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Will Generative AI Make Marketers Obsolete?

8 minute read | July 7, 2023

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Will AI lead to the end of marketing jobs?

AI will replace over 30,000 ad agency jobs, report says

Nearly 80% of women’s jobs could be disrupted, automated by AI

These are just a random sampling of recent headlines from major media outlets, each prognosticating how marketing pros and other employee groups should be warming up their resumes and looking for an employment Plan B. 

Whenever a new and disruptive technology hits the scene, the response from pundits, media outlets, and the market generally is always the same: lead with gloom and doom. 

Artificial Intelligence (AI), for example, has been part of the cultural zeitgeist for decades and is almost always associated with some dystopian future. From “The Terminator” in the 80s to Spielberg’s “A.I.” in the 90s to this century’s “The Matrix,” letting technology hop into the driver’s seat almost always leads to our demise. 

So, as AI hit the mainstream at the end of 2022, it’s no surprise that cautionary tales soon followed. And since this is the Golden Age of clickbait and doom-scrolling, those sentiments have been amplified, and you get articles and think pieces similar to those listed above. 

Let’s just put an end to all of this craziness right now, shall we? Time to do a little myth-busting.

Will AI disrupt the role of today’s marketer? Unquestionably, yes. 

Does the advent of ChatGPT and Midjourney mean the demise of copywriters and designers? Of course not. 

What this does mean, however, is that marketers (and other professions) now have a viable way to automate the repetitive, optimize execution, and deliver personalization that scales. But before all these new tools can be utilized to their fullest potential, the teams behind the tech need to acquire, embrace, and consistently enhance their skills so they can keep pace. 

Making AI your own

Spend five minutes at the keyboard playing with ChatGPT, and you’ll quickly realize just how adept it is at synthesizing and delivering remarkably engaging content. Even Ryan Reynolds got into the act and used ChatGPT to script a 60-second spot for Mint Mobile. (Currently at 1.9 million views and growing.) The major drawback is that it uses a curated slice of internet-based information to generate content, so there are inherent risks. 

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Think through this scenario: say you’re a marketer with a cybersecurity software company, and you’re using ChatGPT to a) help identify key challenges impacting your target audience and b) develop email content highlighting how the right cyber platform makes those problems disappear. Great way to accelerate a time-consuming process, particularly if you’re doing multiple iterations for A/B testing purposes. However, if your competitors are also utilizing the same approach, you run the very real risk of sending undifferentiated and ultimately forgettable content. 

One way to work around this problem is prompt engineering or prompt-tuning, the act of tailoring and refining AI queries to improve the output. Asking the AI to make a piece of content “more irreverent” so you can personalize the tone or “more concise” so you can make the text more scannable will help to make the output more distinct. 

A better (and safer) approach would be feeding unique information – such as a creative brief detailing your organization’s unique tone, positioning, and value propositions – as a baseline, then asking the AI to develop content armed with that additional information. The output will be more personalized, approachable, and scalable in a relevant and impactful way. 

Granted, this is a relatively simple use case, but it illustrates how AI supports marketing professionals, not replaces marketing professionals. It also highlights how creative teams can spend their time focusing on big-picture strategic pursuits while automating more mundane practices. 

There are some tools, services, and platforms available that ostensibly help with tasks such as defining your brand’s voice or serving up pre-built prompts for different marketing activities and outcomes. They’re not quite ready for primetime. Bringing your team up to speed on the fundamentals of Generative AI and LLM, how they work, and how to integrate them into the day-to-day workflow will deliver a much bigger bang for the buck.  

Focusing efforts, energy, and resources

Another powerful use case is leveraging AI’s ability to analyze and synthesize vast amounts of data to identify patterns, map sentiment, then utilize that data to shape the next steps in the prospect or customer journey. AI-powered analytics can look at browsing behaviors, social media interactions, discussions with chatbots, and any number of interactions through different channels to isolate and identify behaviors. 

Some cohorts, for example, might prefer conducting independent product research, asking contacts via social media for opinions and input, then directly engaging with a sales team for a demo or pricing. At the same time, another might start their journey with a demo, then visit peer review sites to check ratings and comments before making a decision. These two distinct sets of buying behaviors require two distinct marketing approaches to optimize performance. Armed with this information, marketing teams can develop campaigns that will boost conversions, increase revenue, and maximize the use of marketing funds. 

As prospects become customers, AI tools now have access to an additional pool of data – purchasing behaviors – from which to draw. This helps marketing teams develop and automate programs to drive more sales, such as sending a coupon for in-store use to a customer who typically visits a brick-and-mortar location but hasn’t recently been in the store. This sort of predictive modeling is incredibly challenging to perform at scale, but AI does the “heavy lifting” so marketing teams can focus more energy on running campaigns and driving behaviors and less on crunching numbers.

The more marketers learn about customers (or prospective customers), the better they can meet the need for hyper-personalization. Creating dynamic content templates, for example, allows the AI to capitalize on the patterns it has discovered by assembling the ideal offer delivered at the perfect time in the ideal format, all based upon the creative content developed by the marketing team.

One particularly impressive example of hyper-personalization done right comes from one of the most popular brands in the world of confections, Cadbury. Many small businesses struggled to return as the world emerged from the lockdowns triggered by the COVID pandemic. This problem was particularly acute in India. Cadbury’s advanced AI capabilities allowed store owners to create an ad for their shops using the likeness of Bollywood star Shah Rukh Khan. This campaign, dubbed “NotJustACadburyAd”, highlights how AI can create personalized experiences at the retailer and the consumer level—very powerful stuff. 

As with the case of prompt engineers, the ability to develop and define programs that fully utilize the capabilities of AI first requires a firm understanding of the fundamentals of data-driven decision-making: where data is sourced, how it’s used, and what actions should be taken based on the results. Without access to the correct set of information, AI can have a detrimental effect on marketing efforts.

Garbage In / Garbage Out (GIGO) is rooted in computer science and has become a shorthand for what happens when decisions are made based on flawed, incomplete, or erroneous data. In the case of AI, a resource that can be used to synthesize data and automate actions at scale, getting the datasets wrong or failing to set up a campaign properly could lead to disastrous results. 

We’ve all received emails that included “Hello <<FIRST_NAME>>” before, right? Now imagine something like that, but ten times worse on a macro scale. Yeah… not pretty.

That’s why ensuring your entire team is “leveling up” their data and AI skills at once is vitally important. 

Creating a step-change in marketing capabilities

For all the incredible efficiencies and opportunities that AI unlocks, the ability to put those tools to work rests squarely on the shoulders of a well-informed, well-trained team. Virtually every member of the marketing organization – from ad purchasing to creative to campaign management and beyond – will be impacted by AI, so leveraging a human-led, cohort-based approach to mastering these skills will ensure everyone can drive the business forward. 

But you don’t have to take our word for it.

Here’s what ChatGPT has to say:


Why should marketing teams learn about data-driven decision-making and AI as a group instead of one at a time?


Marketing teams should learn about data-driven decision-making and AI as a group rather than individually for several important reasons:

  1. Collaborative Understanding: When the entire marketing team learns together, it fosters a collaborative environment where team members can share knowledge, insights, and ideas. It ensures that everyone is on the same page and speaking a common language, which is crucial for effective communication and teamwork.
  2. Holistic Perspective: This will impact the entire marketing function, including areas such as customer segmentation, targeting, personalization, campaign optimization, and performance measurement. By learning as a group, marketers gain a holistic perspective on how these technologies can be applied across different marketing activities, leading to more comprehensive and integrated strategies.
  3. Cross-Pollination of Ideas: When marketers learn together, they bring diverse perspectives, experiences, and expertise to the table. This diversity can spark creativity and innovation, as team members can share their unique insights and ideas on how to leverage data and AI effectively. It encourages collaborative problem-solving and enables the team to explore new possibilities collectively.
  4. Knowledge Sharing and Skill Development: Learning as a group allows for effective knowledge sharing. Some team members may already possess relevant skills or expertise in data analysis or AI implementation, and they can share their knowledge with others. This facilitates skill development across the team, enabling marketers to develop a broader skill set and become more versatile in their roles.
  5. Consistent Implementation: By learning together, marketing teams can ensure consistent implementation of data and AI practices. Consistency is essential to avoid silos or fragmented approaches within the team. When everyone understands the fundamental concepts and principles, they can align their efforts toward a common goal, ensuring a unified and coordinated approach.
  6. Change Management: Adopting these technologies often involves a shift in mindset and practices. Learning as a group helps create a supportive environment for change, as team members can navigate the transformation together. They can discuss challenges, address concerns, and support each other throughout the learning process, making the transition smoother and more successful.
  7. Efficiency and Time Savings: Training the entire marketing team collectively can be more efficient and time-saving compared to individual training sessions. Instead of repeating the same information for each team member, a group learning approach allows for centralized training, reducing duplication and optimizing resources. It also enables the team to start implementing data-driven practices sooner, leveraging their collective knowledge.

Overall, learning about data-driven decision-making and AI as a group empowers marketing teams to embrace these technologies effectively, drive innovation, and achieve better outcomes collectively. It fosters collaboration, knowledge sharing, and a holistic approach, ultimately leading to improved decision-making processes and marketing performance. 

I couldn’t have said it better myself. 

Since you’re here…
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About Marc A. Moschetto

Marc is a B2B technology marketing executive, with nearly 30 years of global experience and Cloud/SaaS-based solution domain expertise. He's spent decades working within the HR technology sector, including holding senior marketing roles at WorkForce Software, Workscape (now ADP), Workbrain (now Infor) and PeopleFluent. Marc has also held senior marketing roles with large global organizations such as General Electric and Schneider Electric, and has built and managed teams throughout North America, Europe, APAC, and the Middle East.