3 Ways to Adapt to How AI Is Changing Marketing

Just over half of all marketers are using or experimenting with Generative AI at work, according to a recent survey. Basic content creation, writing copy, inspiring creativity—these top three AI-generated content use cases point to some of the ways in which marketers are already integrating this new technology into their processes and workflows. We cannot entirely predict how AI is changing marketing jobs. We can, however, proactively adapt our ways of working to take advantage of AI’s expertise while optimizing our own.

The following three recommendations are based on what Generative AI, or GenAI, is capable of doing today and the strategies marketers hope to pursue with it. By thinking about what GenAI does well and where the gaps are, marketers can focus human time on adding the most value.

To start, let’s recap where marketers benefit from using Generative AI today.

How AI is changing marketing focused on what it does well

Today’s general-purpose Generative AI solutions are largely prediction machines. They work by predicting which combination of words best responds to a user query, or “prompt.” During the past 18 months, a large share of consumers have seen first-hand how powerful that predictive ability is. A majority of marketers have tried it at least once.

There are some limitations, however. Data scientists train the large language models underpinning Generative AI using large sources of text. OpenAI’s large language models are trained using content available on the internet, along with other sources. The same is true of Google’s Bard (now rebranded as part of Gemini). That is one reason why these general-purpose Generative AI are so good at answering general-knowledge questions. The internet is filled with that kind of information.

Yet if you ask a more esoteric question, one for which there is not a lot of information or consensus, or one related to a fast-changing area of expertise, the results are less consistent. Sometimes the resulting text is factually incorrect. The “predictions” related to what the sentences should say just don’t have enough data to back them up.

Using large sources of generic language data also creates the tendency for GenAI to hallucinate. That is, they invent answers that are not in their training data. The scope and scale of text they have seen makes it more likely that the algorithms will stray from their source material.

Both technology providers and enterprise AI users can get around those limitations by training their Generative AI on specialized data sets. Another option is fine-turning AI for specific use cases, among other techniques. Yet technical solutions should also be complemented by having humans take the lead. 

Where GenAI should lead and where human marketers should

The strengths and limitations of Generative AI highlight some of how AI is changing marketing and how humans can adapt.

High value use cases where AI can take the lead

Nearly every commercial Generative AI can efficiently write new text in familiar forms on well-known subjects that are fairly static. As a result, it is an excellent collaborator when writing first-draft SEO blog posts or glossary entries communicating established thinking.

Email subject lines and body text, webinar titles and landing pages, and social text also benefit from GenAI assistance. This is particularly true when you have a source text you include in your prompt to show the GenAI. This equips it to highlight specific information or write in a certain style. These use cases represent how AI is changing marketing jobs.

Where humans take the lead in marketing

How does a Generative AI application know what to write about in that SEO blog? How does it know which customer segment to write for? Does it know what season it is or what overstock your retail marketing team wants to promote?

It doesn’t—at least for now. Humans still need to define the task they are trying to fulfill for the AI. Generative AI is changing marketing execution, but not the pre-work of considering digital marketing campaign strategies and cultivating customer insights. Problem framing and strategy development are still human-centric activities.

Generative AI is also less effective than human marketers at taking the lead on writing true thought leadership or future-oriented texts. This is especially true if there is little existing information on the subject. Humans will need to take the lead for this type of writing.

These ideas about where today’s GenAI excels and where it needs your support point to three areas where human marketers could dedicate their time to increase the value they deliver to their organizations.

Area #1—Focus on inspiration

Since GenAI solutions can craft fluent text at impressive speed, they appear to “understand” a prompt and respond to it. It doesn’t though. It is merely relying on probabilities. The combination of words in your prompt are associated with a corresponding combination of words as an answer.

Similarly, GenAI does not have full insight into every facet of your customer. A GenAI-enabled Voice of the Customer tool may do a good job of sorting through thousands of minutes of call center conversations to identify the five most common problems people need an agent for. It may also be able to identify the words customers use most often when they are likely to churn. Or when they are likely to upgrade. Facilitating and enhancing those analytical activities is another way Generative AI is changing marketing.

It is not changing the need for inspiration, however. Take the example of how a fashion writer drew inspiration from seeing Taylor Swift at a football game wearing a sweater in Kansas City Chiefs red. Living in the world and engaging with your customers or people like them gives you in-the-moment insights. Your knowledge of human motivations that go beyond the static data in a customer data platform. While those insights are limited to your personal experience, they allow for fast associations.

A GenAI trained specifically to optimize marketing can then be your writing copilot. It can take your inspiration and translate it into text that motivates customers to act. (That is what the Persado Motivation AI Generative AI solution is specially trained to do.) Studies even find that when people who write for their jobs use GenAI tools to draft and iterate on written outputs, they produce more creative results.

This is just one option for how AI is changing marketing jobs: you focus more on having good ideas to feed the AI, which provides the writing collaboration.

Area #2—Focus on strategy

The sheer scale potential of GenAI can dramatically increase the volume of messages you send. The ability to quickly achieve volume and scale is necessary to meet the expectations marketing leaders have of driving personalization across a growing number of digital channels and segments. GenAI can create content for all of them. They may also link to a customer data platform or be fed web session data so it can personalize what it says for a given segment.

No human can equal it. Nor do you need to. Marketing success is not all about scale. A customer who sees more of your marketing is not more likely to engage if the product does not appeal to them. In fact, seeing more may annoy them, and make them less likely to buy from your brand. 

Instead, marketing success is more likely to come from a high-quality strategy that focuses on relevance and customer loyalty. Marketing should respond by front loading their time and attention to focus on the goals and strategic approach to a marketing campaign. You can then accelerate campaign creative ideation and production using GenAI. The strategic phase could validate the targeted customer segments, their mindsets and behaviors, and how they want to engage.

Execute on the ideas with a human-AI collaboration to ideate topics, themes, and narratives that excite customers. Then run experiments testing multiple versions to see which ones resonate.

Internal strategies and processes 

Another area of strategy that requires human leadership is changing internal processes. Adopting GenAI allows teams to get more done faster. However, these tools require organizations to change the way they produce content and output. Copywriters that once wrote copy that “sounded good” and followed brand guidelines will now begin the creation process by entering AI prompts and evaluating AI-generated content. It’s a different process. While change can be challenging at the beginning, it pays off in the end. These internal changes will future-proof the organization’s marketing strategy and the careers of the people on the team. 

Area #3—Focus on accuracy and transparency

To repeat, many of the general-purpose GenAI applications of today do not know when AI-created text is inaccurate. Let alone when it might be offensive or controversial for your audience. GenAI can be trained or prompted to eliminate common mistakes or to avoid certain subjects. It can also be trained to preferentially use certain words and avoid others. Even then, there is the potential for AI hallucinations and inaccuracies to slip in.

The need to mitigate those risks is another example of how AI is changing marketing. In this case, by increasing the need for checks and controls to ensure accuracy and transparency.

One change is that AI-enabled marketing teams need to either add fact-checking or make how they do it more robust. This process is less onerous for teams that start with a GenAI application that is especially trained for your use case and which includes built in controls.

A cousin of AI accuracy is transparency. This takes on many forms in the world of AI. Two considerations are whether to be transparent with customers when they are interacting with or exposed to AI content. Another relates to the AI’s explainability. The latter refers to AI whose results can be reverse engineered to show how they came about. Explainable AI has its opposite in “black box” solutions, the results from which developers cannot explain.

Each organization must set their own policies about transparency. The social sentiment appears, however, to show an increasing desire for transparency. Consumers say that knowing how a business is using AI will build trust. TikTok is getting ahead of those trends by implementing AI transparency policies. YouTube has since added labels for AI-generated content and Meta requires the same on political ads and posts. Depending on your organization’s policies around transparency, marketers will need to ensure every piece of content reflects it. Doing that with a GenAI that produces explainable results will help.

Embrace your new Generative AI collaborator with Persado Motivation AI, an enterprise Generative AI text solution for marketers in retail, financial services, travel, and other industries. Trained on text from more than 10 years of Fortune 500 marketing campaigns, Persado AI-generated content emphasizes the words and phrases that motivate customers to act. Learn about our risk-free trial options today.