Artificial intelligence (AI) is reshaping every corner of the retail landscape—from the way customers shop to how stores manage their operations, staff, and inventory. But deploying AI effectively isn’t as simple as flipping a switch. It requires a structured, strategic approach. Based on insights from American Public University and Bailiwick’s own real-world deployment experience, here are the five steps retailers should follow to implement AI successfully.

Step 1: Define Clear Business Goals for AI Integration

Before diving into any technology investment, retailers must ask: What problem are we trying to solve?

Whether it’s optimizing labor schedules, improving customer service, managing inventory, or enhancing loss prevention efforts, AI should support measurable business outcomes. As seen with Tractor Supply’s “Gura” assistant and Amazon’s AI-powered product recommendations, top retailers start by identifying the operational or customer experience challenges they want to solve—and then build their AI strategy around those goals.

“What makes this moment unique is the maturity and affordability of AI,” says Jimmy Hinshaw, director of sales at Bailiwick. “Powerful insights that once required enterprise-scale infrastructure can now be generated using compact edge devices. These small, affordable units can deliver real-time insights on store traffic, dwell times, and labor needs—making AI accessible even to smaller retailers with limited resources.”

Step 2: Leverage and Evaluate Existing Infrastructure

Retailers don’t need to start from scratch. In fact, most begin by layering AI onto their existing infrastructure. Video analytics platforms, for example, can analyze footage from current camera systems to measure customer behavior and identify suspicious activity. However, the effectiveness of these AI tools depends heavily on data quality.

“Retailers looking to adopt AI will often start by using their existing systems—feeding current data into AI-enabled platforms or cloud services,” Hinshaw explains. “For video analytics, they may layer AI on top of their current cameras. But the quality of insights is tied directly to the quality of inputs. If the existing video infrastructure delivers low-resolution images, accuracy may be limited. In those cases, upgrading to higher-quality cameras could be necessary to meet performance goals.”

Step 3: Start Small with High-Impact Use Cases

AI adoption doesn’t require a massive, enterprise-wide rollout on day one. Smart retailers choose one or two high-impact use cases and build them from there.

For instance:

• Predictive analytics can be used to optimize inventory planning and reduce stockouts, and line as Walmart has done during peak demand cycles.
• AI-powered customer service bots can handle product inquiries and process returns automatically, improving the shopping experience while reducing staff workload.
• Strategically placed cameras can monitor checkout lines to help determine if more staffing is required to service customers.
• Smart shelving and virtual try-ons, like Sephora’s Virtual Artist, can personalize in-store and online shopping journeys and reduce returns.

Starting small allows teams to test the effectiveness of AI solutions, measure ROI, and gain stakeholder buy-in before scaling up.

Step 4: Build the Right Data Foundation

The power of AI lies in the data it can access and analyze. AI tools need clean, structured, and relevant data to produce meaningful insights. Retailers must ensure their data systems—from point-of-sale to supply chain to customer engagement platforms—are integrated and secure. AI tools can support dynamic pricing models, adjusting costs based on demand, inventory, and competitor activity.

AI also enhances loss prevention and asset protection, with smart surveillance systems able to flag unusual behavior, monitor checkout accuracy, and support fraud detection in real time. Some companies are using AI to monitor self-checkouts and identify theft or scanning errors.

Step 5: Address Ethical Considerations and Scale with Confidence

As AI becomes more embedded in retail operations, retailers must adopt it ethically and transparently. Consumers are increasingly aware of how their data is collected and used. Clear communication around consent, privacy, and data protection is essential.

Retailers also need to plan for workforce transformation. While AI can automate repetitive tasks, it can also free up staff to focus on more meaningful customer interactions if properly supported through training and upskilling.

AI deployment isn’t a one-and-done project. It’s an ongoing journey that evolves with business needs, customer expectations, and technological advancements. A scalable, ethical foundation allows retailers to expand AI capabilities confidently and responsibly.

Partnering for AI Success

The retailers succeeding with AI today didn’t go alone. They built a long-term strategy and partnered with experienced technology providers.

At Bailiwick, we understand the challenges and opportunities of deploying AI at scale. Whether you’re piloting smart cameras, integrating predictive analytics, or rolling out AI-based fraud prevention, our team of engineers, project managers, solution architects, and technicians simplifies the complex. From initial planning to full-scale deployment, we help you unlock the full potential of your technology investments.

Note: Jimmy Hinshaw will join Joe Hinrichs, Bailiwick VP of IT and Product Development, for a panel discussion entitled “Securing the Future: AI-Powered Loss Prevention at Scale,” at ISC East in New York, November 18th at 11:00 AM.