We hear two extremes and everything in between from retailers about their use of AI, ranging from, “It’s just another tool in the toolkit” that they’ve been integrating for years, or they fear they are years away from starting their AI journey.
The retail and consumer sectors have an inherent advantage – they possess troves of data that is the backbone for AI. Whether you’re an early adopter or a laggard in your space, we’ll inspire you to think clearly and pragmatically about planning and accelerating the right approach for your business.
AI defined.
Let’s start with a quick primer. AI is a broad category with many subsets, including machine learning, deep learning, natural language processing, and generative AI. The latter supports the buzzy tools many people are focused on now, such as ChatGPT, CoPilot, Gemini, and Claude, to name a few. While generative AI is often the first thing that comes to mind, it’s essential to understand which flavors of AI will help you achieve which results, and to have a strong business case focused on benefits and ROI when you start or evolve the journey.
- Traditional AI – Leverages a company’s proprietary data to provide answers to very specific questions. It’s great for various use cases, such as automating repetitive tasks, making recommendations, forecasting, or monitoring and healing routine system issues.
- Generative AI – Creates new content, such as text, images, music, or other data, by learning from existing datasets. It can also answer general questions that are not dependent upon a company’s data and don’t require a great deal of precision.
- Proprietary generative AI – Brings a company’s proprietary data and the world’s data together to address questions with greater precision and, therefore, value. This area is the most nascent and has the most opportunity to meaningfully redefine what’s possible.
Please see our “Demystifying AI to Deliver Impact” blog for more details on the AI landscape.
High-impact AI use cases implemented by retail and consumer companies.
As a backdrop, we used AI to explore news articles, press releases, and other media to learn how 100 retail and consumer companies use AI today. We discovered 32 types of use cases spanning the enterprise. These targeted a range of themes around personalization, optimization, and automation. In many instances, the use cases are the next evolution of capabilities that have been around for a while, allowing retail and consumer companies to improve their accuracy, timeliness, scale, or efficiency. However, we also discovered some examples that chart new avenues of innovation and demonstrate the emerging power and potential of AI.
Of the use cases we cataloged, the top three most common are product recommendations (12%), product design and development (9%), and chatbots to handle customer inquiries (8%). Let’s explore these more.
- Product recommendations: The evolution of AI capabilities enables companies to personalize product recommendations based on a broader dataset. An innovative retailer we looked at uses image recognition to inform its recommendations.
- Product design & development: Companies have long used customer sentiment and trend data to inform product design and development. However, leaders are mining broader data sets to improve the timeliness and accuracy of customer sentiments and using generative AI to create preliminary designs and shape R&D efforts.
- Customer inquiries: Companies have been using chatbots to handle common customer inquiries for a while. More recently, leading QSRs are leveraging chatbots with enhanced natural language processing to take customer orders, while others are creating advanced chatbots that can engage customers more deeply on specialized topics.
Some of the other exciting use cases we cataloged include:
- Automating the creation of web content, such as product listing pages, product descriptions, and attribution.
- Automating editing of product images, such as changing backgrounds or showing models of different sizes.
- Increasing conversions and reducing returns by enabling visualization of products through virtual try-ons or viewing products in a specific space.
- Optimizing search experiences, such as showing items similar to an image and returning results for vague or conceptual searches.
- Automating marketing capabilities to enable hyper-personalization at scale, such as product SEO, virtual influencers, customized messaging, and computer-generated marketing journeys.
- Optimizing store operations such as plan-o-gram compliance, on-the-shelf inventory availability, and store labor schedules.
- Creating supply chain efficiencies and improving accuracy through automated communications, truck packing optimization, localized demand planning, and inventory management.
- Reducing shrinkage by predicting porch piracy and using computer vision to check the contents of carts.
The expansive datasets available for retail and consumer companies create a fantastic playground for AI. Companies can uncover patterns and trends that inform decision-making and shape strategic planning, which can be leveraged to deliver value by automating functions, optimizing performance, and personalizing customer engagement at scale.
Whether you’ve not yet started your AI adoption journey or have already completed a few laps, check out our point-of-view, “A 3-step approach to drive AI value creation”, to learn more about how to sequence and right-size your efforts and avoid common pitfalls. Spoiler alert: The most crucial part of the answer is data first, AI second—a strong data foundation is the fundamental enabler.
For more insights on how to leverage data, analytics, and AI in your business, contact an expert at Cuesta Partners today.