And with tens of thousands of vendors offering tools with varying capabilities, there is no shortage of options. In a crowded and noisy landscape, it can be challenging for leaders to cut through the hype and make the best decisions for their enterprise. This is where ºÚÁÏÉçsteps in, with Edelman’s 2024 AI Landscape Report: The Communicator’s Guide to Finding AI Tools You Can Trust.
The first in a series of evaluation guides, this report’s contributors vetted over 180 of the most relevant vendors for marketing and communication teams. We did this through the lens of professional use cases, with vendors undergoing rigorous evaluation against set criterion points across three solutions categories and six common use cases. The year-long process included demos, testing, market research, and blind scoring, thus ensuring accurate and unbiased results.
This inaugural report is a trusted guide for decision makers as they navigate the complex and growing AI vendor landscape, and in the process, identify the right, and most enterprise-ready tools for their business.
There are thousands of AI tool options available – and seemingly, just as many opinions about what works best for certain scenarios. But not all solutions are enterprise ready, or useful for Marcom teams.
So, we created a set of prescriptive, data-backed criteria based on these requirements to give you impartial, fact-based and trusted guidance. This list of criteria points included elements such as model data, ethics, user-friendliness, and data safeguards.
We covered three Marcom-related categories: Major Large Language Models (LLMs), Creative and Design, and Analytics/Social Listening.
LLMs were evaluated across the use cases most sought out by today’s Marcom decision-makers. Evaluations for the other categories focused on tools with those specific purposes and capabilities.
Each category includes a list of recommended vendors, as well as honorable mentions for those identified as rising stars.
What are enterprise Marcom teams using (or considering using) generative AI for, and what are the best tools for these jobs?
We reviewed the major LLMs against six universal use cases, drilling down to the everyday components that these use cases are comprised of, including prompting capabilities, content summarization, web-based versus enterprise ecosystem research, analysis integration, and more.
.