Capturing and keeping the gaze of a distracted digital shopper is a marketer’s holy grail. But in the face of short-form content, endless scrolling, and social-native behaviours, traditional eCommerce experiences are struggling to keep up.
Enter AI, a game-changer not just in automation, but in how we reimagine online shopping itself.
The structured, linear shopping journey is being replaced by an AI-powered experience that adapts in real-time, curating a continuous, personalised stream of products, much like a social media ‘For You’ page, but within a brand’s digital storefront.
And the impact? It’s undeniable.
✅ Brands that get personalisation right generate 40% more revenue than their competitors (McKinsey)
✅ 71% of consumers expect brands to deliver personalised experiences
✅ 41% of shoppers would use an AI assistant to help them discover products (StoryStream research)
But… it’s easy to get lost in the buzzwords. Whether you’re in strategy, product, creative, or customer success, understanding the basics helps everyone stay aligned and spot the real opportunities. Consider this your cheat sheet.
THE GLOSSARY
Agentic
AI systems that act independently, make decisions, and pursue goals, with some level of autonomy, like AI agents that schedule meetings, troubleshoot, automate campaign tools that adjust product feeds, or even write and run code on their own
Bots
Software that handles repetitive tasks, responding to FAQs, scraping the web, curating products, or updating catalogue content, so your team can focus on creative strategy. Chatbots are conversational AI tools that interact with customers, offering everything from style advice to post-purchase support, helping brands stay present and helpful 24/7.
Content Enrichment
Adding product tags, AI recommendations, and contextual metadata to make every image or video SEO optimised.
Deep Learning
A type of artificial intelligence that teaches computers to learn from lots of data using layers of algorithms called neural networks. It helps power things like voice assistants, image recognition, and product recommendations.
Discovery Loop
That addictive scroll pioneered by TikTok and Instagram: personalised, visual, and never-ending.
Encoding
The process of converting information like text, images, or video into numerical formats that AI can understand and work with. It’s how AI makes sense of data to recognise patterns, make predictions, and deliver personalised experiences.
Foundation Models
Large AI models trained on vast amounts of data (like GPT or DALL·E). Once trained, they can be fine-tuned for all sorts of tasks: writing, translating, coding, creating images, or even helping with medical research.
Gen-AI
AI that can create new content: text, images, video, music, code. It’s what’s powering tools like ChatGPT, Midjourney, and Runway. A huge shift from AI just recognising patterns to actually generating them.
Hybrid
Combining AI approaches, e.g., mixing symbolic logic with machine learning, to create tools that can both interpret user intent and generate content in real time.
Intent Detection
Understanding what a shopper really wants. Whether they’re searching “everyday moisturiser” or “gift for a runner,” AI helps surface the most relevant products, can be used in chatbots, search, and voice assistants.
Bonus Letters (Because we’re human and we can break the rules):
LLMs (Large Language Models): AI models trained on massive text datasets to understand and generate human-like language (e.g. GPT-4).
ML (Machine Learning): The process of feeding data into an AI model so it can learn patterns and make predictions.
Personalisation: The use of AI to tailor content, products, or experiences to individual users based on their behaviour, preferences, or data. From “recommended for you” product carousels to personalised emails and homepages, it’s how brands create 1:1 experiences at scale.
Prompt Engineering: The art (and science) of asking better questions to get better AI outputs (a hot skill right now).
Query Understanding: Interpreting shopper searches with context, e.g. knowing that “spring jacket” in March means fashion, not gardening.
Scraping: The automated process of extracting data from websites or platforms, often used to gather training data for AI models (e.g. pulling thousands of product descriptions or reviews).
Workflow Automation: AI-driven tools that eliminate repetitive tasks, from email responses to ad copy generation.
AI now sits at the heart of a new kind of eCommerce: from sourcing user-generated content at scale to enriching every click and swipe, AI is transforming how people discover, interact with, and ultimately buy products online.
👀 Prefer to see real people break this down? Our in-house experts will be diving into some of these terms in real time on our socials, with practical tips and honest takes. Follow us on LinkedIn so you don’t miss it.
