Many AI projects struggle to move beyond the testing phase. There’s no one perfect way to turn AI ideas into useful tools for businesses. But two companies, Ernst & Young (EY) and Lumen, are showing different paths to success. EY takes a cautious, responsible approach, while Lumen pushes for a broader AI culture across the company.

EY’s Focus on Responsible AI and Risk Management

EY, a global firm in finance and tax, develops frameworks to help clients adopt AI safely. They have documented over 30 million processes internally and operate with 41,000 agents in production. EY uses its own experience to guide clients through AI implementation. The company emphasizes the importance of a strong data foundation. Without good data, AI prototypes are likely to fail before they even start.

Joe Depa, EY’s global chief innovation officer, explains that the pace of AI technology is accelerating. We’re moving from basic generative AI to more advanced agentic AI, and even physical AI systems. Behind these innovations is the rapid development of new foundational technologies, including quantum computing. Companies are constantly updating their IT systems to keep up, often replacing older infrastructure that hasn’t caught up with new tech.

The Role of Governance and Employee Training

Depa stresses that responsible AI frameworks are key to scaling AI projects. These guardrails help companies experiment more freely while managing risks. Organizations that embed responsible AI into their workflows tend to see fewer compliance issues and better value from their AI investments. This structured approach encourages teams to test new ideas in safe environments, or sandboxes, without fear of negative consequences.

Training employees effectively is another critical factor. Depa notes that many clients haven’t invested enough in teaching staff how to work with AI. Instead of traditional training methods, he recommends on-the-spot learning, where employees gain skills during their work with AI tools. He compares this to robotic surgery, where hands-on practice is essential for mastery. When companies focus on practical, real-time training, AI adoption becomes smoother and more successful.

Inspired by

• https://www.computerworld.com/article/4123740/how-two-companies-are-moving-ai-prototypes-to-production.html

Sources

• ey.com
• lumen.com