With nearly 90 per cent of businesses already having developed an artificial intelligence (AI) strategy, it’s safe to say the technology is about to permeate every corner of the global retail industry. 

That figure was included in a report by Qualtrics, Unlock the potential of AI-enabled CX, a practical playbook supporting organisations like retailers that want to deploy AI in customer experience to create a competitive advantage. It also found that 72 per cent of executives polled expect AI to fundamentally transform their organisation’s approach to customer experience over the next three years. 

The report’s author, Isabelle Zdatny, head of thought leadership at Qualtrics XM Institute, spoke with Amie Larter of Inside Retail for an episode of the Retail Untangled podcast, where retail industry experts share business hacks that help them succeed.   

In customer experience, explains Zdatny, AI has the power to unlock hundreds of billions of dollars by transforming how retailers understand and serve their customers. 

The report projects that AI-enabled CX could generate an estimated $440 billion in annual EBITDA for consumer-facing businesses. Furthermore, consumer retail and retail banking each stand to realise $100 billion in EBITDA through measures like hyper-personalised marketing, predictive analytics, and streamlined operations. 

From a customer experience perspective, Zdatny is particularly excited about the potential to use AI in the post-purchase experience. For example, how predictive service models can help remove problems before customers report them. 

“We’re seeing a lot of those best-in-class retailers using AI to detect potential issues like delayed shipments or product defects and proactively communicate that with customers, maybe proactively send them a new offer or a discount code, to turn those potential negative experiences into opportunities to build loyalty.”

Increasingly, she says, retail brands are deploying AI to drive visual search results online, helping customers find products. 

“Retailers like Wayfair and H&M allow customers to upload images to find similar items, removing some of the friction from the shopping process. We’re also seeing increasingly sophisticated recommendation engines that look at not just purchase history or demographic information, but more of that psychographic information, historical browsing behaviour, and customers’ communication preferences, which is allowing for even more personalised recommendations. 

“We’re also again seeing hyper-personalised marketing campaigns for consideration. Dynamic pricing and promotion optimisation is a big one, but for retail, especially ways to create more tailored offerings and recommendations, will help us maximise conversion while protecting our margins.”

The three pillars of AI

Zdatny breaks AI into three distinct pillars: Analytical AI, Generative AI, and the next generation in the making, Agentic AI. 

Analytical AI that allows you to process massive volumes of data to predict future events and uncover hidden patterns. So things like predictive analytics, natural language processing, and sentiment analysis help derive better data and insights. Those AI tools have been around for about 15 years, yet organisations are still not great at deploying them, she says.

Generative AI helps create new content and power natural language conversations with customers. A lot of the $860 billion opportunity across 19 industries comes from Generative AI. 

Agentic AI emphasises autonomous systems that can make decisions and perform tasks without direct human intervention. It can independently orchestrate multiple capabilities across complex workflows, says Zdatny. “Rather than just assisting humans, these systems are going to be able to drive complete end-to-end processes all by themselves.”

The transformation to Agentic AI won’t happen overnight, she says. Humans will need to stay in the loop before the AI can complete end-to-end workflows and processes. But in the future, the technology will evolve to be able to flexibly adapt to changing conditions and make smart decisions along the way. They won’t just follow a rule-based system; they’ll be more flexible. 

“It will be like having a fleet of automated project managers that can coordinate tools and people and other systems, including other AI systems, to accomplish a specific goal. As these systems become more mainstream and mature, this won’t just be an incremental improvement for businesses – this is going to fundamentally transform how organisations understand and serve their customers. It’s going to drive hyper-personalisation, seamless end-to-end experiences, radical operational efficiency, and lots and lots of other things.”

Zdatny says that the concept of Agentic AI might sound scary, but much of that fear is based on confusion. There are already many obvious applications for Agentic AI in a company’s systems: “All of us spend a lot of time on administrative tasks and going through processes and workflows that we don’t want to be doing with our time. We are happy to offload that.”

Most retailers already have an AI strategy

While Qualtrics’ report found that nearly 90 per cent of organisations already have some AI initiatives underway, these activities tend to remain limited and often uncoordinated. 

Only 12 per cent of executives in the study said they had an organisation-wide AI strategy in place with coordinated ownership. Those 12 per cent were 2.3 times more likely to report market share gains compared to the rest of the group that was not taking a systematic approach to AI. 

A roadblock in AI’s more widespread adoption is a nervousness about AI going wrong.  

“Many executives understand the transformative potential of AI – almost 70 per cent think AI will completely transform their industry within the next three years – but only 15 per cent of them aspire to be at the forefront of this AI-driven business transformation,” explains Zdatny. 

“There is this wait-and-see approach because there are no existing playbooks, and it’s scary to be the organisation that’s defining those playbooks. One bad instance of an AI bot going rogue can do lasting damage to your brand. So there is this tension between moving decisively to capitalise on this compounding value while also being responsible and ensuring that you have the proper guardrails in place so that you are not going to harm your business.”

Zdatny says when planning for AI, businesses must start with a holistic strategy based on the business outcomes and brand objectives they are trying to achieve, before looking at how different AI solutions can help meet those goals. 

Early on in their AI journey, organisations focus on how AI can drive productivity gains and process improvements, accelerating existing ways of working and automating tasks, and potentially help employees make better decisions in their roles.

“Over the longer term, where we are going to see the most value is under revenue growth. How can they use AI not just to accelerate existing ways of working, but to unlock completely new ways of understanding and delivering value to customers? That is ultimately going to be the competitive differentiator.”

• Listen to the podcast to hear Zdatny outline the three key pillars of exceptional customer experiences, explain what she terms “pilot purgatory”, where companies falter in implementing AI solutions due to siloed data, and how companies can prepare their people to build an AI-ready workforce and be comfortable with it.