In an era where the hype surrounding AI seems synonymous with innovation, it’s easy to believe that the efficiencies it heralds for retail are unprecedented. 

However, the integration of AI in retail should not be reduced to a narrative of technological prowess or marketing ostentation. Rather, it should be framed firmly as a quest for enhanced business performance, driven by an in-depth understanding of the data that lies at the heart of decision-making.

Retailers have been leveraging data to inform business decisions for decades – forecasting and loyalty schemes are not new to the industry, for example. But the spike in interest catalysed by recent (generative) AI advances has reframed the potential for how much further it could drive critical business improvements.

Some retail players are adopting generative AI with alacrity, while others remain rooted in the rigours of the day-to-day, somewhat overwhelmed by the sheer speed of developments and other macroeconomic disruptions. In addition, as with the advent of any new technology, there is the inertia that could take hold when deciding whether to lead the race or be a fast follower. 

For all retailers, the focus must lie within areas that directly influence business performance, such as forecasting optimisation, pricing strategies, and supplier negotiations. This will lean activity towards machine learning and more “traditional” forms of AI, which can be just as effective as the new generative kid on the block. Of course, the introduction of natural language interfaces will further enhance the insight derived and positive impact in these areas.

There are business areas where change will be more intrinsically anchored to generative AI, coupled to high-quality, classical data engineering. It is quietly revolutionising customer services and redefining call centre dynamics through personalisation at scale and feedback loops for continuous improvement in both human and AI systems. 

Similarly, in areas saturated with content – such as improving searchability and creating hyper-personalised promotional offers. The “behind-the-scenes” applications can be equally groundbreaking, making short – and hugely scalable – work of tasks like producing high-quality product descriptions.

With AI at the helm, metadata becomes smarter, in turn able to inform negotiations with suppliers based on commodity costs, aided by sophisticated should-cost models. Product picking and distribution functions can benefit from smarter factories and logistics operations too, where AI-driven defect spotting and automated warehousing activities are both rapidly on the rise. 

These efficiency milestones are exciting, certainly, but so too is the impact on operational expenses. Labour, often the largest cost centre in stores, can now be more accurately matched to tasks and work dynamics through AI analytics, optimising workforce deployment without compromising service quality.

Business benefits, but at what cost to truly connect?

Despite these advances, retail’s perennial hurdle remains investment-related – a CFO’s conundrum; retailers, particularly grocers operating on such slim margins, grapple daily prioritising investment requests. As such, it is crucial for leaders to not only foster a culture of low-cost experimentation but also to concentrate on a few select initiatives that demonstrate robust potential for high operational impact and tangible returns.

Furthermore, AI’s chances to shine aren’t exclusive to sectors with lean margins desperate for more breathing room. The realm of luxury can also reap the benefits, for example, where experience and behavioural data can help predict emerging trends. Advanced social listening and proactive supply chain monitoring are potential strategic high points too, ensuring that these players avoid falling prey to the next potential ESG misstep.

But how do these innovations connect with the cumbersome legacy systems that form the backbone of so many retail enterprises? Refreshingly, much of AI’s current arsenal doesn’t require deep integration with existing infrastructure; many applications can operate effectively as an ancillary to the main tech framework. Yet the marriage between AI and quality data is essential for achieving the full spectrum of efficiency gains, from customer-facing operations to foundational data improvement – the latter being a critical strategic priority resurfaced through AI’s newly-found seat at the boardroom table.

Level heads will trump hyperbole

The advent of natural language interfaces and generative AI breaks down barriers, potentially guiding organisations towards becoming more data-driven and insightful. While keeping an eye on the competition is prudent, it’s equally critical to evaluate AI’s potential to influence the bottom line.

Consumers continue to demand seamless experiences but will likely accept automated processes as a dominant force behind the scenes, provided service levels remain uninterrupted, secure, and exceptional. Nevertheless, retailers must pre-empt potential issues, fostering policies that empower employees to manage automation effectively and experiment with AI technology more broadly, without putting customer satisfaction or their employer at risk.

In retail’s AI-enabled future, practical application should therefore reign supreme. Whether choosing to lead or follow, making informed decisions driven by clear-cut business benefits will trump unrealistic impact expectations, theoretical speculation, or simply becoming swept up in the hype cycle.


An abridged version of this article first appeared in Retail Week