CIO: How to Prepare for
AI in 2025?

It’s impossible to ignore the artificial intelligence (AI) frenzy. And for good reason! By 2027, the world is expected to have half a billion users of AI-related technologies. According to some studies, AI could even contribute up to $15.7 trillion to the global economy by 2030.
Currently, it’s estimated that around 30-40% of French companies use this technology, compared to 59% of U.S. companies. This leaves significant room for growth, with numerous benefits: task automation, process improvement, data analysis and utilization, cost reduction, better electronic document management… to name just a few.
In this article, we will explore AI and its tangible advantages, asking how to effectively integrate it into your business. Follow the guide.

Assessing Your AI Needs

This is the first step. Every company is different, so it’s important to conduct an initial assessment to define its specific expectations and needs. Here’s how to proceed.

What are the Business Needs within the Organization?

AI has numerous areas of application: healthcare, finance, logistics, communication… the possibilities are vast. That’s why it’s important to conduct an in-depth study: what are your company’s needs? How could AI be used to improve processes? Which teams could benefit, and in what ways?

Be sure to list the needs of each department and possibly even each role.

The Company's Expectations

Think strategically here. Which sectors does the company want to prioritize? In which area would AI investment be most profitable? The aim is to align the company’s strategic expectations and goals with business needs.

Database Quality

The quality of the database is fundamental in implementing AI within a company. Indeed, any artificial intelligence is dependent on the data it relies on. Incomplete, outdated, or incorrect data can create biases and reduce the effectiveness of learning algorithms.

It is essential to ensure rigorous data collection and regular cleaning of the company’s data. Are your data compliant with current regulations, such as GDPR? How are they structured? Are improvements possible?

The CIO should therefore audit the company’s database and establish a specific management strategy.

Identify the Risks Before Starting an AI Project

No project can be implemented without identifying the associated risks. Here are the main elements to consider.

The Variable Cost of AI

AI integration obviously comes at a cost, which varies depending on factors such as the volume of data processed. Resource consumption, particularly computing power and storage, is proportional to the complexity of the models deployed.

Moreover, the continuous improvement of algorithms requires regular investments in training and system maintenance. The upskilling of internal teams or reliance on external experts represents another factor in cost variability. Although AI can generate significant productivity gains, CIOs must anticipate these variable costs to maximize ROI.

To avoid paying (heavily) for unused features, it’s important to clearly define the application scope.

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Data Security

AI relies on massive volumes of potentially sensitive data. Protecting this data is crucial to prevent cyberattacks and information leaks.

Additionally, compliance standards, such as the GDPR in Europe, impose strict obligations on the processing and confidentiality of personal data. Thus, CIOs must integrate robust security measures (encryption, regular audits…) at each stage of the data lifecycle, from collection to analysis by AI, including storage.

Database Structuring

This is an important step. For algorithms to efficiently process and analyze information, data must be organized as accessibly and consistently as possible.

This includes standardizing data formats, establishing clear relationships between various data sources, and using schemas or data models that meet the algorithms' needs.

The Impact of AI on Employees

By altering both roles and skills, the introduction of AI in a company can have a profound impact on employees. Indeed, AI automates certain repetitive tasks, such as data processing or system management, freeing up time for teams to focus on higher-value tasks. The IMF’s 2024 report, “Gen-AI and the Future of Work,” even asserts that AI will impact 40% of jobs worldwide!

This automation (and the paradigm shift in general) is liberating in many ways, but it can also raise concerns about job elimination. Companies must therefore invest in continuous training to enable employees to develop new skills related to AI technologies, such as algorithm management, result analysis, or cybersecurity. It’s also important to address change management, which is central to this issue. If necessary, external experts can help.

Integrating AI into Your Organization

How can you effectively implement AI in your company?

Identifying Required Internal Skills

Identifying AI-related skills within the organization is a strategic step that should not be overlooked.

This involves identifying existing talents with technical skills in AI, data science, machine learning, or data management to maximize internal resources. This process not only helps to better understand available capacities but also to define training or recruitment needs.

By creating a clear skills map, CIOs can effectively assign employees to AI projects, accelerating implementation while reducing outsourcing costs.

An AI Solution Suited to Your Digital Environment

Choosing an AI solution perfectly adapted to the company’s digital environment seems obvious, yet many organizations stumble here.

Indeed, every company has a unique IT ecosystem. To maximize AI’s effectiveness, it is essential to select tools and technologies compatible with this existing environment, while also allowing for future scalability.

A customized AI solution should integrate seamlessly with data management systems, the Digital Workplace, and business tools. Additionally, it should consider specific company needs, such as performance, security, or regulatory requirements.

Finally, it’s best to opt for a solution that handles specific tasks (e.g., automating customer service emails) and meets a particular need rather than an overly general tool.

A specific and personalized approach thus ensures smoother adoption as well as a better return on investment.

Supporting Operational Teams

Finally, don’t overlook this step, which is a key component of successfully integrating AI into your organization.

AI profoundly changes business processes, automating certain tasks, for example. Teams must therefore understand how to use these tools and integrate them into their daily work.

This support includes tailored training and ongoing communication between CIOs and operational teams. Don’t forget to establish continuous support to answer questions and adjust tools based on field feedback.

Conclusion

The potential of AI is undeniable. However, implementing it in companies doesn’t always come without friction.

To minimize these issues, it’s important to understand team needs and align them with company goals. Don’t forget to identify areas where AI investment will be most profitable, train employees, and choose specialized tools focused on specific verticals (sales, customer service…) rather than overly generic solutions.

FAQ

CIO: How to Prepare for AI in 2025?

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