Generative AI has become an unavoidable topic in the technology world. It promises to transform development practices by automating tasks and offering unprecedented creative and assistance capabilities. However, every advancement comes with considerable challenges that organizations must anticipate. At ekino, we have embraced this turning point from its inception, experimenting with it and drawing valuable lessons. Nicolas Prud’homme, Global CTO at ekino, has led this process progressively, seeking a balance between innovation and pragmatism. This article aims to be a guide for CTOs, providing them with strategic and pragmatic advice for the development and integration of generative AI in their companies, in order to maximize its potential while avoiding pitfalls.

I. Defining a Generative AI Adoption Strategy

Before diving into the integration of generative AI, it is essential for you to design a strategy aligned with your company’s priorities. There are several factors to consider.

1. The Choice of Solution: “Ready-to-use” tools like GitHub Copilot, Tabnine, CodeWhisperer, and even ChatGPT-type tools offer simple and quick access to AI for developers. These pre-trained models do not require any infrastructure. However, this ease of use can pose problems of customization or security, especially for sensitive applications. It is important to study the Terms of Service (ToS) beforehand, and therefore the operating and context-sharing model, and what part of it is used for the tool’s learning.
2. Terms of Use: An Essential Point of Vigilance: A key initial factor in defining your strategy lies in the General Terms of Use (ToS) of the considered solutions. Each generative AI tool, whether it’s GitHub Copilot, ChatGPT, or others, relies on specific usage policies that define how data is used. Some platforms retain a portion of the generated content or queries to improve their models. This point is essential, especially if you handle sensitive information, such as proprietary data or strategic elements related to your business. Before any adoption, a rigorous analysis of the ToS and the privacy policy must be conducted. Ensuring that the use of the tool complies with your company’s legal and ethical requirements will save you a lot of trouble.
3. The Quality of the Model: A Decisive Choice: Another crucial element in your strategy is the quality and specialization of the available models. Each generative AI model has its own strengths and limitations. For example, Github Copilot focuses on programming assistance, while ChatGPT excels in conversational interactions or content writing. Relying on a general-purpose model to meet a specific need, such as code generation, could lead to approximate or irrelevant results. Furthermore, some models are optimized for a specific language or domain: a model trained primarily in English might produce poor suggestions in French, or lack the nuances for highly specialized industry needs. The choice of the right model must therefore align with your business objectives to maximize its relevance and effectiveness.
4. Context: A Key Factor for Effectiveness: Context is also a critical factor. It is indeed important to assess the extent to which a model can take into account the specific context of your project. For example, if you use an AI to generate code, it must be able to adapt to the specifics of your environment: technology stack, architectural constraints, security requirements, or work methodologies. If the tool operates in a vacuum, without considering project dependencies or global information, it risks generating generic or even incompatible proposals for your needs. Context is just as essential in other use cases: whether it’s writing documentation, analyzing data, or proposing optimizations, a lack of context can drastically reduce the value of the suggestions produced by the AI. Therefore, make sure that the chosen tool allows for sufficient customization to integrate smoothly into your existing processes.

It is crucial to choose the strategy best suited to your real needs, taking into account your company’s technological capabilities and the cost-benefit ratio of generative AI. A good strategy maximizes efficiency without compromising the security or quality of your deliverables.

II. Experience Feedback: Practical Advice for a Successful Integration of Generative AI

After addressing the technical aspects of AI integration, it is just as crucial to consider practical experience feedback to ensure successful adoption. Indeed, during our own adoption of generative AI, one of the biggest challenges was the acculturation of the teams. A six-month training program was implemented, covering nearly 200 people while maintaining continuous production. This learning process, designed to fit into the daily rhythm, allowed the teams to assimilate the new practices while ensuring the quality of their work. Beyond the initial adoption, it was essential to establish a monitoring system to ensure that knowledge evolved with technological advances and feedback, notably by appointing referents in each team. This approach facilitated constant feedback and nurtured the improvement of practices around generative AI.

1/ Evaluating Operational Efficiency Without Creating Technical Debt: The promise of generative AI is to make development more efficient, but be careful: poor implementation will generate technical debt, and the bugs are sometimes very subtle. The development processes in place should not be cut short; on the contrary, the generated code must be reviewed and verified, and its unit test coverage ensured before entering the merge request review process.

Instead of tracking a single productivity or velocity metric, favor an assessment of operational efficiency that takes into account the quality of deliverables, the time to production, and the teams’ feelings.

2/ Building a Virtuous Circle of Continuous Learning: One of the keys to sustainably integrating generative AI is to create a cycle of learning and adaptation. Skills are evolving at a dizzying pace, and the developer of tomorrow will not have the same practices as those of today. To prevent teams from being overwhelmed, structure a virtuous circle where learnings are continuously enriched by feedback, technological watch, and close monitoring of the progress of these different assistants. This can be done through semi-annual surveys to measure the practices and effectiveness of AI, as well as to capture improvement points suggested by the users themselves.

3/ Avoiding Overexcitement and Rationalizing Uses: The integration of tools based on generative AI must be approached with discernment. These technologies, although effective, consume a huge amount of energy and have a significant ecological impact. It is important to ensure that each use meets a clear and useful objective. Raise your teams’ awareness of these environmental issues to encourage reasoned use.

At the same time, proactive regulation is essential in the face of the growing autonomy of AIs. CTOs must collaborate with security and legal teams to limit permissions and define clear rules: what can the AI do, and within what framework? By combining the rationalization of uses and supervised monitoring, it is possible to exploit AI while respecting ecological, ethical, and strategic priorities.

III. Mastering Generative AI: Combining Human Expertise, Customization, and Responsible Innovation

To overcome the limitations of generative AI, you can rely on key strategies that combine human rigor and technological power.

Strengthening human validation is essential. Every output produced by the AI must be reviewed by experienced developers. These professionals provide a critical eye and adjust the results to ensure that the final product meets the expected quality standards and the project’s software architecture. This validation step offers an additional guarantee, helping to fill any potential gaps in an automated model.

The hybrid approach also proves to be wise. AI can play the role of a powerful assistant, capable of generating insights and proposing optimizations. However, strategic decisions and complex tasks must always remain under the supervision of human teams. This intelligent collaboration allows you to leverage the strengths of AI while maintaining a high level of control and responsibility.

By combining these strategies, you can fully exploit the potential of generative AI, maximizing gains in productivity and innovation. But above all, you will ensure that these advances are never made at the expense of quality or human expertise, two fundamental pillars of any successful technological transformation.

Training and Upskilling of Teams

To maximize the benefits of generative AI, it is essential that teams are trained in its use and its challenges. CTOs must invest in the continuous training of developers, data scientists, and MLOps engineers. The goal is to enable teams to master the tool, but also to identify the tasks where AI can create value, and those where the benefit/risk ratio is reversed.

The upskilling must include learning how to collaborate between humans and AI. Teams must know how to interpret the results produced by the AI, analyzing them critically to adjust the models or correct errors. Continuous improvement is also necessary to adapt the models to the evolution of business needs and regulatory constraints.

Finally, employees must be trained on ethical and regulatory issues to ensure responsible use of AI. This includes data protection, avoiding biases, and complying with legal standards. Investing in the upskilling of teams is a key factor in guaranteeing the success of generative AI adoption, thus ensuring responsible and high-performing innovation. It is with this in mind that we have created a training offer dedicated to AI for developers.

Conclusion: Towards a Human-Algorithm Symbiosis

Generative AI is no longer content with producing lines of code: it is redefining the very role of the developer. It will undoubtedly become a precious ally for automating certain tasks, while freeing up time for activities with higher added value. But to take full advantage of it in the long term, it is essential to adopt a thoughtful and responsible approach. The CTO of tomorrow will be an orchestrator of this symbiosis between human and algorithm, in search of a digital world that is not only high-performing, but also sustainable and ethical. While generative AI creates unprecedented opportunities, it also calls for increased vigilance and a structured approach. This evolution requires CTOs to constantly learn, question, and renew their working methods. More than a technology, generative AI marks a profound transformation in development — a transformation where the human will always remain at the center, guided by a responsible and sustainable vision.

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At ekino, we believe the best technology is meaningful technology. We’re here to help you find and amplify the purpose behind your projects.

Interested in exploring what’s possible? Reach out to us at contact@ekino.vn for a thoughtful discussion.