Podcast Summary:

Podcast: Behind the Numbers: an eMarketer Podcast
Episode: How Marketers Use AI at Work: Skills, Stigma, and the Future of Advertising Jobs
Date: October 3, 2025
Host: Marcus Johnson
Guests: Grace Harmon (Tech and AI Analyst), Lisa Heiss (Senior Editor)

Overview

This episode of “Behind the Numbers” explores how artificial intelligence (AI) is reshaping marketing and advertising professions. The panel delves into topics such as job displacement fears, current practical use cases for AI, vital AI upskilling areas for marketers, the lingering stigma associated with using AI at work, and the risks of “faking it” with AI proficiency. The conversation is rich with insights, research findings, and a candid discussion around challenges and opportunities in the evolving digital marketing landscape.

Key Discussion Points and Insights

AI's Impact on Ad Industry Jobs

• Continued Job Losses: The advertising and related fields have seen job losses for seven months in a row, driven in part by AI adoption.
  • “I hate to say it, but AI is taking over the ad industry.” – Lisa Heiss [03:21]
• Focus on Cost-cutting: Companies are prioritizing short-term efficiency and cost-cutting by replacing certain roles with AI, especially affecting junior/entry-level positions.
  • “The shift that’s prioritizing cost cutting and short term efficiency over having human insight... which is really concerning.” – Grace Harmon [04:31]
• Pipeline for Future Leaders at Risk: Relying too much on AI for entry-level tasks may disrupt the development of future marketing leaders.
  • Quote: “Agencies and brands are embracing the efficiency and cost savings of AI but at the risk of cutting the very pipeline that feeds future leadership.” – Quoting Gaja Savilla [04:52]
• Higher Unemployment for Young Grads: Recent 22- to 27-year-old college grads face a 5.8% unemployment rate, 25% higher than the national average—a trend attributed to entry-level positions disappearing due to AI. [06:10]

How Marketers Use AI Now

• Use Cases Still Basic: The primary use for AI remains content generation (copywriting, product listings, etc.), rather than more strategic applications like content analytics.
  • “The top use case right now is still content generation... but at the same time it’s not necessarily the best idea.” – Lisa Heiss [07:31]
• Analytics Use is Low: Only 9% of marketers currently use AI for content analytics, expected to rise to just 29% by 2028. [07:31]
• Cautious Consumer-Facing Output: Marketers are wary of using AI to generate public-facing content, due to concerns about brand perception. Internal applications are seen as lower risk.
  • “Generated social media content, generated images, don’t necessarily always go over well with consumers.” – Grace Harmon [08:04]

Upskilling and Training: What Marketers Actually Need

• Critical Skills:

  • Tool Fluency: Knowing which AI models are best for specific tasks, and their individual strengths and weaknesses.
    • “One key thing to teach employees is which models are best suited for individual tasks...their strengths, their weaknesses.” – Grace Harmon [09:04]
  • Prompt Engineering: Efficiently crafting AI queries to get useful outputs saves time and increases productivity.
  • Feedback Loops & Support: Employees often lack formal guidance and must figure out AI tools independently.
    • “49% of executives said employees are left on their own to figure out AI tools.” – Grace Harmon [09:04]
  • Technical Gaps: There’s inequity in access to tools and training, dividing staff by generation or gender.

• SEO & GEO in the Age of AI:

  • SEO (Search Engine Optimization) is still crucial; the rise of “Generative Engine Optimization” (GEO) is important as content increasingly appears in AI-powered search results.
    • “SEO isn’t dead...but generative engine optimization is really key to make sure that your content is still appearing in AI overviews.” – Lisa Heiss [11:19]
  • “ChatGPT users are not non-users of search... good SEO strategies are still critical.” – Grace Harmon [12:12]

• Lack of Structured Training: Many companies offer little or no training for AI tools—employees must self-educate, often on their own time.

Stigma and Shadow Use of AI at Work

• Stigma Sources:
  • Fear of being seen as lazy or replaceable if one relies on AI
  • Skepticism about the quality and trustworthiness of AI outputs
• Hidden AI Use: 45% of US employees use AI at work without telling supervisors. [Inc. article; 13:31]
  • “There’s a huge amount of shadow use, but I do think the stigma has two main sources... fear of replacement and skepticism about quality.” – Grace Harmon [13:31]
  • “Using AI as a copilot rather than a replacement I think is really important.” – Lisa Heiss [14:09]
• Company Culture & Transparency: Open dialogue, clear policies, and normalization at the company level can reduce stigma.

The “AI Bluff” and Embarrassment Around Skill Gaps

• Widespread Exaggeration:
  • 80% of professionals (and 90% of C-suite execs) admit to overstating their AI skills to keep up with perceived expectations (PluralSight 2025 report).
  • “AI proficiency is now a baseline job requirement... which is likely why nearly 80% of professionals exaggerate their AI knowledge.” – Paraphrasing Gaja Savilla [15:21]
  • “There’s the stigma of using it, but there’s also a level of embarrassment about how little people actually understand the technology.” – Marcus Johnson [15:21]

Where to Go Next: Training, Equity, and Responsible AI Use

• Training Needs More Focus: Both guests strongly advocate for more accessible, equitable, and ongoing training in workplace AI use.
  • “Honestly, it’s just training. I mean, it’s so easy to get trained on AI and you don’t even have to wait for your company... The only reason that somebody wouldn’t be trained on it is if they didn’t take the initiative.” – Lisa Heiss [16:51]
• Equity and Opportunity Gaps: A lack of training risks exacerbating divides between workers who can upskill (or can afford to) and those who cannot.
  • “The next phase... needs to move past working just on efficiency and working on equity of training and sustainability.” – Grace Harmon [17:15]
• Responsible AI Use: Employees need to understand not just how to use AI, but its limitations, ethical concerns, and especially data privacy risks. Only 17% of marketers report receiving comprehensive, role-specific training.
  • “Learning how to use AI responsibly means understanding its capabilities along with its risks, limitations, and... ethical concerns.” – Marcus Johnson [17:53]
• Shadow Use & Data Privacy Risk: Using AI tools unsanctioned at work (shadow use), particularly for proprietary data, can expose companies to privacy risks.
  • “Using proprietary data and these AI tools can leak the data... that's one big thing... in terms of shadow use.” – Grace Harmon [18:34]
  • Definition: “Shadow use is use of AI, I guess in this case I’m talking specifically at work when it’s not sanctioned or not disclosed.” – Grace Harmon [19:05]

Notable Quotes & Memorable Moments

• [03:21] Lisa Heiss: "I hate to say it, but AI is taking over the ad industry."
• [04:52] Marcus Johnson quoting Gaja Savilla: "Agencies and brands are embracing the efficiency and cost savings of AI but at the risk of cutting the very pipeline that feeds future leadership."
• [07:31] Lisa Heiss: "The top use case right now is still content generation...but at the same time it’s not necessarily the best idea."
• [09:04] Grace Harmon: "One key thing to teach employees is which models are best suited for individual tasks...and another major one is how to write prompts efficiently."
• [11:19] Lisa Heiss: "SEO isn’t dead...generative engine optimization is really key to make sure that your content is still appearing in AI overviews."
• [13:31] Grace Harmon: "There’s a huge amount of shadow use, but I do think the stigma has two main sources...fear of replacement and skepticism about quality."
• [14:09] Lisa Heiss: "Using AI as a copilot rather than a replacement I think is really important."
• [15:21] Marcus Johnson: "There’s also a level of embarrassment about how little people actually understand the technology."
• [16:51] Lisa Heiss: "Honestly, it’s just training. I mean it’s so easy to get trained on AI and you don’t even have to wait for your company..."
• [17:15] Grace Harmon: "The next phase... needs to move past working just on efficiency and working on equity of training and sustainability."
• [18:34] Grace Harmon: "Using proprietary data and these AI tools can leak the data... that's one big thing..."

Timestamps for Key Segments

• 03:21: AI’s effect on ad industry employment & replacement of junior roles
• 06:10: Data on entry-level job loss and recent grad unemployment
• 07:31: What marketers are currently using AI for and missed opportunities (analytics)
• 09:04: Essential AI skills for marketers and self-driven upskilling
• 11:19: The evolving relationship between SEO and GEO in the era of AI
• 13:31: Shadow use of AI and workplace stigma
• 15:21: “AI bluff”—workers exaggerating their proficiency
• 16:51: The critical need for company-wide AI training
• 18:34: Shadow use, data privacy, and risk management

Conclusion

This episode offers a nuanced, evidence-based look at the pressures, opportunities, and pitfalls facing marketers as AI becomes more embedded in the workplace. The conversation highlights the urgency for practical training, responsible adoption, and a cultural shift toward transparency and equity in AI upskilling to ensure sustainable success—not just for companies, but for individual marketers at every stage of their careers.

Next episode teaser: The ethics of AI in advertising.