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Should We Be Storyteller’s or Marketing Engineers?

~ K. Quesenberry ~ Leave a comment

At the end of last year The Wall Street Journal reported in “Companies Are Desperately Seeking ‘Storytellers” that the percentage of LinkedIn job postings including the term “storytellers” doubled as businesses scrambled to cut through AI-generated noise.

Recently, I noticed a new article from the same reporter, “Marketers Are Rebranding as ‘Engineers’ to Survive the AI Era.” Huh? This juxtaposition struck me, not only because we seem to have moved on quickly, but also because I have engineering and storytelling in my background. Which is the important skill?

Which are we to be: Storytellers or Marketing Engineers?

An Eclectic Education Journey

I started out as an Engineering major. I was attracted to process thinking: the systematic, iterative way of solving problems. But there wasn’t much creativity. I spent time as a Marketing major but my first class was accounting where creativity was not encouraged. I finished with Journalism and a concentration in Advertising.

I also took a lot of liberal arts classes along the way: short story writing, poetry, photography, video production, drawing, anthropology, film. But I never forgot what I learned in Engineering. One of my strengths has been uncovering and explaining processes for strategy and creativity.

Process Only Gets You So Far

In my advertising career we followed a strategic and creative process. That got us to good solutions most of the time for our clients. But sometimes we hit it out of the park with creative solutions that exceeded expectations – winning creative awards and delivering exponential marketing performance results. It wasn’t until I began my academic career that I figured out what made the difference.

With time to reflect, I researched what I knew: advertising creativity. What is the difference between okay campaigns and exceptional ones? Three of our own research studies (one on Super Bowl ads, one on YouTube ads, a new one on Instagram static Posts) and a synthesis of other research later I’m convinced the difference is storytelling.

I’ve done research into Super Bowl ads, Onlline Video ads, and Instagram posts to prove the power of story.

You Don’t Need Engineers To Improve Process.

What about the Marketing Engineer? As best I can tell, this is a person taking the same processes and outsourcing some of it to AI. I see how that could be valuable.

But anyone in current positions and job titles can add AI to processes and outsource routine tasks to increase time on more important ones.

Everyone follows a process. Everyone does consumer research to inform decisions. AI can speed some of that up. Rare is the Storyteller.

Storytelling Is An Enduring Skill That Transcends Trendy Titles

Perhaps this becomes a temporary position that bridges a gap, but eventually I think it goes away as everyone just learns to integrate AI into their processes. We’ve already seen this pattern in the job title Prompt Engineer. As Fortune reported last May, “This Six-Figure Role Was Predicted to Be the Next Big Thing–It’s Already Obsolete Thanks to AI” The reporter describes how this once hyped-up career, Prompt Engineer has shifted from a unique job title to a skill needed by everyone to succeed.

A Fortune article about the Prompt Engineer job not becoming what people expected.

I’ve seen in my studies, professional career, research and teaching the value of storytelling. AI doesn’t have lived, physical experiences and can’t feel emotions to truly understand another human’s life. That’s needed in many careers. In the first WSJ article, “Storytelling” rose in listings as a skill in different jobs not a single trendy new job title.

In a College of Management, I see how storytelling is relevant to all our students preparing for careers in sales, human resources, management, analytics, entrepreneurship, finance, and yes, accounting. I’ve also taught marketing and storytelling to engineering students.

Engineers Will Not Replace Marketing And Communications Storytellers

I recently turned in the files for the Second Edition of my book with Michael Coolsen Brand Storytelling (due out next Spring). One thing we learned about storytelling and AI is that Engineers are not replacing Creatives, Strategists and Storytellers. As explained in Agency Reporter,

“Spend a day inside any agency today and you’ll notice something feels different. The brainstorms are still animated. The whiteboards are still full. But ideas are taking shape faster. Generative AI has quietly woven itself into the everyday rhythm of agency life — not as a dramatic disruption, but as a steady undercurrent changing how work gets done.”

Generative AI doesn’t replace or change the foundations of the strategic and creative process. It doesn’t replace the need for storytelling. But AI can accelerate certain parts of the process.

This boost in speed can be reinvested in valuable human thinking time. AI doesn’t replace human instinct, taste, or empathy needed for insights, ideas, and story. This applies to marketing, advertising, public relations, and other communications professionals.

AI Automation Is A Threat To Storytelling

One thing we did discover in writing the second edition is a warning about types of automation that impact story. This includes an example described in the WSJ Brand Engineer article – automating media buying.

Google’s performance max (P-Max) campaigns removes the human from the process. Instead of creating distinct ads for different media, marketers upload an “Asset Group” of raw elements (up to 15 headlines, 5 descriptions, 20 images, and 5 videos). Google’s AI dynamically mixes, matches, and deploys the pieces across YouTube, Search, Display, Gmail, and Maps.

Don’t be tempted by the lure of AI automation when it matters like telling your story.

AI optimization like this sounds great at first, but it removes the brand storytelling. Our Five-Act Story framework (and any story) requires a seqential series of events that capture your attention and move you through the ups and downs of the plot. This is what creates and releases tension. It’s what makes you feel sympathy and empathy for the characters.

Imagine if your favorite movie or novel randomly mixed scenes, dialogue, events and characters. Would it have the same appeal?

The Lesson Is Avoid Shinny Object Syndrome

I think Marketing Engineer may be another sign of what Marketoonist Tom Fishburne calls “Shinny Object Syndrome.” He observed how marketers love chasing the shiny new thing.

His illustration shows a group of dogs in a conference room that should be looking at a marketing chart on the screen, but instead they switch mid-sentence to looking out the window saying “Look, Squirrel!” Storytelling isn’t a shinny new thing or a squirrel.

As we explain in our book, storytelling is one of the oldest strategies in the world. Aristotle laid out the basic structure for story in Poetics, written over 2,300 years ago. Story dramas held audience attention in Greco-Roman amphitheaters across the antient world.

What we know about story began in Greco-Roman amphitheaters. Image by Albrecht Fietz from Pixabay

Shakespeare’s works have been performed in countless cities and countries for more than 400 years – he was a master storyteller.

Modern marketers are called to be storytellers too! Seth Godin wisely says,

“Marketing = storytelling, and everything an organization does supports the story. So everyone is in the marketing department, and a company either tells a story that people care about, or their story disappears.”

Storytelling is a millennia-old practice that always works, is always needed and AI can’t authentically tell. Why would we throw that away to become a Marketing Engineer? Let the Engineers be Engineers. Let Marketers be Storytellers.

Here’s my AI disclosure: I didn’t use AI in writing this. I’m not against using AI in the writing process. I’ve written about AI without using AI and I ‘ve experimented writing about AI using AI. It can be a valuable tool. In this article, I choose not to use it.

AI Doesn’t Make Social Media Audits Outdated. It Makes Human Listening More Important.

Pictures of Social Media Marketing by Keith Quesenberry first through fourth editions.
~ K. Quesenberry ~ Leave a comment

Years before AI became a part of everyday talk about work and education, I developed a social media audit template built around a simple idea: before brands talk, they need to listen.

I developed it after years teaching one of the first social media strategy courses, made it a core part of my Social Media Strategy text, and outlined the framework in a 2015 Harvard Business Review article. Rooted in the 5 Ws I learned in journalism school, it’s designed to be a systematic analysis of brand, competitor, and consumer conversation to shift marketing mindset from top-down control toward authentic, consumer-centric engagement.

Pictures of Social Media Marketing by Keith Quesenberry first through fourth editions.
I’ve revised my social media text many times but the Social Media Audit remains a core component.

Over the years, audits in my consulting work and student projects always surface significant insights busy professionals overlook and students would otherwise never see. In the HBR article and my book, I recommend conducting a social media audit at the beginning of a project and at least every 12 months after. I also emphasize doing the work yourself. Visit each platform, scroll the feed, make your own observations.

That includes social media professionals who manage brand accounts every day. Being in the accounts isn’t the same as stepping back to analyze them. When you’re busy posting, responding, monitoring, reporting, and keeping up with the daily demands of content, it is easy to become buried in the weeds.

A social media audit creates the discipline to pause, zoom out, and look across the company, consumer, and competitor conversation as a whole. It turns everyday activity and a surface level view into deeper strategic perspective.

That first hand listening advice still holds. It matters more now.

What AI Changes and What It Doesn’t

AI has dramatically changed how quickly we can collect, sort, summarize, and compare social media information. In seconds, AI can identify themes in comments, classify content types, summarize sentiment, spot recurring hashtags, and compare competitor activity across platforms. That’s powerful.

AI doesn’t make social media audits outdated. It makes them more necessary.

The goal of a social media audit was never to fill out a spreadsheet. The goal was to understand the conversation around a brand: What is the company saying? What are consumers saying? What are competitors doing? Where is the conversation happening? What earns attention and engagement and what does all of this suggest about strategy?

Those questions haven’t changed. And they require uniquely human skills: empathy, emotional intelligence, nuanced judgment, intuition, and ethical reasoning. What has changed is the process we use to answer them.

The Template Still Works Because the Thinking Still Works

I haven’t changed the core audit template through multiple editions of my book because the structure still teaches the right way to think. It asks students and social pros to examine three areas:

  • Company: What is the brand saying and doing on social media?
  • Consumer: What are people saying about the brand, category, problem, experience?
  • Competitor: What are direct and indirect competitors doing in the same space?

It then organizes that listening through the basics: who, where, what, when, and why.

That framework prevents one of the most common mistakes in social media strategy: mistaking activity for understanding. For students, AI accelerates that mistake. Ask for “social media recommendations for Brand X” and a polished list appears in seconds with no listening required. Polished, but not grounded.

For professionals, the trap is different. Daily management of posting, responding, and reporting can create the illusion of strategic awareness. But being in the accounts every day is not the same as auditing them. A social media manager focused on owned channels may miss revealing conversation happening in a Reddit thread, competitor community, or review site. The audit forces that discovery.

Human Listening Comes First

Before students or pros use AI to analyze social media activity, I encourage them to look at the accounts themselves. This isn’t old-fashioned. Like any relationship, real understanding comes from first-hand experience, not data.

We know this instinctively. A reason people react negatively to AI-generated comments is they feel manufactured. The reply may be fluent, but doesn’t feel real.

Social media is supposed to be social. If people are frustrated when brands or individuals use AI to fake conversation, why would we teach students or professionals to understand those conversations only through AI summaries?

The point of an audit is to listen to real people in real contexts before deciding what a brand should say or do.

You can’t fully grasp a brand’s presence from a summary. You need to see the posts, feel the tone, notice visual rhythm, quality of comments, the way a brand responds or doesn’t. The brand may show “positive engagement” in surface analysis, but a closer look might reveal shallow or sarcastic comments not connected to the analysis.

Another brand may have fewer likes but a far more committed community. A TikTok comment section carries a different meaning than a LinkedIn thread. A Reddit discussion may surface frustrations that never appear in the brand’s owned channels.

Those are human insights, and students need to develop the ability to notice them. So do experienced practitioners. Staying in the feeds keeps you sharp, but managing owned brand channels every day can also keep you buried in posting, responding, and reporting.

A social media manager may be so focused on the brand’s Instagram, TikTok, or LinkedIn activity they miss a more revealing conversation happening in a Reddit thread, competitor community, or review site. A social media audit forces that discovery. It creates the structure to step back from daily doing and see a larger strategic pattern across company, consumer, and competitor activity.

This is especially important in education because students aren’t just learning to collect information. They’re learning to interpret markets, audiences, and behavior. That judgment comes from looking closely, comparing examples, asking better questions, and sitting with ambiguity. AI can support this, but it shouldn’t remove students from it.

Where AI Genuinely Helps

After that first-hand review, AI becomes a valuable tool. It can help:

  • Summarize recurring themes across comments or reviews
  • Identify sentiment patterns across a large volume of content
  • Group common hashtags or keywords
  • Classify posts by content category
  • Surface repeated customer questions or common complaints
  • Compare the content mix of a brand and its main competitors
  • Identify gaps in the conversation
  • Organize messy notes into a cleaner audit observation and presentation

This is especially useful when students and even pros are dealing with more content than they can manually process. They might review a representative sample themselves, then use AI to help scale and organize the broader set.

But there’s a critical distinction: AI can identify patterns. The student and professional still needs to decide what those patterns mean.

AI might report a brand’s comments are mostly positive. But positive about what? The product, price, or humor? The packaging, nostalgia, or customer service? Sentiment is an input. Strategic interpretation is the job that still belongs to humans, who can pick up on nuance AI misses.

Social Media Audit Template To Improve Social Media Marketing Strategy.

(Click image for a downloadable PDF of the social media audit template.)

An Updated AI-Assisted Workflow

The core social media audit template has not changed. What I’ve changed is how I recommend students and social pros use it.

  1. Start with human observation. Look at the brand’s accounts directly including recent posts, captions, comments, replies, visuals, hashtags, engagement. Do the same for key competitors. Then search further looking for consumer conversations beyond the brand’s owned channels. Don’t start with AI. Start by looking.
  2. Capture evidence in the template. Record specific examples: posts, comments, themes, content types, timing, engagement, strategic choices. The goal isn’t to collect everything. It’s to build evidence. What does the brand emphasize? What does the audience respond to? What do competitors do differently?
  3. Use AI to organize and scale. After forming your own observations, use AI to help summarize larger content sets, group themes, compare post types, or clean up your notes. This is where AI saves time. Saving time, however, is not the same as outsourcing thought.
  4. Verify AI output. Don’t assume AI is right. Check its claims against the actual posts and examples you reviewed. AI can miss context, flatten nuance, misread irony, and make a weak pattern sound stronger than it is. If AI says customers are frustrated, you should be able to point to real evidence. Evidence still matters.
  5. Interpret strategically. This is the most important step. The real value of an audit isn’t the list of observations. It’s the interpretation. What should the brand keep doing? Stop doing? Improve? Test? What audience insight should guide future content? What competitor opportunity exists? AI can organize the inputs. You make the strategic argument.
  6. Disclose how AI was used. A simple note is enough: what tool you used, what you asked it to do, what you provided, and how you checked the output. For example:

I used AI to summarize recurring themes from a sample of Instagram and TikTok comments. I compared the AI summary to my own manual review and included only themes I could verify with examples from the accounts.

That transparency teaches responsible AI use and it’s a reminder that AI support doesn’t remove responsibility for the final analysis.

My new Social Media Audit GPT. Available as an AI assited social media strategy tool.

Why I Built a Social Media Audit GPT

To support this process, I created a custom Social Media Audit GPT. Its purpose isn’t to complete the audit for you. It’s to guide you through it with firsthand listening.

A good educational AI tool doesn’t hand you an answer. It helps you ask better questions and move through the work with more confidence. The GPT prompts students to think about company, consumer, competitor, platforms, content, engagement, and strategy. It scaffolds the process and doesn’t replace the learning. It’s also place for students to turn for help when they’re up late and I’m probably already asleep.

A social media audit is valuable not because students manually count things that software could count faster. It’s valuable because it teaches them to listen before they recommend, to compare brand, consumer, and competitor activity, to move from observation to insight, and to ground strategy in evidence.

The future of social media education shouldn’t be students staring at feeds for hours with no assistance from modern tools. But it also shouldn’t be students handing the thinking to AI and accepting the first polished answer.

The better path is in the middle: look with your own eyes first, use AI to organize and scale what you find, then return to human judgment to decide what it means.

Social media audits aren’t outdated. They’re one of the best ways to teach one of the most important skills: listening before you speak.

This post was created with the assistance of ChatGPT and Claude. The ideas, experiences, and opinions are my own.