If AI saves you time at work, that doesn't mean you'll end up working less. Marketing expert Jessica Apotheker argues that companies could use those gains to produce more content, while losing the originality that makes their brands different.
In her TED talk, she makes the case for building stronger data and predictive AI capabilities while protecting human creativity. Her starting point is an earlier productivity revolution whose promise sounds familiar.
Better tools haven't meant less work
Apotheker opens by looking back roughly 30 years, to the promise of word processors and spreadsheets. These tools would reduce the time people spent writing, making presentation slides, and calculating numbers.
She jokes that the promise came true: everyone now has plenty of leisure time, and she works only two days a week. The reality, of course, is different.
Instead of working less, people write longer documents. Presentation decks have grown from six slides to 50, an observation she makes as a consultant. Meanwhile, the amount of data people must process has exploded, making decisions more complex.
Those tools made work faster, but organizations expanded what they expected people to produce.
Generative AI brings another major productivity opportunity as companies embed it into their operations. For Apotheker, the important question is how organizations will direct that opportunity, rather than allowing faster production to become an end in itself.
Generative AI reaches the core of marketing
Apotheker has spent her career in marketing and advising marketing professionals. She cites estimates that marketing could see a productivity impact as high as 50 percent, making this an immediate concern for business leaders and consumers alike.
Marketing's creative foundations
She describes marketing as a traditionally "right-brained" function. Marketers understand consumers' emotional needs, develop products and innovations to meet them, and find messages that reach people at the right place and time.
Over the 15 years preceding her talk, digital marketing and analytics added more specialized skills. Digital marketing and marketing technology became important areas of expertise alongside broader creative abilities.
Generative AI goes further because it changes the core activities of marketing.
The performance gains are already substantial
Apotheker cites a study conducted by Boston Consulting Group and Harvard that found ChatGPT improved marketers' right-brain performance by 40 percent, using the version available at the time of her talk. BCG's research on generative AI performance examines the gains and risks behind that finding.
She then asks what marketers might do with a hypothetical day and a half of free time each week. More yoga or family time? Would companies allow that, or would they cut large parts of the marketing function?
Her expectation is neither. Without deliberate direction, she believes marketers will spend the time on what they already do well: producing more content and more ideas.
More content could mean personalization or overload
Producing more marketing content has a useful potential outcome for consumers: messages that fit their interests and needs more closely. However, Apotheker also sees a risk that people will face much more content, with less variety.
A brand email could feel more relevant
Her example is the weekly email you receive from a favorite brand. AI could help make that email entirely tailored to you.
The images might show people of your age and gender, even wearing T-shirts featuring your favorite rock band. Every product could be relevant to you. A bot could also provide a more human-like experience.
That kind of personalization would make increased content production useful to the person receiving it.
Repetition could become harder to escape
The downside starts with an experience many consumers already recognize: feeling chased by the same content repeatedly online.
If AI causes the volume of marketing content to grow sharply, that experience could become worse. Apotheker also warns that the content might all sound alike.
Because generative AI learns from existing content and data, she argues that it can reduce the range of outcomes. More production could therefore lead to what she calls a "great equalization of marketing," with brands becoming less distinct.
Her proposed response has two parts: develop a "left-AI brain" within the organization and protect its strongest creative talent.
Build a "left-AI brain" into marketing decisions
By a "left-AI brain," Apotheker means the skills and teams needed to build, use, and spread predictive AI tools throughout a function. Those capabilities should sit at the heart of decision-making.
Give marketers access to data expertise
For marketing, she proposes teams of marketing data scientists and marketing data engineers. These specialists would develop tools that marketers across the organization can use to understand performance and predict outcomes.
The tools could help answer questions such as:
- Which combinations of audience and creative are working in the market?
- Which products work with which consumers, and why?
- How is the marketing funnel changing?
- How does consumer behavior respond across channels and touchpoints?
The purpose is to put analytical capabilities into everyday marketing decisions. That requires both reskilling people and reorganizing teams so the tools reach the marketers who need them.
Predictive AI has a different role from content generation
In Apotheker's proposal, this analytical capability helps marketers understand what works and anticipate what might happen next. Generative AI, meanwhile, creates a separate question about how much creative work people should hand over to a model.
An organization needs to develop its ability to use data without surrendering the human ideas that distinguish its brand.
A consumer goods company put that approach into practice
Apotheker describes partnering with a consumer goods company that chose to build a left-AI brain advantage.
The company developed tools for use across its organization. For each marketing initiative, those tools helped marketers predict the sales outcome and how consumer behavior would change across channels and touchpoints.
They also helped teams examine execution in detail, including which creative was working and why. Apotheker describes the result as a positive feedback loop across the organization.
The effort included a team of more than 30 marketers with left-AI brain capabilities. They built and customized the tools, then helped colleagues develop the skills to use them.
That example shows the breadth of the work she has in mind. Creating the tools was only part of the effort; making them useful to the wider organization also required training.
Expand beyond the data your company already has
Even a capable team can limit its own progress if it trains models only on the company's current content and data. Apotheker warns that this can leave a brand stuck in the territory it already knows.
Existing audiences don't tell the whole story
She uses the example of a brand that's strong with millennials but wants to succeed with Gen Z.
Its existing millennial-focused data and content won't necessarily help it understand the new audience. If the brand never succeeds with Gen Z, it could also miss innovations and trends that would strengthen its appeal to millennials.
The limitation therefore affects both expansion into new audiences and future relevance to existing ones.
Useful partners may sit outside your industry
Apotheker recommends looking beyond a company's immediate ecosystem for relevant data and content partners.
Her example is a construction company that wants to market to architects for the first time but has no data about them. Other construction companies might have that data, but they're direct competitors.
Financial institutions or insurance companies could be possible partners because they may have relevant information about architects.
She suggests setting up a federated model with such partners and training algorithms through that arrangement. In her example, this could make the construction company better equipped to market to a new customer segment.
The broader point is to seek relevant knowledge outside the data the business has already collected.
Protect original ideas from overreliance on AI
Better data and stronger analytical skills don't complete Apotheker's proposal. An organization can still lose its creative range if it hands too much of its right-brain work to generative AI.
Better performance can coexist with fewer different ideas
Apotheker cites another finding from the BCG and Harvard study: when people over-rely on generative AI, the collective divergence of ideas drops by 40 percent.
"Divergence" here means the range of different ideas people bring forward. A smaller range can mean fewer new ideas reach the surface, which she argues can stifle innovation.
The two 40 percent findings concern different outcomes: improved performance on creative work and reduced variety of ideas when people over-rely on generative AI.
For a brand, reduced variety also creates a risk of losing its identity and becoming harder to distinguish from competitors.
Give innovators a clear boundary for AI use
Apotheker urges companies to identify their artists, differentiators, and innovators. In marketing, she observes, these may be the people who regularly disagree with everyone else.
These people still need to learn how to use AI well. She sees useful roles for AI in finding inspiration in ideas and trends, creating quick prototypes, and expanding the impact of a strong idea once someone has developed it.
However, she draws a boundary around originating original ideas. That work should remain with the human brain.
Her goal is to keep human creativity active while giving creative people tools that help them develop and extend their work. In turn, that protects the brand's identity and its difference in the market.
Marketers need to choose which strengths to develop
Apotheker closes by asking marketers to examine what they're good at. Her recommendations differ depending on whether someone is drawn to creative innovation or analytical work.
The two paths emphasize different strengths.
| If your strength is... | Apotheker's recommendation |
|---|---|
| Creativity and original ideas | Cultivate that ability. Being the true innovator in the room can become your superpower. |
| Data, rational thinking, and fact-based decisions | Specialize, build technology skills, and invest in predictive AI competencies. |
Organizations need both kinds of capability: people who build and use AI tools, and people who preserve original thinking.
Apotheker's closing message is direct: "Every marketer out there needs to choose their brain."
Productivity gains need room for human originality
Faster tools have already shown that saving time can lead to more work rather than less. Apotheker sees the same possibility in AI-powered marketing, along with the risk of more repetitive content.
Her response combines stronger predictive AI capabilities with deliberate protection of human originality. Marketing can use AI to understand performance and develop ideas while keeping the source of its most distinctive ideas human.
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