Artificial intelligence (AI) is reshaping how enterprises operate. But while pilots and prototypes have generated excitement, many organizations are now facing a more sobering challenge: How do you actually make AI work at scale?
In this white paper titled, “Operationalizing AI: Overcoming the Challenges,” sponsored by Atos and AWS, Harvard Business Review Analytic Services (HBRAS) uncovers the realities of operationalizing AI in large organizations. We’ve reached deep into our Atos network to help HBRAS bring to this paper the expertise and vision of some of the most experienced voices in the field— executives who aren’t just talking about AI but are actively deploying it under real-world conditions.
Operationalizing AI isn’t about chasing the next big model; it’s about building sustainable systems, resilient practices, and clear governance frameworks. Often, success depends on access to external expertise and guidance. Complete the form below to download this paper for a grounded look at the factors that are keeping organizations from realizing the full potential of AI and how to move forward strategically with clarity and confidence. It offers insights and direction for anyone looking to move beyond experimentation and unlock the full value of AI in the enterprise.
Download the white paper
At Atos, we’re not just consultants guiding companies with recommendations—we’re also AI users ourselves. We’ve experienced first-hand the technical, organizational, and ethical hurdles that stand between proof-of-concept success and production-grade systems. From performance drops in live environments to unpredictable compute costs and security vulnerabilities, the road to AI at scale is anything but smooth and straight.
Ready to talk about the AI challenges that keep you up at night?
Contact Brian Ray, Head of Data & AI, to take the next step on your AI journey.
Brian Ray
Head of Data and AI
