As retailers capture increasingly large amounts of data, consumers expect to be provided with a personalised experience, and for their individual needs and values to be understood. Combine this with the much-hyped potential for AI technologies like ChatGPT and Midjourney to ingest large datasets and produce compelling outputs, and there is a clear opportunity for retailers to make use of AI.

In this blog, we’ll discuss some of the barriers preventing retailers from applying AI, as well as some of the approaches they can take to overcome them and use this transformative technology to deliver tangible customer benefit.

What's standing in the way of applying artificial intelligence in retail?

We expect retailers to know who we are, how we shop, and what our previous experiences have been with their brand, both in-store and online. But as retailers look under the hood of their organisation to access these insights, many are met with three key challenges:

• Legacy technology foundations limit progress: Many retailers have grown organically and bolted on new systems and technology over time. This has resulted in a complex web of features and functionalities that inhibit timely access to data, and present a risk to overall data security.
• Historic data challenges: Disparate data silos, incomplete information, and being unable to stitch the necessary information together leads to challenges creating personalised experiences, as well as an inability to analyse data internally for operational efficiencies.
• Legislation, regulatory restrictions, and ethical challenges: As a society, we are still grappling with the ethical considerations of AI. Combine this with the ‘death of the cookie’ and shifting consumer attitudes towards sharing personal information and we see a data minefield to navigate in the imminent future.

With all the buzz in this space, it’s easy to get carried away with blue-sky thinking. However, without robust technology and data foundations it’s difficult, if not impossible, to implement AI use cases that could take your organisation to the next level.

This is why legacy technology estates throttle innovation and development in this area, hampering retailers’ ability to swiftly adapt new tech like AI. As a result, brands should imminently consider modernised, component-based MACH architectures (microservices-first, API-based, cloud-native, headless) as a means to onboard and retire technology as required – this enables more streamlined processes that can have a positive effect on customer and employee experience. The next step would be to truly get a handle of your structured and unstructured data and use these insights to begin creating next-generation retail experiences while optimising your supply chains and driving innovation to continually differentiate. That’s no mean feat.

Once the foundations of technology and data are in place, you can begin to ask how AI can be implemented in your business as a force for change. Start by looking inwards and think about how an AI companion can be used to accelerate the outputs of your staff and colleagues. For example, AI tools are increasingly being used to generate marketing content and artwork, as well as being used by developers to create and verify code for new features.

Then you can look at how AI could help you deliver better service in customer-facing roles. A natural next step for customer-facing chatbots is AI integration to allow for more realistic, adaptive conversations to improve customer experience and resolve complaints.

Be aware, ethical use of AI is also a key consideration. When ChatGPT launched in November 2022, it became the fastest growing consumer app in internet history, and generative AI is now available to the public for broader use. We must ensure that any initial use cases we build are done so in a way that enhances the consumer or colleague experience. The rapid development of AI has meant businesses and corporations are leading the charge on its implementation, and especially with an industry such as retail, any use cases we build may have touchpoints with thousands of customers every single day. Make no mistake, AI will become a critical capability you must develop and own and will provide the foundations for successful digital innovation initiatives. However, for anything we build we must first ask ourselves about its impact on both people and the planet.

Once the most obvious internal and customer-facing implementations have been considered, you can look to more disruptive use cases.

“The new solutions we’ve developed with BJSS are powering better decision making and reducing operational risks. With a single view of the truth and a reliable, scalable platform, we have improved our service levels and can now focus on enhancing the customer experience. We now have the elasticity to support high traffic and to reduce compute capacity to lower levels during slow periods to manage our cost base effectively.”

Marcus Sims - Chief Technology Officer, Beauty Bay

Read the full case study >

The advice for retailers

Don’t feel comfortable because others are being complacent. A lack of action in fixing existing data challenges presents a significant risk of losing out to the competition on the point of experience. This is not a story unique to retail – look at how the likes of Monzo and Starling lit a fire under the legacy banks a few years ago. New, digitally-native brands will capitalise on your hesitation to modernise and provide customers with a next generation experience that becomes the best-in-class standard you’re unable to replicate.

Modernise your legacy IT estate and create an evolved data strategy. Create a short, medium, and long-term view of your data and personalisation strategy. Consider what information you need to capture, to what level of detail, and how you can store and access this for actionable insights. Initially, we’d suggest focusing on creating a clean and visible data pipeline to allow you to draw upon requested insights, on a customer-by-customer level if needed. This will increase the richness of your internal data and, via robust analysis, lead to multiple fresh, new recommendations.

Determine what facets of your data strategy are the most important to create the experience needed. Consider the following questions: What data do I need to capture? What insights and trends do I need to be aware of? What kind of experience am I trying to create? It comes back to understanding the signals customers give you, building the right models to action those signals, and tweaking algorithms for continuous improvement. Give yourself the foundations first and foremost - too frequently, retailers have tried to run before they walk in this area.

Start small and scale fast. A hypothesis-based approach is key. Brainstorm use cases which you think will provide the most value to your business. Discuss with cross-cutting functions to obtain a clear picture of the wider considerations, such as clear governance and security requirements. Once established, build out your use case to quickly demonstrate value back to the business. This will help cut through some of the ubiquitous hype around AI and prove to your colleagues the real benefits of this new technology - working alongside them to make their day-to-day lives easier.

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