AI and Digital Transformation: Impact, Use Cases and Adoption

AI digital transformation is the use of artificial intelligence to take the digitization of a business further: automating repetitive tasks, supporting decisions with data and personalizing customer and employee experiences. The gains depend on three conditions: well-chosen use cases, reliable data, and employees trained to use AI every day inside their business applications.

AI and Digital Transformation: What Do We Mean?

Artificial intelligence and digital transformation are two closely linked concepts. The former refers to technologies that enable machines to imitate certain human cognitive abilities such as learning or problem-solving. The latter refers to the integration of digital technologies by businesses to improve their business processes, customer relationships, and ultimately, their business performance.

If we delve a little deeper, we realize that digital transformation involves both the digitization of internal processes, greater empowerment of employees, and the deployment of innovative customer journeys based on self-service and online interactions.

The link between these two concepts is therefore easily understood; AI is, in fact, the driving force behind companies' digital transformation at various levels:

• Intelligent automation of back-office processes to increase productivity.
• Precise personalization of user journeys leveraging predictive analytics and BPMN process analysis
• Upskilling of employees with conversational agents.
• Data-driven decision-making for business units.

From a disruptive technology only a few years ago, AI is now establishing itself as the key ingredient in the winning recipe for digital transformation.

How are generative AI and AI agents changing digital transformation?

Since conversational assistants reached the general public in late 2022, generative AI has widened the scope of AI in the enterprise: it drafts, summarizes, translates, writes code and answers questions about documents in natural language. AI agents go one step further: starting from a goal you set, they chain several actions, such as finding information, filling in a form or triggering a workflow.

Adoption is accelerating. According to Eurostat, 20% of EU enterprises with 10 or more employees used at least one AI technology in 2025, up about 6.5 percentage points from 2024, and 55% of large enterprises did. The most common uses were analyzing written language (11.8% of enterprises), generating images, video or sound (9.5%) and generating written or spoken language or code (8.8%).

The practical consequence: AI mostly arrives inside the tools your teams already use. Vendors embed it in their ERP software, CRM, HRIS and collaboration suites. The question is no longer only "which AI should we choose?" but "how do we make sure employees use these new features correctly and with confidence?"

How Does AI Facilitate Digital Transformation Efforts?

AI Automates Processes

Automation is one of the primary use cases for AI in the digital transformation of businesses. In practice, learning algorithms (machine learning) and natural language processing (NLP) can mimic human reasoning to perform tedious and repetitive tasks.

For example, one can mention robotic process automation (RPA), which uses software bots to perform tedious manual operations such as data entry, accounting transactions, or email management. These digital robots analyze and replicate human actions in sequence to automate a whole range of critical procedures.

Another example is chatbots, which assist or even replace agents in customer support. Equipped with NLP, they interact with users in natural language to resolve technical issues or answer questions. Behind the scenes, they also relay relevant insights to support teams.

Finally, we can also mention business process analysis via BPMN (Business Process Model and Notation). Indeed, AI can automate this sequence to precisely identify the steps of a business process.

An NBER study of 5,179 customer support agents found that a generative AI assistant raised productivity by 14% on average, and by 34% for novice and low-skilled agents. By freeing them from low-value-added tasks, it allows them to focus on strategic work that cannot be entrusted to algorithms.

Improved Decision-Making

Beyond automating operational tasks, AI also optimizes decision-making within digitized organizations. Indeed, machine learning algorithms analyze colossal volumes of structured and unstructured data to extract actionable insights.

In this regard, we can mention predictive maintenance in industry, which, through real-time sensor data analysis, detects at-risk equipment of failure. Machine downtime is anticipated, and asset availability is optimized.

Improved Customer Knowledge and Experience

By leveraging data collected from user journeys, AI enables a better understanding of needs to personalize digital experiences.

The case of predictive marketing is an excellent example: thanks to machine learning, it models the behavior of B2C prospects and customers to anticipate their needs. Campaigns are thus hyper-personalized and delivered at the right time according to the predefined customer journey.

Competitive Advantage

It is by enabling optimized operations, streamlined decision-making, and smoother customer journeys that AI offers a competitive advantage to companies pioneering its adoption.

We can cite insurers who optimize the targeting of potential customers and personalize their offers to increase sales.

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The same applies in the retail sector, where pioneers in analytical data intelligence see their average basket value grow faster than latecomers, thanks to better customer segmentation and optimized promotional campaigns.

Attractiveness to talent

Finally, by adopting AI and the latest technological innovations, companies also strengthen their attractiveness to talent, especially younger employees seeking purpose and agility at work.

They favor organizations offering modern work environments where new technologies play a central role.

And AI precisely meets these expectations by offering them the opportunity to upskill in disruptive technologies set to transform their jobs. Tomorrow, few positions will remain untouched by intelligent automation and predictive analytics.

Furthermore, AI greatly influences the employer brand and companies' ability to attract the talent of tomorrow. Those that have successfully integrated augmented intelligence into their information systems will also secure the talent needed to sustain their competitive advantage.

How to integrate AI into digital transformation?

The widespread adoption of AI within information systems is a major undertaking requiring a tailored strategy. This strategy involves:

1 – Choose the right AI technology

The first step is to clarify priority use cases based on their complexity and potential business impact. Technologies vary depending on the use cases.

Once the use cases are identified, the next step is to select the appropriate technological building blocks from the many existing solutions.

2 – Feed AI with data

Data is the essential fuel for AI engines. Without massive, high-quality data, it's impossible to train algorithms and enable them to make autonomous decisions.

It is therefore essential to plan ahead for the necessary datasets for machine learning, depending on the intended use cases: customer data, sales history, application logs, open data, etc. The challenge is also to keep them updated to optimize the reliability of predictions.

3 – Employee training and adaptation to AI

Introducing AI inevitably leads to business and organizational transformations that need to be supported. Rethinking job roles in terms of necessary skills or developing a data culture within the company is essential for its adoption.

Appropriate interactive guidance must be deployed to encourage employees to adopt these technologies, a prerequisite for their success. Employees will be all the more inclined to adopt AI tools if they have the necessary skills to use them and understand their daily benefits.

AI accelerates the digital transformation of businesses and impacts their short-term competitiveness. However, its adoption requires overcoming certain technological and human challenges within the IT system. Otherwise, organizations risk falling behind competitors who are quick to leverage AI's potential.

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What are the risks of AI, and what does the EU AI Act require?

Bringing AI into your information system creates risks you should address from the scoping phase:

• Data quality and bias: a model trained on incomplete or skewed data reproduces those flaws.
• Generative AI errors: answers can sound plausible yet be wrong, so human review remains necessary on sensitive topics.
• Confidentiality: personal or strategic data entered into an AI tool must stay compliant with the GDPR and your security policy.
• Shadow AI: when no approved tools are available, employees turn to services the IT department does not control.

If you operate in the European Union, the AI Act regulates these uses with a risk-based approach. It entered into force on August 1, 2024, and its Article 4 has required providers and deployers of AI systems to take measures on the AI literacy of their staff since February 2, 2025. Amended in 2026 by the Digital Omnibus on AI, the article does not set a required level of literacy, but deployers of high-risk systems must still train the people in charge of human oversight, and national authorities supervise the obligation from August 2, 2026 (European Commission). The Omnibus also postponed most high-risk rules to December 2, 2027 and August 2, 2028 (European Commission). Clear IT governance, with approved tools, usage rules and training, is therefore inseparable from any AI project.