When “AI-first” becomes more marketing than strategy 

Artificial intelligence is widely described as the most consequential technological shift since the internet.

Unsurprisingly, a growing number of companies now position themselves as AI-first. The phrase appears frequently in strategy presentations, investor communications, and product marketing. In an environment where AI is shaping both competitive expectations and capital allocation, few organizations want to appear hesitant.

Yet the rapid spread of the term has given rise to a new phenomenon.

AI washing.

AI washing occurs when organizations communicate that AI sits at the center of their strategy, while the actual role of AI inside the company remains relatively limited.

Often this gap emerges unintentionally. Many organizations are experimenting with AI in useful ways: deploying chatbots, introducing automated summaries, or embedding isolated machine-learning features into existing products.

These initiatives can create meaningful improvements.But they rarely represent the kind of transformation implied by the label AI-first. And that distinction matters.

The difference between using AI and becoming AI-first 

Across most industries today, AI adoption follows a familiar pattern. Companies begin by identifying specific tasks that might benefit from automation or prediction. A recommendation engine improves product discovery. A classification model organizes documents. A language model accelerates internal workflows. These applications can deliver measurable productivity gains. But they rarely alter the operating logic of the organization.

A genuinely AI-first company approaches the question differently. Instead of asking where AI can be added, it asks a far more fundamental question:

What would this organization look like if it were designed around AI from the beginning?

Answering that question forces a deeper reconsideration of how work is structured. Processes are redesigned rather than simply optimized. Data becomes a strategic asset rather than a byproduct of operations. Decision-making increasingly becomes model-assisted rather than purely experience-driven. 

Perhaps most importantly, employees begin working with AI systems rather than simply using software tools. In this model, AI is not a feature layer. It becomes part of the organizational architecture.

Why AI washing is more than a messaging problem

At first glance, AI washing may appear harmless. After all, companies are simply signaling that they are participating in one of the most significant technological shifts of our time. 

But according to Katja Andersen, AI expert and former SVP at PwC, the challenge many organizations face today is not the technology itself, it is understanding the scale of change AI actually requires.

“Many companies start their AI journey with isolated experiments. That is a natural first step,” Andersen explains. “But the real transformation happens when AI begins to influence how decisions are made, how information flows through the organization, and how people interact with technology.”

When the narrative around AI moves faster than the underlying transformation, a number of strategic risks begin to emerge.

If organizations believe they are already advanced in their AI journey, they may underestimate how much structural change still lies ahead. Investors, partners, and customers are becoming increasingly sophisticated in how they evaluate AI capabilities. When messaging outpaces reality, trust can erode quickly.

Instead of investing in the foundations that enable AI at scale:  data architecture, redesigned workflows, and model-assisted decision-making, companies prioritize features that signal innovation but leave the underlying business largely unchanged.

In this sense, the real challenge of AI adoption is rarely technical. It is organizational. 

Technology revolutions rarely begin with transformation 

History provides useful context. When the internet first became commercially relevant, thousands of companies rushed to launch websites. At the time, having a website was widely perceived as evidence of digital transformation.

But the internet’s true impact only emerged when companies began redesigning entire business models around digital infrastructure. Retail evolved into e-commerce. Media evolved into streaming platforms. Services evolved into digital marketplaces.

The organizations that ultimately defined the internet era were not those that adopted the technology earliest.

They were the ones that restructured their businesses around it.

AI will likely follow a similar trajectory. 

The companies that ultimately lead in the AI era will not necessarily be those that use the term AI-first most aggressively today. They will be the ones that methodically build the foundations required to make AI part of how their organizations actually operate.

Technology revolutions rarely begin with transformation 

One reason AI washing persists is that the most valuable applications of AI often appear unremarkable. They do not always produce impressive demos or viral product launches. Instead, they improve the underlying mechanics of complex systems.

In financial operations, for example, the real opportunity often lies in automating reconciliation, structuring unorganized financial data, detecting anomalies in transactions, or assisting users in navigating administrative workflows.

These improvements rarely attract headlines. But they fundamentally reshape how efficiently organizations can operate and how easily systems can scale.

A technology still in its early stages

The reality is that most organizations remain in the early stages of their AI journey. That should not be surprising.

Major technological transitions rarely unfold quickly. They evolve over time as organizations gradually adapt their infrastructure, their processes, and their ways of working.

Seen through this lens, the most important question may not be which companies can claim to be AI-first today. It may be which organizations are building the conditions that will allow AI to shape how they operate tomorrow.  Because if previous technological revolutions offer any guidance, the companies that ultimately define the AI era will not be those that adopted the label first.

They will be the ones that quietly built the foundations that made it true.