With customers expecting fast, personalized service, retailers must rapidly adapt to new demands driven by AI—or risk falling behind in a competitive marketplace. Cloud enables retailers to better meet demand at peak moments, unlock data-driven product insights, and stay agile amid a fast-changing economy.
Importantly, cloud modernization is foundational for successful AI deployments that help businesses engage customers more effectively and innovate continuously. Yet converting AI enthusiasm into business value seems beyond many enterprises, especially in retail.
Foundry’s 2025 AI Priorities Study found that less than a quarter (23%) of businesses across sectors had implemented AI technology enterprise wide.1
Recent research from EPAM suggests that while 30% of retail companies have piloted AI programs with their customers and 29% have done the same with internal teams, only 6% are leveraging AI at scale.2
Missed opportunity
Martin Ryan, VP Retail Europe at EPAM Systems, sees this as a missed opportunity.
Retail organizations have access to vast quantities of customer and logistical data, yet only a few are using it to drive decision-making in high value areas like price optimization, supply chain resilience, inventory management or merchandising.
Lack of investment is a big part of the problem in a sector where budgets are tight and investment capital contested. The resulting lag in investment, Ryan suggests “means they have the data, but it’s locked away.”
Jon Kadis, Managing Principal, Technology Consulting at EPAM concurs.
“They can’t monetize it and they can’t really pull the true value out of it,” he argues. “They need to take steps and fix their infrastructure and data issues before they can take advantage of the AI opportunities.”
The answer, both feel, lies in treating data as a strategic asset and connecting disparate systems together in a modernized cloud infrastructure.
Vital transformation
Budgets are tight, and Foundry’s AI Priorities Study also shows that business case justification and competing business priorities are two of the biggest challenges for getting AI projects off the ground.3
Yet this is vital transformative work that could fortify businesses in a tough sector where many high-profile brands face difficult conditions.
For CIOs wondering how to begin their AI journey, many experts recommend focusing on cloud rather than on-premises platforms.
Ryan notes: “On-premises is much, much harder because the tooling isn’t there, the connectivity isn’t there, the scale isn’t there. It’s hugely capital intensive, and it’s difficult to innovate.”
Kadis feels that with a cloud infrastructure, “it is easier to get the data into a place where you can feed it to a model to pull the insights out”.
This doesn’t mean businesses need to go all-in on cloud and AI right now. In fact, it pays to be deliberate.
“You need to spend time working out where the real prize is,” says Ryan, “and if you can combine that with vision and energy and a willingness to invest, then you can start getting some results.”
Success with AI in one area can lead to more willingness to invest in others.
Of course, some retail organizations lack the in-house expertise and experience to identify the areas with the most potential, or to design and implement the cloud data platform necessary to drive AI success.
Partners like EPAM can assist on both points and help retail enterprises take the next step in a sector that will be unforgiving to those who stay still.
1 AI Priorities Study 2025, Foundry, February 2025
2 AI Adoption in Retail & CPG, EPAM, June 2025
3 AI Priorities Study 2025, Foundry, February 2025