Different

Perspectives,

Together.

ON_Discourse is a private membership community for business leaders to participate in the Discipline of Discourse, cultivating perspectives, decision-making, and meaningful relationships.

Learn More

Issue #004

“Influencers are the modern athletes.”

“Fans are the new Free Agents.”

“Insurgent Sports Innovate.”

“Thrive To Survive”

Fans are the new free agents

ON_Sport dives into the last stronghold of the rapidly changing world of professional sports — the fan experience. Unlike other media verticals, the fan experience has been largely insulated against disruption; live sports have been one of the last bastions of linear broadcast media, a vast and mature market where regional relationships drive market activity and predictable consumer behaviors.

This stability is facing a triple threat of unstoppable disruption: the unbundling of ESPN, the rise of online gambling/fantasy, and the memification of highlights. These forces are resetting fan expectations for how they consume the game, what matters most to their experience, and where business opportunities lie in an uncertain future. 

The intergenerational transfer of fanatical devotion of a local team is going to fundamentally change in a new media and digital landscape, but the love of the game will remain. How will that love be witnessed, monetized, and mediated in an uncertain future?

Explore our initial questions and perspective

Discover More ON_Sport

The
secondary
market

issue #003

HAS OWNERSHIP

FALLEN OUT

OF FASHION?

Exploring the relationship
between fashion and
technology and the evolving
landscape that is the
secondary market.

The fashion industry has a strange relationship with technology.

Why does the Fashion Industry treat tech like a seasonal trend?

The secondary market is creeping up on the luxury market.

How might AI-driven entertainment redefine our relationship with anticipatory content?

AI Forecasting in Fashion has a Big Sustainability Problem.

AI forecasting is a noisy guess masquerading as an objective analysis — and manufacturers know it.

Discover More ON_Fashion

On_Entertainment

ISSUE #002

GOOD
ARTISTS
COPY,
GREAT
AIs
STEAL

Issue #002 examined how creativity, ownership, and distribution are
being impacted by the growth and mass adoption of AI and its impact
on the business of entertainment.

DO CREATIVES
KNOW WHAT CREATIVITY IS?

Our event set the stage for a lively, engaging debate over a private dinner and drinks in the Hamptons with ON_Members and specially invited guests in the entertainment, media, and tech industries.

We Are Algorithim-ing Ourselves Into a Monoculture

Content has become mundane and unexceptional as a result of personalized recommendations.

The Copyright Fight for Digital Creativity

The US copyright regime has become an obstacle to this new era of innovation–and not because of what the law actually says.

Would you let Netflix read your mind?

How might AI-driven entertainment redefine our relationship with anticipatory content?

How Artists Turn AI Into Gold

AI has the potential to be an industry-disrupting tool for streamlining creative processes and getting projects out the door faster than ever previously possible.

Discover More ON_Entertainment

AI

is

not

a

new

revolution

Food for thought

It is tempting to think of AI as novel technology that emerged very recently

Overheard at ON_Discourse

From the editor: One of our most credentialed AI experts shared some essential perspectives for companies betting on AI. This speaker has a PhD in Brain and Cognitive Sciences, and has been working on machine learning models for over 15 years. As always, ON_Discourse operates under the Chatham House Rule—no attribution of perspectives without explicit consent.

AI is not magical. It is not new. And the recent surge in attention is not the result of an unexpected advancement in generative software.

The dominant media story about AI is that OpenAI achieved a major technological breakthrough sometime in 2022. This breakthrough unlocked new ways consumers can prompt with models to receive shockingly relevant generative output. In other words, before 2022, the generative systems were still largely theoretical. Nope.

The ability to prompt with generative models has been around for more than a decade. Prior to 2022, researchers in cognitive and brain sciences (not computer science, by the way) were interacting with models as seen on ChatGPT. They did not call this functionality artificial intelligence – it had always been called machine learning, a more accurate representation of the underlying tech.

So what happened? If the software didn’t change, why are we treating AI as a groundbreaking technological revolution? The short answers are that the hardware (the GPU chips) got faster and OpenAI put it all online. In other words: the hardware got faster and the UI made it accessible. 

Everything else that happened is hype.

AI researchers are constantly building models, trying something new, and incrementing the version number. Nevertheless, we have to put things into perspective before the hype takes over. This is the reality: no AI model can suddenly build a rocket from scratch or do anything it couldn’t do before. The headlines brag about it getting a 95 on one benchmark, and ignore the fact that the previous model got a 92. It is all a progression.

The headlines brag about it getting a 95 on one benchmark, and ignore the fact that the previous model got a 92.

The technology is undeniably amazing but the amount of hype around their improvements hasn’t been proportional. The only major innovation is how they made their AI models available to the masses. The technology that’s under the hood remains the same.
  
Companies looking to innovate shouldn’t bother with generative AI models. Innovation in areas like quantum computing, neuromorphic computing, and low-temperature semiconductors is much more likely to bring about the next wave of AI than hiring a bunch of Stanford Computer Science PhDs.

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

Overheard at ON_Discourse
search icon

Can LLMs be optimized like search results?

From the editor: Marketers are starting to test different LLMs with branded prompts, reminding us of the early days of SEO. We convened two experts in brands and AI to sort through their conflicting perspectives on this analogy. As always, ON_Discourse operates under the Chatham House rule—no attribution of perspectives without explicit consent.

search icon

Can LLMs be optimized like search results?

From the editor: Marketers are starting to test different LLMs with branded prompts, reminding us of the early days of SEO. We convened two experts in brands and AI to sort through their conflicting perspectives on this analogy. As always, ON_Discourse operates under the Chatham House rule—no attribution of perspectives without explicit consent.

search icon

Can LLMs be optimized like search results?

From the editor: Marketers are starting to test different LLMs with branded prompts, reminding us of the early days of SEO. We convened two experts in brands and AI to sort through their conflicting perspectives on this analogy. As always, ON_Discourse operates under the Chatham House rule—no attribution of perspectives without explicit consent.

Kind of, yes

Kind of, yes

Sometimes, language models act like certain brands always lead in their industries.

“One of the things that people ask me a bunch is whether there are specific ways they should be thinking about optimizing what they do as marketers so that the LLMs have a better sense of their brand or they become the default brand when the LLM or diffusion model imagines a car or a beer … And my answer is that I don’t think it’s anything fundamentally different.

I think basically what you do when you’re doing brand building—building distinct brand assets over time—is probably the best thing that you can do to build up the LLMs’ understanding of the brand."

Sometimes, language models act like certain brands always lead in their industries.

“One of the things that people ask me a bunch is whether there are specific ways they should be thinking about optimizing what they do as marketers so that the LLMs have a better sense of their brand or they become the default brand when the LLM or diffusion model imagines a car or a beer … And my answer is that I don’t think it’s anything fundamentally different.

I think basically what you do when you’re doing brand building—building distinct brand assets over time—is probably the best thing that you can do to build up the LLMs’ understanding of the brand."

No
way

No
way

But...

We should not be confusing LLM and SEO. They are different topics and functions.

“I think if you are trying to make your brand what an LLM responds with when asked for a default beer, then you’re fundamentally misunderstanding what this technology is, and how to leverage it – it’s likely your reference point is 'how do I get the top result in search'.”

Firstly, the LLM is not imagining a car or beer can like we might. It’s developing something based on the prompt that is entered by a user. This matters a lot. It’s then going to modify that depending on what extra knowledge it receives in further commands.

But...

We should not be confusing LLM and SEO. They are different topics and functions.

“I think if you are trying to make your brand what an LLM responds with when asked for a default beer, then you’re fundamentally misunderstanding what this technology is, and how to leverage it – it’s likely your reference point is 'how do I get the top result in search'.”

Firstly, the LLM is not imagining a car or beer can like we might. It’s developing something based on the prompt that is entered by a user. This matters a lot. It’s then going to modify that depending on what extra knowledge it receives in further commands.

Yeah, but…

Sustained marketing strategy can determine what the LLM does and that is important.

“I mostly agree, but on the flip side, there are some brands for whom this is true (they’re the default brand that the LLM 'thinks' of), and the real point is that those brands that have achieved that status did so through many years of good marketing and branding practices. These are the same brands we think of when we think of sneakers and soda: the LLM is just reflecting back this perceptual reality."

Yeah, but…

Sustained marketing strategy can determine what the LLM does and that is important.

“I mostly agree, but on the flip side, there are some brands for whom this is true (they’re the default brand that the LLM 'thinks' of), and the real point is that those brands that have achieved that status did so through many years of good marketing and branding practices. These are the same brands we think of when we think of sneakers and soda: the LLM is just reflecting back this perceptual reality."

I still don’t agree…

This is not the time to be thinking about what an LLM outputs. There are higher priority issues for marketers.

Honestly, this kind of thing should not even be on the radar of a marketer right now – they should be focused on understanding how LLMs can be used as reasoning engines, how knowledge can be leveraged and stacked, how their workflows and lives can be made easier, and marketing made more effective.”

A brand shouldn’t be worrying about being the car that the LLM 'imagines' because it’s essentially creating a mishmash of a combination of everything it has learned about what a car is when it returns the output. It’s the detailed input that goes into prompts that creates the direction – e.g. futuristic car, Ford car, etc. If a brand wants to be mentioned by an LLM, then it needs to be worried about what is inputted to drive the output.

I still don’t agree…

This is not the time to be thinking about what an LLM outputs. There are higher priority issues for marketers.

Honestly, this kind of thing should not even be on the radar of a marketer right now – they should be focused on understanding how LLMs can be used as reasoning engines, how knowledge can be leveraged and stacked, how their workflows and lives can be made easier, and marketing made more effective.”

A brand shouldn’t be worrying about being the car that the LLM 'imagines' because it’s essentially creating a mishmash of a combination of everything it has learned about what a car is when it returns the output. It’s the detailed input that goes into prompts that creates the direction – e.g. futuristic car, Ford car, etc. If a brand wants to be mentioned by an LLM, then it needs to be worried about what is inputted to drive the output.

So in conclusion...

Google has profited immensely from the marketing world’s obsession with its black-box search algorithm. This obsession unlocked a deeper technical understanding of how online search works so brands can carve out opportunities to differentiate. It is therefore logical that markers begin to explore how LLMs respond to prompts. There is an undeniable symmetry in these two technical processes.

One of the interesting takeaways from this discussion is the inherent strength legacy brands will have in this upcoming AI era. They already possess the collateral that initially fed the data set behind the model. It makes us wonder how new brands should think about this upcoming era. Is there anything they can do?

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

Good brands

will integrate

more friction

into their CX

Hear me out

Let’s get a little inefficient

in the AI era

Dan Gardner

Dan Gardner

Co-Founder & Exec
Chair of Code and Theory
Co-Founder,
ON_Discourse

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

From the editor: Our co-founder spent the past 20 years designing to remove friction, only to see how AI stands to push it too far. Do you think he has a point?

Technology, if you really think about it, is an evolution of removing friction from everything that we do. Successful brands offer their customers value and build their staying power by leveraging technology to remove friction.

AI is just another piece of technology with the potential to remove even more friction. Taken to an extreme, it’s easy to imagine the perfect AI-driven experience: no friction. Whatever you want or need is available exactly when you want or need it. A perfectly frictionless experience.

Friction points let customers
recalibrate ahead and realign
with the brand in a more direct
and human way.

Think about the Humane Ai Pin launch video. There is a moment when a book is offered in front of the Pin with a directive to buy it. Which store? At what price? Is there a membership plan associated with the retailer? The assumption in that video is that the consumer is in some kind of a rush to buy.

We’re not there yet, but we’re getting close. 

When you’ve removed all friction, there’s no moment left for decision making. Brands could no longer need, or even allow the customer to make decisions anymore.

If we were to reach such a point, brands would need to learn where and when to add friction back in. In a world of no friction, the trend of removing friction would have to reverse. This is where good brands will distinguish themselves.

Good brands will define ideal friction points for their customers. Friction points let customers recalibrate ahead and realign with the brand in a more direct and human way.

AI is too

immature

for your
business

From the editor: One of our most credentialed AI experts shared some essential perspectives for companies betting on AI. This speaker has a PhD in Brain and Cognitive Sciences, and has been working on machine learning models for over 15 years. As always, ON_Discourse operates under the Chatham House Rule—no attribution of perspectives without explicit consent.

AI is an

adolescent

This tech revolution is built on bad behavior.

AI behaves like a teenager. It is moody, unreliable, and unpredictable. It needs constant supervision.

Are you sure you want this kind of technology driving business decisions?

Without any guardrails in place, AI models trained on text scraped from the internet swear at you, call you names, go off on sexist and racist rants, and so on. These are very teenager things to do: saying something just to say something, without realizing what it actually means, how bad it is, or what the repercussions in society might be.

Companies form so-called red teams to perform adversarial testing on their AI systems. These tests try to make an AI model do all the worst stuff possible, so that the companies can then prevent it from doing that stuff when it’s talking to their users.

Many people don’t realize that a lot of the work around AI currently involves babysitting models so that the end user doesn’t realize that the tech needs to be babysat. In short, companies are hiding how incredibly immature AI still is.

Keep this in mind before, during, and after you deploy any sort of AI tech across your organization.

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

Expect

fewer

managers

and

‏‏‎ ‎direct‏‏‎
‏‏ reports‏‏‎ ‎‎

From the editor: This overheard passage comes from a virtual event that featured a productive disagreement between members. In this piece you’ll see two opposing perspectives about the well-worn assumption that AI will replace labor. As always, ON_Discourse operates under the Chatham House rule—no attribution of perspectives without explicit consent.

‏‏‎ ‎Yes,‏‏‎ ‎ and here's why

AI could accelerate the flattening of organizations

MBA programs didn’t prepare anyone for this. The accelerating adoption of AI tools could completely redefine how businesses organize. The first dominos to fall will be the HR and management layers. At first glance this seems like a bad thing, but it’s not…

Growing
without
headcount

Today’s model is fundamentally stupid—I don’t mean that pejoratively. Managers are incentivized to grow their teams regardless of need. Why? Having more direct reports leads to more promotions and more raises for managers. This sustains a conventional growth model that leads to overhiring and layoffs.

AI tools will disrupt the need for companies to grow their workforces by injecting intelligence and data into this dumb growth model. The knee-jerk reaction to such a shift typically sounds like, “If we don’t grow our teams, how are we ever going to get promoted and have a career path?”

This thinking is rooted in the assumption that a hierarchy and a climbing mentality are required for growth. AI tools will reveal that this is not the case, and adding people will not be seen as a requirement for revenue growth.

Instead, revenue growth could be achieved while keeping headcount flat, or even through reductions. Many non-revenue-generating roles, like in HR and middle management, will start to disappear. Active executors that have a more direct relationship with the bottom line of the organization will replace them.

Focusing
on what
matters

People managers who remain will have more time to focus on their hands-on efforts. These individuals were good enough at doing something to get promoted. Their employers will develop metrics and goals around those skills, unlocking an entrepreneurial spirit.

Instead of adding more people to manage, people managers will become more focused on outcomes. AI tools could help incentivize individual contributors based on what uniquely motivates them and the impact they can make.

On the HR side, plenty of functions could be handled by AI, possibly with less bias, more clarity, and more transparency. The processes could be opened up and deployed more equally across teams.

More layers and hierarchy only distract from revenue-generating functions and complicate decision-making.

Better
outcomes

This has important knock-on effects. Non-revenue-generating roles pull businesses further away from their customers and the outcomes they want. More layers and hierarchy only distract from revenue-generating functions and complicate decision-making, whereas fewer non-essential roles could mean more focus on translating user needs to products and services.

Some argue that AI can just help bring all the data needed for decisions to the top of the organization, but that’s backward. Why bother having such a structure? Instead, flatten your organization and empower more people to make decisions closer to the customer.

AI tools will disrupt the need for companies to grow their workforces by injecting intelligence and data into this dumb growth model. The knee-jerk reaction to such a shift typically sounds like, “If we don’t grow our teams, how are we ever going to get promoted and have a career path?”

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

I totally ‏‏‎ ‎disagree‏‏‎ ‎

AI will need many more managers

I agree with
you on two
important
points

I agree with you on
two important points

  1. No conventional MBA graduates in management are prepared for the AI era.
  2. The proliferation of AI products and services will change management structures.

The rest, you have backwards. The number of managers is going to grow significantly in the near future. We are going to need more people to manage the adolescent that is AI.

Adding AI products and services to the toolbox creates a new output signal that businesses need to wrangle. This in turn calls for another human layer to figure out how the AI tools are performing and how they are changing the way we ingest new information.

We are going to need more people to manage the adolescent that is AI.

AI tools could create
a lot more noise
for decision makers

Because we have more signals coming in, we will ultimately make better decisions. Those additional signals, however, require more data science teams, more linguists, more operational people, more people managing how the models are performing, more people monitoring any human bias that is going into those models, and so on.

Industry expertise will remain vital. Companies will demand subject matter experts who decipher the truth within AI-generated content.

There are limitations to what AI can produce. Only humans can figure out that last critical bit. Non-technical roles, filled by creative and culturally-aware individuals who bridge technical jargon and diverse global perspectives, will grow in importance.

In fact, we might see high demand for managers who can attract the right mix of these unique, multidisciplinary groups of people, and know how to get them to work together productively and efficiently. AI simply doesn’t have the people skills to manage divergent perspectives and disciplines into a cohesive group.

More technology will once again require more humans to manage it.

Your brand does
not have enough
data for AI

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

From the editor: One of our most credentialed AI experts shared some essential perspectives for companies betting on AI. This speaker has a PhD in Brain and Cognitive Sciences, and has been working on machine learning models for over 15 years. As always, ON_Discourse operates under the Chatham House Rule—no attribution of perspectives without explicit consent.

You need an
enormous amount

This is one of the AI era’s most
under-appreciated assumptions.

In the AI era, everyone seems to have convinced themselves that their company has the best data. You likely believe that not only is your data just the best, but it also happens to be just so unique that the resulting AI model is going to supercharge your whole business.

This is wrong, for two reasons:

  1. You have too little data. You need billions or even trillions of heterogeneous observations to be able to do anything interesting with AI. There are only a handful of companies today that possess that quantity.
  2. Your data is of poor quality. Even most large enterprises do not have a dataset rich enough to make a meaningful AI play. They rely on buying data from multiple sources or stealing it from internet users under the guise of fair use.

Many companies are opting for a shortcut into AI. Any company today can implement base GPT models. That works for experimentation, but it won’t drive revenue or improve efficiency. Why? AI is not trustworthy. You need to hire experienced teams to manage its bad behaviors.

For almost every single company, trying to build something bespoke that will move the needle is simply not worth the investment.

Your brand does
not have enough
data for AI

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

From the editor: One of our most credentialed AI experts shared some essential perspectives for companies betting on AI. This speaker has a PhD in Brain and Cognitive Sciences, and has been working on machine learning models for over 15 years. As always, ON_Discourse operates under the Chatham House Rule—no attribution of perspectives without explicit consent.

You need an
enormous amount

This is one of the AI era’s most
under-appreciated assumptions.

In the AI era, everyone seems to have convinced themselves that their company has the best data. You likely believe that not only is your data just the best, but it also happens to be just so unique that the resulting AI model is going to supercharge your whole business.

This is wrong, for two reasons:

  1. You have too little data. You need billions or even trillions of heterogeneous observations to be able to do anything interesting with AI. There are only a handful of companies today that possess that quantity.
  2. Your data is of poor quality. Even most large enterprises do not have a dataset rich enough to make a meaningful AI play. They rely on buying data from multiple sources or stealing it from internet users under the guise of fair use.

Many companies are opting for a shortcut into AI. Any company today can implement base GPT models. That works for experimentation, but it won’t drive revenue or improve efficiency. Why? AI is not trustworthy. You need to hire experienced teams to manage its bad behaviors.

For almost every single company, trying to build something bespoke that will move the needle is simply not worth the investment.

Can AI

help
consumers

love
your
brand?

From the editor: Ahead of our CES trip, we held a spirited discussion about the future of brands in the AI era. One of our most provocative discussions centered on the idea that AI functionality can unlock deeper relationships with brands. Is this true? Or are post-purchase activation schemes a rehash of old ideas?

Laura Del Greco
Founder and CEO, MUSAY

Yes, here's how...

Invest in differentiated activation experiences - with AI’s help.

Think about the brands you trust and love—the ones you rely on to do a job better than any of their competitors. If they’re smart, they’re always looking for ways to grow their relationship with you by becoming more meaningful. The stronger their relationship with you becomes, the greater their margin and pricing power.

True brand power is a high CLTV to CAC ratio and customers who say, to misquote a line from Brokeback Mountain, “I can’t quit you.”   

AI WON’T change this reality because we’re human and we want the decision shortcuts brands give us.  

AI WILL exacerbate the need for brand relationship building and give brand marketers a powerful tool with which to do it.

AI WILL ALSO muddy the waters for brands fighting for differentiation, and a share of a consumers’ heart and wallet. There is a way around this.

Brands live on a continuum from branded commodities (oatmilk, sugar, etc.) to “n of 1” companies (Instagram, Carta, Slack, Apple, etc.). While AI will impact all of these brands, from their products to how they show up in the world, it’s the brands in the middle for which AI presents the biggest threat… and the biggest opportunity for innovation and relationship building.

The muddy AI water: generative AI for brand differentiation isn’t “all that.”

So, how can brands differentiate in the AI era and how can AI help?

To answer this question, forget about technology and AI for a moment.

Human nature doesn’t change and marketers know what makes consumers tick. They also know their brand’s personality, how it lives in the world and the moments, outside of purchase, where a consumer might find the brand relevant or useful. This is basic brand activation, but done more thoughtfully so a brand can continue to endear itself to the consumer. (And if you’re thinking about activations as “personalized rewards” post-purchase, that’s cool… but that isn’t really this – or it isn’t all of this!)

Brand activation, especially the post-purchase “hours” of 8:00 PM to 12:00 AM on a brand strategy clock, help a brand break free from the sameness of AI-generated digital mind melds and put the consumer at the center to grow the relationship. Using brand activations, marketers can wrest control of AI and use it to their advantage via interactions that unlock contextual customer data and insights, which help marketers ideate ways for a brand to authentically show up in the world and be relevant in ways that extend beyond product use.

Strategic post-purchase brand activation builds a flywheel of engagement, a more efficient allocation of human and financial capital from advertising to brand engagement and, ultimately, to an increase in CLTV/CAC.

In brief, a brand team’s hard-won epiphany of how its brand is differentiated is communicated to the consumer in ways that reinforce past purchase behavior, incentivize repeat purchase behavior, and ideally make the brand part of someone’s life and perhaps their identity.

If you listen to Spotify, you were recently treated to their Wrapped experience. If you fly Delta, you just received a visual summary of all the ways Delta helped you in 2023—your most visited cities, flights, and upgrades. They also reminded you how they could help you in 2024. These weren’t fancy, but they were useful, relevant, and designed to meet the consumer, (me, in this case), where they are beyond the purchase moment.

These examples are only two of the countless ways a brand can authentically have a meaningful activation moment; the best ones will grow their relationship with the consumer. This is where AI insights can help. Traditional and social media channels can do this, but they can be expensive and aren’t designed to deliver the nuanced, relationship building, context that is possible here.

If you know me, you know I’m an early adopter who is always looking for ways to use technology to meet the consumer where they are. I look at AI as a tool to do just that. That said, there is no single “right” way to move forward, but move forward we must. After all, DVDs and CDs used to be billion dollar businesses until new technology came to town.

Even if you’re not 100% sold on the concept of AI-informed post-purchase activations and experiences, I urge you to entertain the possibility. It’s just good business.

Your opportunity

By its very definition, a brand must be differentiated enough from its competitors to outsell its competitors. 

To get the desired output of dynamic, differentiated positioning, you need dynamic differentiated input AND an ability to create connections between disparate elements. This is really hard to do—it’s a never-ending battle. There are conference rooms strewn with takeout containers and overfilled trash cans from weary product teams trying to win the war. (Coke vs. Pepsi, anyone?)

Unfortunately, differentiation isn’t enough. Today’s consumer faces a cacophony of SEO-driven messaging and data overload that threatens to water down the positioning born from years of brand research, insights, and takeout containers.

The instinct to use generative AI as a positioning tool shortcut makes sense and yet, it’s risky. Large Language Models (LLMs) are still relatively new and they’re currently processing and converting structured, unstructured, and dark data from everywhere without nuance or a critical eye. In other words, generative AI positioning is a regression to the mean and without a human’s watchful eye can be a melting pot of average.

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

Not really

There's no research to confirm customers want relationships with brands.

The first thing I have to say before we get into any debate about the future of AI is that no one knows what will happen. Anyone that gives you a more confident answer to questions is definitely full of shit.

Regarding your perspective: we partially agree on an important point:   

Overheard at
ON_Discourse

Anonymous under
Chatham House Rules

My agreement with you comes with a caveat: if we reduced a brand to the values in a manifesto or the colors of a logo, then the averaging out of brands in generative AI experiences would be a problem.

But… that is not how I believe brands work. I believe Byron Sharp’s theory about how brands grow. A brand emerges over time, after the audience embeds its signals in their collective memories. A generative AI model is a reflection of what is already out in the air. I’ve been experimenting with different models and am seeing that many models seem to distinguish between strong brands and vague ones.

We kind of agree that customer-facing generative experiences are homogenizing brand elements.

You place a lot of emphasis on pre and post-purchase activation. I take another angle. Differentiation will come from good creative marketing, which is less common than it should be. Strong brands have been living on the dividends that were paid thirty years ago. Now is the time for creativity. As this technology propagates, more middling brands will elevate their creative output into a generic median – good enough output. Human creativity, augmented by AI capabilities, will further distinguish strong brands from that median.

We also kind of agree that brands need to find a way to distinguish themselves from each other.

You are describing a mass personalization experience across most CPG brands. I’ll just state it like this: there is no research to suggest that consumers want a relationship with brands. The buyers of Crest of Colgate are not interested in joining a social network dedicated to whiter teeth. They buy their respective brands based on the same theories that have driven marketing for generations.

Finally, I do not agree with your point about post-purchase opportunities.

Laura Del Greco
Founder and CEO, MUSAY

Yes, they do!

Now is the time for brands to invest in differentiated activation experiences - with AI's help.

Noah, you’re right.  There are post- purchase activations that are irrelevant, annoying, time consuming, and invasive; no one wants those!  Not having insight into how the survey was conducted, my gut is that when people were asked about brand follow-up they had the annoying kind in mind.  

To quote Scott Galloway from “No Mercy / No Malice”, “brand is Latin for irrational margin”, and the best ones drive that margin by being omnipresent and meaningful, especially post purchase. It’s a way to reinforce behavior, stay top of mind, and reduce the need to advertise in conventional ways. Brands live everywhere they need to and maintain their margin via post-purchase activation and foundational brand building.  
 
Many of the people who said they didn’t want post-purchase brand engagement probably shop at an Apple store, (itself a form of post-purchase engagement), use the post-purchase activation that is the Genius Bar, and tune into Apple events. In other words, post-purchase activations can be covert and feel seamless, which is probably the best way.
 
For a covert activation combined with foundational brand building, look at the Tide. Tide’s environmental research efforts,  (e.g. cold water washing for the environment), their WWF partnership, product innovation in stain removal, and their“Loads of Hope” community program are all authentic Tide moments and reinforce what people think of Tide, and, what they think of themselves.. They’re done to keep the brand top of mind and psychologically reward purchase.  Tide appeals to people’s hearts (we help you save the planet, help others, and keep your, and your family’s, clothes clean).  

Tide’s post-purchase activations are subtle and the “distribution channels” are primarily PR and co-branding, (awards such as ”Better Homes and Gardens”) targeted towards their core consumer.  Because I’m hard to reach via advertising, and a Tide loyalist, my interaction with the brand was via a Malcom Gladwell podcast about laundry! (highly recommend)
 
Think too about Lululemon – in-store classes, free hemming, repairs ... consumers WANT this!  They just don’t think about these as post-purchase activations, so they would say “no” if asked. And if you’re a baker, King Arthur has done a spectacular job of creating a premium flour brand with their cookbooks and digital resources. (I’m a fan of this brand too!)
 
The level of activation has to be commensurate with the meaning a brand has in someone’s life and the job it does.  It’s even better if it’s seamlessly woven into someone’s life in a relevant way.  
 
For overt activation, think of any automotive brand. The brand is always in touch with you via their app, their service and their special edition mugs (that maybe someone buys!). They also rely on their sales force to help you stay wedded to their brand. Also, for overt but “in service to the consumer,” think Apple, Nike, Nespresso, Disney, Delta, Amex, etc.  
 
Can AI help any of the brand teams behind these and other brands gather insights to continue to elevate? Of course!
 
P.S. A bit closer to home I just noticed that Piano.io -  Analytics & Activation is building out a third space in the Flatiron district of NYC. I have no idea if AI was used to deliver consumer analytics that inspired that brand activation experience, but it could have!

True brand power is a high CLTV to CAC ratio and customers who say, to misquote a line from Brokeback Mountain, “I can’t quit you.”   

The

prompt interface

needs a

redesign

No, it doesn't

We need “smarter” humans, not design

Henrik Werdelin
Co-founder of BARK (BarkBox) and Founding Partner of Prehype, a venture development firm

Everybody hates chatbots. And yet, the fastest growing consumer-facing technology in human history is basically a chatbot. Is there a problem here? Is this a good moment for a design intervention?

Let me start with a fundamental question: why do people hate chatbots? Is it the design, or the experience?

Historically, conventional customer service chatbots were insanely annoying and, critically for users, fundamentally dumb. They locked users in a recursive loop of unanswerable questions that yielded no results; the only escape from this torture was to force a connection with a human.

In this moment, the human represents context and the ability to reason, while the bot represents a brick wall. So let me answer the first question: the reason people hate chatbots is the experience. The design is fine.

What happens when the conventional chatbot interface is replaced by a different backend technology? Generative AI is doing this right now by breathing artificial cognition into the brick wall, giving it the ability to convert context into reason, turning the whole interface on its head. No more need for any human intervention.

Layering generative AI into chatbots introduces two problems that a redesign cannot solve.

The first problem: humans are biased against chatbots.

They see the interface and automatically expect a conventionally dumb experience. This also happens when you see a website with a web 1.0 design. As a result, they enter into the experience with a lower level of engagement that affects the quality of their prompts. As the saying goes: garbage in, garbage out.

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

The second problem: humans are mostly lazy.

They imagine that a generative chatbot is going to magically read their mind and solve humanistic conscientious tasks with minimal effort. This perspective influences the quality of their prompts and the scrutiny they give to the responses. This looks a little different than the first bias: generic prompts generate generic responses that are accepted without a second thought because most don’t realize how much better the output can be. 

I have seen user testing experiments that confirm the second problem. Two user groups were asked to brainstorm creative ideas for an imaginary product. One group was given an AI chatbot while the other was given paper and pencils. The AI group produced the least interesting ideas and devoted the least amount of thinking to the assignment (until they were taught how to work optimally with the chatbot).

So let me sum up my argument: The AI chatbot interface does not need a redesign. It needs humans to catch up to its capacity. This involves something bigger than UX design.

Humans need to become better at conversation. This is going to take time, but it won’t be the first time.

I have been working on the internet since the 1990s. I spent time demonstrating internet browsers to executives who were still asking their assistants to print out webpages for them to read. Those executives were uncomfortable by the technology and simply needed more time to evolve. Going back further in the technology timeline, the keyboard and mouse triggered the same response. Sometimes, the design is not the problem and humans just need time to adapt.

Yes, it does

Design can make chat a better experience

Michael Treff
CEO, Code & Theory Co-Founder, ON_Discourse

It is hard for me to disagree with an argument that is so well-structured. But Henrik, this is ON_Discourse and we have to live by our creed. I think your approach to this topic is overlooking the impact of design and where we are in the adoption curve of AI semantic prompt-interfaces like chatbots we see in ChatGPT or text bars like we see in Google Search.

Before we get there, let me start by recognizing your strongest point:

The mouse and keyboard are an excellent analogy for generative AI chatbots. The analogy works because it acknowledges the transformational context that follows these interfaces. To put it bluntly, there was no precedent for either the mouse or generative AI at the release of either of those products. In both cases, it is natural to accept an elongated adoption curve. Sometimes, the burden just falls on people to figure it out.

But I am concerned by the focus of your argument. At the end of the day, the novelty of this tech does not relinquish the role and value of good design. Do you remember the AOL portal? For a limited time in the 1990’s, this was the predominant way most Americans accessed the internet. It had a design language so ubiquitous that it nearly defined the early-internet. Then new designs and platforms emerged for new behaviors that slowly disintegrated this design system.

When I think about the evolution of semantic chat experiences in the AI era, I see the same massive opportunity that you see, but I think of it in a different way. I’ll break it down in a few ways:

Good interface design focuses on behaviors.

The interfaces that win online are those that more accurately deliver an existing behavior in a better way. Tik-Tok pioneered a new interface for video creation and consumption without instruction manuals. Not only did this interface drive historic audience growth, it also fed an explosion of new UI paradigms for non-content experiences (from news feeds to credit card applications). Good design can accelerate the learning curve without being explicit.

At the end of the day, the novelty of this tech does not relinquish the role and value of good design.

The prompt-era is just beginning.

The use-cases for this interface are still in the proverbial laboratory. This is not the same context of the keyboard or mouse which was one of the essential catalysts of the personal computer revolution. It unlocked new ways of thinking about screen design, leading to the GUI, which ultimately brings us to our own digital discourse. AI is different. It is already propagating in the back-end operations of forward-facing companies. They are using this technology to drive internal creative sessions, augment audience research, and develop deeper customer profiles.

The jury is still out on the prompt.

I have no doubt that the prompt will become an essential interface on the internet, but I question whether it will become the dominant interface. The internet has evolved a number of optimized user experiences that are not served by an open-ended prompt experience. Let me put it this way; I’m not convinced that we are solving consumer problems by replacing context clues on a page with an open-ended prompt experience. In this vein, the customer is burdened with prompting in the right way to get the desired outcomes. I don’t feel like that is an improvement to what we have now (though I understand the impulse to consider it).

The technology behind the prompt is undoubtedly amazing and transformational. I agree that it promises to elevate our collective ability to use conversation to better understand our own needs and to find the information we need. Nevertheless, we shouldn’t assume the current interaction models are the future models. Additionally, when we ultimately deliver this tech, as with everything else that goes in front of consumers, it deserves a thoughtful, considered design.

Augmented

Intelligence:

from

UX to HX

Craig Elimeliah
Chief Creative Officer, Code and Theory

Editor’s note: ON_Discourse co-founder Dan Gardner argues that human behavior is the ultimate prompt. This article explores what this means for user interface design.

Augmented Intelligence (AI) is becoming an everyday reality, fundamentally transforming how we interact with our world. Its primary driver is human behavior, the main “prompt” for AI development and application.

This shift challenges us to reconsider the role of AI in our lives, focusing on how it shapes and responds to human actions and decisions. I believe this evolution will be marked by a transition from User Experience (UX) to Human Experience (HX), wherein technological innovation emphasizes, rather than obscures, humanity’s essence.

This is a critical moment. We are at the forefront of a design renaissance, wherein our focus is shifting from mere interfaces to meaningful interactions, from digital screens to the human psyche. Transitioning from UX to HX signals a shift from interface-driven design to a more empathetic, behavior-centric approach.

We are at the forefront of a design renaissance, wherein our focus is shifting from mere interfaces to meaningful interactions, from digital screens to the human psyche.

In this new age, technology will become more than a tool. We won’t just be using AI algorithms and automation, we will be building a symbiotic relationship that marries AI’s analytical strengths with human intuition and insights.

While such a synergy can accelerate and amplify our abilities, it also brings forth a critical challenge: ensuring that AI augments human creativity and intuition without overshadowing them. We must not overlook the ethical implications of an AI-driven HX. Personalization in AI, while beneficial, raises privacy concerns. We must grapple with these ethical challenges head-on, ensuring that AI’s incursion into our lives respects our privacy and adheres to ethical standards.

One of the assumptions in our AI-driven journey is the belief that AI can fully understand and replicate the depth of human emotions and behaviors. Here lies a potential pitfall. We must strike a balance between leveraging AI’s capabilities and preserving human intuition and ethics in design.

For HX to work, I envision AI transcending traditional data analysis to offer a nuanced understanding of human emotions and behaviors. Imagine a customer service chatbot that can detect frustration in a conversation and respond with empathy, or e-commerce AI that suggests products based on emotional cues.

This article is part of The Intelligently Artificial Issue, which combines two big stories in consumer tech: AI and CES.

Read more from the issue:

USER EXPERIENCE

Augmented Intelligence: from UX to HX

Will prompting replace browsing?

The car is the gateway drug to a voice-first acceleration

The prompt interface needs a redesign

RE-ORG

AI will brainstorm your next reorg

Expect fewer managers and direct-reports

AI is too immature for your business

AI is not a new revolution

BRAND

Should we ignore the hardware?

Can AI help consumers love your brand?

Your brand doesn't have enough data for AI

Can LLMs be optimized like search results?

Good brands will integrate more friction into their CX

In health care, AI could evolve to support doctors by providing data-driven insights while preserving the irreplaceable human element in diagnosis and patient care. In education, AI could adapt to students' emotional states, offering personalized encouragement and learning paths.

These scenarios underscore the shift towards ethical AI design, where algorithms must be crafted with diverse datasets and regularly audited to prevent biases, ensuring fairness and inclusivity. 

The AI era must also be characterized by transparency, with companies openly disclosing how their AI technology uses consumer data, thereby fostering trust. AI in smart homes, for example, should recognize and respect individuals' privacy preferences, ensuring that family boundaries are maintained.

As human behavior becomes the new prompt, it will become clear that the role of HX is not just to understand, but to respect and enhance human actions and emotions. The future will not be determined just by the sophistication of experiences that AI can create, but by how these experiences honor and elevate our humanity.