Generative AI is making waves across industries, and in retail, it’s positioned to redefine everything from logistics to customer engagement. According to Praveen Prabhakaran, Chief Delivery Officer at UST, generative AI “has the potential to deliver significant value in the retail operations realm.”
By integrating this technology with core business capabilities, retailers can reduce time to market, lower operational costs, and enhance customer experiences in profound ways.
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He adds, “One of the key opportunities out there [is] to help enterprises become smarter and intelligent with each encounter.”
On the logistics front, generative AI helps retailers optimize routes in real time by analysing traffic patterns, weather, and delivery conditions. “Retailers can generate real-time route plans… automate shipping documents, compliance paperwork… and even support fleet management teams by generating predictive maintenance schedules,” Prabhakaran explains.
With such applications, retailers can streamline their logistics processes, making operations both more resilient and efficient.
In terms of customer experience, generative AI brings a new level of personalisation. Prabhakaran envisions a world where “Enterprises can craft individualized shopping experiences that truly understand and anticipate each customer’s needs.”
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By GlobalDataGenerative AI can enable personalised product recommendations, custom marketing, and even help customers design bespoke products, all of which contribute to an enriched shopping journey.
However, he cautions, “Generative AI is a tool, not an end in itself… By focusing on how generative AI can enhance underlying business capabilities… retailers can realise true value.”
Overcoming implementation challenges
Despite its potential, implementing generative AI in retail comes with its own set of challenges.
Praveen Prabhakaran highlights two main obstacles: the lack of focused investment strategies and the difficulty of accessing and utilising existing enterprise knowledge.
“Retailers… get caught up in edge use cases that are trending but [do] not align with their core business objectives,” he notes, leading to resource-draining efforts with limited returns.
Another challenge is leveraging the vast amounts of internal data and insights that are often fragmented across the business. Without these insights, generative AI can fail to meet its full potential.
UST’s approach tackles these issues with a structured framework that maps generative AI initiatives directly to specific business capabilities. “We start by utilising a Business Capability Model (BCM)… identifying where generative AI can deliver the most significant impact,” Prabhakaran explains.
UST also uses “scenario cards,” a unique feature that embeds knowledge, business rules, and operational insights into the AI framework, allowing generative AI solutions to be customised to each retailer’s needs.
This structured approach ensures generative AI aligns with business objectives, integrates seamlessly with existing systems, and taps into the organisation’s deep knowledge base.
Enhancing profitability and sustainability through AI
The impact of generative AI extends beyond operational efficiencies and customer engagement to areas of profitability and sustainability.
Citing Walmart’s success, Prabhakaran mentions how generative AI has helped the retail giant analyse vast datasets, enabling more accurate inventory forecasting and reduced waste.
“By leveraging generative AI… Walmart has significantly enhanced the accuracy and efficiency of their inventory management,” he notes, reducing excess inventory and minimising the environmental impact of overstocking.
UST’s platform enables retailers of all sizes to apply similar methodologies. “We help enterprises map out their core capabilities… uncovering opportunities to enhance efficiency, reduce costs, and promote sustainability,” Prabhakaran explains.
By leveraging scenario cards, UST’s platform equips retailers with a clear pathway to integrating AI in a way that aligns with both profitability and sustainability goals.
Looking forward, Prabhakaran envisions that generative AI will become “more and more integrated into the retail experience,” enhancing customer relationships, bridging the online-offline gap, and fostering omnichannel experiences.
He believes that by focusing on high-impact, practical applications, generative AI can provide measurable benefits to retailers regardless of size.
Generative AI’s role in retail will continue to grow, especially as retailers unlock its full potential.
With a balanced approach to adoption, Prabhakaran concludes, “Generative AI initiatives… should serve to make operations more efficient and customer experiences more meaningful,” ultimately enabling retailers to set themselves apart in an increasingly competitive landscape.
