AI can lift an entire QSR network to the level of its best restaurants

Most AI programmes fail because of the gap between insight and action.

The greatest opportunity from artificial intelligence in quick-service restaurants is not cost efficiency but the ability to replicate the decisions and behaviours of a brand's best-performing restaurants across its entire network, according to Emma Pitfield, partner at KPMG.

Speaking at the AI-Powered Restaurant: Driving Site-Level Adoption at Scale webinar hosted by QSR Media, Pitfield said most QSR brands had a small number of exceptional restaurants that consistently outperformed the rest, and that AI's real value lay in closing the gap between those sites and the average.

"The real opportunity with AI is how do we replicate those decisions, behaviours and outcomes in those exceptional restaurants and scale across all of your restaurant network," Pitfield said. "This isn't about replacing people. It's very much giving your teams access to the intelligence of your very best restaurant."

Pitfield, who has more than 25 years of experience in the QSR industry, said AI had moved well beyond customer chatbots and marketing campaigns into what she called the economic core of the business, influencing the decisions that directly drive sales, labour costs, food costs and profitability. She said the conversation was no longer about whether to use AI but where it could be used to improve the decisions that affect margins, with adoption now replacing experimentation across the sector.

She argued that leading brands were increasingly treating AI as operating infrastructure rather than a standalone technology initiative, with the technology often becoming invisible as it sat underneath forecasting, ordering, labour planning and customer engagement.

However, Pitfield cautioned that most AI programmes fail for reasons that have little to do with the technology itself. "Most AI programs don't fail because of technology. They fail because organisations don't close the gap between the insight and action," she said.

She identified two components as critical to closing that gap. The first was data quality, with data-rich operators now able to capture detailed operational and transactional data across large numbers of sites. The second, and more difficult, was manager action, ensuring restaurant managers trusted the AI's output enough to act on it and drive team behaviour accordingly.

Drawing on her own experience deploying technology in restaurants, Pitfield said building that trust was essential. She added that embedding insights into operational workflows took time and patience, and that no single metric could sustain the change in behaviour required.

Pitfield said the winners in 2026 and beyond would be those that focused on execution rather than the number of pilots they were running, connecting decisions across the business instead of relying on the siloed, best-in-breed technologies of the past.

"AI doesn't necessarily create the value. Better decisions executed consistently create that value,” Pitfield said.