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The Impact of AI in Business: Insights from the French ‘Trends of AI’ Study with KPMG

The Impact of AI in Business: Insights from the French ‘Trends of AI’ Study with KPMG
The French think tank Les EnthousIAstes has unveiled the findings of its Trends of AI study, conducted in partnership with KPMG, which examines the impact of AI on French businesses. (iStock)

The French think tank Les EnthousIAstes has unveiled the findings of its Trends of AI study, conducted in partnership with KPMG, which examines the impact of AI on French businesses. According to the study, marketing and IT departments have been the most active adopters of AI in recent years. We spoke with Julien Le Dreff, co-author of the study, for further insights.

Conducted between September and October 2024, the study surveyed 200 French companies to analyze the use cases of AI across various business functions, including HR, marketing, finance, and IT. Built in collaboration with 80 experts from leading companies such as BPCE, Orange, LVMH Recherche, L’Oréal, and Renault, the study sheds light on the common challenges and opportunities in deploying AI. These organizations, ranging from large corporations (38%) to mid-sized enterprises (38%) and small businesses (24%), provided insights into both the benefits and hurdles of AI adoption, including technical and financial constraints as well as ethical and environmental concerns.

The study reveals how certain roles are evolving, while others, such as the Chief AI Officer, are emerging to address new demands.

This article summarizes the key findings for marketing and IT departments and includes advice from Julien Le Dreff, co-founder of Les EnthousIAstes and co-author of the study. He shares strategic recommendations for decision-makers to optimize their AI strategies in 2025.

AI: A Responsibility for Everyone

The study emphasizes that businesses and their leaders have high expectations for AI—not only for their specific departments but also for the broader organization.

“Leaders are overwhelmed by the amount of information they need to digest. These technologies promise to revolutionize their industries but come with significant costs and require considerable time to implement. AI systems also need to integrate seamlessly with existing internal tools, which demands a vigilant and well-informed approach,” notes Le Dreff.

The purpose of the study is to help companies navigate these complexities, assess the current state of AI deployment, identify its applications, and share best practices and strategic decisions from peers.

Unlike many other technologies, AI has become a shared concern across the organization.

“These technologies are unusual because their adoption often begins at the grassroots level,” explains Le Dreff. “Typically, new tools or systems are introduced top-down, driven by senior management or department heads. But with AI, especially generative AI, it’s the early adopters who are experimenting with the technology to improve performance, save time, and increase efficiency. These efforts then ripple upward and outward across the organization.”

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The Need for a Cross-Functional Approach

Given this bottom-up adoption, a cross-departmental approach is essential for effectively integrating AI into business operations.

The study highlights marketing and IT as the departments leading the charge in AI adoption, particularly in leveraging generative AI (Gen-AI). These two functions have embraced AI to improve efficiency, enhance decision-making, and unlock new capabilities, setting an example for other business units to follow.

By focusing on these two key areas, this article explores how organizations can harness AI’s transformative power to drive innovation and achieve strategic goals in the years ahead.

Marketing: The Foremost Adopter of Generative AI

According to the Trends of AI study, marketing has emerged as the function most actively embracing AI over the past two years, particularly with the rise of tools like ChatGPT.

Delegating Repetitive Tasks

The study reveals that businesses are increasingly delegating low-value, repetitive tasks to AI. Over half of marketers (54%) use AI for content translation, 47% leverage it for creating and adapting visual content, and 32% employ it to produce marketing campaigns.

Julien Le Dreff, co-author of the study, explains:

“It’s no surprise that marketing has some of the fastest adopters of AI, particularly generative AI tools like ChatGPT and DALL-E. These applications are relatively straightforward to implement. Whether it’s translating content, generating visuals or videos, or creating localized versions of marketing materials, the use cases for AI in marketing are both practical and scalable. Additionally, one-third of marketers (34%) are using AI tools to personalize customer communications.”

Tools in Use

The tools marketers are leveraging include widely available platforms like ChatGPT and Mistral for content creation, and DALL-E or Microsoft solutions for visual content generation.

Shifting Agency Relationships

Another key finding of the study is that AI is transforming the relationship between companies and their external agencies. By adopting AI tools, businesses are becoming less reliant on external providers, reclaiming negotiating power in the process. Nearly half of marketing professionals (46%) expressed a desire to renegotiate creative budgets with agencies that use AI.

Le Dreff illustrates with a concrete example:

 “Take Coca-Cola. Their marketing department has sidelined some translation agencies and graphic design vendors. For an international campaign spanning 120 countries, agencies used to manage country-specific adaptations—adding pine trees for Denmark or specific cultural elements elsewhere. That’s no longer the case. This process has been internalized using AI assistants like DALL-E.”

Limited Adoption in Pricing

One area where AI remains underutilized in marketing is pricing optimization. Only 5% of marketers have deployed AI tools for pricing or assortment analysis.

AI algorithms are capable of processing vast amounts of data to determine optimal pricing and promotional strategies. However, implementing such tools requires access to extensive internal datasets (costs, margins, inventory, sales history, and competitive data), which are often dispersed across multiple systems. These datasets must also be aggregated, cleaned, and regularly updated—a complex and resource-intensive task. For now, the process is too challenging for widespread adoption, but its potential to boost revenue and competitiveness is immense.

The Marketer of Tomorrow

In conclusion, Olivier Laborde, Head of Marketing, Innovation & Digital at BPCE and a participant in the study, emphasizes the transformative role of AI for future marketers:

 “With its ability to analyze vast quantities of data in real time, AI helps identify emerging trends, anticipate market shifts, and detect risks to brands early. It enables businesses to act proactively, refine strategies, and stay ahead of competitors while making monitoring processes more efficient and cost-effective.”

Laborde believes the marketer of tomorrow must evolve into both a brand manager and a technically skilled practitioner:

 “As technology advances, marketers must not only adopt these tools to remain competitive but also use them to shape the future of customer experiences.”

By leveraging customer data, AI enables businesses to segment audiences with unprecedented precision and deliver highly personalized messaging. This hyper-personalization can manifest as targeted email campaigns, tailored advertisements, or personalized product recommendations—ushering in a new era of marketing effectiveness.

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IT: Driving AI Adoption Across Organizations

The IT sector is at the forefront of integrating AI technologies, particularly large language models (LLMs), into organizational systems. This transformation is reshaping the IT landscape and redefining operational efficiency.

AI-Powered Development Assistance

AI has become an indispensable ally for developers, with development assistance emerging as one of the most proven use cases, according to the study.

“AI is not only used for coding but also for documenting code, resolving incidents, understanding issues, and ultimately training developers. A key advantage of this use case is the improved code quality, which enhances both system performance and fosters more responsible coding practices, such as reducing server resource consumption.”

By streamlining these processes, AI significantly boosts productivity and saves time. Indeed, 90% of respondents cited increased productivity as the primary reason for deploying AI. When paired with AI-driven software testing, IT functions are heavily investing in AI to accelerate the development of new digital solutions.

Generative AI technologies for programming assistance are also gaining traction, with nearly 40% of respondents employing these tools during production or testing phases.

Advanced Cybersecurity Applications

AI’s role in cybersecurity is becoming increasingly mature, with 26% of respondents reporting its deployment for this purpose.

AI enhances threat detection by making it faster and more accurate while optimizing incident management. As the study notes:

“Cybersecurity tools, especially at the network level, have transitioned from reactive to proactive and predictive approaches. By analyzing massive volumes of logs and alerts and identifying recurring patterns, these tools can anticipate potential issues and trigger preventive actions before incidents occur.”

Additionally, incident management benefits from AI integration. With natural language processing capabilities, AI tools can generate detailed, comprehensible reports, simplifying the work of IT analysts.

“Contextual recommendations provided by LLMs enable quicker and more effective incident resolution, reducing service interruptions and improving IT system resilience.”

Tools of Choice

More than 50% of respondents favor AI solutions from Microsoft (Azure, Copilot) and OpenAI, largely due to seamless integration with existing IT infrastructures, particularly the Microsoft Office suite.

For cybersecurity, AI-enabled SIEM tools such as CrowdStrike, Palo Alto Networks, and SentinelOne have been adopted by over 50% of respondents. Atlassian and ServiceNow have gained traction as well, with adoption rates of nearly 40% and 30%, respectively.

Interestingly, Google Cloud Platform (GCP) only convinced 14% of IT professionals, despite its strong reputation in the field. Amazon Web Services (AWS), however, emerged as a notable alternative, securing a 25% adoption rate.

Build vs. Buy

The study found that 56% of respondents currently prefer a “buy” strategy over developing AI tools in-house. However, Julien Le Dreff notes a growing trend toward building internal AI tools:

“More companies are investing in creating their own GPT models and internal copilots. This trend reflects the increasing focus on data privacy and security concerns.”

By developing proprietary AI solutions, organizations can address unique operational challenges and ensure greater control over data and security.

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Risks and Constraints of AI Adoption

While the implementation of AI has become as crucial as the adoption of digital technologies in recent years, organizations face significant challenges and risks. These risks impact jobs, brand identity, and organizational investments, creating a complex landscape for AI adoption.

Employment Risks 

A key concern among executives is the potential for job displacement caused by AI. Certain roles, such as graphic designers specializing in visual creation and adaptation, face direct competition from AI tools, particularly in marketing.

Autonomous AI agents, capable of performing tasks independently, represent the next stage in the AI revolution. These agents go beyond responding to queries; they analyze data, make decisions, and take action without human intervention. And this could permanently change customer relationships and customer service.

Salesforce’s Agentforce platform is a leading example, with Bruno Katz, Senior Vice President at Salesforce, stating:

“AI agents represent an unprecedented revolution. We aim to deploy one billion autonomous agents by the end of 2025.”

Lack of Training

A lack of training and organizational acclimatization further complicates matters, affecting 67% of organizations surveyed. This points to an urgent need for internal upskilling and better understanding of AI-related challenges.

According to Julien Le Dreff:

“There is a growing divide within companies. Early adopters experiment first, often starting with tools like GPT, while others remain disconnected. This creates fears of being left behind or even losing their jobs.”

Currently, 80% of IT leaders’ efforts are focused on training and educating teams to mitigate risks associated with AI use.

Loss of Brand Identity and Content Uniformity

In marketing, maintaining a recognizable brand style and tone when using AI tools for content generation is a significant concern. While AI can quickly produce content and adapt it to a brand’s universe, it risks homogenizing messages, reducing diversity and nuance. This could ultimately weaken the effectiveness of campaigns.

Olivier Laborde, Head of Marketing, Innovation & Digital at BPCE, emphasizes:

 “AI should enrich our thinking, not replace it. Let’s not delegate our brains!”

Reputational Risks

Using AI irresponsibly—whether due to excessive resource consumption, discriminatory outcomes, or invasive behavior—can severely damage a company’s reputation.

Data management is another critical issue. As the fuel for AI, data raises questions about its collection, usage, and storage. These concerns are shared by the majority of respondents and remain a significant challenge for companies aiming to maintain ethical AI practices.

Financial Costs

Deploying AI tools is expensive, and the return on investment (ROI) is often unclear, particularly during initial phases. Around 59% of companies struggle to justify AI-related costs, and 50% cite a lack of dedicated investment as a barrier to sustainable AI projects.

For 2025:Large enterprises: 60% plan to invest around €1 million in AI. SMEs: 55% intend to allocate between €0 and €50,000.

The study warns that organizations failing to embrace AI may fall behind, just as some did during the digital transformation era.

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Think Tank Recommendations for AI Integration

The authors of the study emphasize the need for organizations to adopt a value-creation strategy centered on AI. According to Julien Le Dreff:

“Before jumping into using a particular tool, it’s crucial to establish a strategy first.”

This strategy should be cross-departmental, engaging the entire organization under a unified governance framework. Key recommendations include:

Establishing an AI Charter

Organizations should begin by creating a comprehensive ethical charter to guide AI use. This approach, supported by 69% of respondents, defines the company’s policy on AI tools, including ethical principles, best practices and employee guidelines for AI training and usage

The charter should also address critical concerns such as data privacy, algorithmic fairness, and transparency in automated decision-making.

“This will help prevent potential misuse and ensure responsible AI deployment within organizations,” Le Dreff adds.

Appointing Chief AI Officers and Creating AI Centers of Expertise

The appointment of Chief AI Officers (CAIOs) is vital to underscore the importance of AI in the corporate strategy. For instance, Schneider Electric has appointed Philippe Rambach as its CAIO, signaling the seriousness of its AI agenda. Similarly, Clara Chappaz, France’s Minister of Digital Affairs, now has “Artificial Intelligence” added to her portfolio.

“The Schneider example is particularly telling. It demonstrates internal commitment to AI and ensures there’s a dedicated leader to oversee its deployment,” explains Le Dreff.

In addition to CAIOs, many respondents advocate for establishing AI Centers of Expertise to accelerate and oversee AI initiatives across the organization.

Reevaluating the Value of Low-Value Tasks

While AI enables employees to focus on high-value tasks such as strategy and innovation, this can lead to an unintended consequence: mental fatigue.

Julien Le Dreff notes: 

“If everyone is focused on high-value tasks requiring intense intellectual effort, it could lead to significant mental strain. Low-value tasks can serve as a balance to alleviate pressure and maintain overall well-being.”

The study highlights that a mix of low- and high-value tasks is essential for sustaining productivity and employee satisfaction in an AI-driven workplace.

The think tank’s recommendations emphasize a strategic, ethical, and balanced approach to AI integration. From establishing governance frameworks to adopting cutting-edge autonomous agents, these steps ensure AI serves as a tool for organizational growth and employee well-being.

For the complete study (in French), click here.

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