In this CIO Edge interview, Danny Walsh, Business & Technology Services Director, details how he blends data, machine learning, and Copilot to shape AI strategy at George Weston Foods.
Danny describes his role as similar to a traditional CIO, overseeing finance back-office functions such as accounts payable, accounts receivable, and payroll.
He manages seven businesses through a shared services model, providing him with broad visibility across operational areas and enabling him to influence technology adoption at scale.
His responsibilities blend business and technology oversight, ensuring processes and systems align effectively with organisational needs.
George Weston Foods’ AI journey began with machine learning, supported by a centralised data lake that consolidated the company’s data and allowed for effective experimentation with AI initiatives.
More recently, the company has moved into generative AI, starting with ChatGPT and progressing to a pilot of Microsoft’s Copilot involving around 200 users over the past year.
Danny highlights that pairing technology rollout with education has been key to their high adoption rate of 94%, ensuring employees understand how to use the tools safely and effectively while raising awareness of potential risks.
Danny emphasises that machine learning often delivers more immediate value than generative AI, particularly in areas like customer forecasting, citing an award-winning example with KFC.
He stresses that AI initiatives require both experimentation and a solid business case to scale successfully.
Currently, AI use at George Weston Foods focuses on end-user enablement, with the analytics team exploring large language models and early-stage application-specific projects like cyber security.
Over the next 12–24 months, Danny plans to expand Copilot selectively, explore new AI technologies, and work closely with the business to identify problems that AI or automation can address, balancing experimentation with practical value.
Key takeaways:
- Integration of business and technology: Effective AI adoption requires oversight that blends operational understanding with technology strategy, ensuring systems and processes align with organisational needs.
- Education drives adoption: Pairing AI tools with targeted training and support is critical, simply providing technology is insufficient; employees must understand how to use it safely and effectively.
- Experimentation plus business value: Successful AI initiatives combine experimentation to explore capabilities with a clear business case to scale, prioritising tools like machine learning for immediate impact while selectively deploying generative AI where it adds measurable value.