Spotting Opportunities: An Executive's Guide To AI Agents In Retail

Forbes

17 hours ago

Oleg Lola, founder and CEO at MobiDev, a custom software engineering & consulting company.
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Big-name retail corporations are already employing AI agents to upgrade shopping experiences, automate operations and boost business performance—setting the bar high for competitors. During the last NRF 2025 Retail's Big Show, where my company MobiDev's team presented, it was impossible to ignore how much AI was being talked about. It was obvious that AI is a must-have technology for the industry.
For me, AI is a driver of the modern tech world. At MobiDev, we use AI not only as a technology for our clients but also for ourselves. There are many tools that can improve our productivity and performance, so I always encourage my teammates to take a look at those tools and try to use them in their work.
At MobiDev, we had been developing AI agents for years before they became popular. I want to share my experience here and explain what retail SaaS executives can expect from AI agents.
The term 'agentic AI' encompasses AI systems that can think and act on their own—learning from real-life interactions, making decisions and managing tasks with little to no human input. In retail SaaS solutions, AI agents automate processes, personalize customer experiences and turn raw data into practical findings.
For an example of this, look no further than one of the big names in the industry. Walmart has achieved a 68% supplier deal closure rate using AI-powered chatbots, saving an average of 3% in costs.
Here is how I've personally seen agentic AI change the rules of the retail game:
• Personalized Shopping Assistants: AI-powered chatbots suggest products based on your customers' preferences, making shopping more intuitive and entertaining. According to the Nasdaq, 71% of consumers desire generative AI integration in their shopping experiences.
• Better Inventory Management: AI agents in retail SaaS tools analyze demand patterns for forecasting, minimizing potential waste and ensuring customer satisfaction.
• Fraud Detection And Security: AI agents can continuously monitor transactions, catching suspicious activity in real time to keep your clients and their customers safe.
Before jumping into AI agent development, SaaS companies need to identify where AI can bring the most value to their users.
First, it's necessary to understand where AI can make the biggest impact on your clients. Here are a few pain points I've identified:
• Customer Experience: Incorporated into your retail SaaS, AI chatbots and shopping assistants with virtual fitting rooms can create seamless interactions between your client's business and their customers.
• Sales And Marketing: AI agents can examine customer behavior, forecast trends and even facilitate dynamic pricing to boost conversions.
• Inventory And Supply Chain: Automated restocking and warehouse optimization, via POS systems, can allow your clients to have the right products available at any moment.
• Fraud Prevention And Risk Management: Real-time transaction monitoring and fraud prevention mechanisms powered by AI can keep your clients' businesses and customers secure.
• Operational Efficiency: AI agents in retail SaaS can simplify workforce management and automate repetitive tasks, freeing users for more strategic missions.
To turn an AI agent into a valuable tool, it's essential to identify real challenges that your clients experience:
1. Where and when are retail customers getting frustrated?
2. Which repetitive tasks in your SaaS tool eat up too much of users' time?
3. Are valuable data insights sitting unused?
A deep internal audit of the users' workflows within your retail SaaS product will help you discover the best opportunities for AI agent intervention. If you are developing a new product, it's necessary to conduct thoughtful market and product research to understand the role of AI agents in your SaaS product.
Finally, any AI investment should show clear business benefits, so you need to answer these four questions:
1. Can AI decrease operational costs for your clients?
2. Can AI agents add new functionality to your product?
3. Will AI push revenue growth for you by increasing your customer base and decreasing your churn?
4. How do development expenses compare to expected returns?
Adopting AI agents requires detailed planning. Without a solid plan, attempts to implement AI can become overwhelming and result in a huge disappointment.
One of the first considerations I like to look at is market fit and overall product success. A well-executed business analysis phase within the AI consulting process can bridge the gap between technical capabilities and real-world applicability, offering long-term business value.
Having the right technical expertise is also a major part of making an AI agent work. First, you need to figure out if your team has the skills to work with AI in-house or if you require additional resources. It's not just about understanding what an AI agent can do—it's also about its limits in mind and having AI systems work smoothly in the long run.
A clear implementation plan is just as important. It's worth thinking about who should be the first to test AI— users you have randomly picked or those who have volunteered—and what success should look like. Setting clear goals early on will make it easier to track progress and see if the AI agent is delivering results.
AI isn't just a buzzword. The key is to adopt it strategically after thoughtful research or AI consulting, ensuring it solves real problems and drives measurable results. In my opinion, people (and companies) who are not using AI now are akin to dinosaurs. If they are not already extinct at this point, they may become so in a couple of years.
AI agents are redefining the retail industry, and SaaS companies must embrace them to stay competitive. However, they need to do it with intelligence. By identifying high-impact areas, analyzing the pain points of their clients and carefully assessing ROI, retail SaaS providers can successfully integrate AI into their products.
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