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The Guardian recently published a piece on what some are calling “AI-washing”: the growing tendency for agencies and businesses to position themselves as AI specialists, regardless of how deeply AI is actually embedded in the way they work.

The term may be new, but the pattern is familiar.

Every major technology shift goes through a phase where the language moves faster than the reality. Businesses rush to signal relevance. Pitch decks are updated. Capabilities are reframed. New terminology appears everywhere.

AI is no exception.

The difference is that AI is not simply another platform, channel, or software layer. It has the potential to change the operational structure of creative work itself. That means the conversation cannot stop at who is using which tools. The more important question is what those tools are actually changing.

As Andy Berg, CEO at Rhapsody, recently wrote on LinkedIn: “Every major technology shift goes through a phase where branding moves faster than reality. Right now, there are plenty of businesses talking about AI. Far fewer are fundamentally changing how they work because of it.”

That distinction matters.

One of the biggest misconceptions around AI adoption is that transformation begins the moment a business introduces new tools. In reality, most organisations are still operating inside systems designed long before AI became commercially viable.

The approval structures are the same. The duplicated effort is still there. Production processes remain layered. Work continues to move through the same complex handoffs between teams.

When AI is added to those environments without redesigning them, it simply accelerates whatever already exists.
Sometimes that creates value.

Often, it just means inefficient processes happen faster.

This is why AI-washing is not only a credibility issue. It is also a systems issue. If AI is treated primarily as a positioning exercise, businesses stop asking the harder operational questions.

Where is AI creating measurable value?

What friction has disappeared?

Which workflows have improved?

What outcomes are better?

And which parts of the process still depend on human judgement, experience, and creativity?

These are the questions that separate adoption from impact.

For a while, saying “we use AI” was enough to create a sense of momentum. That window is closing. Clients, teams, and stakeholders are becoming more sophisticated in how they evaluate AI claims. They are less interested in whether AI appears in the process and more interested in what has actually improved because of it.

That shift is important for creative industries because creativity has never been defined by speed alone. AI can accelerate production. It can remove repetitive work. It can support versioning, adaptation, research, analysis, and execution. Used well, it can create more space for better thinking.

But meaningful creative work still depends on interpretation, cultural understanding, emotional intelligence, strategic judgement, and the ability to recognise relevance before the data fully validates it.

Those qualities still matter. In fact, they matter more than ever.

At Rhapsody, we see the real opportunity not as AI in isolation, but as creative intelligence, orchestrated. That means designing systems where technology, talent, workflows, and decision-making work together more intelligently.

The goal is not simply to generate more content faster.

The goal is to remove the friction around creative people so they can spend less time navigating process and more time applying judgement, taste, strategic thinking, and original ideas.

That requires a different mindset. It means looking beyond individual tools and asking how work should actually flow. Where decisions should happen. How teams should collaborate. Which tasks should be automated. Which moments should remain intentionally human.
The businesses that create lasting value over the next few years will not necessarily be the ones talking most loudly about AI adoption.

They will be the ones redesigning workflows, collaboration models, production systems, and decision-making around what AI actually changes.

Because AI alone is not a strategy.

The advantage comes from understanding how AI changes the structure of work itself, and building systems designed for that reality rather than trying to retrofit old ways of working around new technology.

AI-washing may be the visible symptom.

But the deeper issue is operational.

The market will eventually separate substance from positioning. When it does, the organisations that invested in real structural change will stand apart from those that simply learned the right language.

The future will not belong to the companies that say they use AI.
It will belong to the companies that redesign themselves around what AI makes possible.