Across industries, nearly every leadership team now claims they are “using AI.” Surveys show that more than eighty percent of organizations have introduced AI into at least one workflow. Yet only a small share are seeing real financial outcomes.
The common explanation is that these technologies are “still early.” But that does not explain why some companies are already outperforming others by a wide margin. The real difference is not access to AI, it is how they execute.
The companies gaining ground start with the workflow itself. They study how information moves, how decisions are made, and how teams coordinate. For them, AI is not an add on but the spark for rethinking the operating model. They build workflows where software handles coordination and people focus on judgment.
Why Now
We are still in the early part of this shift, but the direction is becoming clear. As companies test new tools across their operations, the early results point to the same pattern. Across use cases, the limits are no longer in the tools themselves but in the processes around them. The tools are advancing faster than the workflows built to use them, and the gap between capability and process is widening.
This is the moment before the real change. As the tools advance, the pressure to rethink the workflow will only increase. The advantage will go to the companies that move first. They will not just adopt new capabilities. They will redesign how work moves through the business in ways that lift speed, reduce cost, and become difficult for others to match.
The Workflow Shift
The real gains do not come from making individual tasks faster. They come from changing how work moves through an organization.
Performance is shaped by the flow of information, decisions, and responsibility. When that flow is fragmented or coordinated manually, friction builds and progress slows. The companies seeing meaningful results have recognized this. They are not adding intelligence to old routines. They are redesigning the workflow itself.
Workflows built for this new environment operate with a different logic. Software manages coordination. Information moves continuously. Decisions are settled at the point of work. People step in only for judgment or unusual cases.
When the workflow is rebuilt this way, speed rises, variability falls, and margins expand without adding headcount.
Elevance Health shows this clearly. By rebuilding its claims workflow so that software handles classification, routing, and resolution, work that once took days now takes minutes. The advantage came from a new workflow, not a new tool.
A similar shift is unfolding in local commerce. Trexity rebuilt the delivery workflow for small and mid sized merchants, replacing individual courier calls with a single integrated process. Orders flow in at once, and the platform creates shared routes across many businesses, turning scattered trips into coordinated runs.
Two very different companies point to the same conclusion. Workflow redesign is the unlock.
The same workflow redesign is showing up in healthcare, logistics, finance, retail, and many other domains.
“This isn't a model problem. It's an execution gap. The real gains don't come from making individual tasks faster — they come from changing how work moves through an organization.
We see this in every deal we evaluate. Companies want workflow transformation but lack three things: the integration layer to connect AI to their fragmented systems, the ability to identify and redesign the right workflows, and the organizational capacity to drive adoption.
That's why platforms that enable workflow transformation capture more value than tools that automate individual tasks. The bottleneck isn't capability anymore — it's execution.”
The Structure of Leverage
To understand whether a company is building real workflow leverage, we look across six layers. Each layer captures a turning point that determines whether the product becomes stronger over time or easier to copy. Together, they show how deeply a product sits inside the flow of work and how durable its position can become.
Identifying Real Workflow Leverage
Not every company using AI is creating meaningful value. The signals of durable advantage are not found in the presence of intelligence alone, but in the shape and impact of the workflow beneath it.
Across companies, four signals consistently reveal when real workflow leverage is forming. To make them concrete, we apply the framework to the two examples introduced earlier.
Companies that demonstrate all four signals are not simply adding intelligence to existing processes. They are reshaping how work moves, creating operating leverage that compounds with scale and becomes increasingly difficult to replicate.
What This Means for Founders
Companies are testing new capabilities, and expectations are rising with them. The early gains are coming from teams that rethink how work moves through the business and build systems that run with greater speed, consistency, and leverage. US startups offer a clear example. They tend to adopt new operating models earlier than peers abroad and are growing far faster even outside AI categories. Their advantage is not in having more tools but in changing how work actually runs and letting the compounding effects of better workflows take hold.
The real opportunity is not in adding more features or attaching new tools to old routines. It comes from redesigning the core workflow and letting that structure compound. A well built workflow becomes a speed advantage, a margin advantage, and a retention advantage at the same time. Teams that move early will build operating models that are hard to copy. Teams that wait will find themselves competing with systems that are already a step ahead.
Where This Is Going
The companies achieving meaningful gains are not asking how to add intelligence to their existing processes. They are asking what the workflow should look like now that software can coordinate work at the point where it happens.
This shift from tools to workflows, and from tasks to systems, marks the difference between incremental improvement and structural advantage. As capabilities continue to improve, the gap between these approaches will widen. Organizations that treat AI as a surface level enhancement will see diminishing returns. Those that rebuild workflows around software coordination and continuous learning will move faster, operate leaner, and expand margins as they scale.
The strategic question is no longer whether a company is using AI.
The question is whether the workflow has been redesigned to create leverage. That is where the next category defining companies will be built.
