Smart Retail: AI cheat sheet for retail execs

Hyper-personalised experiences. Intelligent, highly-localised ranging decisions. Customer-driven pricing strategies, and more. Today, artificial intelligence (AI) isn’t just a pipedream or some distant possibility; it’s a practical, tangible reality, one that’s beginning to redefine the way that retailers operate.

As AI’s transformational impact spreads, it’s becoming increasingly vital for senior retail execs to get a handle on it—not just the overarching AI trend, but its specific vocabulary too. In the past 12 months, AI’s explosive growth has brought terms like "large language model" into mainstream conversation. As a result, senior leaders are finding themselves expected to know the difference between Agentic AI, Generative AI, and everything in between.

With that in mind, we thought it would be useful to explore today’s most commonly used AI terms, and some of the ways (both actual and potential) they relate to the business of retail. Asking yourself how Autonomous AI differs from Adaptive AI? Want to make the most of Multi-modal AI? Take a look through our AI cheat sheet below.

1. Generative AI

Of all the terms we’ll touch on this list, Generative AI (GenAI) is probably the most recognisable. Generative AI refers to any kind of artificial intelligence that has the ability to create original material, be that text, graphics, sound, or video. It does this by learning statistical patterns from datasets—books or code, for instance. Well-known GenAI tools include ChatGPT and DALL-E.

Key features

• GenAI can creatively produce new content.
• It does this by recognising patterns within large datasets and reshaping them into new content.
• And it personalises output to a specific context.

How could GenAI be used in retail?

We’re seeing it already (albeit with mixed results). GenAI is being used to create everything from personalised product descriptions and promotional campaigns, through to product imagery and models that are almost indistinguishable from the real thing.

Why does it matter?

GenAI can power deeply personalised customer experiences, aid in the marketing process, and reduce the time and expense associated with content creation dramatically.

2. Autonomous AI

Autonomous AI refers to any system that has the capability to carry out complex, multi-step tasks independent of (ongoing) human intervention. Self-driving vehicles provide a good example of Autonomous AI in action. Here, the AI uses a combination of sensors, real-time data processing, and Machine Learning techniques to help it navigate roads and control the vehicle.

Key features

• Autonomous AI is task-specific, focusing on defined steps and objectives.
• It operates independently, based on pre-programmed or learned rules.
• It is typically used in structured or predictable environments. These give the AI a clear framework in which to operate.

How could Autonomous AI be used in retail?

Again, it already is. Autonomous warehouse robots tap into computer vision and pathfinding algorithms that help them to store, pick, pack, and sort inventory independent of human operators.

Why does it matter?

Autonomous AI can have a dramatic impact on efficiency and scalability in operations. Automating the management of complicated logistical operations also frees people up to focus on strategic decisions and higher value tasks.

3. Adaptive AI

An Adaptive AI system is exactly what it sounds like. As new information arrives, environmental conditions change, or users interact with it, an Adaptive AI will dynamically change and refine its behaviour. By way of an example, think about a floor cleaning robot: not only does it learn which "zones" are most likely to need cleaning based on past experiences, it can also react to obstacles like people or pets.

Key features

• Adaptive AI is continually learning and reacting to real-time changes.
• It uses feedback loops to improve its decision making.
• It responds dynamically to new data, rather than being programmed to follow rigid rules.

How could Adaptive AI be used in retail?

Using Adaptive AI, a demand forecasting engine could make real-time projections based on data like sales trends, seasonal variations, and evolving consumer behaviour.

Why does it matter?

Adaptive AI gives retailers the ability to remain agile in the face of changing circumstances, optimising their strategy as market conditions and customer needs shift.

4. Agentic AI

Alongside GenAI, Agentic has gained major traction recently. Like Autonomous AI, it operates independently—but Agentic AI focuses more on strategic, adaptive decision-making. It can work alongside humans and other systems, adjusting its approach as conditions change.

While definitions are still evolving, Agentic AI typically combines autonomous action with initiative, planning and the ability to break problems into smaller, solvable parts. The recent announcement of Manus—a new Agentic AI tool—has created a fair amount of buzz, and technologists are reviewing its promise as we write.

Key features

• Agentic AI takes the initiative rather than waiting to be told what to do next.
• It develops overarching strategies to achieve specific goals.
• Agentic AI can break a problem down into individual tasks and execute each independently.

How could Agentic AI be used in retail?

An Agentic AI-powered merchandising platform could automatically decide which products to promote, dynamically distribute marketing spend, and then negotiate terms with suppliers using live market data.

Why does it matter?

Agentic AI doesn’t just react to data—it acts on opportunity. For retailers, that could help them to optimise outcomes and make smarter business decisions.

5. Multi-modal AI

Multi-modal AI brings together various forms of data—text, images, speech, and video—and then interprets those to sources to create richer, more context-aware outputs. Multi-modal AI can handle data from a variety of sources and create content in a format that differs from the original input. In healthcare, for instance, a Multi-modal AI could scan medical images and lab reports before generating a patient summary for a doctor to review.

Key features

• Multi-modal AI supports multiple data types for analysis.
• It provides context-sensitive, end-to-end insights on what it has learned.
• It can enhance customer interactions by integrating different data sources.

How could Multi-modal AI be used in retail?

A good application for Multi-modal AI would be in the creation of advanced recommendation engines. Here, everything from visual preferences, voice commands, and a customer’s purchase history could be used to provide highly personalised product recommendations.

Why does it matter?

Multi-modal capabilities can improve customer experience, allowing for more intuitive interactions and deeper insights into behaviours and needs.

6. Synthetic Data

This isn’t a form of AI in and of itself- Synthetic Data is data that’s artificially generated rather than collected from real-world events. It’s designed to look and behave like real data—without using actual customer information. This is especially useful when real data is limited, sensitive, or expensive to obtain and is becoming increasingly important because it can be used to train AI models.

Key features

• Synthetic data is artificial but realistic.
• It can preserve confidentiality by negating the need to use real customer data.
• It enables mass testing and model training.

How could Synthetic Data be used in retail?

Simulated shopping behaviours could be used to forecast demand, optimise stock levels, or generate new store layouts—all without real data needing to be accrued or processed.

Why does it matter?

Synthetic Data enables retailers to speed up their adoption of AI by overcoming data scarcity and privacy concerns. In turn, that allows them to innovate quickly and responsibly.

7. Explainable AI (XAI)

Explainable AI refers to models where the decision-making process is transparent and understandable by humans. Explainable AI seeks to tackle the "black box" problem that AI sometimes runs into—one in which decisions are made with no way of knowing how they were reached.

Key features

• Explainable AI prioritises transparent and understandable decision-making.
• It helps to achieve compliance with various regulatory standards.
• And it builds trust by providing clarity over the decisions made by AI.

How could Explainable AI be used in retail?

Explainable AI should be used in retail. If retailers use a pricing and recommendation engine, for instance, said engine should be able to clearly explain how and why certain choices have been made.

Why does it matter?

At its core, explainability fosters transparency and accountability. As well as addressing regulatory concerns, that can also help to assuage and consumer worries over fairness, privacy, and data ethics. With various stipulations like those coming into force under the EU AI Act, it also allows retailers to justify their decisions and show that they’re committed to the responsible use of AI.