Use cases and solutions typically fall into one of the following three approaches: leveraging AI that is natively available in SaaS tech platforms; partnering with academic institutions to co-develop first-of-its-kind solutions; or, leveraging traditional AI, like machine learning, to automate operations.
Across the diversity of solutions, there is one thing that is universally true – all require a strong data foundation to get started, hence the theme of our most recent Cuesta Summit, Data First, AI Second.
Our team recently hosted several retail and consumer company c-suite executives, senior leaders, and board members for an exclusive discussion based on the prompt, “what most companies in the retail and consumer industries get wrong about artificial intelligence.”
Beyond the buzz, companies of all sizes are realizing value from AI, yet they are facing both common and unique challenges. We believe one of the best ways to chart a course in the complex AI landscape and create outsized value is to share our experiences through peer-to-peer networking– failures and successes alike. We asked each of the participants to bring a use case to share and the conversation was so insightful we decided to pass along some of the key learnings in this recap.
Technology approaches to benefit from AI are varied.
The companies represented at the Data First, AI Second Summit primarily used three complementary strategies to enable AI technology:
- Partner Strategy: As part of their software selection process, one company prioritizes SaaS solutions with native AI capabilities. They give a strong preference to solutions that effectively use AI to provide them with differentiated benefits. By taking a pragmatic buy-vs-build approach, they leverage AI across most of their business without incurring the significant operational costs of in-house development
- Co-Development Partnerships: Two participants shared about partnerships they have struck to co-develop custom AI solutions. One is working with a leading academic to co-develop a commercial solution, allowing them to direct the feature-development of the solution. The other is working with an offshore firm who, in exchange for the data and product inputs, is doing the work pro bono to build out their capabilities. In both examples, these companies are realizing the benefits of Gen AI with minimal capital investment.
- In-House Custom Solutions: One company integrated a handful of commercially available AI tools into their technology stack to automate processing and expand their ability to use their data. This company is building their AI talent while progressively realizing benefits from their extensive data assets. Another participant, a world-wide leader, ranked among the top 20 S&P 500 companies on AI readiness according to Cuesta’s proprietary Index42 research, has built and commercialized their own proprietary Gen AI model, revolutionizing their business and their customer experience.
Although the AI landscape can be intimidating, the varied approaches these companies are using to bring AI into their organizations illustrates that cutting-edge solutions are accessible to companies of all sizes and technical maturities. Only one of the companies present built its own proprietary Gen AI model; the rest took pragmatic, cost-effective, measured steps to advance the goals of their respective companies with AI as a critical enabler.
Winning use cases span the value chain, with the customer as the focus.
Many of the use cases participants shared were aimed at solving decades-old problems that retail leaders have been progressively improving, though not completely perfecting, such as customer engagement, in-store operations, and predictive analytics. Some of the most compelling examples shared include:
- Customer Service Transformation: The customer is at the heart of any successful retail operation and often differentiates one retailer from another. However, consumers’ needs tend to follow predictable patterns which makes transforming service a compelling set of AI use cases. For example, one participant deployed advanced chatbots that help customers with answers to routine problems such as “where’s my order?” in a fully automated and conversational way. Another company prioritized their AI development on uses cases that empower store employees, emphasizing optimal task management, suggesting the highest priority work to be done such as managing inventory, store merchandising, and customer engagement.
- Automated Personalized Experiences: Personalized search results and product recommendations have been on the scene for ages, yet the fact remains that algorithms are far from perfect. Well, some of our attendees are actively working to close those gaps. One participant is applying AI to identify like matches or patterns across disparate datasets to scale their ability to personalize offer delivery at a very granular level (i.e., offer generation tailored to specific stores and customers). Another participant is automatically generating product detail pages with individually tailored product copy to reflect each consumer’s desires, derived from their social media activity and search terms. Imagine, without manual site merchandising, being able to suggest an outfit for a local event, with content, such as made with sustainable materials, tailored to an individual’s preferences!
- Precision Cost Savings: Efficient operations are always important, but evermore so in years where retail sales are soft for so many companies, like they are in 2024. For some of our participants, AI proved to be a boon to deliver in-year cost savings. For example, one company is leveraging gen AI to automatically edit product and marketing images, reducing their photo shoot costs. Another is applying AI to combine their fraud-related data with a network of other retailers to dramatically reduce fraudulent purchases and build cases against organized crime. Another evolved a shrink report – that required human analysis and intervention – to an AI system that identifies the causes of shrink. As a result, for example, they saved millions in losses when the solution uncovered that an item had high theft rates at a particular warehouse because it was located near an exit.
At the beginning of the conversation the attendees were segmented into two camps – AI evangelists and AI skeptics. By the end of the evening, everyone was inspired and jointly brainstorming how to solve a variety of future problems. One of the more intriguing future opportunities discussed was how to dramatically increase throughput for live entertainment venues (think football games or live performances at large venues) – from robotic drink mixing to taking a page out of the autonomous store’s play book.
Data transformation is the key to sustained success ahead.
Participants joined Cuesta Partners for our Data First, AI Second Summit for assorted reasons. All left inspired by the conversation, smarter from learning about how their peers are tackling the opportunities that the advancements of artificial intelligence have to offer.
While the technical approaches used varied along with the relative maturity of each organization, two things were universally true: First, enterprise data transformation is the single greatest lever to unlocking the full potential of AI. Second, the journey has just begun.
To learn more about how to leverage your organization’s data and apply it to your AI opportunity and vision, please contact one of our experts today.
