In the 2023 Gartner Marketing Technology Survey, 77 percent of MarTech leaders said that they are already deploying GenAI in some fashion (14 percent) or plan to in the next 24 months (63 percent). The ones that have already deployed GenAI report having seen increases in productivity across proven use cases including content creation and next-best action optimization.
With such a wide range of GenAI use cases and many ways to measure its success, it may be worthwhile to take a step back from the front lines and look at AI from the perspective of both marketers and IT in terms of what they are hoping to gain from GenAI in the first place. When the time comes to deploy GenAI with specific use cases and objectives in place, having a base understanding of what it can do for the enterprise as a whole – for marketers and IT – might help clarify how to maximize its value.
Why is it important to better articulate the purpose of bringing GenAI to the organization? Because, according to Gartner, “the field is cluttered with consultants and vendors positioning GenAI as the thing that will solve the challenges of marketing. It will not.” GenAI is not a magic bullet; its effective use depends on having a clear view into its various applications.
We will attack this from the marketer’s perspective first, and turn our attention to the IT point of view in a follow-up blog post.
Where Will GenAI Make its Mark?
Ask an operational marketer about what they hope to get out of AI, or GenAI, and the answer will likely relate to one of two themes. Either anything that helps make their day-to-day job easier or helps deliver a better customer experience.
The question helps divide out GenAI’s use from an internal perspective (making the marketer’s job easier) and an external perspective (a better CX). Externally – customer-facing – common GenAI applications include better product recommendations, deeper personalization, improved journey design and doing a better job answering questions such as a chatbot or call center. The goal in each of these use cases is to improve customer experience to meet marketer goals like retention, brand awareness, basket size, etc. This is how marketers think about AI in terms of CX – how can GenAI induce change to a customer interaction that somehow improves things for the customer and the brand?
Inward-looking, the focus is more on how GenAI will help simplify daily tasks. The end result – a better CX – may be the same, but in this telling GenAI has a direct impact on the marketer, i.e., GenAI in a co-pilot function such as a natural language user interface where a marketer can ask questions about customer data, an audience, or a campaign. A co-pilot might also generate insights, alerts and recommendations based on monitoring and understanding data, campaigns and customers. In other words, GenAI might speed up or even automate manual processes, or generate insights that a marketer might otherwise miss.
There is certainly some overlap between the internal and external use of GenAI in the marketing realm. Using GenAI to, say, generate email subject lines both helps simplify the marketer’s job and, ultimately, creates a better CX by coming up with (hopefully) amazing content by having trained by ‘reading’ millions of other email subject lines. A marketer will want to both use GenAI to improve personalization outcomes and use a smart personalization framework with optimization, A/B testing, etc., to test and improve AI recommendations and content.
The GenAI Marketing Scorecard
An assessment of what a marketing department wants to get out of GenAI will help determine a starting point. Gartner helps organizations evaluate how to make the best use of GenAI with a use case scorecard for marketing. (downloadable GenAI Use Case Prism). The scorecard ranks 20 potential use cases, from Content Copilot to Federated Collaboration, assessing each on six different metrics, three each in terms of business value (increased revenue/operational efficiency/managed risk) and feasibility (technical feasibility/organizational feasibility/external feasibility). Organizations can use the scorecard to plot one or more desired use cases and rank each one based on what’s most important – generating business value vs. the feasibility of getting an application up-and-running.
There is no question about GenAI’s soon-to-be pervasive use in the enterprise. For marketers to effectively integrate GenAI into their processes, nailing down a purpose is a good start for identifying where quick wins are possible. A follow-up blog will focus on the IT approach to GenAI, and explore potential overlap.
