One in five retailers will deploy customer-facing generative AI applications by 2025, according to Forrester research. Alarmingly, the success rate of these projects is also only 20%. After interviewing AI/ML engineers, the RAND researchers found misaligned data, infrastructure, and objectives to be the main causes of failure.
Rather than taking a tech-first approach, retailers must first consider their business goals, then look at structural areas for improvement, and internal GenAI expertise. There is little room for error when it comes to customer-facing tools — one data breach or poorly handled experience could send customers away for good. In 2023, over half of the customers believed GenAI was a problem for customer service. Difficulty reaching an agent, receiving the wrong answers, and not being treated equally were top concerns.
Retailers and their IT practitioners must think carefully about their customer strategy, looking first at non-human interactions where GenAI can seamlessly integrate into the user experience (UX) and incrementally build their talent expertise for the best results. Security and empathy will be pivotal priorities for retailers to build consumer confidence in 2025.
Here are three things retailers must consider as they implement generative AI:
1. Align data understanding with AI functionality
A 2024 PMI Generative AI in Project Management Survey identified that the crucial skills for GenAI usage include the ability to work with data (45%), define task requirements (42%), prompt-writing skills (34%), validate GenAI outputs (30%), programming and logic skills (28%), and understanding LLM and NLP (22%).
Retailers and their employees should familiarize themselves with systematic services like GenAI-powered product recommendations, before trying their hands at more complex tasks. For example, GenAI-powered personalized product recommendations can use a simple, rule-based method such as, “Customers who bought X also bought Y.” The data input is more straightforward, too, using customer purchase history and basic demographics.
However, algorithms for targeted advertising campaigns, generating ad copy tailored to specific customer segments, and identifying optimal ad placement and timing are much more complex. These tools need up-to-date purchase history, browsing behavior, social media engagement, demographics, and location.
AI chat outputs are a complex function — don’t be fooled into thinking you can simply build a wrapper to a widely known large language model (LLM) like OpenAI’s ChatGPT, or Google’s Gemini. If you do not have a team of technical experts, consider working with leading no-code tools or hire a long-term AI partner who can multi-turn conversations across leading AI models.
2. Ensure airtight security
The most urgent security risks for GenAI users are all data related. The widespread adoption of GenAI has led to a 46% increase in data policy violations, primarily due to the sharing of sensitive source code.
Using public AI tools like ChatGPT or GitHub Copilot with sensitive code can inadvertently expose information. Moreover, the more disconnected systems are, the more entry points for security vulnerabilities. Threat actors can use GenAI to analyze existing malware, identify patterns, and then generate new, more sophisticated threats. They could rapidly generate new strains of malware that are harder to detect, or large volumes of targeted phishing emails, widening the attack surface.
Retailers and IT leaders should aim for a solid data foundation, streamlined workflows, and a well-connected network of applications. Developers must also ensure that access controls are carefully configured to reduce these risks. By using private, secure repositories, and conducting regular security audits to identify and address vulnerabilities, IT leaders can ensure a safer GenAI landscape for retailers.
As a consumer of an enterprise application, you rely on the provider to implement effective security controls. To assess their security posture, investigate their control implementations, review design documents, and request independent third-party audit reports.
3. Make room for empathy where it is needed
After surveying 10,000 US customers across 282 brands on the six pillars of experience, empathy fell the most in 2023. Customers felt technology had become a substitute for human connection and care.
Rather than solely relying on technology going into 2025, companies should curate a blend of human and technological interactions. They can start with simple AI functions such as product recommendations and FAQ chatbots, and direct customers to an agent for more complex tasks. By setting aside a team to closely monitor the queries that do reach agents, retailers can begin to create chatbot decision trees to answer these needs automatically.
When a customer does get through to an agent, the representative must be ready. They must stay updated on product features, benefits, and promotions to provide accurate information and assist customers in making informed decisions. They should also engage customers in loyalty programs, track their points, and offer exclusive rewards based on the problem or need at hand. GenAI-powered alerts can help keep agents up to date with the latest company and product changes. These tools can improve and facilitate live agents’ work, increasing productivity in a hybrid approach, and ultimately enhancing the service offered.
GenAI-powered experiences are reaching customers across all industries, and retailers are no different. However, customers still desire that human touch. When retailers can seamlessly integrate automated services into their UX, customers can appreciate faster access to their suited products. But retailers must ensure they don’t bite off more than they can chew and start with limited problem-solving before integrating more advanced technology into their workflows.
