Customer journey optimisation using large language models: Best practices and pitfalls in generative AI
Vaikunth Thukral, Lawrence Latvala, Mark Swenson, J. Ethan Horn · Applied marketing analytics · 2023
Today's business environment is moving faster than ever, and the expressive and adaptive capabilities of generative AI (GenAI) and large language models (LLMs) are redefining the enterprise rails of tomorrow. Given the abundance of industry hype, investor expectations and leadership pressure, the initial impulse is to ‘get in the game’. But how does one implement initiatives that drive business outcomes within ethical parameters while avoiding technical pitfalls? Marketers need practical guidance to navigate through these changes. In this paper, the authors examine multiple considerations for deployment of GenAI in marketing and customer experience. How does the marketer decide on which initiatives and opportunities to begin with? Which use cases will drive value as the organisation adapts to deploying these new capabilities? Once a marketer has identified the opportunities to capitalise on through GenAI, how is the capability deployed? There are a variety of approaches that can be considered given the level of organisational capability with AI and resource levels to be applied. As with any cutting-edge capability, there are potential missteps that must be avoided to ensure success. This paper provides some insight based on practical experiences to date that cover ethical, technical and process concerns. The paper presents thoughtful approaches to the deployment of LLMs and GenAI that can result in concrete ROI and reduced risk even in this early stage of adoption. With this information, marketers can be prepared to confidently begin their journey using GenAI to transform their customer experience and drive enterprise value for their organisations.