Practical AI in Retail: What’s Hype vs What’s Real in 2025

Original source: https://content.yudu.com/web/69r/0A44arm/PCRSeptOct2025/html/index.html?page=6&origin=reader

Over the past few years, ‘AI in Retail’ has become a buzzword dominating headlines, but it’s essential to distinguish between what’s truly transformative and what’s merely trend driven. Today, intelligent automation is at every level of retail operations, delivering deeper personalisation and efficiency.

In PwC’s 27th Global CEO Survey, 76% of leaders acknowledge the need for reinvention, yet many are still uncertain about where to start. Retailers that embed AI across their operations are securing a competitive advantage. With 49% of CEOs expecting generative AI to increase profitability within the next 12 months, AI has moved from an operational enhancement to a core driver of business reinvention.

Retailers are embedding AI across operations. Some impactful examples include hyper
personalisation, AI-powered inventory and forecasting, customer service, automated checkout process, fraud detection and security, AI agents, and seamless point-of-sale (POS) systems. In fact, Gartner predicts that by 2026, 80% of retail executives will have successfully implemented AI in their day-to-day operations. AI is no longer peripheral; it is core.

As AI adoption accelerates, the lines between what’s hype and reality can blur. While many AI solutions deliver measurable value, some struggle to create seamless customer experiences – often due to a lack of clarity around where AI is most valuable, such as post-purchase engagement.

AI-powered personalisation is no longer a choice

Just a few years ago, AI in retail was confined to narrow use cases such as recommendation engines on e-commerce sites, basic chatbots, and some predictive analytics in supply chain planning. Fast forward to 2025, and retailers are integrating AI in every layer of retail operations.

From the shop floor to the back office, brands are now deploying AI to:

• Deliver hyper-personalisation that moves beyond “customers who bought this also
  bought that” to create tailored experiences across digital and physical touchpoints
• Automate inventory forecasting with models that consider weather, local events, and shifting consumer sentiment to prevent both overstock and stock-outs
• Improve customer experiences through AI-driven agents capable of handling complex queries while seamlessly escalating to human teams when needed
• Secure transactions with real-time fraud detection that learns and adapts faster than traditional rule-based systems
• Streamline the checkout process with seamless shopping experiences powered by
  computer vision and biometric payment
• Reimagine point-of-sale (POS) systems that integrate data across stores, e-commerce, and supply chains to give retailers a unified view of customer behaviour

Hyper-personalisation: From novelty to necessity

Customer personalisation has long been an ambition for retailers, but until recently, most approaches were generic discounts, broad segmentation, and simple product recommendations. However, advancements in generative AI tools and machine learning have made genuine hyper-personalisation possible at scale. Today, brands can analyse browsing data, past purchases, real-time context, and even social sentiment to create offers and experiences uniquely tailored to each shopper.

For example, a customer researching hiking boots might be presented not just with products, but also content on local trails, seasonal accessories, and loyalty offers timed to coincide with pay-day. This shift is not optional. Customers now expect brands to understand them intuitively. As per the McKinsey & Company research in the past, over 70% of consumers say they are more likely to engage with retailers who provide personalised interactions. If executed strategically, hyper-personalisation strengthens customer relationships, drives loyalty, and increases lifetime value. However, there is a caveat: personalisation must respect privacy. Collecting and applying data without transparency risks eroding the very trust retailers are trying to build. Responsible AI governance is therefore a prerequisite.

Establishing consumer trust in retail

Consumer trust is the foundation of personalisation. Without it, even the most advanced systems can fail to create meaningful engagement. Shoppers are becoming increasingly aware and sceptical of how their data is being used. Concerns range from whether AI-driven recommendations are biased, to whether chatbots are making decisions without human oversight, to whether their personal data is being sold or exposed. Brands need to address these concerns by being transparent, ensuring ethical AI models are bias-free and providing shoppers the choice to opt out from sharing their data. Forward-thinking retailers are already adopting responsible AI frameworks to guide their deployments.

What’s still just hype

Not every shiny AI tool delivers business value. In fact, one of the biggest risks for retailers in 2025 is chasing hype. Vendors are quick to market every new capability as “game-changing,” but retailers must cut through the noise. Identifying overhyped AI applications is critical to ensuring that deployments deliver tangible business value. Some solutions generate excitement but fail to increase return on investment (ROI) – yet may prove more valuable in areas such as post-purchase interactions, including delivery updates and returns automation Brands can consider virtual AI shopping assistants.

These tools can generate significant excitement, with promises of guiding customers through entire shopping journeys via conversational interfaces. Similarly, integrating AI into existing systems such as customer relationship management (CRM) tools can be significantly helpful. Yet for many retailers, uptake remains low, interactions are clunky, and the ROI is questionable. This does not mean these solutions lack future potential. But it does mean that C-suite leaders must apply a rigorous filter: will this technology improve operational efficiency, customer experience, or profitability in measurable terms? If not, it is hype – at least for now.

One area where hype often outpaces reality is post-purchase engagement. Brands invest heavily in pre-purchase AI such as recommendations, advertising, website optimisation. Yet far fewer focus on post-purchase, where customer loyalty is truly won or lost.
AI-driven updates on delivery, proactive returns automation, and intelligent customer service follow-ups remain underutilised. Here lies an untapped opportunity: retailers can differentiate not by flashy tools, but by applying AI where it matters most to customers.

Where AI in retail is heading

Looking beyond 2025, AI will not simply be a tool for optimisation but a force reshaping the industry, with three themes set to dominate. First, seamless omnichannel AI will move to the forefront, as retailers focus on unifying online and offline data to deliver consistent shopping experiences across every customer touchpoint. Whether a customer enters a brick-and-mortar store or shops online, AI will help brands ensure continuity and personalisation throughout the journey.

Second, AI’s role can also expand from driving efficiency to underpinning operational resilience. Brands are likely to deploy AI to forecast supply chain shocks, optimise coordination, and support dynamic workforce planning. This will enable retailers to absorb disruption with agility. Finally, with significant focus being placed on sustainability in retail, the industry can see sustainable AI emerge as both a regulatory requirement and a customer expectation. As regulators and consumers demand more transparency, AI systems will need to optimise not only for profit but for environmental responsibility. They should aim to extend efficiency gains beyond cost savings to include energy use, carbon reduction, and ethical sourcing. Retailers that thrive will be those who harness AI not just as a competitive edge, but as a strategic enabler of trust, resilience, and sustainable growth.

Advice for retailers

For retail executives, the leadership imperatives around AI are clear. Anchor AI in strategy, not hype. Retailers should deploy AI where it supports core business goals, not as a distraction. Brands must demand accountability from vendors on ROI, bias, and sustainability, ensuring solutions deliver measurable outcomes. Retailers should place trust and ethics at the centre of every deployment. It is critical to understand that retailers should invest not only in technology but also in people, training employees to see AI as a partner rather than a replacement. The story in retail is not about AI replacing people. It is about how AI can empower brands to connect with customers in smarter, faster, and more trustworthy ways. Retailers who use AI to empower their staff than replacing them will thrive in the long-term, balancing efficiency and human value.

Preparing for the future

AI in retail is not just about buzzwords or flashy demos. It is about reshaping the fundamentals of how retailers serve customers, run operations, and create value. The hype will come and go, and robot shop assistants may excite today and fade tomorrow – but the underlying reality is profound.

By focusing on hyper-personalisation, operational efficiency, and responsible adoption in tandem with human value, brands can use AI tools to build stronger, more resilient businesses. Those who see through the noise and focus on practical, human-centred application will stay ahead.