The Challenge
Southwest Airlines' marketing team relied on largely manual go-to-market campaign processes, including repetitive copy-paste actions between tools and documents, inconsistent templatization, and limited visibility into where friction was slowing execution. The AI Programs team needed a structured method to identify which parts of the campaign process were genuine candidates for AI or automation — without defaulting to applying AI everywhere indiscriminately. The challenge was surfacing the right problems before jumping to solutions.
What They Built
Nicola's team conducted cross-functional discovery interviews and workshops across Southwest Airlines' marketing function, codifying a reusable five-signal framework that identifies where AI can add value — available data, repetitive tasks, templatization, reusable infrastructure, and high error rates.
Nicola's team began by establishing a disciplined separation between problem identification and solution design — a step that prevented the common failure mode of applying AI to the wrong processes. Using the Double Diamond Design Thinking Framework, the team conducted one-on-one interviews and cross-functional workshops with representatives from social media, paid media, brand, creative, strategy, analytics, digital, and technology teams across Southwest Airlines.
Interviews were designed to surface where work was slow, repetitive, error-prone, or dependent on manual copy-paste actions between systems. Workshop sessions validated and pressure-tested findings across functional boundaries. From these sessions, the team codified five signals that reliably indicate where a process is ripe for AI: availability of data or historic assets, repetitive task structures, existing templatization, reusable technical infrastructure, and zones of high human error. The resulting framework now serves as a reusable assessment tool for future AI opportunity identification across the Southwest enterprise, giving the AI Programs team a structured, repeatable method rather than a one-off analysis.
Enterprise organizations with large, distributed workforces that need a structured, research-grounded approach to AI adoption — particularly where leadership wants to move beyond "AI for everything" toward strategic prioritization. Well-suited to organizations in transportation, hospitality, retail, or any sector with complex internal marketing and operational workflows.
