Decades ago, the alcohol industry launched its ubiquitous “Drink responsibly” marketing campaign. In 2024, it’s time to “AI responsibly” in marketing technology (martech). There are parallels between drinking and using generative AI (genAI): People often start because others are trying it; it might make things look more appealing; and you could embarrass yourself if you’ve had too much.

Now is the perfect time to be methodical with genAI. Despite the hype, genAI in martech is still in its relative early stages. Last year, we envisioned the evolution of genAI use cases in martech playing out in multiple stages over time:

 

This is still an accurate picture of where we are today. Marketers are still dabbling in creative design, with only limited adoption for the analytics, insights, and operational assistants identified in the midterm.

So this month, we published two research pieces to help marketers “AI responsibly” when planning for and incorporating new genAI capabilities into their martech ecosystems. Incorporating these learnings and resources should help marketers move past early-stage adoption stagnation.

1. Prioritizing GenAI Use Cases In Martech

The B2C Martech AI Use Cases Planning Tool provides definitions for 26 martech use cases and helps marketers define the scope of their genAI adoption. The interactive tool allows marketers to select which use cases are in current use vs. prioritizing which go on the martech roadmap. The most common use cases today are content generation as well as natural language interfaces and application assistants within tools.

Forrester also offers a B2B Revenue Technology Use Case Template, which can be used as a guide for creating outcome-focused use cases to gain buy-in for AI and other technology requests.

2. Operationalizing GenAI In Martech

Shift Generative AI In Martech From Theory To Reality guides B2C and B2B marketers on genAI activation across four critical aspects:

  • People. GenAI adoption is a group project across key stakeholders. Identify your core personas, which typically will span the marketer, IT, data scientist, and steward personas.
  • Process. You’ll need an iterative approach to incorporating genAI. Follow five steps: ideate, forecast, prototype, prioritize, and activate.
  • Implementation. There are multiple ways to access genAI for the many martech use cases. Consider genAI tools embedded in third-party technology, open or closed public large language models (LLMs), or building your own LLM.
  • Measurement. Make a plan now for how you’ll measure genAI’s impact. Too many marketers lack defined metrics. They should measure both efficiency and effectiveness goals.

There’s a lot to consider with genAI and martech, so let’s continue the conversation. Schedule a guidance session or inquiry with Katie Linford, Joe Stanhope, Rusty Warner, or Jessica Liu.