Give AI context to improve competitiveness

During the UBA 60 minutes session presented by Michaël Verbinnen, Data & Tech Lead at iO, and Tom Van Mierlo, Strategy Director at iO, participants explored how context-driven AI is becoming a key lever for brands. The session focused on three major shifts: search, digital experiences and marketing operations.

AI is becoming the new gatekeeper

The way people find information is shifting fast. For years, digital discovery followed a familiar pattern: search, click, read, compare and decide. Today, users increasingly ask AI platforms a question and receive a synthesised answer. Tomorrow, they may simply give an AI agent a goal and let it compare, plan and act on their behalf.

This changes the role of brands in the customer journey. Visibility is no longer only about ranking on page one. Brands must also be readable, understandable and useful to AI systems. If an AI platform cannot interpret your content, data or services, your brand risks becoming invisible in moments where customers are making decisions.

Context is more than content

Context-driven AI means giving AI systems the right information, in the right structure, at the right moment. This includes product data, pricing, FAQs, policies, user needs, brand rules, tone of voice, customer journey stages and real-time information from business systems.

Content alone is not enough. A page may be well written for a human reader, but difficult for an AI system to extract, compare or cite. Brands therefore need to structure their data more clearly, use formats such as schema markup, create answer-first content and build topical authority around the subjects they want to be known for.

From browsing to intent-based experiences

Context-driven AI also changes what happens on a brand’s own platforms. Users no longer want to navigate complex menu structures or browse like they are reading an encyclopaedia. They expect faster answers and more relevant experiences.

Instead of forcing visitors through fixed navigation, brands can capture user intent and use their own validated data to create adaptive experiences. This goes beyond adding a chatbot. The real opportunity is to use AI inside the customer journey, helping people reach the right answer or action faster.

AI as an orchestrator for marketing

In marketing operations, context is what turns AI from a writing shortcut into a business tool. A large language model on its own does not know a brand’s priorities, customer segments, legal constraints, tone of voice or campaign performance. It needs context from the organisation.

When connected to the right systems, AI can help orchestrate tasks across the marketing stack. It can query analytics, support content production, personalise messages, connect CRM data with campaign workflows and help teams act faster.

The Gazelle case showed how this can work in practice: instead of manually creating many content variants for different personas, segments and lifecycle stages, an AI-driven content platform can generate variations based on centrally defined brand rules, tone of voice and prompt templates. Human review remains essential, but the workload becomes more manageable.

Where brands should start

The first step is to audit the data that customers and AI systems need: product information, pricing, FAQs, policies, service details and content assets. Brands should ask what AI can currently read, understand and reuse.

Next, they should improve structure through schema markup, clean APIs and modular content. Search Console can also reveal which content is crawled but not indexed, which may point to a content quality issue rather than only a technical one.

Finally, brands can explore how AI can enrich their own platforms and internal operations. A sensible approach is to start with read-only use cases, such as analytics or search, before moving towards automation. The goal is not to add AI everywhere, but to make trusted brand context available where it creates value.