Whole-brained is defined as working so that strategic questions are addressed from a left-brain, a right-brain and a heart perspective at once, meaning analytical, creative and empathetic. The study finds overperformers are twice as effective at bringing in whole-brained talent.
The session’s premise is that most companies are unbalanced. A decade-long swing toward analytics needs rebalancing, and the session works through what it takes to hold all three perspectives together.
Key findings
- The three dependencies are stated as a chain: it takes human insight to unlock the power of data, human creativity to unleash the power of technology, and human empathy to inspire with a true brand and corporate purpose.
- Data is no longer the constraint. The earlier Marketing2020 study coined infobesity for the surplus, and the recent growth study confirms that nobody complains about the availability of data anymore.
- The cost of the swing is creative. The session argues that many companies threw out the creative baby with the bathwater, producing a significant decline in creativity and brand differentiation.
- Four common pitfalls are named: one dominant side, data for data’s sake, unbridled creativity, and a lack of empathy.
- Even inside an AI-powered advertising market, human creativity still accounts for most of the effect. Of the impact of digital advertising, 70 percent is still attributed to human creativity, and that share has held steady for ten years.
How marketing became so analytical
The account starts with reputation. Marketers were long seen as the spenders of the company, with advertising budgets drawing scorn from finance and sales, and marketers themselves joking that they did not know which half of the spend was wasted.
The internet, digital spend and analytics changed that, and the rise of technology companies accelerated a more analytical and programmatic approach. The study confirms overperformers start strategic planning from solid data.
The pandemic pushed in the other direction, showing companies the importance of bringing more empathy, true understanding, clear purpose and humanity into the marketing mix.
The session also reframes the order of questions, invoking the argument that deciding who is on the bus matters more than why, what or how, and asking who is on the team that spots unmet needs and builds the solutions.
Leading whole-brained
The first practice is enabling people to work at their best. Creative rhythms are not uniform, so if your creative time is early in the day, starting with email wastes it, and the same applies to the people you lead.
The example extended outward is telling. Asking an agency where and when they feel most comfortable presenting ideas probably does not produce the answer of a client’s corporate office.
The other practices are exposure to diverse experiences, encouraging differing perspectives, and representing diverse consumers. The session adds listening as arguably the most important, so that every team member can contribute their own view.
What AI can already make
The practitioner’s view is that AI value creation over the next couple of decades will come from marketing and services rather than supply chain, infrastructure or transport.
The visible proof is the digital advertising market, powered by algorithms that decide what content and messaging is put in front of which person and when.
The work described is about production capacity. The volume of work keeps rising as new channels and new data points appear, requiring ever more variations, which is why the team designs what it calls creative workflows of the future.
The worked example is a wildlife campaign where the birds could not be filmed, because preventing wildlife trafficking is the charity’s own mission, so they had to be generated instead.
The pipeline required segmenting 20,000 images into corresponding maps of which region was the bird, then training for a couple of days, after which a human sketch produces a photo-realistic image in about four and a half seconds.
The unexpected consequence is that the system can produce things that could not exist, which the speaker argues will matter for architecture, vehicle design and anything involving aerodynamics.
Synthetic people, and the objections
After content, the biggest cost in advertising is the people who appear in it, because shooting large numbers of people is prohibitively expensive.
A face is not a face to the system. It is a collection of pixel values, closer in spirit to a spreadsheet, and labeled datasets allow features such as glasses, smiles or wavy hair to be applied back onto a face.
The risk identified is what happens when generation is connected to targeting. The more you click on smiling people, the more the ads smile back, and the same applies to hair color and age.
Bias in the training data is amplified rather than diluted. Turning up an attractiveness label made a face whiter, because more white people had been labeled attractive in that dataset.
The datasets were never meant for this. Labeling tens of thousands of images takes a long time, so people reach for whatever already exists, and the biases carried in them will grow.
Reactions split by age. Executive MBA audiences over 45 are deeply concerned, while a 17 year old is likely to be indifferent and already using such features on social media.
The forward projection is specific. Video avatars will keep improving, video calls with them are expected within five years, and celebrity endorsement changes when a person can record generic lines once and never be shot again.
Homophily is the concern the speaker keeps returning to. Generating people based on what someone clicks converges on the face of the person clicking, and in mass media that creates a landscape that looks like the viewer.
On using AI to manufacture diversity he takes a position against, describing it as neither transparent nor morally right for anyone in the equation, while acknowledging the counterargument that no one in ads was ever real anyway.
What participants take away
The three key learnings are that whole-brained means analytical, creative and empathetic together, that leadership requires appreciating and promoting diversity of perspective and experience, and that AI will be a tool for driving creativity rather than a replacement for it.
The workbook asks where the participant’s organization is leading and lagging on whole-brained working, what is blocking or facilitating success, and what action can be taken now, stated with who, what and by when.
Scope and limitations
The AI material is one practitioner’s account from a single agency group, describing research done several years before the session, and the ethical positions in it are explicitly presented as personal views on a contested question.
Source
This page summarises SESSION 10: Whole-Brained, by Frank van den Driest, Perry Nightingale, 2024.