Introduction
What the figures ultimately reveal is not just an adoption rate.
They tell something more subtle: the way each sector reacts to uncertainty.
Is it seeking to accelerate? To stabilize? To understand? To protect itself?
Across marketing, customer service, finance, human resources, industry and healthcare a map begins to take shape. Incomplete, shifting… but revealing.
Not of what AI can do but of what organizations are willing to let it do.
AI in marketing
Within the broad family of data-driven and customer-facing professions, marketing is often cited as one of the most advanced functions in terms of AI adoption: for example, in figures compiled by Incremys, 88% of marketers say they use AI for personalization content creation or performance analysis… a rate that is hard to ignore when considering the scale of the movement.
But the perspective can be widened a bit: according to sector surveys and benchmarks, marketing and content creation functions are among those where organizations perceive AI’s value the fastest, precisely because these technologies accelerate tasks that were once slow or costly.
This is perhaps where the question becomes interesting: what makes marketing truly “AI-ready”? Is it the technical ability to integrate models, or the strategic vision of a marketing function that is both predictive and narrative?
At Eminence, this figure does not come as a surprise… it confirms a deeper intuition. Marketing has always been a field of experimentation, but AI marks a turning point : we are no longer talking about optimizing campaigns but about reinventing the relationship between the brand and the individual.
What we observe is a silent transition: marketing ceases to be a sequence of actions and becomes a living adaptive sometimes unpredictable system. The real question therefore is not how many campaigns are automated, but how far a brand is willing to let AI shape its story… without losing its voice.
AI in customer service
In the field of support and day-to-day customer interactions, AI is already well established: 72% of organizations say they use it to automate customer responses, reduce waiting times and make support more efficient, according to Incremys data.
Beyond this figure, these uses must be placed in a broader context: service-oriented functions have become a preferred application ground for AI because they handle large volumes of routine requests. Where a team could once be overwhelmed, systems driven by language models or intelligent rules can now perform the first level of triage, handle standardized responses and free up human time for more complex cases often leading to an improvement in long-term customer satisfaction.
It is not uncommon to see, in certain industries, nearly a third of basic interactions handled automatically, with no loss in perceived quality for the user… quite the opposite.
At first glance, these figures speak of efficiency. But when examined more closely, they tell something else… a redefinition of the very notion of service.
At Eminence, we view customer service as a space of truth: where the brand promise meets reality. AI here acts as a revealer. It highlights what is clear, structured and predictable… and just as brutally exposes what is not.
What we retain is not the speed of responses, but the ability of organizations to become understandable to machines and thus paradoxically more readable to their customers.
AI in finance and technology
This is a sector often cited as a driver of overall AI adoption: according to several market reports, financial services rank among the most advanced industries in AI integration, particularly for functions such as fraud detection, risk scoring, or predictive analysis, with very high usage rates ( between 61% and 85% depending on the source ), well ahead of many other industries.
What must be remembered is that AI in finance is not a gadget: it processes massive volumes of data in real time, supports critical decisions and in some cases even replaces traditional, costly and slow processes. To the point that this sector, often vigilant about risk, has paradoxically found itself at the forefront of technological adoption.
That said, behind these figures, one question must be asked: is it operational efficiency that draws financial companies toward AI, or the speed of execution that AI promises? Perhaps a bit of both…
In finance and tech, AI is not perceived as an emotional rupture, but as a functional inevitability and that is precisely what makes this sector fascinating.
At Eminence, we observe that these industries do not adopt AI to innovate… they adopt it to hold complexity together. Massive data flows, real-time decisions, constant regulatory pressure: AI becomes a stabilization mechanism as much as an accelerator.
What strikes us is less the displayed performance than the silent maturity: here, AI does not make noise, it structures.
Conclusion
Taken together, these sectors tell a story that is less spectacular than one might imagine… but far more interesting.
AI does not win because it is more intelligent.
It wins because it responds to imbalances that were already present: information overload, time pressure, regulatory complexity, fragmented customer expectations, talent shortages.
Where marketing uses it to explore new narratives, customer service uses it to make visible what was no longer so.
Where finance and tech employ it to hold systems under tension together, HR tests it with restraint, almost with philosophical caution.
And in industry as in healthcare, AI settles in quietly, where error is not an option.
At Eminence, this panorama confirms a strong conviction:
AI adoption is never neutral. It reflects a culture, a level of maturity, a way of looking at risk and responsibility.
So it is not a question of who adopts fastest.
The real question lies elsewhere…
Who adopts with the greatest clarity?
And perhaps, in the years to come, it will be precisely the sectors that are most cautious today that will define the most sustainable standards tomorrow.
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