From AI Adoption to AI Advantage

In the late nineteenth century, when electric dynamos arrived in factories, most managers made a reasonable decision to replace the steam engine with the new motor. Everything else, which included the same shafts, pulleys, and factory floor primarily arranged for a different era of power, would remain unchanged. 

It was only almost three decades later that factories started to redesign themselves around the actual possibilities that electricity brought to the table. They placed individual motors on every machine, rebuilt the layout, and tracked the productivity gains. Studying this pattern in 1989, Stanford economist Paul David termed it the “productivity paradox:” a phenomenon where the presence of dynamos was widespread, but their measurable impact on economic output remained elusive. This was because the operation model had fundamentally remained unchanged.

In my view, enterprise AI is currently at a similar inflection point. AI adoption is broad and accelerating, but the underlying model that organizations follow, be it for competing, serving customers, or pricing their service delivery, is largely intact.

The law of diminishing competitive returns

AI technologies are available to every player in the market today. Most organizations deploy the same set of tools against similar workflows and the gains experienced become industry baseline. AI investments may be soaring yet sustained financial impact on performance is elusive.

This point is further reinforced by data. MIT Sloan Management in 2025¹ confirmed that AI may become pervasive, enhance processes, operations, but it does not provide any differentiated advantage to the users. Productivity gains powered by AI are self-eroding in the long haul. Both McKinsey² and BCG³ put this more plainly in their respective global studies that even with near-universal adoption of AI (nearly 90-94% of global enterprises surveyed) only a tiny fraction (5-6%) generate substantial financial value from AI.

So, what can these companies do differently?

What AI makes newly possible

For starters, there has to be a clear idea of how to deploy AI, focused on the new possibilities it can create for their competitive strategies.

We can categorize this further into three distinctive shifts.

The first shift is when they use AI to access customers and market segments that were previously considered uneconomic. Be it targeting lower price-point segments, expanding into new geographies, or addressing an underserved customer cohort, these become feasible as delivery cost reduces, response time accelerates, and the quality of personalization at scale improves. This is the argument made for revenue, not efficiency. Productivity is nice, but revenue is the most critical piece.

The second shift is when enterprises reshape how they deliver outcomes and pricing accordingly. Forward-looking organizations assess how work can be restructured to ensure AI-driven outcomes are contractible, reliable, and measurable. When you produce and measure outcomes consistently, the commercial relationship with customers shifts, aligning price with the value delivered and not the effort expended.

The third shift centers around how AI is treated as a lens for entering categories and capabilities that were structurally out of reach previously. This can be considered a massive transition from capital-heavy, monolithic corporate structures to a model of lean disruption. With the right stack, an AI augmented team, lower build costs, and faster iterations, these companies are able to move into new service/product lines with less time and capital.

The fact is only a few firms are converting AI into enterprise value at scale. Those that do are widening the gap with their competitors in growth, margins, and shareholder returns. They are compounding the advantage, and competitors’ opportunity to secure it is narrowing.

What is holding the majority back from making these shifts?

Efficiency is table stakes, but growth is the real prize

The real barrier is not technical, but organizational and gravitational.

Geoffrey Moore, in work that followed Crossing the Chasm, described what he called the pull of the past. It was a simple observation about the tendency successful companies have when it comes to letting go of the business model that made them successful. They will keep optimizing it even after the ground beneath them shifts.

Look at how this plays out when it comes to AI in 2026, where this pull takes a specific form. The existing operating model rewards efficiency gains, and this shows up in margins and utilization metrics that are tracked by boards quarterly. But the returns from structural investment in new commercial models, customer segments, and delivery architectures are difficult to measure and pays off rather slowly. Optimizing for quarterly efficiency metrics is quite rational behavior at the end of the day, but only if you accept the premise that AI is primarily a cost and productivity tool, instead of being a growth lever.

The companies pulling ahead seems to have found a way to hold on to both. They are able to sustain operational discipline and carve out a deliberate, protected space for the growth-oriented work that AI makes possible.

Clearly, executing these shifts will not be easy.  Most large organizations have built their tech landscapes over decades, and their legacy systems, data silos, and accumulated architectural decisions cannot be replaced overnight. While getting data foundations right and driving modernization can take years, this should not be a reason to defer the strategic reframing. Instead, it is an opportunity to initiate it with clear priorities and genuine organizational commitment. The companies that generate durable advantage from AI are not necessarily the ones that start with clean infrastructure. Instead, they are those that start with a clear answer to the question of what they were building toward.

Separating adoption from advantage

Organizations today shouldn’t be focused only on how much AI they are using. Nor should they only use conventional productivity metrics to measure ROI. Rather, they should question the many ways AI can be used to do something that compounds, opening new revenue streams, creating new commercial relationships, evolving capabilities that deepen with use.

At its core, the shift from adoption to advantage is in the strategic question being asked. This does not necessarily require new tech investments but instead a strategic frame that questions what AI makes newly possible for the organization.