Has your organization flirted with the idea of using generative AI to improve your productivity, output, and marketing efforts?
The adoption of generative AI has been rapid, but we wanted to determine how it’s being used, where the hesitation exists, and the extent to which it’s being implemented at an individual, team, and organizational level. iPullRank and MarketMuse conducted a survey of over 100 marketing leaders to understand how they are approaching the strategy, implementation and governance of this technology.
Listen in as Garrett Sussman, Demand Generation Manager of iPullRank, and Jeff Coyle, Co-founder and CSO of MarketMuse, discuss the findings of this survey. Don’t miss their views on the current perception, adoption, and future of Al-generated content.
Generative AI is becoming an essential part of content strategy, but trust and adoption remain key challenges
The state of generative AI report revealed that a majority of content creators and marketers are now utilizing AI-generated content to some degree. However, the panelists agreed that trust and adoption of AI-generated content still face challenges, particularly in ensuring quality and factual accuracy.
Garrett mentioned that he was surprised by the 73% of respondents who said they trust content generated by AI, as he personally would say no. “I assume that there’s going to be issues with the content until I QA it. And it’s interesting as we’re seeing things like Bing integrated with Chat GPT or the search generative experience and Bard integrated with the internet.” Jeff also expressed surprise at the high trust levels, noting that he belongs to the minority who do not trust AI-generated content entirely.
In terms of adoption, Jeff emphasized the importance of considering AI-generated content as part of the overall content strategy and workflow, rather than merely focusing on the payload or the end product. “We’ve got to be thinking about how do we get faster and better and more efficient with our time? And in my case, me personally, I’m ripping out 40 hours of working 4 hours.”
AI-generated content requires a shift in focus towards editing and refining outputs
As the use of AI-generated content becomes more prevalent, content creators and marketers must adapt their workflows and focus more on editing and refining the outputs produced by AI. This is because AI-generated content often requires fact-checking, brand voice consistency, and the removal of any biases.
Garrett noted that editing is becoming more critical in the AI-generated content landscape: “It changes my workflows in some good ways and some bad ways.” Jeff agreed, adding that understanding the types of edits needed for AI-generated content is essential: “If you don’t know the different types of editing, this is the time to learn what editors do.”
The panelists also discussed the need for organizations to establish content governance policies and frameworks to ensure that AI-generated content adheres to specific standards and guidelines. This includes building standardized prompt libraries, ensuring quality assurance and editorial processes, and providing training and education for employees.
AI-generated content should be seen as a complement to human expertise, not a replacement
The panelists agreed that AI-generated content should be viewed as a way to enhance and streamline content creation processes, rather than a threat to human writers and content creators.
Garrett emphasized that AI-generated content is not going to replace human content creators: “It’s the people who understand how to use these tools that are going to replace you. Now is the time to learn how to do it. Learn how to edit if you don’t already know that. Learn how to infuse humanity into the writing.” Jeff also noted that AI-generated content can offer significant benefits in terms of efficiency and productivity, allowing content creators more time to focus on higher-value tasks.
Ultimately, the panelists concluded that generative AI is here to stay and presents enormous opportunities for content creators and marketers. However, it is essential for organizations to approach AI-generated content with a focus on trust, adoption, and refining workflows to ensure the highest quality and most accurate outputs.
- Text is the most common use case for generative AI, followed by imagery and video.
- OpenAI is the most commonly used AI software, with other options like Jasper, Writer, and Mid Journey also in use.
- Most users are editing AI-generated content, with fact-checking and subject matter expertise being the most common edits.
- AI can both help and hinder workflows, depending on the specific use case and implementation.
- Trust in AI-generated content is mixed, with some users feeling confident in the quality and others being more skeptical.
- “I think to your point, there will be a lot of tools that are built on these other LLMs in the same way that almost like Amazon AWS has websites built in the cloud.”
- “I do think this also kind of highlights a new paradigm shift as we get to talking about content, strategy and workflows of like, editing is going to become more of the puzzle than original generation.”
- “I think it’s difficult to predict because to your point, I do think we’ve got Adobe Firefly has just recently come out and they already have a partnership with Google.”
- “I think it also speaks to the idea of as SEOs, as content marketers, our skill sets need to expand.”
- “I think the future state, which we’ll talk about a little bit, is hopefully eventually search engines can surface what is actually the best, most useful content, and you’re not just trying to create what you think the search engine wants.”