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Feeling stuck with generative AI?
Here's a way to get unstuck.
I would like to ask you to consider two questions.
Did you see the new movie Ghostbusters: Frozen Empire first? If not, just think about the last movie you saw (for me it was the remake of Road House – ugh). Did you like it?
I'm only referring to Ghostbusters because it did poorly with critics (44% on Rotten Tomatoes) compared to audiences (84%). In addition, “Ghostbusters” scored well at the box office with sales of $45 million in its first few weeks of release.
But here's the thing: whether you thought the movie was wonderful or terrible, you're right and you're wrong. Even the data can't tell you if you're right. If you liked the recent action-spy movie “Argylle,” the data — box office, critics and audience reviews — would say you’re wrong. But if you answer “But Henry Cavill,” you’re not wrong.
Now the second question: When you experiment with generative AI to create your marketing content, do you think you consistently get valuable results, decent but not great results, or poor results?
I've been asking this question to a few listeners lately, and most people choose the middle – decent across the board, but not great. However, regardless of your answer, I know one thing. You are completely wrong. And you're fine.
It's all a question of perspective. All marketing content is like a movie. What moves you may not move me. The data may say your content is successful, but whether a person values it or feels motivated by it is subjective.
Stuck between uncertainty and doubt with generative AI
A recent talk featured a conversation about a bank that approached an AI company with 500 use cases to which they wanted to apply large language models.
Yes, they are stuck.
I think the trend is omnipresent. Companies of all sizes are looking for the right uses for generative AI. Managers are putting extreme pressure on their teams to find the “efficiency” and “usefulness” of generative AI. It's so new and so innovative; There HAS to be something you can do with it.
With every swipe of a social media feed, podcast episode, webinar, or industry event, applications of generative AI become visible. It's hard to keep up with all the options because every day someone thinks of something you don't do.
But is generative AI helpful for marketers?
It is not. That's why you feel stuck. It's a classic choice paradox. They believe the wide range of use cases makes it easier to apply generative AI to your content and marketing. But it really makes it harder to decide which applications to use.
The most insidious part? You can't know if AI-generated content is better until you choose one.
Here's what I mean.
Generative AI's response to a prompt is designed to be unpredictable. When you ask the tool to rewrite, edit, or create something, it never responds the same way twice. If you press and ask if that's the best it can do, it usually responds with a different variation. Rewriting will only stop when you stop the process. It will never be, “Well, the third iteration was the best version, so stop asking.”
Generative AI doesn't always give you the right content, nor the best content. It simply provides the most likely content. If you think that's good enough, you're right. And you're wrong.
Novelty and efficiency provide the necessary insight
I started helping customers out by taking a more structured approach to putting together their use cases. In marketing, two spectrums can be applied to generative AI. The first concerns a new or existing capability. Is the use case already a task that could be made more valuable through generative AI? Or is it something that wasn't possible or was so difficult that the human effort wasn't worth it?
Real-time translation of customer service calls is an example of an existing feature facilitated by AI. An example of a new skill is rewriting a research paper into more user-friendly versions for different people using AI.
The second spectrum revolves around efficiency. Will you become more efficient by using generative AI? Does it save time and resources? Or is it less efficient? Will it take more time and resources?
An example of greater efficiency is using a generative AI tool to generate SEO keywords or correct grammar. An example of less efficiency is an AI tool that scans both your CRM data and LinkedIn to produce a content gap report. You would add the task to a person's to-do list because the result represents a valuable new use of their time.
With these spectrums in mind, you can create a four-quadrant chart to evaluate generative AI usage. The vertical line runs from the new ability at the top to the existing ability at the bottom. It is cut in the middle by the efficiency line, which runs from less efficient on the left to more efficient on the right.
The four quadrants fit into these categories:
- Improvement – a new feature that makes you more efficient. For example, a generative AI tool learns your brand guidelines, tone, and editorial jargon (new feature). It automatically points out these flaws (more efficiency) to help you create consistently well-branded content.
- Refinement – an existing skill that makes you more efficient. For example, a generative AI tool can create real-time (more efficient) translation of content for customer service requests (existing feature).
- Complement – an existing skill that is less efficient but more valuable. A good example is competitive research. By investing a little more time and resources using AI, you can conduct comprehensive competitive analysis on an ongoing basis.
- Complement – a new feature that makes you less efficient. These applications are real innovations. For example, you create a new chatbot using a custom learning model that scans all training documentation to provide an interactive assistance application for customers. The amazing new experience requires greater attention to the quality and structure of your training manuals.
These categories may seem esoteric. As I noticed, the cases can fall on a spectrum, so one usage might be up in the upper right quadrant (very new feature and highly efficient), while another might be closer to the center of the graph in that quadrant (somewhat new). ability and generally efficient).
However, this categorization table is handy.
Use case categories are confusing you
One of the biggest tensions in generative AI planning comes when use cases don't align with your priorities and what executives deem important.
Let me explain.
I have collected over 230 use cases for generative AI in content and marketing. They are divided into four categories:
- Improvement (new ability, more efficient): 6%
- Refinement (existing ability, more efficient): 31%
- Supplement (existing capacity, less efficient): 45%
- Complement (new ability, less efficient): 18%
A third of the use cases fall into what you would consider the most common – the tasks that are completed in everyday work become more efficient. But interestingly, it's only a third.
By far the most popular use cases (45%) are jobs that were once downgraded because they were too demanding and are now worthwhile thanks to generative AI. They actually increase the need for more resources. This finding is consistent with the initial anecdotal evidence I gathered while working with clients. Most generative AI integrations in marketing impose new budget and resource requirements and complement existing capabilities.
It's also not surprising, but nice to see that few use cases fall into the “improvement” category – things you couldn't do before that also make you more efficient. Put this in a nutshell: “We don’t know what we don’t know.” This generative AI adventure is still in its infancy and new possibilities are just beginning to be discovered.
The key takeaway, however, is not to force some balance in use cases in your work. Rather, it's about understanding where to prioritize so that you meet leadership expectations. When you prioritize generative AI deployment in the supplementary category, but management expects AI to deliver a more refined deployment, conflict and tension arise.
If you don't properly promote the use of generative AI, you run the risk of failure. I know a company recently proposed a new generative AI solution to create a set of content that automatically creates targeted/personalized content on their website. It was a real extension use case, but they presented it as a refinement case – a way to save money. Of course, these two things didn't fit together and her pitch failed.
Only you can say what's good
As you assemble your teams and develop the use cases for generative AI in your marketing and content plan, remember to truly understand the value they provide.
They will all look fantastic and give good results. They'll all look terrible too, like big money and time pits. Only you and the team can determine which is which. But if you agree on what challenge each will solve, at least you'll know what matters most – the critic's score, the audience's score, or the box office.
It's your story. Say it well.
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Cover image by Joseph Kalinowski/Content Marketing Institute
Create your very own Auto Publish News/Blog Site and Earn Passive Income in Just 4 Easy Steps
