With more than 450 laboratories across the globe, ALS Limited is a trusted provider of testing, inspection, and certification services for industries as diverse as mining, environmental sciences, and food safety. From determining the quality of drinking water to certifying the purity of gold, the company’s data is central to high-stakes decisions worldwide.

The first part of this three-part series explored how ALS organizes its data and AI strategy and what the future of “Smart Labs” looks like in a decentralized, high-stakes global testing enterprise.

In the second part of this conversation, Thibault Bonneton, Chief Digital and Information Officer at ALS Limited, sits with Julian Schirmer, Co-founder of OAO, to discuss how the company organizes its decentralized structure, harnesses early cloud adoption and generative AI (GenAI), the role of analytic translators, and balancing agility with scale.

Standardization in a decentralized network

ALS’s reach spans continents, but consistency remains essential. “Providing reliable data to our customers is the essence of what we do,” Bonneton affirms. “We are a trust provider, and we’re very proud of that.”

Despite the diversity of its industries, including mining, environmental testing, and food safety, ALS has built standardized ways of working across its 450 labs. Much of this standardization is powered by internally developed Laboratory Information Management Systems (LIMS), which have been evolving for decades.

By developing its own operating software, ALS maintains complete control over lab processes, ensuring data quality, consistency, and reliability. “We have complete control over the processes, the quality of the data, and the way of working of each lab in the world.”

Further, by internalizing the genes of how the labs work, ALS Global has gained the advantage to control the output and provide consistent and reliable information to its customers. Development teams in Spain and other regions continue to invest in developing the software and rolling it out to multiple of its labs, he says.

Early cloud adoption and GenAI priorities

Another key differentiator for ALS is its early embrace of the cloud. “We built a database in the cloud in Microsoft Azure more than 10 years ago,” Bonneton recalls. This long history of cloud maturity underpins ALS’s ability to manage vast datasets with high-quality standards.

Looking ahead, generative AI is a strategic priority, he adds. “Gen AI will give our lab technicians a better ability to respond in a much faster way to the customers and also some efficiency we can have internally,” says Bonneton. From faster responses to greater efficiency, AI promises both external and internal impact.

Shared enablers across industries

With such a diverse portfolio, synergies across business lines are critical. He explains that enablers developed centrally often serve multiple industries. “Our objective is to build the enablers that will help all the business lines and support their activity from an IT perspective or data perspective.” 

“Computer vision in the mining industry, in food, and environmental space are not so different,” he notes. “They are the same use case applied to multiple business lines.”

Other shared tools include machine learning for numerical validation of results or knowledge bots for knowledge retrieval and decision support. Solutions like this enable ALS to scale its use cases more quickly and consistently.

“It’s very important to have a very good balance between centralization and decentralization,” says Benneton. On the development side, operations are fully centralized, and data management is also largely centralized. Yet the core mission of the IT, data, and AI function is to provide the business with tools and enablers that help scale the use cases delivering the greatest value, he adds.

This approach reflects ALS’s culture as a trust provider, both to its clients and internally, says Bonneton. Trust is built on strong relationships and on the principle that business leaders know their operations best. “We are here to give you the tools, enablers, and use cases that are the most valuable for you and for your customers.”

The role of “analytic translators”

To bridge the gap between business needs and technological enablers, ALS relies on what Bonneton calls “analytic translators.”

“It’s key to have these people,” he says. “It’s very important to find the people who understand the business and the technology at the same time and can build bridges.”

These translators, embedded in business lines, help drive roadmaps, filter ideas, and identify common use cases. Their presence ensures that AI and data priorities align with real business value.

AI factories and the human side of transformation

When asked how decentralization efforts ensure scalability from one organizational area to another, Bonneton says, “Decentralization brings you agility and lots of positive energy from the ground and at the same time, an industrial mindset, which builds things at scale.” To achieve both, ALS has established development factories and is setting up AI factories to ensure consistency in GenAI use cases.  

Bonneton stresses that the AI factories create consistency in how the company approaches use cases and ensure a unified way of addressing emerging challenges. At the same time, ALS remains highly mindful of resources.

More importantly, he says, “Beyond the organization and the organizational chart, the real importance is of good relationships between the people.” Bonneton concludes, “Transformation is like a muscle. You need to exercise it and build a healthy relationship with the business and between the people to move forward together.”

CDO Magazine appreciates Thibault Bonneton for sharing his insights with our global community.