How do I use AI across a large brand?

Enterprise-level AI can describe AI used across a large organisation to optimise operations and reshape how functions work over time. This goes beyond deploying a single tool: it can involve reconfiguring multiple business functions within an AI framework, often through substantial institutional programmes with long delivery timelines. A separate, more focused approach is department-level AI, where teams use emerging digital tools to reduce costs, improve accuracy or increase efficiency against quarterly objectives. For brands and marketers, both approaches need more than access to technology. AI output requires collaboration with people who bring professional expertise, strategic judgement and an understanding of customer needs. The aim is not simply to generate more content or automate more activity, but to create experiences that align with brand values and serve audiences well.

Enterprise-level AI can be brought to life through a real customer experience, particularly where a brand has a distinctive capability to demonstrate. IBM's partnership with Wimbledon illustrates this approach. In 2017, a campaign built around Wimbledon's "In pursuit of greatness" brand promise used IBM Watson to lead a debate around "What makes great?". It combined data captured through the IBM and Wimbledon partnership with insights from live social listening. The campaign showed AI as an active part of an audience-facing experience, rather than an unseen technical claim. IBM's broader role at Wimbledon has included innovations that enhance the fan experience, provide broadcasters with live data and offer deeper insights to the public and players. This gave the campaign a concrete connection to the role IBM played in the event.

AI should support a brand's strategy rather than determine it alone. Marketers still need to create experiences that reflect their values and deepen their understanding of what customers want and need. Expert involvement matters because access to generative AI tools does not by itself produce quality work, just as access to creative software does not make someone a designer. Large transformation programmes may be expensive and extend beyond a year, while team and department projects are more likely to be shaped by quarterly KPIs. In either case, a practical starting point is to identify one or two things the organisation can do better than anyone else. A new AI-enabled experience can then be built around those distinctive strengths, making the technology relevant to the brand and meaningful to its audience.