AI for everyone

Bouygues Telecom had already experimented with AI/machine learning (ML) models by developing a few simple, one-off apps, such as for chatbots and text analysis. Yet the proof-of-concept (POC) approach used to develop these models was unaligned with corporate IT standards and couldn’t support full production. In addition, many line-of-business (LOB) and IT managers had trouble envisioning how to best use AI and hesitated to take the time to help develop solutions.

The company needed to effectively involve managers in brainstorming viable AI use cases. It also required a uniform, integrated approach to scaling AI solutions beyond experimentation. In other words, if Bouygues Telecom wanted to drive enterprise innovation, it had to fully democratize use of AI.

Yet the company lacked in-depth AI knowledge and skill sets. “We knew we had to go fast and strong to build a global AI offering for our company. However, AI is a very specific domain that requires not only technical but also management, ideation and design skills,” says Dutot.

To fast-track AI adoption, Bouygues Telecom sought a consultancy company with deep, multivendor AI expertise and experience. The company had a complex IT landscape, with numerous data systems and governance requirements. The consultants would need to support Bouygues Telecom’s evolving multicloud strategy, which gives teams flexibility to select cloud and AI providers based on their departmental and application needs.

Bouygues Telecom chose IBM. A leader in ethical AI, IBM embraces an open-technology, collaborative approach to digital transformation and has highly experienced data scientists. In addition, IBM provides the IBM Consulting for AI at Scale services offering, a comprehensive consult-to-operate approach for effectively scaling AI enterprise-wide. The offering guides companies on their AI journeys by delivering the framework, methods, assets and technology for creating and scaling AI solutions, from minimal viable product (MVP) to production, with rapid time to value.

Integrating AI design, ethics, data science, architecture and engineering, the AI at Scale offering helps businesses consistently integrate and scale AI/ML PoC and other tested models into production and then continually revise and optimize those models over time. It also includes components to help businesses evolve their AI culture and governance. To structure this project, the team followed the IBM Garage™ Methodology — proven practices that guide a company through designing, building and scaling solutions for end-to-end transformation. IBM co-designed custom data and AI reference architecture covering multiple cloud scenarios that can be extended to all AI and data projects across the Amazon Web Services (AWS) cloud as well as other cloud/on-premises platforms.

The initiative to democratize use of AI across Bouygues Telecom began with an AI Boost program led by the corporate Innovation team, which was tasked with proving the value of AI by creating nine MVPs of cloud-native AI apps. It also provided training to fuel an enterprise-wide cultural transformation around AI and data.

To help generate ideas for the MVPs, Bouygues Telecom participated in several IBM Enterprise Design Thinking® Workshops where consultants held brainstorming sessions with the Innovation team and several LOB managers.

“Imagining what you can do with AI can be quite difficult and requires specific skills and methods. The IBM Garage approach really helped accelerate the ideation process,” says Dutot. IBM consultants also assisted in rapidly building the MVPs following agile principles.

Once the Innovation team successfully completed the AI Boost project, the company was ready to move beyond experimentation. At this point, the IT Innovation Department stepped up to operationalize and scale AI capabilities across the organization, including setting AI-at-scale standards for the company. A small team that tests and scales new technologies, the department could have chosen to work with a different consulting company, but it wanted to keep the momentum going with IBM.

“IBM did really great work during the AI Boost program. We wanted to bring some of the MVPs — and also some new ones — to scale in just a few months, but we didn't want to start from the beginning. We wanted to accelerate the process, and IBM was the perfect partner,” says Dutot.

The quest to democratize AI has just started at Bouygues Telecom, Dutot emphasizes. Now that the AI platform is operational, the focus shifts to continuing to facilitate cultural transformation and upskilling AI talent across departments, including through training, codesign and codelivery. New users to the platform are given access to AI tools relevant to their roles and assisted in learning how to effectively use them. The team also plans to identify more use cases, improve the AI/ML models and build a robust MLOps framework to standardize at-scale processes.

That said, the work accomplished to date represents a huge leap forward toward helping Bouygues Telecom gain competitive advantage through enterprise AI adoption. Now, nearly any team or department can optimize virtually any business application and work process using AI. Bouygues Telecom is also better positioned to capitalize on its vast data stores and continue rolling out a multicloud strategy.

“AI is no longer an experimental subject for us,” says Dutot. “Everyone is now AI ready, and that’s a real game changer for us.”

Founded in 1996, Bouygues Telecom is France’s third-largest B2B provider of services for mobile telephony, internet and IPTV services. It strives to bring people closer together by providing a high-quality fixed and mobile network, innovating in a constantly changing market and fostering collaboration. Part of the Bouygues Group, the company serves 25.3 million customers, operates 500 stores and employs 9,550 people.