“Hope is not a strategy.”
A former partner used that line as a governing principle. It was not philosophical. It was operational. Decisions were grounded in evidence, not intent.
Over time, I have come to a more balanced view. Hope has a role. It sustains effort in uncertain environments. It gives founders and operators a reason to persist when outcomes are not yet visible.
But when it comes to building competitive advantage, hope remains insufficient.
A similar misconception is now shaping how organizations approach artificial intelligence.
The prevailing narrative: AI creates value through productivity. And in the near term, it does. According to McKinsey and Company, leading organizations are already seeing meaningful returns from targeted AI deployments, in some cases approaching three dollars of value for every dollar invested.¹
That is the hook. It is also the trap.
Because those gains are not durable.
As AI capabilities diffuse across competitors, vendors, and platforms, the benefits of efficiency compress. Costs decline across the market. Output increases across the market. And the economic value of those gains is competed away.
What appears to be advantage is often just early adoption.
Efficiency is not differentiation. It is convergence.
The organizations that recognize this early will treat AI not as a productivity tool, but as a strategic lever to reshape how value is created and captured.
The Productivity Paradox
The first phase of any general-purpose technology is almost always defined by efficiency. Artificial intelligence is following that pattern with unusual speed.
Organizations are using AI to automate workflows, accelerate knowledge work, and reduce the cost of execution. These applications produce immediate, visible results. Cycle times compress. Headcount requirements shift. Margins, at least initially, improve.
From an operating standpoint, this is progress. From a strategic standpoint, it is incomplete.
Productivity gains are inherently transient. They are replicable by competitors, transferable through vendors, and quickly embedded into industry baselines. As adoption scales, firms are forced to pass those gains through in the form of lower prices, higher service expectations, or both.
We have seen this before. Enterprise software improved coordination. Cloud computing improved scalability. Digital tools improved access. Each created value. None, on their own, sustained advantage.
AI is not exempt from this pattern. It is accelerating it.
“If your AI strategy is centered on doing the same work faster, you are not building advantage. You are accelerating parity.”
Sam Palazzolo
The paradox is straightforward. The more successful AI becomes at driving productivity, the less useful productivity becomes as a differentiator.
Where Value Actually Accrues
If efficiency is not the source of durable advantage, then where does AI create value?
The answer lies in structural change.
McKinsey’s research makes a critical distinction: the majority of current AI value is being realized through improvements to existing processes, but the largest future gains will come from redefining how businesses operate and generate revenue.¹ This is not a marginal shift. It is a categorical one.
Organizations that capture disproportionate value from AI are not simply optimizing workflows. They are redesigning what they offer, how they price it, where they compete, and how they scale. Three patterns are emerging.
First, products are becoming adaptive systems. AI enables continuous learning and real-time responsiveness, turning static offerings into evolving platforms. That increases both customer dependence and lifetime value. Second, pricing models are shifting. With improved measurement and prediction, firms can move toward outcome-based or usage-based structures, aligning revenue with delivered value and expanding margin potential when execution is strong. Third, the source of scale advantage is changing. Historically, scale was driven by labor or physical assets. Increasingly, it is driven by data, model performance, and the integration of intelligence into core workflows.
These are not efficiency gains. They are economic reconfigurations.
“AI does not create advantage by making you faster. It creates advantage by changing what you are fast at, and how that translates into revenue.”
Sam Palazzolo
AI and the Reallocation of Profit Pools
One of the more underappreciated aspects of AI adoption is that it does not create value evenly. It redistributes it.
McKinsey estimates that generative AI alone could add between $2.6 trillion and $4.4 trillion annually to the global economy, with a disproportionate share concentrated in functions such as marketing, sales, and software engineering.² That concentration matters.
Value will migrate toward organizations that control or access high-quality data, integrate AI into revenue-generating workflows, and scale intelligence across customers and use cases. It will move away from activities that become commoditized through automation. That is not nuance. That is a capital flow.
This aligns with broader economic analysis. Research from Goldman Sachs suggests that generative AI could raise global GDP by up to 7 percent over time, but with uneven distribution across industries and labor segments.³
AI is less a rising tide and more a shifting current. The strategic question is not whether value is being created. It is whether your organization is positioned on the right side of that shift.
Why Execution Breaks Down
If the opportunity is this clear, why are so many organizations struggling to realize it?
The answer is not technological. It is organizational.
Most AI initiatives fail to progress beyond pilot stages because they are layered onto existing operating models without meaningful redesign. Workflows remain intact. Incentives remain misaligned. Success is measured in activity, not economic impact. The result is localized improvement without enterprise transformation.
Research from MIT Sloan Management Review underscores this point: organizations that derive significant value from AI are those that pair technology adoption with changes in processes, roles, and management systems.⁴ AI does not fail because it lacks capability. It fails because it is not integrated into how the business actually operates.
Leading organizations take a different approach. They concentrate resources on a limited number of high-impact areas, redesign workflows end-to-end, and tie outcomes directly to financial performance.
They are not experimenting with AI. They are operationalizing it. There is a difference, and the P&L knows it.
From AI Deployment to Capital Strategy
As AI moves from experimentation to execution, its implications extend beyond operations into capital allocation.
Decisions about AI now influence which business lines receive investment, how quickly those lines can scale, the durability of margins, and the valuation of the enterprise. This is particularly relevant in investor-backed environments, where small shifts in growth or efficiency can materially impact enterprise value.
AI, in this context, is not a feature. It is a driver of economic structure.
“The organizations that win with AI will not be the ones that deploy it most broadly, but the ones that align it most tightly with where capital creates the most value.”
Sam Palazzolo
This reframing moves AI out of the domain of IT and into the core of corporate strategy. Most boards are not there yet. That is the window.
Closing Perspective: From Efficiency to Advantage
Efficiency matters. It always has.
But efficiency, on its own, does not create lasting advantage. It improves performance within an existing system. It does not change the system itself.
Artificial intelligence presents a choice.
Organizations can use it to optimize what they already do, capturing short-term gains that will, over time, be competed away. Or they can use it to redefine how they create and capture value, positioning themselves ahead of where profit pools are moving.
The distinction is not academic. It is economic.
Efficiency is not a strategy. But in the hands of disciplined operators, aligned with capital and growth, it can become part of one.
Sam Palazzolo
12+ years ago I led a Tech (SaaS) startup to PE exit. Since, I have scaled 15+ organizations from $5M to $500M (2x $1B+).
References
¹ McKinsey and Company. Where AI Will Create Value and Where It Won’t. 2026. ² McKinsey and Company. The Economic Potential of Generative AI: The Next Productivity Frontier. 2023. ³ Goldman Sachs. The Potentially Large Effects of Artificial Intelligence on Economic Growth. 2023. ⁴ MIT Sloan Management Review. Expanding AI’s Impact with Organizational Learning. 2024.
