
  • Could we offer a recurring option that creates more predictable cash flow for both sides?
  • Could a service we deliver today evolve into a product that scales tomorrow?

These questions reframe growth as something that begins inside the business, not outside of it.

A Different Kind of Growth Mindset

For years, “growth mindset” has been shorthand for raising another round. But durable, controlled growth rests on a stronger foundation.

Equity buys time. Debt buys leverage. Customer funding buys freedom.

As competition intensifies and capital tightens, more leaders are discovering that the most dependable source of growth capital may be the customers they already serve. When those customers become partners in funding the next stage, the question changes from “Who will give us the money?” to “How fast can we deliver?”

Sam Palazzolo

Strategy Dies Without Storytelling

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The memo is out: CEOs aren’t just decision makers anymore—they’re the company’s Chief Storyteller.

McKinsey called it years ago, but let’s be honest—most leaders still confuse “strategy deck” with “story.” And while anyone can parrot the phrase vision and values, very few can make you feel them.

The question is no longer if you should tell stories. It’s this: How do you build the storytelling skill that separates forgettable leaders from the ones people would follow through fire?

From Talking Points to Real Narratives

Here’s the trap: executives think storytelling is about polishing a speech. It isn’t.
It’s about noticing the raw material of a good story in real time—a customer breakthrough, a failure you learned from, a moment that shaped the culture—and turning it into something people can connect with.

That’s the first discipline: listen for the signal.
Once you catch it, you can shape it: Where did we start? What changed? Where are we going next? That simple arc beats any “bullet-point manifesto” you’ve been reciting.

Building the Muscle

Knowing what a story looks like isn’t enough; you need to craft it.
Think of three tools that separate the pros from the amateurs:

  • Structure (setup → tension → resolution)
  • Emotion (people don’t move on facts; they move on stakes)
  • Detail (tiny moments—“the look in her eyes”—burn into memory)

Want to practice? Take a major win or failure from your last quarter. Rewrite the email you sent about it as a story instead. Make it human. Then share it with a peer and ask them this: Would you tell someone else about what you just read?

Living the Story You Tell

Here’s where most leaders blow it: they craft a narrative, then behave in ways that contradict it.

Your story is worthless unless your team sees it in your actions. That’s why McKinsey says the CEO has to embody the story. Every channel—LinkedIn post, boardroom presentation, town hall—is either reinforcing or eroding it.

Ask yourself: if someone watched me work for a week with the sound off, would they still understand what I stand for? If not, you’re just a narrator, not a storyteller.

Repetition Is Not Redundancy

Good storytelling isn’t a one-time TED Talk. It’s a cadence.
Top leaders build a rhythm: weekly internal moments, quarterly external narratives, crisis stories that clarify direction when everything’s on fire. Done well, people don’t think, I’ve heard this before. They think, I know exactly where we’re going.

This habit turns “CEO vision” from wallpaper into a compass.

Test, Learn, Evolve

Finally, understand this: stories aren’t precious—they’re prototypes. Share, watch reactions, and adjust.
You’ll know you’ve hit a nerve when your team starts repeating your stories back to you without prompting. Until then, keep iterating.

McKinsey’s data is clear: the leaders who thrive aren’t just telling stories—they’re learning from the way those stories land.

Real Strategies. Real Results.

Storytelling isn’t a soft skill. It’s a leadership edge.
Listen. Shape. Live. Repeat. Refine.
Do that, and you’ll stop giving speeches and start creating movements.

Because in the end, strategy may set direction—but story is what makes people move.

Sam Palazzolo

Sam Palazzolo Strategy Dies without Storytelling

4 Reasons AI Adoption Stalls: What Smart Leaders Do Differently

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We all want the benefits of AI—faster decisions, deeper insights, automated efficiency. But if there’s one pattern I’ve seen repeated across industries, it’s this: the tech usually works. It’s the organization that doesn’t.

In nearly every AI initiative I’ve been called into midstream, the problem wasn’t lack of ambition or capability. It was a lack of organizational learning or upskilling. And that’s the real reason AI adoption stalls.

The tech usually works. It’s the organization that doesn’t.

Sam Palazzolo

So here are four of the biggest reasons I see AI adoption failing—and how the most strategic leaders counteract them.

1. Start Where the Energy Already Exists

Most execs assume AI adoption starts with a top-down strategy deck. It doesn’t. The real spark usually comes from someone on the front lines—marketing ops building smarter lead scoring, finance reducing reporting time, or a product team testing an ML model for recommendations.

The leaders who succeed don’t try to centralize too soon. Instead, they take a “gardener’s approach”: spot where things are already working, then scale those ideas by making the infrastructure reusable across teams. Think shared data access, faster experimentation cycles, and cross-functional visibility.

If you find a win, don’t isolate it—institutionalize it.

2. Incentivize Learning, Not Just Output

You want innovation? Don’t just reward ROI. Reward learning. Smart organizations create the right incentives around curiosity, iteration, and insight—not just outcomes.

That could mean recognizing internal champions, hosting innovation days, or promoting people who bring others along for the ride. When you celebrate the process—not just the success—you get more engagement from more people.

If you find a win, don’t isolate it—institutionalize it.

Sam Palazzolo

3. Run Experiments That Actually Teach You Something

Here’s a trap I see too often: building a shiny AI model, running a 6-month pilot, and then declaring “it didn’t work” without knowing why.

Real AI transformation doesn’t come from proof of concept. It comes from proof of learning. That means:

  • Testing clear hypotheses (“Can we reduce response time by 40% without sacrificing accuracy?”)
  • Running small-sample, short-cycle experiments
  • Capturing why it worked—or didn’t

If you’re not learning something new in every sprint, you’re just burning time and budget.

4. Stop Celebrating Everything

When every experiment gets a trophy, the signal gets lost. Smart leaders know that too much recognition—especially for inconclusive or low-impact work—erodes focus, urgency, and standards. Recognition should be earned, not automatic.

This doesn’t mean punishing failure. It means being intentional about what gets amplified. Celebrate experiments that move the needle, generate transferable insight, or unlock repeatable processes—not just anything that checked a box.

Smart leaders don’t reward motion—they reward momentum. Recognition isn’t a participation trophy—it’s a spotlight for what’s repeatable, scalable, and strategic.

Sam Palazzolo

AI Adoption

AI doesn’t fail because the models aren’t good enough—it fails because organizations aren’t structured to learn fast enough. We walked through four breakdowns that stall adoption: ignoring grassroots innovation, incentivizing the wrong behaviors, mistaking activity for insight, and celebrating everything instead of what actually moves the needle. The common thread? Smart leaders build cultures that learn, adapt, and scale—faster than the tech evolves.

Sam Palazzolo
Real Strategies. Real Results.

PS – If you’re serious about scaling AI—and scaling your business—I share insights like this every week in my newsletter. Sign up at sampalazzolo.com and get a free copy of my 50 Scaling Strategies eBook (a $50 value) instantly.

Sam Palazzolo AI Adoption