Scaling generative AI 4 ways from experiments to production

Despite the boardroom buzz around generative AI, businesses are struggling to convert experimental projects into full-scale production services. A Deloitte study shows more than two-thirds of executives believe less than a third of their generative AI experiments will reach full deployment within six months, highlighting the significant gap between AI exploration and meaningful implementation.

The big picture: Warner Leisure Hotels’ head of cloud and IT security, Madoc Batters, is bucking the trend by actively pushing AI from experimentation into production, offering valuable lessons for organizations facing similar challenges.

What they’re saying: “There’s a lot of talk about gen AI, and a lot of people saying they’re going to put the technology into certain areas of their business, but there aren’t many people doing it,” observes Batters, highlighting the implementation gap.

1. Build from the bottom
Rushing into AI implementation without proper groundwork can lead to failure. Batters emphasizes that organizations should resist the pressure to exploit AI hastily: “Many people focus on gen AI because it’s that burning sun in the sky. They feel like they have to do work in this area. And I think, sometimes, you need to get all the other bits of the foundations in place first.”

2. Experiment in new areas
Testing is crucial for successfully transitioning generative AI into production environments. Batters advocates for the “fail fast” philosophy commonly used in IT development: “You need to experiment, make sure it works or doesn’t work, and be able to change things quickly.”

3. Give workers a choice
Warner Leisure Hotels avoids mandating specific AI tools, instead providing guardrails while empowering developers. “We don’t enforce anything,” Batters explains. “We can put guardrails on to stop people deploying things if we think it’s too much. But we believe in giving developers the autonomy of choice and being able to decide if it’s a good or bad thing.”

4. Keep exploring carefully
Organizations need to balance innovation with data protection. “You must embrace gen AI. If you don’t use it, your business could be left behind. However, you have to use gen AI responsibly, so that you’re not exposing any of your company’s data,” cautions Batters.

Behind the numbers: Warner Leisure Hotels has discovered that data readiness represents a significant challenge for AI initiatives, requiring careful preparation before implementation.