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The RCM Wheel Is Lying to You

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There’s a revenue story most healthcare leaders don’t see… It usually starts the same way. Revenue is up. Headcount is up. Systems are in place. On paper, the business looks healthy. Yet cash feels tight, growth has slowed, and leadership meetings keep circling the same question: “Why does it feel harder to scale now than it did at half the size?”

In healthcare (but not the exclusive applicable industry), this moment often triggers a closer look at Revenue Cycle Management (RCM). Leaders pull up the familiar RCM “wheel” (See below) – patient access, coding, billing, collections, reporting – and reassure themselves that each function technically exists. The wheel is intact. The problem must be execution.

That assumption is usually wrong.

The issue isn’t that the wheel is broken. It’s that the wheel is optimized locally-by function, by vendor, by department-rather than architected as a system designed to scale.

And that distinction matters far beyond healthcare.

Sam Palazzolo's The RCM Wheel Is Lying to You

Revenue Cycle Management Is a System, Not a Department

At its core, Revenue Cycle Management refers to the end-to-end process of capturing, managing, and realizing revenue-from initial customer engagement through final payment and reconciliation. In healthcare, this includes eligibility, coding, claims submission, payment posting, and collections. In SMBs more broadly, the labels change, but the mechanics don’t.

Every RCM wheel attempts to do three things:

  1. Protect revenue integrity
  2. Optimize cash velocity
  3. Create predictability for reinvestment

When any one of those breaks down, growth stalls.

Industry research consistently shows that revenue leakage-caused by process fragmentation, unclear ownership, and misaligned incentives-costs organizations 3-5% of net revenue annually (HFMA; Change Healthcare). For a $50M organization, that’s $1.5M-$2.5M evaporating every year, often invisibly.

That’s not an execution problem. That’s an architecture problem.

Why “Revenue Integrity” Is the Hidden Constraint

Most leadership teams focus on top-line growth. Fewer rigorously manage revenue integrity-the assurance that revenue earned is revenue realized, accurately, completely, and on time.

In healthcare, weak revenue integrity shows up as:

  • Inconsistent coding standards
  • Payer contract misalignment
  • Poor upstream data quality
  • High downstream rework and denials

Across SMBs, it looks like:

  • Discount creep
  • Inconsistent pricing enforcement
  • Contract terms not reflected in billing
  • Sales closing deals ops can’t efficiently fulfill

Different industries. Same root cause: growth outpacing system design.

The RCM Wheel Breaks When Scale Arrives

Here’s the uncomfortable truth:
Most RCM models are designed for operation, not scale.

As volume increases, small inefficiencies compound. Manual workarounds become institutionalized. Reporting lags reality. Leaders rely on lagging indicators instead of leading signals.

The wheel still spins-but it wobbles.

This is why organizations with strong revenue growth can simultaneously experience:

  • Rising AR days
  • Margin compression
  • Cash flow volatility
  • Leadership frustration

The wheel isn’t aligned to strategy. It’s aligned to history.

“Revenue problems rarely start in billing. They start when leadership mistakes functional coverage for system design.”
– Sam Palazzolo, Managing Director, Tip of the Spear

RCM as a Scaling Discipline, Not a Back-Office Function

High-performing organizations treat Revenue Cycle Management as a cross-functional growth system, not a downstream cleanup function.

That means:

  • Revenue integrity is owned at the executive level
  • Contract economics are operationalized, not assumed
  • Data flows forward, not backward
  • Incentives reinforce system health, not local optimization

In healthcare, this shift consistently correlates with:

  • Lower denial rates
  • Faster cash realization
  • Improved forecasting accuracy

In SMBs more broadly, it unlocks something just as valuable: decision confidence.

When leaders trust the revenue engine, they invest faster, hire earlier, and scale with intention instead of caution.

Why Stalled Growth Is Usually Structural

For $5M-$100M organizations, stalled growth is rarely about ambition or effort. It’s about structural constraints that were invisible at smaller scale.

Revenue Cycle Management is often where those constraints surface first-but not where they originate.

They originate in:

  • Strategy that outgrows operating cadence
  • Leadership bandwidth stretched too thin
  • Processes built for survival, not scale
  • Financial systems optimized for reporting, not insight

RCM is the mirror. Not the root cause.

What Leaders Need to Internalize

Let’s bring this home.

  • Revenue Cycle Management is not a wheel to maintain-it’s a system to architect.
  • Revenue integrity is the difference between growth that compounds and growth that leaks.
  • Stalled organizations don’t lack effort; they lack alignment between strategy and execution.
  • The same RCM principles that constrain healthcare systems quietly stall SMBs across industries.

If revenue feels harder than it should, the issue isn’t effort.
It’s structure.

And structure can be diagnosed.

Sam Palazzolo, Managing Director @ Tip of the Spear
Fractional CRO / CRMO · Revenue Architecture & Scale

McKinsey’s AI Workforce Shift

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Over the last decade, I’ve been brought into dozens of companies between $5M and $100M in revenue that all sound different on the surface – but break in exactly the same way underneath. The pipeline is inconsistent. Forecasts aren’t trusted. Sales, marketing, and operations don’t agree on the numbers. And investors are asking a question leadership can’t confidently answer: “How predictable is your revenue, really?”

What’s changed isn’t effort or ambition. What’s changed is the operating model. And McKinsey’s recent analysis on AI-driven workforce transformation puts hard data behind what I’ve been seeing in the field for years: revenue growth is no longer a headcount problem – it’s a productivity and leadership problem.

So is consulting dead? Maybe, but this is exactly why fractional CRO and RevOps leadership isn’t a stopgap anymore. It’s becoming the default growth model for successful companies of the future.

McKinsey’s “25 Squared” Project: Shrinking Teams and Expanding Output

One of the most telling McKinsey takeaways is this: the firm increased client-facing roles by roughly 25%, reduced non-client-facing roles by 25%, and still grew overall output.

I see the same pattern in SMBs and portfolio companies. Revenue teams were built for a world where scale required more people: more SDRs, more analysts, more RevOps admins. AI has blown that assumption apart.

Today, many of those tasks – reporting, data cleanup, pipeline hygiene, even first-pass outreach – are automated or AI-assisted. But here’s the uncomfortable truth: most companies didn’t replace those tasks with better leadership or strategy. They just layered AI on top of a broken revenue system.

Fractional RevOps works because it matches this new reality. You don’t need more bodies. You need fewer, better decisions – executed faster.

Productivity Gains Are Real – But Only If Someone Owns Them!

McKinsey reports saving 1.5 million hours annually by using AI for research, synthesis, and repetitive work. That time didn’t disappear – it was redeployed toward higher-value thinking and execution.

This is where I see companies struggle.

They invest in tools. They buy AI platforms. They expect productivity gains to magically show up in revenue. But productivity without ownership just creates faster chaos.

Fractional CROs and RevOps leaders exist to convert productivity into performance. We decide:

  • Which metrics matter
  • How pipeline should actually flow
  • What “forecastable” really means
  • Where AI accelerates execution – and where it introduces risk

Without that leadership layer, AI becomes noise. With it, AI becomes leverage.

The “25 Squared” Model Is Why Fractional Leadership Wins

McKinsey described its approach as “25 squared“: fewer support roles, more value-creating roles, and higher overall output. That’s not a consulting insight – it’s an operating principle.

Fractional revenue leadership follows the same logic.

Instead of hiring:

  • A full CRO
  • A full RevOps team
  • Multiple layers of management

Companies bring in experienced leadership exactly where it creates leverage, without long ramp times or fixed overhead. I’ve seen this reduce cost, increase clarity, and – most importantly – restore trust in the numbers.

For investors, this matters. Predictability beats growth theater every time.

AI Agents Are Replacing Tasks – Not Accountability

McKinsey now operates with roughly 40,000 human professionals and 25,000 AI agents, with near parity expected soon.

This mirrors what I see across revenue organizations. AI agents can:

  • Update CRMs
  • Generate reports
  • Draft emails
  • Analyze trends

What they cannot do is own a revenue number.

Fractional CROs sit precisely at this intersection: translating AI output into executive decisions, aligning GTM teams around shared metrics, and making tradeoffs when data conflicts. AI accelerates execution – fractional leadership ensures it moves in the right direction.

Junior Work Isn’t Disappearing – It’s Being Rewritten

Another subtle but critical McKinsey takeaway: junior roles aren’t going away – they’re changing. AI absorbs the grunt work, forcing humans to operate at a higher level sooner.

This is happening inside revenue teams right now. The old apprenticeship model – “learn by doing admin work for two years” – is collapsing. Without strong RevOps leadership, that creates confusion and burnout.

Fractional RevOps introduces structure, playbooks, and guardrails so teams can actually grow into this new reality instead of being overwhelmed by it.

Why Investors Are Driving This Shift Faster Than Founders

VCs, PE firms, and family offices don’t want heroics. They want repeatability. Traditional revenue teams are slow to adapt, expensive to fix, and difficult to unwind.

Fractional CRO and RevOps leadership gives investors:

  • Faster time-to-impact
  • Clear diagnostics across portcos
  • Consistent revenue governance
  • Predictable forecasting frameworks

That’s why I increasingly see fractional leadership deployed before full-time hiring – not after failure, but as a growth accelerant.

Revenue Leadership Has Changed – Permanently

McKinsey’s workforce data confirms what many operators already feel: output is being decoupled from headcount. AI has rewritten the rules. The companies that win aren’t the ones hiring faster – they’re the ones aligning smarter.

From my seat, fractional CRO and RevOps leadership isn’t a trend. It’s the natural response to an AI-augmented world where productivity is abundant, but clarity is scarce.

If revenue feels stalled, forecasts feel fragile, or investors are pushing for predictability – the problem isn’t effort. It’s the model.

And the model has already changed.

Sam Palazzolo, Managing Director @ Tip of the Spear

McKinsey’s AI Workforce Shift

The NIL Playbook for High-Velocity, High-Impact Growth in the Attention Economy

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The Point: From a capital markets perspective, the NIL economy has moved beyond novelty and access. The real question is whether athlete influence can be operationalized into a repeatable, measurable growth engine. At Tip of the Spear Ventures, we evaluate opportunities through the same lens we apply to any scalable business: disciplined execution, clarity of value creation, and the ability to perform under pressure. As Managing Director at Tip of the Spear, I sit on the Advisory Board of Directors at Contested, an NIL Agency focused on delivering an Operating System for Brand-Athlete Partnerships. The attention economy may reward speed, but capital rewards performance. This piece outlines why the next generation of NIL leaders (Capital, Brands, and/or Athletes) will not be defined by hype or reach, but by a performance-driven operating model that treats athlete influence like any other high-impact asset: trained, measured, and continuously optimized.

From Talent to Performance Systems

In both sports and business, raw talent is rarely the limiting factor. Systems are.

Elite athletes do not rely on moments or motivation. They rely on structure: clear objectives, disciplined preparation, continuous feedback, and constant adjustment. Organizations operating in the NIL ecosystem face the same reality. Success does not come from access to athletes alone, nor from one viral campaign or marquee partnership. It comes from the ability to produce outcomes consistently under variable conditions.

A performance mindset reframes NIL from a creative experiment into an operating discipline. Athlete selection, campaign design, execution cadence, and post-activation analysis all become part of a repeatable system. Over time, this system compounds. Each activation informs the next. Each outcome sharpens the model. The result is not just better campaigns, but a stronger operating engine.

What Capital Actually Underwrites

Capital does not underwrite narratives. It underwrites execution.

Investors evaluating opportunities in the NIL and attention economy look for evidence that value creation is understood, measured, and repeatable. The strongest signals are not celebrity associations or headline partnerships, but operating clarity: defined success metrics, disciplined prioritization, and early proof points that demonstrate control over outcomes.

A performance-driven NIL model communicates seriousness. It shows how athlete influence translates into revenue, how brands reduce acquisition risk, and how results can be replicated across markets and campaigns. From a capital standpoint, this reduces volatility and increases confidence in scalability.

Organizations that can demonstrate this discipline are not dependent on market hype cycles. They are building durable operating models that perform regardless of sentiment.

Brands Want Outcomes, Not Optics

Brands today are not short on content. They are short on certainty.

The most effective NIL programs treat athlete partnerships as performance channels, not publicity stunts. Demonstrated lift in engagement, conversion, or revenue matters more than impressions. Clear objectives, defined KPIs, and post-campaign analysis are no longer optional. They are table stakes.

Equally important is the ability to balance speed with learning. High-performing organizations test quickly, measure rigorously, and refine continuously. Successful activations become playbooks, not anecdotes. Over time, this creates institutional knowledge that compounds returns and reduces risk.

Brands that approach NIL this way move from experimentation to advantage. NIL becomes a growth lever, not a line item.

Athletes as Strategic Operators

Athletes are uniquely positioned to thrive in this environment because the performance mindset is already ingrained.

The most effective athletes in NIL treat their personal brand as a business asset. They show up with clarity on goals, discipline in preparation, and accountability for outcomes. They understand that consistency and professionalism increase their value to brand partners over time.

When athlete impact is measured and documented, influence becomes tangible. Audience growth, campaign performance, and sales contribution tell a story that brands and capital understand. Athletes who operate this way are viewed as strategic partners, not interchangeable endorsers.

This shift elevates the entire ecosystem. Brands gain confidence. Platforms gain credibility. Athletes gain leverage.

The Compounding Effect of Performance

The real advantage of a performance-driven NIL model is compounding.

Each activation generates data.
Each campaign sharpens execution.
Each iteration improves predictability.

Over time, this creates a flywheel where better performance attracts stronger brands, more serious athletes, and aligned capital. Not because of hype, but because of evidence.

In crowded markets, durable operating models separate themselves by how they perform, how they measure results, and how they improve over time. Performance becomes the differentiator.

The Bottom Line

The future of NIL will not be shaped by reach alone. It will be shaped by discipline.

Organizations that win will treat athlete influence like any other high-impact asset: trained, measured, and continuously optimized. They will prioritize outcomes over optics and systems over stunts.

In an attention economy defined by noise, performance is the signal that endures.

Sam Palazzolo, Managing Director @ Tip of the Spear

The Contested Advantage

For those evaluating how NIL can be applied as a serious growth lever rather than a marketing experiment, this is how Contested approaches the problem. Contested treats athlete influence as a measurable, optimizable growth asset that delivers advertising ROI for brands, durable value for athletes, and a scalable, execution-driven model for capital partners. This is not an open marketplace; engagements are structured, measured, and performance-led. To explore brand partnerships, athlete representation, or investment opportunities, visit Contested.com or reach the team directly at hello@contested.com.

The NIL Playbook - Contested

Getting Clarity on AI ROI: You’re Looking at It All Wrong

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The Point: For all the noise surrounding artificial intelligence (AI), most organizations still fail to capture meaningful return on investment (ROI). Leaders chase models, vendors, and pilots, yet the business case stays fuzzy and the outcomes underwhelm. The problem rarely lies with the technology. It lies with how organizations think about value, structure their operating models, and measure performance. AI ROI isn’t elusive. My experience: It’s simply being approached from the wrong direction!

Where AI ROI Breaks Down

94% of executives expect AI to create competitive advantage… Only 27% can point to meaningfully scaled outcomes. (Deloitte, State of AI in the Enterprise)

Across industries, the pattern is remarkably consistent. Organizations overestimate what AI can deliver in the short term, underestimate what it can deliver in the long term, and misdiagnose where value actually comes from.

A 2023 Deloitte global AI survey found that while 94 percent of executives expect AI to create competitive advantage, only 27 percent can point to meaningfully scaled outcomes (Deloitte, State of AI in the Enterprise). The gap is not conceptual; it’s operational. Most companies deploy AI as tools layered onto legacy processes rather than rethinking the processes themselves.

MIT Sloan’s research reinforces this. Companies that report the highest AI ROI invest 70 percent more in change management, workflow redesign, and capability building than their lower-performing peers (MIT Sloan Management Review, The Cultural Dividend of AI). In short: organizations expect returns from AI without transforming the environment needed to produce them.

Chasing the Wrong Metrics

Another root cause is measurement. Leaders track AI ROI as if they’re buying a server rack: cost in, efficiency out. That narrow frame kills strategic value before it ever emerges.

McKinsey’s 2024 Global AI Survey notes that top-performing AI organizations measure value across three horizons:

  1. Immediate efficiency gains
  2. Mid-term revenue acceleration
  3. Long-term business model transformation

Only 18 percent of companies track all three. Everyone else gets trapped in short-term savings, missing where the exponential value sits. AI’s highest ROI rarely comes from automating tasks. It comes from redefining how the enterprise creates value, from dynamic pricing to predictive operations to personalized product ecosystems.

BCG’s research found that companies using multi-horizon metrics are 2.5x more likely to report positive AI ROI (BCG, AI and the Future of Business Value). You can’t capture value you never measure.

You Don’t Have an AI ROI Problem. You Have an Operating Model Problem.

Technology is the easy part. Integrating it requires an operating model built for speed, experimentation, and cross-functional execution. Instead, most organizations treat AI as a feature bolted onto old habits.

Harvard Business Review’s analysis of failed AI programs points to three recurring issues (HBR, Why So Many AI Pilots Fail):

  • Fragmented ownership that leaves strategy unclear
  • Lack of data readiness, forcing teams to innovate on unstable foundations
  • Underinvestment in talent, training, and adoption

Executives want AI to behave like a neatly scoped IT project. But AI is an organizational capability, not a tool. It requires governance, decision rights, and incentives aligned with how algorithms learn and iterate. Without those, ROI will always look disappointing.

Sam Palazzolo Getting Clarity on ROI

The Organizations Getting AI ROI Right Do Five Things Differently

Across Deloitte, MIT, BCG, and McKinsey’s research, the pattern is strikingly consistent. High-return AI organizations:

1. Tie AI to fewer, higher-stakes business outcomes.

They don’t launch 30 pilots. They launch 3 that matter. They begin with revenue-critical, customer-facing, or operational choke points.

2. Invest in foundational data and infrastructure.

The companies realizing the strongest returns spend 40–60 percent of their AI investment on data readiness, not models (McKinsey). They optimize the soil before planting the seeds.

3. Rewire workflows, not just tasks.

AI ROI accelerates when companies redesign how decisions get made. Not “automate the process,” but “transform the process.”

4. Build skills and adoption into the plan from day one.

Top performers devote disproportionate energy to change management, training, and user enablement (MIT Sloan). AI only creates ROI if humans actually use it.

5. Measure value across efficiency, revenue, and transformation.

Companies that restrict AI ROI to cost savings stunt their upside. The biggest returns come when AI reshapes the business model.

If You Want AI ROI, Stop Treating AI as an Experiment

The uncomfortable truth for executives is this: AI isn’t underperforming. Your strategy is. Many organizations still treat AI as a sandbox initiative rather than a lever of enterprise value creation.

When leaders shift the conversation from “Which model should we buy?” to “Where is the bottleneck in our value chain that AI can fundamentally rewire?” everything changes. That’s when ROI stops being anecdotal and starts being systemic.

The next wave of competitive advantage won’t come from who deploys AI first. It will come from who deploys AI intelligently. Clarity on ROI requires clarity on strategy, operating model, measurement, and adoption. Get those right, and AI becomes not a cost center but a force multiplier.

Getting AI ROI isn’t hard.
Looking at it correctly is.

Sam Palazzolo, Real Strategies. Real Results.

Sources

AI + Ivy Lee: The Productivity Hack You Didn’t Know You Needed

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In 1918, Charles Schwab—then one of America’s wealthiest industrialists—asked productivity consultant Ivy Lee for advice on improving executive performance. Lee’s proposal wasn’t a grand strategy session or a complicated system. It was a single sheet of paper with six lines and a deceptively simple rule:

Each evening, write down the six most important things you must accomplish tomorrow. Prioritize them. Start the next day with #1 and don’t move to #2 until #1 is finished. Anything left undone moves to tomorrow’s list.

That’s it. No dashboards, no color-coded software, no buzzwords.
The story goes that Schwab tested it for 90 days—and wrote Lee a check worth $400,000 in today’s dollars.

A century later, in an era obsessed with optimization, the Ivy Lee Method still wins. Because it solves the root problem: not time management, but decision management.

Why Simplicity Still Scales

James Clear, author of Atomic Habits, revisited the method and made one point unmistakable: its genius lies in simplicity. Six tasks (or five, or four—it’s not the number that matters) force clarity. Limitation breeds focus.

Most executives lose productivity not because they’re lazy, but because they’re drowning in options. Slack pings, inbox chaos, and endless dashboards have turned “work” into a scavenger hunt for where focus went to die.

The Ivy Lee Method gives you a map:

  • Decide what matters most before the day begins.
  • Work on one thing until it’s done.
  • Repeat.

AI doesn’t replace that clarity—it magnifies it.

The New Era: AI as the Co-Pilot of Focus

AI can’t make you care, but it can make your discipline effortless.
When paired with the Ivy Lee framework, it transforms a century-old method into a 21st-century operating system for leaders.

Here’s how to fuse Lee’s structure with today’s AI capability:

1. Use AI to Generate Your Six (and Remove the Guesswork).
Ask your AI assistant:

“Based on my goals, deadlines, and current pipeline, what six outcomes should I prioritize tomorrow?”
The machine does the sorting—you do the choosing. Decision fatigue disappears, replaced by strategic intent.

2. Let AI Enforce the Rule of Focus.
Distractions are inevitable. But AI-integrated systems can mute notifications, block irrelevant meetings, and remind you—politely but persistently—when you’re veering off your priority. It’s digital discipline without micromanagement.

3. Automate Reflection and Recovery.
The Ivy Lee Method ends with review. Traditionally, that meant pen and paper. Now, AI can summarize your day:

“You completed four of six tasks. Task #3 took twice as long as estimated. Suggest reprioritizing tomorrow’s list to focus on Task #5 first.”
Reflection becomes real-time learning, not afterthought.

4. Adapt in Chaos.
James Clear reminds us: interruptions happen. The goal isn’t perfection; it’s recovery. AI thrives here—it can reorder tasks dynamically when a meeting runs long or an urgent issue appears. The method bends but doesn’t break.

Why It Works: Constraint + Intelligence

The Ivy Lee Method’s psychological edge lies in constraint. By forcing yourself to pick a few priorities, you sidestep the illusion of progress created by endless checklists. Each completed task triggers momentum—what psychologists call “the progress principle.”

AI compounds this advantage. It observes patterns, learns your working rhythms, and highlights where your focus actually drives impact.
That turns daily discipline into data-driven self-awareness.

  • Human judgment defines what matters.
  • Machine intelligence ensures you act on it.

The result? Focus without friction.

From List to System

Think of the modern Ivy Lee system like this:

Evening Setup (5 minutes)
AI analyzes your calendar, communications, and objectives. It suggests the six most strategic tasks for tomorrow. You review, adjust, and commit.

Morning Execution (2 minutes)
You open your workspace to one thing—Task #1. AI suppresses noise until it’s done.

Midday Review (1 minute)
AI checks progress, reprioritizes if needed, and prompts for delegation.

Evening Reflection (2 minutes)
AI summarizes results, lessons, and trends, preparing the next day’s six.

Ten minutes total. The same mental payoff Ivy Lee promised in 1918—now compounded by machine precision.

What AI Doesn’t Change

The core of the Ivy Lee Method still stands untouched: constraint, clarity, completion. Technology adds leverage, but not meaning. AI won’t tell you why a task matters—it just helps ensure you actually finish it.

That’s the paradox of progress. The tools evolve; the truth doesn’t.

If you want more productivity, you don’t need more systems. You need fewer—but smarter—ones.

The 90-Day Challenge

Lee asked Schwab’s team to try his system for 90 days. That’s all.
So here’s your modern equivalent:

  • Use an AI assistant (ChatGPT, Claude, whatever your choice) each night for 90 days.
  • Generate, prioritize, and execute your six daily outcomes.
  • Reflect every evening using AI prompts and pattern insights.

Watch what happens to your focus, decision quality, and throughput.

You’ll discover the same truth Schwab did: the method works because it respects your time as finite and your attention as sacred.

The 100-Year-Old System That Still Outperforms Your Apps

The Ivy Lee Method is not a relic—it’s a reminder. The future of productivity isn’t about doing more; it’s about thinking less about what to do next.

AI gives you back the mental space to lead, create, and decide. Pair it with Ivy Lee’s clarity, and you’ll build something far more powerful than a to-do list—
you’ll build a repeatable rhythm of success.

Simplicity met intelligence.
And together, they made focus scalable.

Sam Palazzolo, Managing Director @ Tip of the Spear

Ivy Lee himself (Graphite Pencil version I created w/AI)