Over the past year, more and more companies have started calling themselves “AI-first.” From banks to retailers, everyone wants to signal they’re ready for the next era of innovation. The problem is that most are not.
A 2024 PwC survey found that 73% of large companies plan to integrate AI across key functions, but what are the real results of these eye-catching figures? For many legacy players, AI adoption starts and ends with mostly marketing slogans. The AI-first label has become a branding exercise rather than a business transformation. Let’s look closer at how the current landscape looks and why it’s the case for many global companies.
The AI-First Rush
If it feels like you’re seeing the words “AI-driven,” “Artificial Intelligence,” or “GenAI” everywhere, you’re not imagining it. Pew Research Center found that by early 2025, seven percent of all page visits in their study included at least one AI-related term. The term itself has saturated online language, from press releases to marketing copy to thought-leadership articles. Everyone wants to sound future-ready, which means nearly every piece of content you encounter today has some flavor of AI in it.
Why? Because putting “AI-first” in the marketing copy is certainly low risk. But the risk piles up when internal expectations are raised and not met. While most leaders recognize AI’s potential, 76% find implementing it within their organizations challenging. According to Vention’s 2025 AI Adoption Statistics report, success often depends on a well-defined strategy, clear KPIs, strong data foundations, and dependable cross-functional teams.
Yet barriers remain. Many companies struggle with defining an AI operational model, ensuring data quality (a concern for 56% of companies), and securing employee buy-in. Other challenges include limited budgets (19%), lack of strategy (18%), and regulatory hurdles (15%).
From a business perspective, that’s the heart of the problem: leadership declares “we’re AI-first” but keeps the same old three-year roadmap, same legacy platforms, and same data silos.
The PR-Tech Loop
The same rebranding effect is fueled by pressure from the market. Public companies want to demonstrate AI progress to shareholders, even if it is limited. Gartner analysts warn that “AI washing”, overstating AI capability to attract investors or talent, is becoming a growing problem.
These efforts follow a familiar loop:
- Make a public announcement of an AI strategic pivot.
- Launch some proof-of-concept projects.
- Hype AI capabilities through PR.
- Stall through inner friction or data jams.
This loop erodes trust. Workers lose faith, customers perceive inconsistencies, and leadership must devote more time to managing the message than driving the change.
The Missing Middle Ground
The majority of companies overestimate the effort it takes to bring AI out of prototypes into business operations. The middle layer, such as the people, processes, and governance that connect technology to outcomes, is absent. AI labs run experiments that never make it into core business systems, while IT teams continue to maintain legacy CRMs and ERP platforms that can’t handle real-time data integration.
Transformation comes to recognize that, in most cases, it is not poor technology but poorly aligned incentives that cause it to stall. Marketing wants the AI story out fast. Engineering needs time to validate models. Compliance would prefer to review each dataset for privacy risk. With no single point of responsibility, AI becomes everyone’s priority and no one’s responsibility.
McKinsey’s 2024 State of AI report brings into context that less than one out of four firms have embedded AI in multiple business functions. These companies cited having clearer alignment between leadership, product, and data organizations as the most important driver of success. AI maturity, in other words, develops from governance, not slogans.
Legacy players can take note. Before making an AI-first proclamation, clarify who controls AI outcomes, what metrics are important, and how success will be measured over time. Getting incentives aligned early on can help maintain the momentum after the hype dies down.
Rebrand Last, Transform First
From where I sit, here is the strategic advice I can give to the modern-day brand:
- Use “AI-first” internally, but on the marketing side, wait until you have a tried-and-true system in place that serves the company well.
- Raise the conversation beyond tools and hype: map workflows, data, and decision rights.
- Measure early what matters: pipeline latency, insight-to-action time, clean-data ratio, not just “AI pilots launched.”
- If you feel the need to announce “we’re AI-first” before December this year, ask yourself: What will we point to in six months when the slides need proof?
The rush to call yourself “AI-first” has become the new corporate arms race. But for legacy companies, achieving the badge only matters if something of note has changed. If the narrative outruns the capability, you don’t look like a leader, but rather, a promise unkept.
