The race to AI-driven operations in retail is on. But most retailers are just starting their journey toward the finish line. According to a recent survey conducted in collaboration with ServiceNow and Retail Dive’s Studio by Informa TechTarget, nearly three-quarters (72%) of retail executives say that manual processes noticeably impact their retail operations. The cost of maintaining this status quo is huge. Tedious back-end processes pull associates away from the floor for up to three hours per shift, which contributes to more lost sales, a weaker customer experience and a higher risk of associate talent.
The good news? With the right strategy, even small steps toward AI-driven operations can yield significant benefits, streamlining operations and improving customer service. We talked to Ellie Quartel, head of global retail and hospitality at ServiceNow, about the steps retailers can take today to lay the foundation for AI transformation and realize real gains along the way.
Focus on business objectives to find your starting point
With so many potential use cases for AI in operations, retailers often feel overwhelmed about where to start. So Quartel recommends grounding your decision in what you know best: The friction points immediately impacting your day-to-day operations.
“Start by looking at where manual handoffs are costing you the most — not in abstract operational terms, but in ways your store teams feel every day,” she advises. “Our survey found that 54% of retailers cite integration challenges as their top barrier to AI adoption. That tells me a lot of organizations are trying to innovate on top of a fragmented foundation, and it's slowing everything down.”
She advises picking one workflow that crosses at least two systems or teams, where a delay or error has a direct, visible impact on customers or associates. Task management between HQ and store teams is a good example: “Think about what happens when merchandising rolls out a new planogram, marketing launches a new product, or IT pushes a new in-store technology release. In each of those cases, a directive comes down from corporate and there's often no reliable visibility into whether it's actually been executed at the store level — or when, or by whom,” she advises. “That's a workflow problem you can fix without a massive infrastructure overhaul, especially if you’re working from a platform that already connects across those systems and can route that right action to the right team automatically.”
Measure what you value to drive meaningful results
Of course, realizing incremental wins relies on having a solid strategy to track and measure the right KPIs. “A good leader once told me 'you treasure what you measure,' and I've seen that play out repeatedly in retail AI deployments,” says Quartel. “Without a formal accountability structure, measurement becomes ad hoc, adoption quietly slips, and by the time someone notices a workflow isn't performing, you've lost months.”
Quartel recommends a regular quarterly check-in among the executive steering committee to help keep transformation initiatives on track. Not because the strategy needs to change each quarter (it doesn’t) but because having that milestone on the calendar drives the right behavior between meetings, she says.
“When store teams and program owners know they'll be presenting results to leadership in 90 days, they stay close to the data. They catch issues early. They course-correct before problems compound,” she says.
To reap these benefits, build your measurement framework during the pilot phase, not after. Decide upfront which KPIs matter: task completion rates, error rates, time-to-resolution, and adoption by store or region. Capture clean baseline data before pilots go live so you can meaningfully compare before-and-after. And track adoption alongside operational metrics.
Task completion rates can look fine on paper while associates are quietly working around the system. Adoption data, such as who's using the tool, how often, and where drop-off is happening, tells you whether your strategy is actually working or just appearing to. “Both sets of signals belong in your quarterly review, and both need to be tracked from day one,” Quartel says.
Maximize adoption by engaging, and upskilling, associates
Retailers that see strong AI adoption share a common habit: they involve associates early, not as an afterthought. 41% of retailers who have deployed AI report that their associates have more time to assist customers, a significant benefit that should motivate employees to use the new tools.
However, retailers treat training as a checkbox when rolling out technology upgrades. Quartel says that a 30-minute session before going live or emailing a PDF version of instructions won’t suffice.
“Your store teams know where the friction is. They know which manual processes are eating their time,” she says. “If you can show them, concretely, how a new workflow tool addresses the specific pain they've described, adoption follows much more naturally. It's not about selling them on AI. It's about solving their problem.”
But involvement shouldn't stop at selection. The retailers who see the strongest adoption are the ones who keep store and business teams in the room continuously — not just during rollout, but through the first year of operation. “Put them on the AI council. Include them in vendor selection committees. Have them present at project read-outs at the 3, 6, and 12-month marks,” she says. “When the people who could be your biggest skeptics become co-owners of the solution, the dynamic shifts entirely. They're no longer evaluating whether the tool works; they're invested in making it work.”
For cross-skilling, Quartel recommends looking internally for the skills they need to leverage AI. Retailers don’t necessarily need advanced data scientists or developers, she says. They need people who know how to use AI.
“A lot of the skills you need to use AI well — analytical thinking, attention to process, customer orientation — are already in your organization,” she says. “You're looking to develop those skills, not necessarily hire your way to AI readiness.”
Focus on progress, not perfection, to gain a competitive edge
The data around AI adoption in retail tells a clear story: Most retailers are behind on AI, and they know it. Right now, fewer than 10% of store operational tasks are AI-assisted for the majority of retailers. However, 83% expect to cross that threshold within the next 12 months.
“The challenge for most retailers isn't identifying where they want to go with AI. It's the gap between where their systems and workflows are today and where they'd need to be to scale AI effectively,” says Quartel. “Integration complexity, data quality, internal expertise. Our survey puts all three in the top barriers retailers face. Those aren't technology problems in isolation. They're workflow and connectivity problems, and they’re solvable.”
ServiceNow connects the operational systems retailers already have, surfaces what needs attention across workflows, and automates the next action to streamline retail operations. Visit us online to learn how to put AI to work for retail.
