When generative AI exploded onto the scene, Ouest-France was well-placed too take advantage of the new technology thanks to its long history of working with AI.

Already before the current buzz around the tech, the French publisher’s dozen or so data scientists and engineers had been using AI for a while, for example when working on the digitalisation of the company’s archives, saeid David Dieudonné, Head of AI at Ouest-France.

Ouest-France is France’s top newspaper by paid circulation, with 480,000 print and digital subscribers. The title, founded in 1944 and mostly circulated in the west of the country, has a sizable network of 780 journalists. In September, it expanded to broadcasting with the launch of a new TV channel in France.

Read more: Ouest-France bets on TV to diversify revenue streams

The publisher’s experience with AI gave it a “luxury of approaching AI in a very principled way,” Dieudonné said during his presentation at WAN-IFRA’s Jakarta AI forum 2025. A key part of this was a commitment to investing in internal AI expertise and tech capacities.

More specifically, Ouest-France’s principles for the use of AI include:

Putting AI at the service of the mission. The aim is for AI to be “not something we work for, but something that works for us, especially the newsroom,” Dieudonné said.

Ensuring a secure tech environment. Protecting data and copyright is paramount, including protecting journalists’ work from being used as training data by AI platforms, Dieudonné said: “We block crawlers, and we are very protective of the content, making sure it’s not circulated in data centres that are not in our control.”

Human in the loop. In principle, every piece of AI-generated content is verified by staff before publishing.However, the company is identifying exceptions to this rule, such as weather forecasts and other comparable use cases.

Openness. Being open with the audience about the use of AI is critical, as is transparency about correcting possible mistakes.

Maintaining employment. The publisher is committed to designing AI solutions without disrupting the newsroom organisation, as well as having a commitment to upskilling and mobility.

Notably, Ouest-France has opted out of making deals with AI platforms, unlike some publishers who have entered into such arrangements. Instead,Ouest-France collaborates with public research institutions,and has

“`html

AI Prototyping: From Idea to Executive Approval

Artificial intelligence (AI) prototyping is a crucial stage in developing and implementing AI solutions. It bridges the gap between initial concepts and fully realized applications. This process involves creating preliminary versions of AI systems to test feasibility, gather feedback, and refine designs before significant investment. Successfully navigating the prototyping phase, and ultimately securing executive approval, requires a strategic approach focused on demonstrating value and managing risk.

The AI Prototyping Process

AI prototyping isn’t a single step, but rather a series of iterative stages. Each stage builds upon the last, progressively refining the AI model and its potential applications.

1. Ideation and proof of Concept

The process begins with identifying a business problem that AI can potentially solve. A proof of concept (POC) is then developed – a small-scale experiment to determine if the core AI technology can function as intended. This frequently enough involves using readily available datasets and pre-trained models to quickly validate the initial idea. The goal is to answer the basic question: “Is this technically feasible?”

2. Data Collection and Planning

once the POC is successful, the focus shifts to data.AI models are only as good as the data they are trained on.This stage involves collecting, cleaning, and preparing relevant data for model training. Data preparation can be a significant undertaking, often requiring data scientists to address issues like missing values, inconsistencies, and biases. IBM provides a detailed overview of data preparation.

3. Model Advancement and Training

with data in place, the AI model is developed and trained. This involves selecting an appropriate AI algorithm (e.g., machine learning, deep learning) and using the prepared data to train the model to perform the desired task. Model training is an iterative process, requiring careful monitoring and adjustment of parameters to optimize performance. Google Developers offers extensive guides to machine learning.

4. Prototype Development and Testing

The trained AI model is then integrated into a functional prototype. This prototype simulates the intended request, allowing stakeholders to interact with the AI and provide feedback. Testing is crucial at this stage, evaluating the prototype’s accuracy, reliability, and usability. Different types of testing, such as A/B testing and user acceptance testing, are frequently enough employed.

Securing Executive Approval

After the prototyping phase, the AI prototype is presented to an executive committee for review and approval. This is a critical juncture, as executive buy-in is essential for securing the resources needed to move the project forward.The primary consideration at this stage is return on investment (ROI).

Key Criteria for Executive Approval

• Demonstrated Value: The prototype must clearly demonstrate how the AI solution will address the identified business problem and deliver tangible benefits.
• ROI Analysis: A comprehensive ROI analysis is essential, outlining the expected costs and benefits of implementing the AI solution. This should include quantifiable metrics such as increased revenue, reduced costs, and improved efficiency.
• Risk Assessment: Executives will want to understand the potential risks associated with the project,including technical challenges,data privacy concerns,and ethical considerations.
• Scalability and Integration: The prototype should demonstrate the potential for scalability and seamless integration with existing systems.
• clear Implementation Plan: A detailed implementation plan, outlining the steps required to deploy the AI solution, is crucial.

Presenting the Prototype Effectively

A successful presentation to the executive committee requires clear communication and a focus on business value. Avoid technical jargon and instead emphasize the benefits of the AI solution in terms that executives understand. Visual aids, such as demos and dashboards, can be highly effective in illustrating the prototype’s capabilities.

Key Takeaways

• AI prototyping is an iterative process that transforms ideas into tangible solutions.
• Data quality is paramount for successful AI model training.
• Executive approval hinges on demonstrating clear ROI and managing risk.
• Effective communication and a focus on business value are crucial for securing buy-in.

As AI technology continues to evolve, the importance of robust prototyping and executive alignment will only increase. Organizations that can effectively navigate this process will be well-positioned to harness the transformative power of AI.

Published: 2025/