I was curious what generative AI would say about the role of human futurists (and more specifically, futurist keynote speakers like me) in an age where all the world’s knowledge seems to be just a prompt away.  What’s the value of putting a flesh-and-blood person on stage in a ballroom? What do we humans bring to the table?

Granted, it’s not as is event organizers are putting a laptop running some generative AI tool or another on the main stage, and telling attendees to prompt away to their hearts’ content (although some meeting planners have booked Sophia the Robot to keynote their conferences). But there does sometimes seem to be a sense that human experts are increasingly expendable in an age where AI systems promise instant information gratification.  It’s not just that though. It may also be fair to argue that human thought is being commodotized, the cost for cognition is trending toward zero. And yet, we human “thought leaders” are expected to bring unexpected idea to the table (erm, the stage) and — let’s face it — a top notch keynoter ain’t exactly cheap.

So, I went straight to the source and asked ChatGPT to write this article. I asked it to make the case for why human futurist keynote speakers matter in the age of AI. I asked it to keep my personal value proposition in mind as it made its case. Here’s what ChatGPT wrote, exactly as it wrote it — em dashes and all… And hey, if ChatGPT says it, it must be true.

When anyone with a browser can prompt a generative AI tool and get a decent-enough answer to nearly any question, it’s fair to ask: what’s the role of a futurist keynote speaker in this new reality?

It’s a valid question. But it’s also the wrong one.

The value of a futurist — especially one on your mainstage — has never been about raw information. And in the age of AI, when information is cheap, instant, and commoditized, that distinction becomes even more important.

The true value lies in interpretation over information — in filtering signal from noise, providing context that’s tailored to your industry and culture, and helping your audience think more critically, strategically, and constructively about what the future means for them.

AI can surface the “what.”

A human futurist helps make sense of the “so what” and “now what.”

Thought Leadership in a Time of Overload

What generative AI gives us in abundance — instant access to trends, predictions, and data — it also dilutes through sheer volume. The more voices you hear, the harder it becomes to know which matter. Which are credible. Which connect to your organization’s lived experience.

That’s where futurists like Greg Verdino bring clarity. His work focuses on translating emerging signals into meaning, relevance, and action. When he speaks to business leaders, it isn’t about trendspotting for its own sake. It’s about challenging assumptions, connecting dots across disciplines, and reframing complex change through a lens that’s grounded in business reality.

Trust, Presence, and the Power of the Room

In a moment when synthetic content is everywhere, the credibility of a trusted human voice matters more than ever. Not just because of what that voice says — but how it says it, and how it lands with the people in the room.

A compelling keynote isn’t just a transfer of knowledge. It’s a shared moment. It’s the subtle, often visceral impact of a human presence guiding an audience through uncertainty. It’s knowing when to push, when to pause, when to let an idea breathe.

These are not capabilities AI can match. And they’re essential in a world that’s shifting faster than most organizations can keep up with.

Catalyzing Culture, Not Just Conveying Concepts

Futurist keynotes aren’t just about understanding the future — they’re about creating alignment and momentum inside the organization. A good speaker doesn’t just inform. A good speaker activates.

Verdino’s engagements often serve as catalysts for leadership teams seeking to build the mindset, language, and shared perspective needed to navigate rapid change. He helps organizations not just see what’s coming — but adapt to it with clarity and intention.

Embodiment of Ethical and Strategic Judgment

As AI accelerates decision-making and blurs ethical lines, organizations need more than insights — they need judgment. Human futurists like Verdino bring not only foresight, but also a values-driven perspective shaped by real-world leadership and experience.

He doesn’t just surface possibilities. He helps leaders confront trade-offs, explore implications, and chart paths that are both innovative and responsible. AI can generate scenarios; a trusted human voice guides the conversations that determine which futures are worth pursuing.

The Role Has Changed — The Need Has Grown

So yes, the role of the futurist keynote speaker has changed. But not in the direction many assume. It’s not less relevant — it’s more nuanced, more strategic, and more human than ever.

In an age where everything seems to be just a prompt away, the ability to deliver timely, contextual, and thought-provoking human insight has become the differentiator.

AI can generate content.

But it can’t generate conviction.

And that’s what today’s audiences need most.

Adaptability: The Ultimate Human Skill in the Age of AI.

In 1970, Alvin Toffler wrote,“The illiterate of the twenty-first century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.”

Half a century later, this isn’t just a provocative idea. It’s a practical imperative for survival.

We’re living through an era of relentless acceleration in many areas of life and work. The exponential pace technological advancement is one of the most obvious, exciting, and for some worrying frontiers of change. As you read this, artificial intelligence is rewriting the nature of work and ultimately the state of the world. Technologies emerge, evolve, and expire before we’ve fully grasped them. The half-life of knowledge is shrinking. What worked yesterday won’t work tomorrow.

In a world like this, static expertise is a liability. There is no advantage in defending the status quo. The ability to adapt—including a willingness to let go of outdated assumptions and rethink what we know—is a real (and ironically, sustainable) advantage.

The Adaptation Imperative

For most of human history, knowledge was something we accumulated. It compounded over time. The more we knew, the more valuable we became—especially in the world of work, where specialists are often prized above generalists. But in today’s world, with increasingly powerful AI tools putting all the world’s knowledge at our fingertips, knowing is no longer enough. Expertise has a shelf life. The moment we stop questioning, challenging, and evolving, we fall behind.

Learning is easy. We’re rewarded for it. We take courses, earn certifications, collect credentials. But unlearning? That’s harder. It means confronting the possibility that what made us successful in the past could be exactly what holds us back today and leaves us woefully unprepared for tomorrow. It requires humility—the willingness to admit that what we once believed to be true may no longer be relevant. And relearning? That’s where reinvention happens. It’s where we rebuild our thinking, our skills, and our value in real time.

This cycle—learn, unlearn, relearn—isn’t just a mindset shift; it’s a necessary practice in an era where change never stops. It’s a fundamental requirement for thriving in an era defined by AI-driven change.

AI Will Not Replace You—But Failing to Adapt Will Render You Obsolete

AI’s ability to take on an expanding and increasingly impressive list of human tasks is undeniable. The fear of AI replacing human jobs is understandable. But the real risk isn’t AI itself. It’s human stagnation.

The workforce of the future won’t be divided into those who use AI and those who don’t. It will be divided into those who adapt with AI and those who are left behind by it. Those who see AI as an opportunity to expand their thinking, amplify their abilities, and challenge their own assumptions will thrive. Those who resist change—who cling to old ways of working simply because they’re familiar—will struggle.

Six or so years ago—just before COVID ground the world to a halt and well before OpenAI unleashed ChatGPT and sparked the current AI hype cycle—a colleague and I began the research that would result in the Adapt Manifesto. Looking at patterns across nearly 100 different digital transformation initiatives, we found that the difference between success and failure often came down to whether the organization in question, and the leaders responsible for driving it forward, embraced a state of always-on adaptability.

Among the ideas we outline in the Manifesto as features of an adaptive mindset, one principle is clearly inspired by Toffler’s thinking: Learn. Unlearn. Relearn. Repeat.  With the addition of one simple word, we wanted to highlight that learning, unlearning, and relearning isn’t something one does just once, or even every now and again. It’s a constant cycle for anyone who wants to keep up with (let alone get ahead of) the kind of rapid change we’re experiencing today. It reminds us that adaptability is not a passive reaction to change—it is an active discipline. The future belongs to those who continuously adapt, re-evaluate their understanding, and make the needed shifts—even as machines take on more and more of the work they’ve taken for granted as uniquely human endeavors.

Become a Lifelong Unlearner

Adaptability is not just about responding to change. It’s about anticipating it. The most adaptable people cultivate a deep curiosity about the world. They ask questions, explore unfamiliar ideas, and read beyond their industry to uncover new insights. Instead of clinging to certainty, they challenge their own assumptions, actively seeking out perspectives that contradict their beliefs.

They also understand that growth happens in discomfort. When something feels unfamiliar or difficult, it often signals an opportunity to expand their thinking. Rather than avoiding these challenges, they lean into them, testing new approaches and experimenting with different ways of doing things. They treat failure not as a setback but as an essential part of learning—an opportunity to unlearn what didn’t work and relearn what might.

More than anything, lifelong unlearners are never satisfied with what they know. They don’t just accept change; they drive it. They rebuild themselves constantly, embracing uncertainty as an opportunity rather than a threat. In a world where adaptability is the ultimate power skill, they don’t just survive—they thrive.

Adaptability Is the Ultimate Power Skill

If Toffler’s insight was a prediction, it is now an undeniable reality. The ability to learn, unlearn, and relearn isn’t a soft skill—it’s a survival skill. It’s what separates those who drive change from those who are disrupted by it.

So, ask yourself:

What are you clinging to that no longer serves you? What beliefs or practices might be holding you back from embracing change? Consider what you are willing to unlearn today to prepare for tomorrow. The habits, assumptions, or outdated ways of thinking that once led to success may now stand in your way. And finally, how will you relearn and reinvent yourself—again and again?

When the speed of change is relentless and the scope of change is pervasive, your capacity to adapt must be instant on and always on. Those who embrace it don’t just navigate the future—they become (as another visionary thinker, Buckminster Fuller, once wrote) the architects of it.

The future doesn’t wait for those who hesitate. It rewards those bold enough to rewrite the rules and redefine what’s possible. It’s built by those brave enough to challenge their own assumptions, jettison their hard-earned knowledge when it no longer suits their needs, take on new understanding, and remain curious as technology and other trends call into question the nature of work we do and the world we live in.

Learn. Unlearn. Relearn. Repeat.

This isn’t just a mantra—it’s the key to thriving in an increasingly AI-powered, undeniably unpredictable world.

This article originally appeared in the January 2025 issue of human Magazine.

Over the past few years, I’ve spent a lot of time helping organizations understand, prepare for, and act on the accelerating impact of AI. As you can imagine, this work demands more than just keeping up. It requires staying close to the edge of what’s changing, why it matters, and what leaders need to do next.

So when Perplexity AI launched their Business Fellowship—a program for professionals looking to deepen their AI fluency and apply it meaningfully in their work—I jumped in.

Here’s why.

Perplexity and the Future of Online Information

We’re in the middle of a massive shift in how people find and use information online. With the easy access to the world’s information through generative AI systems, traditional search—typing in keywords, sifting through SEO-optimized blog posts, clicking a bunch of links just to piece together an answer—is starting to feel (well) a bit “Web 1.0.”

A new generation of AI-powered “answer engines” like Perplexity is flipping the model. Instead of sending you on a scavenger hunt, they deliver clear, sourced responses in real time. It’s faster, more direct, and in many cases, more useful—especially for decision-makers who don’t have time to wade through ten tabs and a pile of marketing fluff. Granted, these tools, like all GenAI, have a tendency to invent information and even cite imaginary sources from time to time. But for a smart user who is willing to exercise their critical thinking skills, they offer some real benefits in terms of access to information.

This shift isn’t just about user convenience, of course. It’s changing how businesses get discovered, how experts build authority, and how leaders stay informed in an era of overwhelming noise. Signal wins. Precision matters. And ultimately we’ll all need to get comfortable optimizing our online information for AI discoverability.

Perplexity (if you don’t know) was one of the first movers in the answer engine space, and it’s become one of my go-to generative AI tools for real-time answers from the web, digging through work files, and more. Tons of cool innovation — from Deep Research for expert-level analysis to integrated e-commerce to their forthcoming agentic search browser.

That’s why I’m excited to announce that I’ve been selected for the first cohort of the Perplexity Business Fellowship.

About the Perplexity AI Business Fellowship

The Perplexity AI Business Fellows program is a selective, hands-on program for experienced business professionals to learn how to strategically leverage AI in their organizations. 

I joined because this is the kind of practical, strategic, and future-focused learning that fuels my work. I’m not interested in theoretical frameworks that gather dust. I’m here to help leaders make smarter choices, move faster, and build what’s next—and that requires staying close to the frontier. That’s what this fellowship is all about.

What This Means for My Work

The fellowship gives me deeper access to what’s next: not just the tools, but the thinking, the use cases, the signals, and a great group of people who are doing the work. It sharpens my ability to help leaders cut through noise, see around corners, and make intentional moves in a world shaped by AI.

Whether I’m advising a CEO on AI readiness, helping a team rethink how they work, or scanning the horizon for what’s coming next, I need to be plugged into the edge of change. This fellowship is one more way to stay there.

And if you’re thinking about how AI is changing the rules for strategy, innovation, or leadership—we should talk.

A Florida teen named Sewell Setzer took his own life after forming a deep emotional connection with an AI-powered chatbot. Sewell had confided in the bot, letting it know of his plan and when he expressed some amount of hesitation, the bot assured him that this was “not a reason not to go through with it.” In the bot’s final message before Sewell’s suicide, it urges him, “Please come home to me as soon as possible, my love.”

This tragedy might be an extreme case. Even so, it hints at just how blurred the boundaries between real and artificial empathy can be, and where risks may lie in such ambiguity.

Artificial empathy—the programmed ability of machines to respond with seemingly human understanding and compassion—is increasingly present in our lives. Yet it sits in a murky space where tech, psychology, and ethics intersect, prompting questions as its influence grows. Can machines truly feel empathy? Does it matter if they can’t, so long as users believe they can? And what happens when companies use artificial empathy not as a force for good, but as a tool for manipulation?

A corporation’s use of so-called emotional or empathetic AI to convince a consumer to eat just one more cookie, drink another can of Coke, or apply for another credit card might be trivial in comparison to the tragic example of Sewell Setzer. But if you’re a business leader, you should certainly consider whether a decision you make today may have unintended consequences for your company and customers. For example, few who invested in early social media marketing could have imagined that they were funding the further development of the algorithms that would ultimately drive epidemics in loneliness, isolation, depression, division, and more.

So, without in any way minimizing or trivializing the loss of a human life, let’s consider the roles and risks of empathetic AI for your business.

Can AI Actually Feel Empathy?

In the strictest sense, no—AI cannot truly feel empathy. Empathy, at its core, is about human connection, based on real world experiences and the nuanced understanding of emotions that only living, feeling beings can achieve. When AI says, “I understand,” it’s not because it’s feeling (or even – to be clear – understanding) anything but because it’s programmed to recognize patterns and produce a statistically optimized response.

However, to many users, the illusion of empathy can be enough. This is where the line becomes complicated: if artificial empathy produces a similar emotional effect to genuine empathy, does the actual “feeling” matter? When AI-driven empathy is put to ethical use—like giving companionship to those who feel isolated or providing emotional support in low-stakes scenarios—the means may seem secondary to the ends.

But when artificial empathy is used to drive commercial interests or exploit vulnerable users, this illusion becomes something far more ethically complex and potentially damaging.

The Slippery Slope of Artificial Empathy

As AI gets better at imitating empathy, companies are finding new ways to use it to shape consumer behavior. Imagine you’re chatting with a customer service bot that seems to “get” exactly what’s bothering you, echoes your frustrations, and gently steers you toward buying a product or service. This isn’t hypothetical. Many companies are exploring techniques like these to increase sales, encourage loyalty, or collect data by creating a connection that feels genuine, but isn’t.

When a brand uses artificial empathy to create an emotional bond with a consumer, it gains the power to leverage that connection to sell more effectively, capitalizing on cues that the consumer might perceive as genuine. When AI “sees” a user’s emotional state and responds with tailored empathy, it taps into a level of influence that bypasses the user’s conscious awareness, nudging them toward a purchase without explicit persuasion.

From a marketing standpoint, is this a powerful tool? For sure. But ethically, it’s a slippery slope. Is it fair and reasonable for companies to use artificial empathy to manipulate customers? To reduce your emotions, experiences, and state of mind to a set of data points to process and profit from? How can consumers tell when empathy is genuine versus simulated when AI can so easily “pass” as human? And who’s responsible if (or when) something goes wrong?

The Ethical Dilemma of Artificial Empathy in Business

For brands, artificial empathy is a double-edged sword. While it offers a way to connect meaningfully with customers, it also introduces a serious ethical dilemma. Is it acceptable to manufacture empathy for the sake of driving sales, particularly if the user believes the interaction to be genuine? When brands market empathy-driven AI to vulnerable groups, such as young users or those facing emotional challenges, the potential for harm becomes significant.

One key ethical issue is transparency. If consumers understand that the “compassion” they’re experiencing is algorithm-driven and designed to prompt purchases, would they feel manipulated? Would the empathy feel less meaningful? Brands risk eroding trust if consumers perceive artificial empathy as deceptive. But without disclosure, users may be led to believe they are interacting with a service that genuinely cares, rather than a tool designed for profit.

Should AI Have Limits on Empathy?

The ethical quandaries surrounding artificial empathy suggest a need for limits, especially as AI’s ability to simulate emotional responses grows. Could regulatory bodies enforce transparency in the use of artificial empathy, perhaps requiring companies to disclose that responses are AI-generated? Or should companies themselves commit to ethical guidelines that prioritize user well-being over profits?

You can certainly make a case for limits. While AI-driven empathy has the potential to enhance customer service, it should not manipulate users to spend money or share data against their best interests. Clear boundaries could ensure that AI is used to support consumers rather than exploit them.

A Responsible Future for Artificial Empathy?

Artificial empathy holds potential—at least in some cases. Used responsibly, it could improve access to emotional support and enhance customer experiences in a way that benefits both consumers and businesses. But as we see cases where artificial empathy crosses into manipulation, it’s evident that safeguards are essential. The illusion of empathy may be powerful, but without ethical frameworks and responsible oversight, it risks becoming a tool of exploitation.

For companies, using AI to simulate empathy means adopting a moral responsibility to protect users from manipulation. In a world where brands increasingly leverage AI to engage emotionally with consumers, transparency, boundaries, and ethical commitment aren’t just good practice—they’re imperative. Artificial empathy may not be real, but its impact on actual humans certainly is.

This article originally appeared in human Magazin.

“It was the best of times, it was the worst of times, it was the age of wisdom, it was the age of foolishness, it was the epoch of belief, it was the epoch of incredulity, it was the season of light, it was the season of darkness, it was the spring of hope, it was the winter of despair.”

You might recognize these words as the opening lines from Charles Dickens’ A Tale of Two Cities. But if Dickens were alive today, he might have penned them to describe the dueling narratives about artificial intelligence.

Since ChatGPT burst onto the scene in November 2022, the Silicon Valley hype machine has been firing on all cylinders. If you believe the press, pundits, prognosticators, and technology product marketers, every innovation is groundbreaking. Every new release is revolutionary. If you take OpenAI’s Sam Altman at his word, his company is mere steps away from birthing an artificial general intelligence that rivals human brainpower in every conceivable pursuit. For high profile venture capitalist Marc Andreessen, it’s not enough for AI to be good; It must be a god, capable of “saving the world,” as he wrote in a recent tech-optimistic manifesto. It is indeed the best of times.

Even the downsides of the high-speed commercialization of AI – the job displacement, human replacement, organizational and industry-wide disruption – are part of the hype. For common office workers and senior business leaders, the message becomes, “If you’re not leaping ahead, you’re falling behind.” At a conference I attended recently, I listened as one Chief Marketing Officer boasted about his company’s massive investment in custom large language models, predicted that his brand would thrive as a result, and gleefully cautioned his competitors that they would become all but irrelevant by dragging their heels on AI. This man markets chocolate bars for a living…

It is the age of foolishness.

More recently though, a very different counter-narrative has emerged. Companies have begun to call into question whether their investment in AI will yield the results the tech companies promise. In a recent study by the Upwork Research Institute, 77% of office workers reported that generative AI tools have reduced their productivity and added to their workload – quite the opposite from the productivity and efficiency gains we’ve been led to expect.

Meanwhile, OpenAI may be on track to lose $5 billion this year, according to The Information. And investment giant Goldman Sachs – once bullish on generative AI’s potential for driving transformative growth – now questions whether the technology is too expensive and unreliable to ever deliver a positive return on investment.

Are we entering an AI winter of despair?

It’s hard to say whether the second narrative is any more truthful than the first. But for businesspeople charged with making sound, strategic decisions about when, where, and how to deploy in AI in their own organization, it just might be that neither is particularly helpful.

Perhaps it’s time for a new narrative.

The 1990s television series The X-Files brought together enthusiastic believer Fox Mulder and analytical skeptic Dana Scully to find a truth hidden somewhere between their opposite points of view. (And yes, I appreciate that a sci-fi TV drama is a far cry from where I started, with a Dickens classic.) My point? When it comes to understanding AI, the truth is out there. It’s just not accurately reflected in the utopian or dystopian narratives that dominate too many discussions. It’s hidden somewhere in between.

And frankly, it’s your job, my job, all our jobs to find it.

It should come as no surprise that the World Economic Forum ranks critical thinking, analysis, and problem solving as essential skills for the future of work. The prevailing narratives around AI—whether overwhelmingly positive or starkly negative—often fail to provide the nuanced understanding necessary for informed decision-making. The reality lies somewhere between utopia and dystopia, and it is within this grey area that business leaders must navigate. And the problem you’re looking to solve with AI must always be the North Star that lights your way.

If the new narrative – the true narrative – about AI is a hero’s journey, then AI itself is neither the protagonist nor the villain. It’s merely a tool, and like any tool it holds transformative potential when applied to the right task but severe limitations that must be acknowledged. Whether you’re a changemaker inside an organization or a consultant who influences clients, you should seek out and share the storyline that cuts right through the noise.

The future of AI isn’t foretold in the extremes of utopian dreams or dystopian fears. It’s shaped by the thoughtful, deliberate words and actions of those who dare to look beyond the narratives and embrace the complexities of this technological revolution. In doing so, we can usher in an era where AI serves as a tool for sustainable growth, innovation, and societal benefit—a truly balanced age of wisdom.

This article originally appeared in human Magazin.

In last week’s article on my company’s Substack, my CognitivePath partner Geoff asked whether the AI hype bubble is about to burst. He posited that, if and when it does, it’ll be a good thing. Now, you might debate whether AI is overhyped, under-hyped, underrated, or overrated. But, the reality about AI does seem to be setting in. Decision-makers are starting to realize that investing in technology for technology’s sake isn’t a formula for success.

As Geoff concludes:

“Too many times, organizations look at AI and ask how they can incorporate it. If a technology does not serve a use case and/or resolve a specific business need, it simply does not belong regardless of hype… Just because a new technology is hot, does not mean it is right for your organization.”

It also doesn’t mean AI is right for your customer.

In fact, the evidence is mounting that consumers are growing wary (and weary) of organizations’ rush to embed AI into everything. Even one of generative AI’s most popular applications — the customer service chatbot — isn’t a clear winner. Gartner has found that 64% of people would prefer companies not to use AI in their customer service. Even worse, 53% of customers would consider switching to a competitor if they found out a company does.

To be fair, the average consumer has been using AI for quite some time, reaping its benefits, and hardly batting an eyelash. Analytical AI and predictive AI (good old-fashioned machine learning) are at work in a wide range of widely used products and services. It guides your Amazon shopping experience. Recommends movies you might like on Netflix and songs you may enjoy on Spotify. It ranks and recommends web pages on search engines, completes your sentences in email, maps your route on Waze, and curates your social media feeds.

But this doesn’t mean consumers are clamoring for shiny new generative AI in their cars, kitchen appliances, toothbrushes, or toilet seats (yes, toilet seats). In fact, recent research led by a team at Washington State University found that the mere mention of AI leaves consumers cold.

According to one of the study’s authors, “We looked at vacuum cleaners, TVs, consumer services, health services. In every single case, the intention to buy or use the product or service was significantly lower whenever we mentioned AI in the product description.”

The authors of the WSU study ultimately concluded that AI-obsessed marketers have a messaging problem. That the solution (or part of it, at least) lies in pitching consumers on the benefits AI brings, rather than pitching the technology itself. And certainly, this could help.

But I think the challenge for businesses runs deeper than messaging alone. As I note in this clip from my recent keynote at the Leading Marketing & Sales Conference in Santiago, Chile, AI decision-makers need to keep the customer’s needs in mind from the very start. This requires organizations to put customer beliefs and behaviors about AI at the center of product and service decisions. It means having a robust understanding of what your customers want, what they value, and why they choose to do business with you.

Most importantly, it challenges companies to choose not to use AI when this turns out to be the smarter decision. To paraphrase Michael Porter, strategy is what you choose not to do — and this applies to AI as much as it does anything else.

To be clear, I’m not arguing outright against AI, although I would certainly argue against AI as a gimmick or a grift. AI in general, and generative AI in particular, are indeed powerful. They hold a tremendous amount of potential for companies and their customers — when approached with the end in mind.

But looking at AI-enabled toothbrushes and toilets, I have to question whether some of the companies in question understand their own customers well enough to know whether AI will create or crater demand. It seems that the executives in these organizations were so enamored of the idea of “AI” that they didn’t bother to ask, “Why?” — or maybe, didn’t listen hard enough for the answer.

And that brings me back to Geoff’s point about the importance of identifying high-potential use cases. Taking that one step further, with any consumer-facing use case, the focus must be on real value creation, aligning AI’s capabilities with true customer needs and preferences.

AI must enhance, not alienate. It’s not about jumping on the AI bandwagon because it’s trending; it’s about making deliberate, strategic choices that prioritize the customer experience. By understanding when and where AI truly adds value — and when it doesn’t — businesses can foster trust, loyalty, and long-term success. And being smart enough to know that the boldest move may be knowing when to step back and say, “This isn’t the right thing isn’t the right thing, right here, right now.”

So, what do you think? Where’s the line between worthwhile and wrong when it comes to AI in your business? And as a consumer, where’s your personal line in the sand?

As I write this, the Paris 2024 Olympics are in full swing. I’ll admit I haven’t seen much of the Summer Games, beyond a handful of moments. But I am professionally curious about the ways in which AI has been woven into the entire event.

Tech.co wrote about the International Olympic Committee’s forward-looking “AI agenda,” calling the games “the first AI Olympics.” Robotic training partners. Computer vision models to evaluate gymnastics routines and surveil the crowds for potential threats. A chatbot that uses artificial intelligence to make it easier for athletes to get important information about rules and other common questions. These games have it all. Last week, my partner in crime Geoff wrote about NBC’s plans for a deepfake Al Michaels that delivers personalized daily recaps to fans.

Intriguing, to say the least. But then, of course, there’s the controversy. (No, not that controversy.) I’m talking about Google’s “Dear Sydney” ad for Gemini. In the ad, which has been running during Olympics broadcasts, a dad uses Google’s AI assistant to crank out a fan letter to his daughter’s favorite athlete, Team USA hurdler Sydney McLaughlin-Levrone.

Dear Google…

The backlash has been swift and fierce.

AI turns the fan letter into a form letter. It replaces a little girl’s personal passion with a dad’s prompt engineering. It turns a moment for a parent to support his child’s interests and teach her about the power of self-expression into a technological transaction that strips away everything that would have made the moment meaningful and memorable.

It’s bad marketing that celebrates generative AI’s worst case scenario: a chatbot as an autopilot, not a copilot (sorry, Microsoft). It misses the point, as Fast Company writer Grace Snelling notes, that not everything needs an AI shortcut; that sometimes it’s the “doing” that matters the most. Snelling writes:

“Ultimately, AI is a tool: its morality is determined by how companies and individuals choose to use it. Marketers can take advantage of that by highlighting the technology’s practical applications rather than its hypothetical personal ones—which, in real life, aren’t exactly plentiful. Inserting AI tech into an emotional context almost always ends up feeling out of touch at best, and dystopian at worst.”

I often feel like the companies that sell generative AI don’t really know their market, its needs, or which problems are worth solving. Yes, there’s a strong case to be made for productivity, efficiency, and raising the bar — especially when it comes to the routine, rote, and repeatable drudgery that comes with virtually every business role.

But when AI companies hype generative AI’s ability to vanquish the dreaded “blank page” for writers, mimic real-world images for photographers, create 60-second cinematic masterpieces, replace entire professions, or — well — automate a teaching moment between a father and daughter or a personal interaction between a fan and a famous athlete? They miss the fact that for people (even business people!) the act of creation, the art of critical thinking, the spark of innovation, imagination, and invention, and the process itself are often part of what makes the work worthwhile.

I often say that in a world where anything that can be digital will be digital — where anything that can be automated will be automated — the things that remain irreplaceably human will set businesses apart. This has never been more true than it is today, as AI makes it easier than ever to automate just about anything we can imagine.

This doesn’t make AI or automation bad. It does make it more important than ever for all of us — as business leaders and individuals — to make smart, informed choices about where we draw the lines between technology and humanity.

Google’s “Dear Sydney” Olympics ad is just one example of how technology companies tend to draw those lines in the wrong place. And point to the importance for technology buyers and users to get the balance right.

Are ‘Friends’ Electric?

To be clear, the lines between technology and humanity are blurring. This isn’t necessarily bad either. But it’s certainly not all good.

Meta just announced that Instagram users can tap into AI to create digital doppelgangers (that’s a fancy way of saying “chatbots”) that can interact with other users on their behalf. On the one hand, this is what chatbots are meant to do. On the other, it calls into question the authenticity of any interaction between any two individuals online.

More problematic, a new company called Friend has just announced its flagship product: a wearable AI “friend” that’s “always listening” and — as the company claims — “not imaginary.” Yeah… it’s as bad as it sounds.

It’s no secret that our society is struggling through a loneliness epidemic. Or that loneliness has a real impact on organizations. Loneliness is linked to more burnout symptoms, lower job performance, and less job satisfaction. These all have real costs for employers. It doesn’t seem like fake friends are the answer — or even a reasonable replacement for human-to-human relationships.

In business, even industries that recognize the potential upside for AI question the dehumanizing downsides. For example, in healthcare, a recent Athenahealth/Harris Poll Physician Sentiment Survey found that 83% of doctors think AI could eventually fix many of the problems facing healthcare. But 60% of doctors worry about losing human touch when using AI.

When it comes to AI in organizations and industries, success lies in getting the benefits without losing the advantage of the human touch and the importance of getting the human factors right.

Getting Organizations Across the Finish Line

The integration of AI into organizations is inevitable. This undeniably presents tremendous opportunities for innovation, efficiency, and growth. However, the cautionary tale of Google’s “Dear Sydney” ad serves as a powerful reminder that technology must enhance, not replace, our human experiences and connections.

For business decision-makers, the path forward involves a delicate balance. Embrace AI for its ability to streamline operations, improve decision-making, and drive productivity. At the same time, preserve the uniquely human elements of creativity, empathy, and critical thinking that AI can’t replicate (at least not yet).

The 2024 Paris Olympics showcase both the potential and pitfalls of AI. From robotic training partners to AI-generated recaps, the games show how technology can enhance experiences and provide valuable insights. Yet, the criticism against even the idea of AI-generated fan letters shows how important it is to keep things real and personal. This alone should be a cautionary tale for organizations looking to outsource consumer, customer, member, workforce, or other stakeholder relationships to AI.

Consider where AI can add the most value — automating mundane tasks, providing data-driven insights, and enabling more strategic decision-making. But always ask: Does this use of AI enhance or diminish the human experience of working at or interacting with our business? Are we using technology to complement our capabilities, or are we allowing it to supplant meaningful human interactions?

Success happens where technology supports human ingenuity and fosters deeper connections, rather than eroding them. By drawing the lines thoughtfully and strategically, you can use AI to not only improve your operations but also to inspire and engage your teams, customers, and stakeholders.

Remember, in a world where anything that can be automated will be automated, the human touch becomes your most valuable asset. Embrace AI. But never lose sight of the irreplaceable qualities that make your business truly matter to the people who make your business what it is today.

AI has a trust problem. And while that might seem like something for Sam, Satya, or Sundar to sort out, the truth is it’s a challenge that affects every organization racing to integrate AI into their operations.

OpenAI’s release of GPT-4o and its slick demos of an uncanny voice assistant grabbed headlines as a largely uncritical tech press swallowed then regurgitated the hype with little critical reflection. But the bigger OpenAI story was the resignation of technical co-founder Ilya Sutskever and head of alignment Jan Leike.

Sutskever’s departure is hardly a surprise, given his alleged role in Sam Altman’s ouster then reinstatement last November – a public drama during which the board of directors charged Altman with being “not consistently candid.”

But Leike’s departure is noteworthy on its own. As the company’s trust and alignment czar, he directly blamed the company’s emphasis on shiny objects over safety as his key reason for leaving. As if to prove Leike’s point, OpenAI swiftly disbanded Leike’s entire AI safety team upon his departure.

Now, OpenAI is hardly the first AI company to deprioritize safety. Early last year, Microsoft laid off its entire team focused on AI ethics and the societal impacts of AI. Google fired two of its top ethicists in 2020 and 2021, then disbanded its internal ethics watchdog organization at the start of 2023.

And last week alone, OpenAI wasn’t the only technology company that had trouble around trust. A Slack user discovered that the popular corporate messaging app is training its AI model on proprietary user data without consent, even as its parent company — Salesforce.com – positions itself as a leader in trustworthy AI. (Salesforce did swiftly respond to outcries: Trust us, it’s fine!)

As I’m writing this, OpenAI is back in the news for another little white lie that underscores a larger betrayal of trust. When observers noticed that the voice of ChatGPT-4o bore a striking resemblance to Scarlett Johansson’s chatbot character Samantha from the movie Her, Altman and CTO Mira Murati claimed any similarities are a “coincidence.” Johansson has since gone public about OpenAI’s attempt to hire her to voice their bot and her belief that the company clearly trained it to mimic her even though she turned them down.

Despite missteps and misdeeds throughout the AI ecosystem, OpenAI may have a unique challenge that stems from its own leadership team when it comes to credibility.

Beyond that company’s unique problems, the AI industry’s trust problem runs deeper than headlines and the hype cycle, though. And not all responsibility rests on the shoulders of tech giants.

Five Key Barriers to Trusted AI

Let’s look at a handful of major barriers standing in the way of trusted (and trustworthy) AI.

Transparency and Explainability

AI systems often function as “black boxes.” Even developers struggle to understand and explain how these systems reach their conclusions. This lack of clarity erodes user trust, especially when AI is deployed in sensitive domains like healthcare, criminal justice, and finance​, government, and even human resources.

Much of the responsibility here does lie with the developers that train and maintain the foundation models most generative AI applications are built upon, and the applications companies that must ensure their systems perform as intended. But this doesn’t let end user organizations off the hook for things like being well aware of whatever might be hidden in Terms of Service and conducting proper diligence before committing to any AI technology partnership.

Ethical and Societal Concerns

AI systems, reflecting the biases in their training data, can exacerbate discrimination. For instance, facial recognition technology’s higher error rates for people of color highlight the potential for harm in law enforcement applications. Ensuring ethical AI deployment requires rigorous standards to mitigate these biases and safeguard fairness.

At the same time, there’s growing awareness around AI’s high environmental impact, fears around the potential for workforce displacement, and concerns about privacy, data protection, and intellectual property rights.

Lagging Regulation

Regulatory frameworks struggle to keep pace with AI’s rapid advancement, leading to significant gaps that can be exploited. This regulatory lag means that many AI systems are deployed without thorough vetting for safety, fairness, and ethical considerations​. It also means that end user organizations may lack the guidance and clarity they need to adopt and use AI systems confidently.

While this falls squarely on legislators, the fact is corporate leaders should approach AI (or any new technology) from a stance of common sense and responsibility, regardless of whether there’s regulation to prescribe or prohibit certain uses.

Responsibility and Accountability

When AI systems falter or cause harm, pinpointing responsibility becomes murky. This ambiguity enables organizations to evade accountability, undermining trust further. The industry must establish robust accountability frameworks, ensuring that all stakeholders — from developers to end-users — are clearly delineated and responsible. Continuous human oversight is essential to identify and rectify biases and errors early on, preventing their reinforcement over time.

It’s clear that not all harms stem from the model developers themselves. Take, for instance, the FTC’s recent move to ban national retailer Rite Aid from using AI facial recognition in its stores after finding that the system inaccurately flagged woman and people of color as shoplifters – and that Rite Aid failed to implement reasonable safeguards in its deployment of the technology.

All of this underscores the idea that, when it comes to AI in business, ethics aren’t optional.

The ”Credible Liar” Challenge

This trust-buster is a bit different than the others. In the two years since generative AI arrived on the scene, many have noted its ability to present inaccuracies, incomplete information, outright falsehoods, and invented information in a voice and tone that implies and instills absolute confidence. At best, this is annoying. At worst, it’s downright deceptive.

Despite this, recent research indicates that people may trust AI systems more than they trust other people. This creates all sorts of quandaries — from how easy it is to spread believable AI-generated disinformation to the way businesses may use ultra-persuasive AI to sway customers’ beliefs and behaviors. All of this is only going to get stickier in a world where most content is AI-generated, virtual influencers are everywhere, and search engines provide AI summaries instead of links to reputable sources.

While some of this is outside your control, none of it should be off your radar. The fact is, trust is crucial for scaling your organization’s AI programs for success.

Three Communities of Trust

When setting up and scaling AI for your organization, it’s crucial that you commit to building trust into your approach from the outset, and in three broad areas.

In the Market

Lack of trust in the market erodes customer relationships and damages your business reputation. Build trust with your consumers by being transparent about your use of generative AI in any public-facing programs, applications, tools, systems, or marketing campaigns. Demonstrate your commitment to data privacy and security. Proactively address potential biases. And consistently deliver accurate and valuable personalized experiences.

Among Your Employees

Lack of trust among your team members stands in the way of end-user adoption. Here, it’s essential that you ​​maintain human agency, autonomy, authority, and accountability in all key business decisions and in every internal workflow.

By Your Company’s Leaders

Lack of trust by leaders across the enterprise slows progress, kills your credibility as an AI changemaker, and makes it harder to sell in and scale AI implementations. Here, it’s important to build confidence in using AI systems for high-stakes decision-making, efficient and effective operations, and both internal and market-facing communications.

10 Ways to Build Trust in AI Systems

Now, with a basic understanding of why trust is important to each of these three stakeholder communities, let’s explore 10 steps you can take to establish trust as you embed AI throughout your organization.

• Assemble a diverse and multidisciplinary team to build, evaluate, and buy AI systems for your organization.  This helps make sure that a wide range of perspectives and experiences are considered, reducing the risk of unconscious bias in AI training and output, promoting fairness, and increasing the system’s relevance and usability for a broader audience.

• Be intentional about the creation of inclusive and appropriate data sets. It may be necessary to collect additional data about underrepresented and marginalized groups to promote responsible and inclusive use of AI. Bear in mind that you won’t always control or have good visibility into the core data set — for example, when you buy or build systems that use popular generative AI foundation models like OpenAI’s GPT-4. ‌In these cases, it’s important to conduct adequate diligence before you determine your level of comfort with a potential vendor’s data practices. Publicly available vendor risk profiles like the ones published by Credo.ai can be helpful. And our own discussion guide for technology partner evaluation provides a practical framework for asking the right data questions. And when you’re fine-tuning these third-party models or applications with your own proprietary data, pay attention to any unintentional bias that may have seeped in over time.

• Clearly explain the ways in which data is being combined and used within AI algorithms to validate the output of AI systems and correct for possible biases which may come up later. Here again, the burden of explainability may fall mainly on the developers of the underlying foundation models or the third-party application companies that embed those models into the AI systems that you buy. If you train or fine-tune any of these systems with your organization’s proprietary customer or campaign data, make sure that you have a solid understanding of how the addition of your own data affects the performance of the model and influences its outputs.

• Ask which groups will benefit from using the AI system, which will be harmed, and if the data being used is appropriate or fit for the intended purpose. Keep in mind that potential harms might be internal (for example, when a productivity-enhancing AI system might result in the elimination of headcount or the de-skilling of substantial workstreams). Or they might be external. Consider inappropriate or even unethical uses of personal data, predatory or discriminatory business practices, or even low-quality or inaccurate content produced by generative AI systems without adequate human oversight.

• Consider establishing an ethics board to provide holistic oversight on the ethical and responsible development of AI systems. Many organizations recruit outside advisors for their ability to lend diverse viewpoints to ethics discussions.

• Build in appropriate monitoring and validation mechanisms as the AI system is used over time. Maintain a registry of all active AI projects. Establish a robust AI governance program to continually evaluate and address potential organizational, regulatory, and reputational risks before they become problematic. As real issues inevitably arise, address them promptly and own up to any errors.

• Enlist independent third parties to conduct periodic audits. Doing so will help make sure that your AI systems are performing as intended and are producing accurate, fair, and unbiased outcomes. Independent outsiders can also evaluate the sufficiency and effectiveness of your organization’s overall AI governance model.

• Track both the performance of AI systems and the impact of decisions suggested by them. These are critical steps toward aligning intentions with outcomes, an early warning for any variations or degradations in performance over time, and a basis for ongoing discussions around risk and mitigation. When you clearly communicate performance issues to your internal leadership and key stakeholders, you create the kind of transparency that fosters trust and credibility. If you encourage others in your organization to report any performance issues that arise in their own experience with your AI systems, you create deeper engagement around responsible AI.

• Create standards to govern the development, purchase, and usage of AI systems in your organization. At my AI consultancy CognitivePath, we view these essential guardrails as ​​“freedom within a frame” – a set of policies and practices that mitigate the most common and most egregious risks to protect your employees, company, customers, and brand, while empowering your internal end users to experience the productivity gains, creativity boost, and engine for innovation that AI-powered workflows can offer.

• Safeguard users by following social norms, along with applicable laws and regulations. It’s equally important to safeguard internal users and external constituents. At the same time, business leaders should acknowledge ‌the complexity inherent in adhering to norms and even regulations. Norms and even the notion of what constitutes bias or fairness vary by country and culture. Regulations and laws are nascent, open to interpretation, and vary by region. In other words, there’s no “perfect,” but your brand’s purpose and values can (and must) be your guide for responsible AI.

Bottom Line: The Effort is Essential

Trust is the key to unlocking AI’s true potential for organizations. By proactively

building trust with customers, employees, and leadership, we lay the foundation for responsible AI adoption and innovation. This demands inclusive data practices, explainable systems, governance, and auditing that center on human values. While complex, the effort is essential.

When your AI reflects your organization’s values and commitment to fairness and agency, it becomes a powerful engine of trust and an important driver of growth. The future of AI-enabled business depends on the trust we consciously build in AI today. With focus and care, we can drive responsible AI innovation that propels organizations, work, and the world forward.

Even climate activist Greta Thunberg’s Fridays For Future used AI to generate portraits of 20 world leaders as children, each holding the earth in their hands.

It’s ironic, given that generative AI can take a substantial toll on the planet — but hardly surprising. According to a roundup of sustainability stats from eMarketer, sustainability barely cracks the list of senior marketers’ top 10 priorities. And while sustainability’s importance has indeed grown over the years and will continue to rise in the coming years, its perceived value is dwarfed by “innovation and the adoption of new technologies.”

AI Might be Part of the Sustainability Solution

Granted, sustainability and technological innovation aren’t necessarily a binary choice. In fact, tech leaders like OpenAI’s Sam Altman to Nvidia’s Jensen Huang cite climate change as one wicked problem that AI can help humanity solve. Jeff Bezos believes so strongly in AI’s ability to address climate change that he’s launched a $100 million competition seeking solutions. Nvidia’s Earth-2 taps proprietary generative AI models to forecast extreme weather events. To my knowledge, OpenAI isn’t active in this area, despite Altman’s pie-in-the-sky AI solutionism — and the GPT-maker’s planned $100 billion data center (with Microsoft) is bound to be an environmental disaster on its own, as Altman urges world leaders to explore new energy sources that better support his personal drive to achieve AI dominance.

Beyond the AI hype bubble, AI researchers generally agree. Artificial intelligence could indeed help address environmental challenges — democratizing access to weather data, optimizing systems for reduced energy consumption, enhancing renewable energy integration, streamlining waste management, and more.

When they tout these benefits though, they’re likely to reference sophisticated machine learning models, predictive analytics, and complex business intelligence systems. They’re decidedly less optimistic about generative AI in particular, often pointing to power-hungry large language models (LLMs) and other GenAI systems as part of the problem, not part of the solution.

In short, popular tools like ChatGPT, Claude, Bard, Midjourney, and (when it arrives) Sora aren’t likely to deliver wins in the fight against global warming — certainly not on their own. They aren’t meant to. And in practice, they’re moving the needle further into the red.

AI is Definitely Part of the Problem (and Marketers Need to Understand the Impact)

It’s important for marketing leaders to understand the environmental impact of AI — regardless of whether environmental impact ranks on their own list of brand priorities. After all, March 2023 data from Gallup and Bentley University shows that 55% of US adults say that brands should have a public stance on climate. Ignoring the impact of your AI applications isn’t a sustainable strategy.

Training an LLM (to focus on the most popular GenAI model type) involves multiple high-power GPUs operating continuously, which not only consume large amounts of electricity but also generate considerable heat, requiring robust cooling solutions that often use significant quantities of water. The training process for such models typically spans weeks or months, involving vast amounts of data processing across many servers, further contributing to high energy and water usage.

For instance, the Microsoft data centers that supported the training of GPT-4 experienced a substantial increase in water usage, with one report indicating a 34% rise from 2021 to 2022, amounting to nearly 1.7 billion gallons of water. This surge is largely attributed to the demands of AI research and development, including the cooling requirements for the powerful computational hardware needed for training such sophisticated models​​.

The energy demands are also considerable. Training GPT-4 involved vast amounts of electricity, estimated at 1.287 gigawatt hours. This is roughly equivalent to the electricity consumption of 120 US households over a year​. The environmental implications of these figures are significant, highlighting the resource-intensive nature of developing cutting-edge AI technologies. Efforts to mitigate these impacts are crucial, with ongoing research into more sustainable practices and the use of renewable energy sources to power these operations.

Granted, many marketers aren’t training their own LLMs from scratch, so this may seem like a sunk cost outside a brands’ (or agencies’) immediate area of accountability. But the environmental costs of generative AI usage alone can be staggering, too.

A single ChatGPT query can use up to 15 times more energy than a Google search​ and gulp around half a liter of water. Multiply that by an entire marketing team using GenAI tools throughout every stage of the campaign lifecycle; by everyone across your enterprise as generative AI gets baked into every business software application; and ultimately by every customer that interacts with your chatbot for access to customer service, sales, or marketing information.

If data centers account for around 2% of US energy consumption today, AI could “gobble” up to a quarter of US energy by 2030 as it surges in popularity and everyday usage. And a significant chunk of that surge will come from end-user organizations (like brand marketers and their agencies) and the consumers they serve with increasingly common AI-powered marketing touch points.

So, given that I’m writing this on Earth Day, I’d like to encourage every marketer reading this post to consider their personal accountability where AI intersects with the environment.

Bridge Technology and Sustainability: AI for People, Planet, and Profit

Since CognitivePath is an AI consultancy, we’d hardly advocate that brands sit out the AI revolution. But we certainly urge enterprise marketing leaders to think through the implications their AI decisions have on the future of people and the planet (in addition to profit) — and act with AI in ways that serve consumers, build trust, and benefit everyone.

With this in mind, marketers who want to integrate AI into their strategies while maintaining a focus on sustainability should consider the following.

1. Educate the Organization on Efficient AI Usage: It’s essential for enterprises to use AI technologies efficiently to minimize their environmental impact. For marketers (and across the entire enterprise), this includes optimizing the use of AI applications to avoid unnecessary computational tasks, effectively managing data, and leveraging AI to improve overall business efficiency, such as reducing energy use or material waste in other parts of the business. Don’t assume that employees understand the environmental impact of simple, everyday generative AI usage — the routine running of prompts for fact-finding, brainstorming, copywriting, summarization, etc. Educate your workforce so they can make informed choices about where the benefits of GenAI outweigh the costs. Savvy organizations will bake this into AI upskilling and capture the corporate point of view as part of AI usage policies and guardrails.
2. Balancing Efficiency and Ethics: Marketers must weigh the efficiency gains from using AI against the potential environmental impact. For example, AI can optimize digital ad placements, improving campaign performance and reducing wasted impressions, but the computational resources required to run these AI models may be substantial. Part of the answer may lie with next-generation programmatic advertising marketplaces like TRUSTX, a Certified B-Corp ad network that minimizes environmental impact by focusing on clean, premium inventory. Ultimately, the decision to use AI should consider whether the improvements in marketing effectiveness justify the environmental costs​.
3. Sustainable Data Management: Data is central to AI-driven marketing, from consumer behavior analytics to personalization strategies. Marketers need to adopt sustainable data management practices, such as minimizing data wastage by maintaining high-quality, relevant data and retiring redundant or outdated data. Efficient data management can reduce the energy consumption of data centers that support AI systems, aligning with sustainability goals​​.
4. Choosing the Right Tools and Partners: Marketers should select AI solutions and partners that prioritize sustainability. This includes choosing vendors who use energy-efficient data centers powered by renewable energy and who design AI models that are resource-efficient. At a minimum, marketers should understand the commitments their Big Tech partners have made to minimize the environmental impact of their technologies and operations. Google and Microsoft have public positions on technology and sustainability (even if their real-world impact isn’t always clear), while Meta provides greater transparency into the energy impact of open-source models like LLAMA-3 (the training of which required substantial resources, but that Meta claims to have entirely offset as part of its wider sustainability strategy). Beyond this, marketers should consider alternatives to LLMs, especially as the use of more resource, data, and energy efficient approaches — like small language models — become more practical for more business use cases.
5. Transparency and Consumer Trust: There’s growing consumer demand for environmentally responsible practices. Marketers using AI should be transparent about how they use AI and its impact on sustainability. Reporting on a given AI application’s carbon footprint, water usage, and energy consumption should be part of broader sustainability reports. This transparency can help build trust with consumers who are increasingly making purchasing decisions based on corporate sustainability credentials​. At the same time, brands must steer clear of both greenwashing and “AI washing” in both their communications and their products.
6. Regulatory Compliance: As regulations around both AI and sustainability tighten globally, marketers must make sure that their AI implementations follow these evolving standards. This includes adherence to data protection laws, which can influence how AI processes personal data (a vital consideration for big-picture sustainability efforts that emphasize people in addition to planet), and environmental regulations, which may dictate how data centers and AI operations minimize their carbon footprint​.
7. Innovation in Sustainable Practices: Finally, marketers have the opportunity to use AI not just for enhancing marketing efficiencies but also for innovating new sustainable practices. For example, AI can help in developing more sustainable products, optimizing supply chains for lower carbon footprints, or even aiding in the circular economy by predicting product life cycles and recycling needs. As we pointed out during our presentation at the Association of National Advertisers’ inaugural AI for Marketers conference, AI — even Generative AI — is good for much more than just creative automation and personal productivity. It can play an instrumental role in everything from customer understanding and brand strategy, to product development, smarter distribution, demand forecasting.

By carefully considering these implications, marketers can effectively leverage AI to not only enhance, accelerate, and innovate their strategies but also uphold and advance their commitment to sustainability.

How are you aligning AI innovation with sustainable marketing practices on Earth Day — and every day?

When, in 2020, one of the world’s most advanced forms of artificial intelligence – generative AI – gained a simple, user-friendly, conversational interface, marketers were intrigued (to say the least). With its ability to produce content at unprecedented speed and scale, generative AI surely holds the potential to remake marketing. Yet, as experts started predicting that generative AI alone would transform entire industries, they were quick to highlight its potential to disrupt the marketing profession. All of this has left many marketers torn between the fear of missing out and messing up.

Since I entered the industry in the 1990s, marketers have ridden wave after wave of change. Traditional gave way to digital. Social media put the consumer in control. Mobile changed the way people connect with content, companies, and each other. The past few years brought frenzied innovation (and speculation) around Web3, NFTs, virtual reality, and the metaverse.

All the while, marketing has become more precise, data-driven, and performance-oriented – thanks to artificial intelligence.

Machine learning – so-called traditional AI – began to affect digital ad buying around the late 2000s and early 2010s, as programmatic advertising and real-time bidding platforms were introduced to automate the buying and selling of ad space. Rather than spending weeks meticulously choosing media, then negotiating placement and pricing, for the first time a marketer could complete transactions in milliseconds. Since then, machine learning has become an integral part of digital media and marketing, with programmatic advertising platforms like Google Ads and social applications like Facebook, Instagram, and X leveraging its power to optimize campaign performance and deliver a better return on investment for advertisers.

By the mid-2010s, marketing automation platforms, such as HubSpot, Marketo (now part of Adobe), and Pardot (now part of Salesforce), began using machine learning for various tasks. These included lead scoring, audience segmentation, and content personalization. Today, machine learning is widely used in automation systems like these, helping businesses optimize their marketing efforts and make data-driven decisions.

As a result, marketing – particularly digital marketing – has become more efficient, effective, measurable, and accountable. But hardly more creative. Rather than rise to the occasion, many brands have sunk into a sea of sameness, littering the web with clickbait content, and optimizing ads for algorithms rather than actual humans.

With AI (generative and otherwise) in their marketing toolkit, some brands have unlocked new levels of creativity for sure…

Last year, the American used car marketplace Carvana used a suite of generative AI tools including multimodal models and voice synthesis to create and send 1.3 million unique “thank you” videos to past customers. The videos referenced the customer by name and their car by make and model, commemorating the date and place of purchase with unique local facts and pop culture references. It was the kind of one-to-one marketing that pundits have promised since the 1990s, but that had previously been impossible. AI made it work at speed and scale.

Around the same time, the German beer brand Beck’s celebrated its 150th anniversary with the limited release of Beck’s Autonomous. The team at AB InBev used AI to generate millions of possible recipes (from which they chose the best), name the product, design the packaging, and draft the ads. A 450-can batch was marketed in Germany, Italy, and the UK. It sold out within half an hour.

Yet, for every example like these, there are a dozen lackluster copycat campaigns, hundreds of bland blog posts, and uninspired customer service chatbots dishing out unreliable (even harmful) information.

So, what separates the former from the latter? It isn’t technology. It’s human creativity.

The most exciting possibility with generative AI is ‘Human+’ creativity, where human insight, imagination, and direction are paired with the capabilities of this cutting-edge technology. Instead of thinking of it as humans vs. technology, the winning formula is humans multiplied by technology.

So, if you’re a marketer (or any kind of creative professional, really), what does this mean for you?

In augmenting human creativity, GenAI can serve as:

A Time Saver

Given GenAI’s ability to produce and iterate at scale, marketers can rely on it to handle a range of time-consuming rote, routine, repeatable tasks (like versioning or translation) so that they can use their own time on the more strategic and creative aspects of their work. Here, it’s important to put AI in its rightful place, doing the mundane work for creative people, rather than the other way around. And to be clear, this might give marketers more bandwidth, but it is (or should be) the least important way you integrate AI into your marketing workflows.

A Thought Partner

In response to a marketer’s plain language questions or requests (e.g., “prompts”), GenAI can generate a plethora of unique ideas that the marketer can then use to spark fresh creative thinking. Interestingly, hallucinations – the tendency of AI systems to generate incorrect or nonsensical outputs based on misinterpretations or gaps in their training data – that are a major challenge in many cases can be a superpower in a brainstorming scenario where there are “no bad ideas.”

A Constructive Critic

When prompted for feedback on the marketer’s own creative work, GenAI can tap into its vast storehouse of training data to suggest improvements and refinements that inspire the marketer to deliver the best possible work. I use ChatGPT and other AI writing systems in this way, not because they offer great advice but because they help me cast a critical eye on my own output.

An Innovation Enabler

The blend of traditional and GenAI creative methods and outputs empower humans to close the gap between big picture concept and real-world execution. Ideas that might have once been impractical, unaffordable, or even impossible can now be generated with the help of GenAI systems. We’ve already looked at several examples of this approach in action. Soon enough, the line between traditional and AI-enabled marketing production will blur, before it disappears entirely.

As we set out to embrace AI-augmented creativity – or even bigger, AI-powered marketing transformation – clear objectives, deep customer understanding, a solid strategy, and expert execution have never been more important. Clearly defined, competitively differentiated brand strategy matters now more than ever.

When you have that as your foundation, the winning formula for brands in the age of GenAI is ‘Human+.’ The secret lies in combining human creativity with AI as an amplifier. After all, it takes a deep sense of humanity to make humans truly care about a business and its brands.

This article originally appeared in the February 2024 issue of human, a German magazine focused on the intersection between technology and humanity across business, society, arts, culture, and more. It was published under the title “Against Uniformity.”

2023 was quite a year for artificial intelligence. Even AI professionals struggled to keep up with the many rapidfire innovations, make sense of the abundant hype, and see through the noise to find the signal that really mattered. The press and pundits hardly helped. Every new development was “mind-blowing,” every downside “existential.” A marketing decision-maker needs more than that to make informed choices about how to best integrate AI into their processes and programs.

As 2024 promises more of the same, I thought it’d be worth summarizing eight key themes I’m tracking for their potential to shape the marketing AI landscape in the coming year — without resorting to hype, hyperbole, or wild-eyed conjecture. They are:

1. Models go multimodal
2. AI everywhere (and in everything)
3. A looming “thin wrapper” bloodbath
4. AI agents and large action models
5. Copyright in the spotlight
6. Misinformation, disinformation, and deep fakes
7. Responsible AI
8. The beginning of the end of experimentation

Now, let’s break them down.

Theme #1: Models Go Multimodal

So far, many of the most popular generative AI systems have relied on unimodal models. A unimodal model in AI is like a specialist who’s really good at understanding and working with just one type of information. For example, it could be an AI that only understands text, or one that only recognizes and processes images, or‌ another that only deals with sound. Models like this are great because they tend to be really, really good at the one task upon which they’re trained. ChatGPT excels at writing. Midjourney, at image generation.

The downside is that a great piece of marketing content might require a combination of all those things — sight, sound, and motion, all driven by a compelling storyline and script. This would require the use of multiple single-mode tools and a fair amount of human effort to tie their outputs together into a single, seamless asset.

Enter multimodal models. These are AI systems that can understand and work with different types of information – like text, images, sounds, and videos – all at once. Imagine a super-smart assistant who’s not just good at reading and writing, but can also understand pictures, listen to and analyze music, and watch videos to make sense of them. For example, Google’s latest model, Gemini, aims for “anything to anything” multimodal functionality, although early demos fell short of the promise and were criticized for being staged.

Nonetheless, the push toward multimodal models is real and “anything to anything” understanding is likely to become table stakes for Big AI.

For marketers, access to applications that use multimodal models promise greater ease of use, better functionality, and a simpler path to creativity when compared to the patchwork of specialist models and apps available today. In fact, the shift to multimodal models may be the innovation required to unlock levels of productivity that generative AI vendors have been promising (but not quite delivering). Like everything riding the AI hype wave, we’ll need to see proof points and real-world deployments to know for certain.

At the same time, ‌with advances in areas like image and video generation, marketers should expect new multimodal AI systems that deliver higher quality, bordering on, meeting, or even exceeding traditional creative methods in many areas. This translates into a variety of applications, including hyper-realistic synthetic humans serving as models, actors, or spokespeople; video production that mirrors the aesthetics of film; faux photoshoots that can easily substitute for the real thing; and more. Every marketer needs to draw the line between authentic and artificial based on their brand, ethos, audience, and outlook.

Theme #2: AI Everywhere (and in Everything)

In 2023, we saw technology companies — software providers, in particular — rush to incorporate generative AI features and functionality into their core offerings. HubSpot introduced its ChatSpot assistant and incorporated AI-powered writing assistance into its marketing suite. Salesforce announced Einstein GPT to integrate public and private generative AI models with customer CRM data. And Adobe rolled out its own proprietary Firefly model, making AI features available in the company’s widely used creative applications. If you sold software, you had to find your AI angle to remain competitive in a fast-moving marketplace.

If the news from the 2024 Consumer Electronics Show is any indicator, manufacturers may now be under similar pressure to integrate advanced AI features. Automakers BMW, Mercedes-Benz, and Volkswagen all debuted in-car chatbots. Sony, Honda, and Microsoft announced a partnership to integrate the latter’s AI systems into a planned smart car. Beyond carmakers, Bosch previewed rearview mirrors with facial recognition technology, Samsung and LG showed off AI-enabled household robots, and Kohler added Alexa to a new line of toilets.

One implication is clear. For marketers, generative AI is good for more than just promotion. It’s finding its way into the first of the four Ps: product. If product innovation is within your purview as a brand manager or marketing decision-maker, it might be time to start thinking about how you can make your (connected) product smarter with AI.

That said, not everything with an algorithm is AI. Neither is every instance of automation. Brands and businesses need to be careful not to overpromise on the extent to which their smarter product truly integrates artificial intelligence.

In fact, faking it rather than making it is so prevalent among marketers touting AI features that the Federal Trade Commission had to issue a warning and promise penalties. Just like any other claim you might make — from taste to fit to product effectiveness — your claims about AI must be true, accurate, and aligned with customers’ expectations.

As the integration of AI becomes ubiquitous, its mere presence in your product ceases to be an advantage. Decisions about when, where, and how to employ AI — whether in your marketing campaigns or your manufacturing processes – must be driven by a keen understanding of your consumer. In short: What unmet consumer needs are you addressing with AI, and how does AI allow you to meet that need better than earlier alternatives?

Theme #3: A Looming “Thin Wrapper” Bloodbath

In generative AI, a “thin wrapper” application is one that adds little value beyond the core functionality of the underlying model. One unfortunate outcome of the past year’s AI hype cycle is that anyone with access to OpenAI’s APIs (or LLAMA or any other GenAI model) could slap a thin interface on top of a GPT and call it a company. To be clear, many (if not most) AI solutions are built on top of foundation models. The issue is the extent to which the application provides substantial, specialized functionality beyond what the foundation model itself offers.

The directory site There’s An AI For That lists more than 15,000 applications, with dozens more being added daily. An analysis by venture firm Sequoia Capital found that few AI applications have nailed their value proposition. As a marketer, you know this can be the kiss of death.

I believe a vast majority of thin wrapper AI startups, along with a significant number of more robust but essentially undifferentiated competitors, will fall by the wayside. This will  cause headaches for end users and organizations that have standardized around any given provider for key tasks, functions, or processes. For better or worse, marketing-oriented solutions — including more than a few of the AI writing assistants  — arguably fall into the thin wrapper category.

As a marketer, diligence has never been more important if you want to avoid the pain associated with onboarding new technology partners, retraining employees, recalibrating workflows, and even reworking integrations between AI tools and the rest of your martech stack. The right rigor in screening and selecting AI technology partners will go a long way.

Always aim to understand the extent to which the provider delivers meaningful and differentiated functionality you can’t get anywhere else, incorporates a reasonable amount of proprietary features, and has optimized its offering to meet modern marketing organization’s needs. At the same time, it’s worth investigating (even asking about) the company’s financial situation to determine whether they have the resources to stay in business for the length of your contract.

Theme #4: AI Agents and Large Action Models

One of the biggest stories coming out of this year’s CES was the launch of the Rabbit R1 – a boxy little handheld device that uses AI to perform actions on behalf of its user. The company reportedly sold $10 million worth of product within days of its introduction. I can’t say that I’m sold on the idea of a standalone AI device, but the actual AI is intriguing.

The Rabbit R1 runs not on a large language model, but a large action model. So, what’s the difference? A large language model takes a request (your prompt) and generates a response (like text or an image or even computer code) based on its training data. Basically, information in, information out.

A large action model, on the other hand, takes a request (your prompt) and performs an action without any further human intervention. To put this into context: If you ask ChatGPT to book you a hotel room in Chicago, it might recommend a list of hotels but won’t reserve a room because the underlying LLM lacks that capability. But a large action model will actually go ahead and book the room.

Where today’s more common generative AI tools are best thought of as “assistants” that work alongside you, LAM-powered applications might be thought of as “agents” that do the work for you. Even if the LAMs don’t quite live up to the hype just yet, this is a development worth watching over time (with or without a hardware angle.)

What are the implications for marketers then? From a workflow perspective, marketing departments are likely to deploy both AI assistants and AI agents to streamline routine tasks and improve productivity. That much seems obvious (right tool for the right job), but the larger implication is more profound and likely to play out over a longer timeline.

In 2019, I gave a talk about what marketing might look like in 2025. Among other things, I suggested that every consumer would have access to trusted AI systems (agents, if you will) that’d know their personal preferences and purchase patterns well enough to autonomously conduct retail transactions on the consumer’s behalf. While many brands already market to algorithms, the coming reality that machines will increasingly act as unbiased intermediaries between brands and their customers will be one of the most disruptive changes for marketers.

AI investor Jeremiah Owyang wrote about this theme recently as well. Marketers should expect to hear more about AI agents as the year progresses.

It’s more essential than ever for marketing decision-makers to have their finger on the pulse of fast-moving AI developments like the ones I’m highlighting here. At CognitivePath, our AI Advisory services are designed to help marketers sift through the hype to find the themes that really matter, so they can make better AI decisions with clarity, confidence, and speed. Get in touch to learn more about how you can gain one-on-one access to our analysts, unlock all of our research, and empower your organization to win with AI in 2024.

Theme #5: Copyright in the Spotlight

You’re probably aware that the New York Times Company is suing OpenAI and Microsoft, alleging that the companies used millions of the media company’s copyrighted articles to train their AI models. It’s a landmark case, in which the NY Times seeks not only monetary damages but also the decommissioning of foundational models like GPT-4. Nobody knows how this case will play out, but many agree its outcome will shape the future of generative AI one way or the other.

This is far from the only copyright claim to be levied at Big AI. Creators are fighting back against the image generators’ use of their work, as end users, pundits, and AI critics point out the frequency with which these tools output images that clearly ape artists’ signature styles, reproduce movie and television stills with uncanny accuracy, and depict trademarked characters without attribution.

While copyright is important, it’s also just one part of a broader set of concerns related to ownership, intellectual property rights, fair use, transparency, explainability, deep fakes, and even government regulation.

While some AI providers claim they’ll indemnify users against copyright claims, it’s incumbent upon marketers and their agencies to do their own homework. They need to understand what risks they may be taking when they use AI-generated content, insist on better transparency from their vendors, and know how AI affects their own ability to claim copyright or protect brand IP. Marketers should get used to spending a lot of quality time with their corporate counsel, be prepared to pivot, and have a Plan B (C, D, and even E) ready if needed.

Theme #6: Misinformation, Disinformation, and Deep Fakes

Brands will also need to navigate a web (and a world) that’s likely to become overrun with AI-generated content — much of it designed to mislead. Misinformation, disinformation, and deep fakes rank among the true risks presented by artificial intelligence. But for the purposes of this article, we don’t need to look beyond the affects on our own industry to understand why this matters for marketers.

Brand Fakes

Taylor Swift doesn’t endorse Le Crueset. Tom Hanks isn’t a dental plan spokesperson. And internet celebrity MrBeast didn’t promote an iPhone giveaway. But millions of consumers saw online ads featuring unauthorized, AI-generated versions of the stars. Meta and YouTube struggle to police their own platforms, even as they remove thousands of AI scam ads.

It’s hard to say whether these fakes are ‘targeting’ the celebs, the brands, or the consumers themselves — probably all of these, to one degree or another. But it’s worth considering how they might damage brand credibility and reputation, and to what extent they upend celebrity endorsements as a tried-and-true advertising tactic.

None of this should take away from the opportunity brands have to innovate with synthetic spokespeople, AI influencers, and other legitimate creative applications of the same types of technology.

Narrative Attacks

Brands need to be aware of — and prepared for — AI-powered narrative attacks. A narrative attack is any attempt by a malicious actor to harm an organization or institution by spreading a false narrative about it. This type of brand safety challenge predates even the internet. But with generative AI, this type of attack is easier than ever to propagate and has the potential to spread out of control. It doesn’t require celebrity star power.  Just imagine someone using AI to generate and distribute millions upon millions of false articles that “sound” authoritative (as GenAI often does).

In some ways, this is a 100x evolution of the old social media crisis — those usually weren’t based on false claims, but they did create headaches for brands, sometimes (frankly) well deserved. Dell did put writer Jeff Jarvis through customer service hell and United did break (and initially refuse to repair) Dave Carroll’s guitar. If you remember how even one consumer complaint could go viral and become a movement (back in the day), this is like that but bigger, faster, and harder to control.

AI Clickfarms

Last, advertisers must grapple with an entire underground industry that trades in fake news and aims to attract ad dollars. At last count (Jan 22, 2024), web watchdog NewsGuard had identified more than 600 AI-generated sites masquerading as legitimate news or information sources, except the content is entirely fake, published without human oversight, designed to fool the average user, and set up to siphon ad dollars away from legitimate websites.

On the one hand, these sites might aim to spread mis- and disinformation. On the other hand, they’re designed to attract readership and earn ad revenues spent with programmatic networks. Brands and their agencies routinely review what’s included in the ad networks they buy, creating block lists and such, but because these sites are proliferating so quickly, it can be hard, if not impossible to do this effectively.

Theme #7: Responsible AI

If 2023 was the year of Generative AI, 2024 will be the year of Responsible AI. Amid all the excitement, experimentation, and more than a few warnings about an AI-driven extinction event (bah!), few marketers have paused and asked the hard questions about the risks AI poses. That’s about to change as more marketers turn their attention to Responsible AI — and that’ll be a good thing for individual brands and the entire industry.

In practice, I’d expect marketing organizations to put a premium on guidelines, governance, and standards. Ethical stances and brand safety will be front and center, as it hits home that the marketing end-user — not the model or app provider — is the last (and maybe even first) line of defense regarding the responsible use of AI technologies.

Marketers will deploy AI in ways that deliver results for them while reflecting the values of the company and (this is critical) respecting the consumer’s rights. In the end, 2024 will be the year marketing AI starts growing up — and responsible AI practices will be at the center of this vital evolution.

Naturally, there are specific things marketing leaders can do to make sure AI is implemented responsibly within their own organizations. Begin by defining and documenting clear end-user guidelines. ​​Think of guidelines as “freedom within a frame” – a set of policies and practices that provide three key benefits:

• Mitigate the most common and most egregious risks
• Protect your employees, company, customers, and brand
• Empower your marketers to experience productivity and performance gains

Advanced AI marketers take an even more rigorous approach to AI technology governance, maintaining strict protocols around which systems are used, when, where, by whom, and for what purpose — and carefully tracking and reacting to any potential business, reputation, or regulatory risks that may emerge.

Responsible AI is about doing what’s right. Defining “right” can be a challenging task. Brands might consider establishing an ethics council and even a customer council to help ensure broad, unbiased perspectives factor into the definition and any resulting decisions. Ultimately, your goal as a marketing leader is to implement AI in a way that engenders trust within your organization and between your company and its customers.

Theme #8: The Beginning of the End for Experimentation

We all know that marketing AI success requires more than “random acts of ChatGPT.” So, while experimentation is valid, valuable, and necessary in the early days of any major transformation, it’s important to make the leap toward a more structured, strategic approach.

As more brands stand up and scale up marketing AI programs, ad hoc experimentation will give way to objective-driven, strategic implementations. This will require marketing leaders to:

• Establish and communicate a powerful yet realistic vision for the role of AI in marketing processes and programs
• Align efforts to objectives
• Identify and prioritize high-potential use cases; and design structured, properly resourced plans to pilot and scale AI programs that deliver results.
• Invest in upskilling your team and rethinking your workflows.

In short, 2024 is the year smart marketers get serious about AI and set the stage for a long-term strategic advantage unmatched by ‌brands that stand still, stall, or sit on the sidelines.

Conclusion

I’ll be returning to these themes throughout the year, as I work with advisory clients and produce new research to guide marketing decision-makers toward AI-powered success. But at the end of the day, remember, your path to AI success lies not in technology but in humanity. If the past year (or for that matter the past decade) has taught us anything, it’s that anything that can be digital will be digital. Anything that can be automated will be automated. It’s the things that can’t be — the tasks, attitudes, behaviors, and attributes that (at least for now) remain uniquely human — that will differentiate your business and your brand.

Put people at the center of your marketing AI strategy, and make sure that the humans in your marketing organization have the agency, autonomy, authority, and accountability they need to thrive in the AI era.

Around a year ago, I began a quiet pivot into artificial intelligence.

Granted, AI has featured in my work as a futurist, speaker, and digital transformation specialist for quite a while. But when OpenAI released ChatGPT in November 2022, I recognized the start of something big — a major shift akin to the advent of the worldwide web or the emergence of social media; two among a string of technology-driven transformations I’ve had the opportunity to guide leaders through over the years.

I dove in. I earned a graduate-level certificate in AI strategy from Cornell University’s SC Johnson College of Business. And another in AI ethics from the London School of Economics and Political Science. Fellow marketing veteran Geoff Livingston and I launched our podcast — No Brainer: An AI Podcast for Marketers — which has gone on to be ranked as one of the top marketing AI podcasts on the web. AI started to feature even more heavily in my keynotes. And I quietly launched an AI consulting practice, CognitivePath, through which I’ve been partnering with the Association of National Advertisers (ANA) to create original reports about marketing AI and delivering hands-on AI workshops for enterprise clients.

Today, I’m excited to announce the next step in my AI journey.

Geoff Livingston (who’s been consulting in AI through his own firm, Generative Buzz) and I are merging our practices to form a new, more focused CognitivePath. The new company — officially CognitivePath Research, Inc. — is the first and only research and advisory firm (aka industry analyst firm) squarely focused on the intersection between marketing and artificial intelligence.

Through our independent work over the past year, Geoff and I both recognized that marketing decision-makers are hungry for the kind of independent and objective guidance only an analyst firm can offer — unencumbered by the pressure to sell-in strategy, technology, implementation, management, or agency-style execution.

Smart marketing decisions (and actions) require CMOs and their teams to cut through the hype, get to the truth, and understand what rapidfire developments in the world of AI really mean to them, their business, their brands, and their customers. The big analyst firms are too broad. The AI pundits, too biased.

That’s where we come in — 100% focused on AI, evaluated entirely through a marketing lens, with a commitment to true objectivity.

We’re excited to launch with three core services — a robust advisory membership, practical hands-on workshops, and original research. And a few early clients, including an ongoing relationship with the ANA.

Our new Substack newsletter — fittingly, The CognitivePath — compiles our AI-related thoughtleadership, dating back to 2016 — and will be the best place to stay on top of our perspectives. A free subscription will put new posts in your email and grant you access to the entire archive. A paid subscription will unlock timely, ongoing, clients-only analysis of key developments in marketing AI — our first paid research offering and a fantastic low-cost point of entry for marketers looking to take the next step on their own path toward AI maturity.

Hear it straight from Geoff and me in a short launch video. And don’t hesitate to get in touch if there’s any way we can help!

As AI technologies increasingly permeate every facet of our lives, businesses are grappling with the transformative power of these tools, particularly in the realm of marketing. Generative AI — a subset of artificial intelligence that creates new content, insights, or predictions — shows promise for a wide range of marketing use cases, including creating dynamic content and having more personalized customer interactions.

But the introduction of AI, especially generative AI, inside an organization isn’t without its challenges. It raises strategic, ethical, and customer-centric concerns that organizations must address proactively. To navigate this complex landscape, Chief Marketing Officers are uniquely positioned to lead (or co-lead) the creation of AI councils that can provide the necessary guidance, oversight, and strategic alignment.

The case for CMO-led AI councils

AI councils are cross-functional groups tasked with evaluating, directing, and overseeing an organization’s adoption of AI. They play a critical role because implementing AI across multiple business functions requires careful coordination. AI councils allow leaders from different departments to align on strategy, share insights, and collaboratively establish policies. This helps make sure that AI projects are ethical, customer-centric, and strategically aligned. Effective AI councils also foster organization-wide buy-in and provide guidance as AI capabilities rapidly evolve.

Given that definition, you might argue that AI council leadership should sit with the Chief Technology Officer for their digital and data prowess. Or the CEO for their broad stewardship of the business. But we’d argue that the CMO is particularly well-positioned to lead these councils in the age of generative AI.

First, CMOs bring a deep understanding of customer behavior and needs. And after a decade or more of digital marketing transformation, many are experienced with data-driven decision making. CMOs can guide the AI council to focus on AI projects that enhance customer experience, leveraging their experience with using data for strategic decisions. The fact that modern marketing is highly data-driven implies that digital-first CMOs are often well-versed in guiding the use of AI, a fundamentally data-driven technology.

Further, the broad, cross-functional perspective that CMOs typically have is invaluable in an AI council, which needs to consider the impacts of AI across the entire organization. Moreover, their experience with technology adoption, particularly in the context of marketing, is beneficial for understanding the opportunities and challenges associated with implementing AI. Given the significant implications of AI usage on customer privacy, trust, and brand reputation, a CMO can make sure that AI initiatives align with the organization’s ethical guidelines and brand values. Their guidance can also foster innovative uses of AI that provide a competitive advantage in the marketplace.

Finally, although generative AI in particular promises a wide range of use cases across all areas of the business, its ability to generate human-like communications at speed and scale makes it ideal for near-term marketing applications. Marketing teams are often among the first business users of Gen AI tools. Marketing cloud providers like Salesforce, HubSpot, and Adobe are quickly embedding generative AI functionality into common martech suites. Looming shifts toward AI-based experiences in search, and the integration of generative AI into paid, owned, and earned media platforms will fundamentally change how marketers understand and engage with audiences.

In short, marketers are at the forefront of generative AI adoption in many organizations and can serve as standard-bearers as this technology gets onboarded into other areas of the business.

Three essential AI councils to guide your path

Let’s look at three different councils. Each serves a different purpose, and together they represent a comprehensive approach to strategic AI guidance.

1. Cross-Functional AI Leadership Council
2. AI Ethics Council
3. AI Customer Council

Cross-functional AI leadership council

Generative AI is a game-changer not only for the marketing function but for the entire organization. Its potential applications span from supply chain management to HR, from customer service to strategic decision-making. Consequently, the adoption and integration of AI shouldn’t be confined to a single department but should be a cross-functional endeavor.

A cross-functional AI council is instrumental for several reasons. First, it ensures that the organization’s AI goals align with its broader business and marketing strategy. This council, composed of leaders and stakeholders across the organization including legal, compliance, IT security, and HR, can set the direction for AI initiatives, ensuring they support key objectives.

Second, it facilitates the sharing of insights and learnings across departments. The benefits and challenges of AI in one area can inform and prepare others, promoting a cohesive, informed approach to AI adoption.

Third, it fosters buy-in, trust, and commitment from all stakeholders. When leaders across functions are involved in the AI decision-making process, they’re more likely to support and champion AI initiatives within their teams.

As a CMO, you’re well-positioned to lead this council. You understand the strategic importance of AI, especially in driving customer engagement and business growth. Your role allows you to bridge the gap between different functions, fostering collaboration and alignment.

AI ethics council

The rapid commercialization of AI has ignited a crucial conversation about ethics. With generative AI, these ethical considerations become even more complex as the technology can create content that may be indistinguishable from human-generated content, harbor hidden biases, produce unchecked inaccuracies, and raise concerns around intellectual property.

An AI Ethics Council, composed of diverse experts from within and outside the organization, can provide the ethical compass that guides AI adoption. This council helps establish positions on the responsible and ethical acquisition and use of AI systems, making sure that all AI initiatives address issues like bias, transparency, and responsible content creation. External experts like academics, civil society advocates, and public interest technologists offer an outside view that can be essential for effective decision-making. Individuals representing diverse geographic, economic, racial, ethnic, and gender perspectives can help companies acknowledge, explore, and address explicit and hidden biases in data sets, AI systems, and approaches.

CMOs are ideally suited to lead the AI Ethics Council as they often grapple with ethical decisions in their roles, particularly around customer data and privacy. Furthermore, marketing is one of the most customer-facing functions in an organization, giving CMOs a deep understanding of the potential impacts of AI on consumers.

AI customer council

At its core, marketing revolves around the customer. AI initiatives, no matter how technologically advanced or strategically aligned, will falter if they fail to put the customer first. An AI Customer Council, made up of real consumers of the company’s products or services, can serve as a vital sounding board.

This council can provide feedback on how the company uses AI in consumer-facing marketing and communications, how the company uses data for personalization, and other decisions that impact customer relationships. Involving customers directly in your AI decision-making helps build trust by demonstrating a commitment to understanding user needs, priorities, acceptable uses, and (most importantly) concerns. With their finger on the pulse of the customer, CMOs can lead this council effectively, making sure the customer’s voice informs the use of human-centric AI systems.

The bottom line

Generative AI holds immense potential for marketing, yet its adoption is a journey filled with strategic, ethical, and customer-centric considerations. As a CMO, you’re uniquely positioned to lead this journey, championing the creation of AI councils that can guide your organization towards a future where AI isn’t just a tool, but a strategic asset that drives growth, fosters trust, and enhances customer relationships.

Imagine a world where your marketing campaigns are precisely targeted to individual customer preferences, behaviors, and needs. Where versioning creative is easy and effortless. Where your team can produce high-performing, on-brand content with unprecedented speed and scale. Generative AI — a branch of artificial intelligence that creates new content — promises all of this, and more. But if there’s one factor that turns this promise into a pipedream, it’s issues with data.

According to S&P Global’s 2023 Global Trends in AI report, companies are finding it more costly and more difficult to start and scale generative AI projects because their data isn’t up to the task. So, if your data is disorganized, saved in different formats, siloed in different departments, or stored in disparate datasets and applications, you’re certainly not alone.

But that’s cold comfort if you’re a marketer eager to embed generative AI systems into your marketing processes today. You can certainly get started with the out-of-the-box, pre-trained models that come with third-party generative AI marketing solutions. But sooner or later, your organization’s proprietary data sets will be essential for generating unique strategic insights, original ideas, and on-brand content.

The sooner you begin building your data foundation, the better you’ll leverage the true potential of marketing AI. So, let’s explore exactly what types of data you’ll need to customize generative AI models for your specific use cases.

Before we do though, rest assured it’s not my goal to turn you into a data scientist or bog you down in detail. In this article, we’ll keep things high-level. But it’s important for any marketing leader to understand exactly what we mean when we talk about “good data,” so that you can engage in productive conversations with the data experts in your organization.

What Types of Data Will You Need?

Look for data that might inform strategic decision-making and marketing plans or allow generative AI systems to understand and replicate the ideas, messaging, style, and creative that resonate with your audience and align with strategy, identity, and objectives.

Data like:

• Customer Data: demographic, psychographic, and behavioral data about the target audience.

• Campaign Data: ad performance metrics, engagement rates, and conversion rates from past and current marketing campaigns.

• Competitor Data: data on competitors’ marketing strategies, such as ad copy, messaging, and offers.

• Content Data: high-performing content, trending content, user-generated content, and existing brand content and creative.

• Trend Data: industry trends, consumer behavior trends, and other relevant research.

• Contextual Data: data on the context in which the content will be consumed, such as the platform, device, and location of the target audience.

• Brand Strategy: values, voice and tone, brand guidelines, brand values, positioning, and messaging.

• And of course, your actual content and creative assets.

What Good Generative AI Data Looks Like

• Available: This may sound obvious, but it certainly isn’t a given for many companies. Your organization needs to have the data. And it should be complete, consistent, and up to date. Poor data practices and spotty data governance over the years can negatively impact the data available for GenAI today.

• Accessible: Marketers require quick and easy access to relevant data to train, fine-tune, and even prompt generative AI models. Departmental, functional, or technical silos in your organization may make it more difficult to get your hands on the data you need. For instance, customer data or campaign performance data may be stored separately from sales data or social media data.

• Abundant: Even when data is technically available, the quantity of the data may not be sufficient. Generative AI models generally require large amounts of high-quality, diverse data to produce useful results. If certain types of data aren’t being collected, or if the data is incomplete, inconsistent, or outdated, it can significantly impact the effectiveness of these projects. Fine-tuning a pre-trained foundation model such as GPT, LLaMa, or Falcon lessens the burden of quantity, but the fact remains: Without a substantial amount of business-specific data, marketers will ultimately struggle to turn generic generative AI systems into highly differentiated, proprietary marketing difference-makers.

• Accurate: Generative AI models learn from the data they’re trained on. Accurate data ensures that the generated content and other outputs are relevant, informative, and compelling. Inaccurate data can result in misleading or irrelevant content that may harm the effectiveness of the marketing campaign or result in poor internal decision-making.

• Fresh: Freshness comes into play in two distinct ways. First, many marketing AI applications, such as chatbots or recommendation systems, need real-time data to function effectively. If data isn’t readily accessible and available, it can hinder these real-time applications. Second, generative AI models often use continuous learning to improve their performance over time. This is a distinct difference between AI-based systems and traditional software and — in fact —  a key decision-making factor when determining whether your use case requires AI or if traditional data processing or analytics is sufficient. Ultimately, when you choose to deploy generative AI (or any type of artificial intelligence), a steady feed of new information is essential for maintaining and improving performance over time.

• Unbiased: Unbiased data is crucial for GenAI outputs that are fair, equitable, and inclusive, and that don’t reflect or perpetuate harmful social, cultural, racial, ethnic, or economic biases. At the same time, it’s important to understand whether your data may be slanted toward a given customer segment or scenario, in a way that might result in skewed outputs. Marketers may need to address any systematic errors in the data that can lead to inaccurate or unfair results.

• Usable: Data that’s easily understood, organized, and processed (by humans and algorithms) helps create efficient workflows, avoid inefficiencies, and minimize inaccuracies. If data is unstructured or in formats that are difficult to process, it can pose significant challenges. Furthermore, the lack of a well-structured data management and governance framework can lead to issues in data integrity and usability. Usability should also consider whether your intended use case complies with applicable regulations and even the permissions your customers opted-into when they provided their personal information.

• Useful: Useful data provides meaningful insights and actionable information that helps achieve your desired marketing objectives. It’s essential for generative AI models to produce market-facing content that resonates with your audience and achieves the desired results, and internal content that supports effective decision-making, communication, and knowledge sharing.

Build a Better Data Foundation

If you’re a marketing leader (or more broadly, a leader who values marketing), you’ve likely already bought into the potential for generative AI for hyper-personalized campaigns, rapid content creation, and strategic insights. However, the key to unlocking this potential isn’t just the technology itself — it’s the data that fuels it. And the truth is that your role extends beyond merely adopting AI solutions. It involves cultivating a robust data ecosystem that’s available, accessible, abundant, accurate, fresh, unbiased, usable, and ultimately, useful.

This requires a thoughtful approach to data management, breaking down silos, and fostering a culture of data literacy within your organization. While you can’t achieve this alone — you’ll need enterprise-wide commitment, and cross-functional collaboration — you can play a key role by understanding what data you’ll need, what “good” looks like, how you can use that data to drive marketing performance, and what you want to achieve by implementing generative AI as a marketing differentiator. After all, good data isn’t just an asset. It’s the key to unlocking your AI advantage.

The prophetic Season 6 opener of Black Mirror, Joan is Awful, propels viewers into a mirrorworld where every actor has been replaced with an AI likeness. Within months of the episode’s release, the fact betrayed by this fiction became a catalyst for a real world Hollywood strike. The actors’ union is protesting a contract clause they say would give big studios the right to use background actors’ digital doppelgangers for free, forever.

From dramatic examples like these to simpler cases like AI-generated articles and emails, it’s clear: Generative AI is remaking media as it’s remaking marketing. And some innovative marketers are already looking beyond the controversy and unlocking new creative possibilities with synthetic talent. In the process, they’re also uncovering some of the challenges and limitations of engaging real consumers with fake celebrities.

In this article, we’ll look at three examples of big brand campaigns that tap into the creative potential of synthetic media. But first, let’s cover the basics.

What is Synthetic Media?

In the broadest sense, synthetic media refers to any use of artificial intelligence to generate or manipulate digital content, such as images, videos, texts, or sounds, so that they appear to be authentic. By this definition, the article your content marketer generated in ChatGPT or Jasper fits the bill.

Here though — as we’re exploring the idea of digital spokespeople — we’re referring specifically to using deep learning neural networks to generate a convincing, photorealistic audio/visual likeness of a specific person, saying and doing things that they didn’t actually say or do. In colloquial terms, we’re talking about deepfakes.

The term deepfake itself carries an aura of malicious intent or — at a minimum — mischief. You likely heard the term when Mijourney images of Pope Francis wearing a Balenciaga puffer coat or Donald Trump’s NYC arrest went viral, earlier this year. Indeed, deepfake technology has been used in everything from online pornography to political misinformation campaigns. But like most innovations, the technology itself is neither good nor bad. Agency holding company WPP has used deepfake technology from one company to personlized employee training. Other companies — like Metaphysic.ai — target the high-end market with sophisticated celebrity recreations for big budget productions.

In one sense, AI-powered synthetic media is an evolution of the virtual influencer trend at play for several years. In my 2019 keynote at BrandManagecamp, I spoke about Lil Miquela — “one of a handful of inarguably artificial influencers, with millions of social media followers and a stable of lucrative brand sponsorships.” And I predicted “a future in which your brand may have its own customized, (eventually) AI-driven digital ambassador that represents your ideal customer and connects with your prospective customers in ways you wish today’s human influencers could.”

Today, given rapid advances in generative AI, brands are beginning to explore the potential of synthetic celebrity spokespeople to achieve previously impossible forms of creative innovation. Now, let’s take a look at three campaigns.

Virgin Voyages Jen AI

Virgin’s cruise line pairs superstar spokesperson Jennifer Lopez with generative AI techniques. A TV spot introduces Jen AI (get it?), a virtual spokesperson that “malfunctions” to expose the actors behind the avatar. It’s a clever spoof of deepfake technology that pokes fun at the current GenAI hype cycle while setting up the call-to-action: the viewer can “control” Jen AI too, and create a personalized cruise vacation promo video.

My sense is that the spot itself uses a living, breathing J Lo and some good old fashioned CGI rather than AI to produce its Jen AI effects. On the website that viewers use to create their custom promos, a synthetic J Lo is brought to life with an AI-powered video avatar created by Deeplocal and the star’s voice cloned by SpeakUnique.

The implementation is rudimentary (although I imagine it was neither simple nor cheap), allowing customization via just a few pointed text prompts and within the limits of tight constraints. The constraints are by no means all negative. In fact, they point to a sound understanding that generative AI is a nascent technology with inherent risks. Particularly for big brands working with star talent, its vital to bake strong guardrails into the brief. Jen AI won’t speak inappropriate language and the generated videos can’t be posted online or shared beyond the intended recipients.

A CNBC piece from late July offers a behind-the-scenes look at the campaign (conceived by VMLY&R), and reports that (at that time) the work had generated around 1,000 new bookings for the travel brand.

Cadbury “NotJustACadburyAd”

The Mondelez-owned confectionary brand combined generative AI, a holiday promotion, and corporate social responsibility in a Diwali campaign designed to sell chocolate and drive shoppers to local mom-and-pop stores. “NotJustACadburyAd” allows local purveyors to create store-specific versions of a video spot featuring Bollywood star Shah Rukh Khan. The campaign uses synthetic media technology from Rephrase.ai to recreate Khan’s face and voice, promoting each store by name to encourage Diwali holiday shopping.

This campaign wrap-up video explains the strategy and execution.

According to a statistic on Rephrase’s website, Cadbury saw its biggest Diwali sales spike in a decade. But what set this campaign apart was the way using generative AI and synthetic media allowed small shops (many of whom were still struggling financially in post-COVID India) to reap the benefits of big budget ad production and the power of celebrity endorsement with nothing more than a prompt.

This said, some consumers took issue with the campaign. One Twitter users called the ad “problematic,” and worried that it highlighted the availability and ease of use for a technology that could be used by bad actors to generate discriminatory, malicious deepfakes. While pushback was minor in this case, this does highlight the need for brands and agencies to anticipate ethical objections, have a plan to mitigate any reputational harms that may arise, and even consider playing an active role in educating consumers about appropriate vs. inappropriate usage of generative AI tools.

Volkswagen do Brasil Resurrects “Elis Regina”

In a Brazilian video ad marking the automaker’s 70th anniversary, musical icon Elis Regina and her adult daughter Maria Rita (a Grammy-winner herself) sing a soul-stirring duet while driving alongside one another in VW vans. The catch? Elis Regina passed away in 1982 at the age of 36. Her likeness is AI-generated, using facial recognition to map her features to the movements of a background actor. The effect is utterly convincing and shows just how good synthetic media can be, even at this relatively early stage of the game.

Millions of consumers who remembered Elis or knew of her by reputation found themselves moved by the emotional video. But others complained that the spot raises significant ethical concerns about AI, its use in entertainment, and its potential societal impact.

While Volkswagen had the family’s permission and participation for the production, the question remains: Under what circumstances is it right to bring a deceased celebrity back to life in a media production through artificial intelligence? To further complicate the matter, Elis staunchly opposed the military dictatorship that governed Brazil at the time of her death — a regime Volkswagen notoriously supported. It’s unclear whether the singer herself would have agreed to this particular brand collaboration if she were alive today.

In the age of AI immortality, who owns your digital likeness? Is resurrecting a late celebrity through AI any more problematic than featuring them through archival footage? Any more objectionable than representing them with a look-alike impersonator?

These may sound like philosophical questions. On one level they are, of course. But they’re also practical considerations. Brazil’s ad industry watchdog, Conar, is investigating VW for a possible breach of ethics after receiving complaints about the campaign. According to The Guardian, Conar is looking into “whether [the use of such techniques] might cause some to confuse fiction with reality, above all children and teenagers.” Clearly, it’s incumbent on brands to take their guidance from a strong ethical AI north star. It’s just as clear that considerations around reputational and regulatory risk must feature prominently in every creative decision — especially when marketers are just beginning to think through the right strategies, use cases, applications, and guardrails for generative AI.

A Real Opportunity for Responsible Synthetic Media

Generative AI is a rapidly developing technology that has the potential to change media and marketing. Creatives can now generate synthetic media that looks convincingly real. Companies like Virgin Voyages, Cadbury, and Volkswagen Brazil have used this technology to create successful campaigns — in the process proving that synthetic media provides a powerful vehicle for marketing innovation.

However, brands need to be aware of the risks of using this technology; must engage in active upfront conversations about unforeseen implications; and should create guardrails and governance to mitigate reputational harms and regulatory risks. Despite concerns, generative AI can fuel creative innovation and create competitive advantage for brands that are strategic and ethical in its application.

The role of AI in marketing has gone from being a novelty to being a game-changing reality that’s reshaping how we connect with consumers and understand their needs. Yet, the power of these advanced technology systems hinges on one profoundly human factor: trust.

Trust has always been the cornerstone of any successful marketing strategy. And with the embedding of AI into nearly every aspect of marketing, it takes on an even greater significance. Our algorithms, models, and systems are more than merely tools‌ — ‌they’re extensions of our brand, our values, and our promise to our customers. The trust we build in the AI systems we develop, deploy, and use will ultimately determine their efficacy, shaping not just individual customer experiences, but the future of marketing itself.

Taking this a step further, trust is more than a bridge between your company and your customers. It’s also an essential pathway to consistent adoption of AI systems by your most important internal stakeholder communities.

Three Communities of Trust

When setting up and scaling AI for your marketing organization, it’s crucial that you commit to building trust into your approach from the outset, and in three broad areas.

In the Market

Lack of trust in the market erodes customer relationships and damages brand reputation. Build trust with your consumers by being transparent about your use of generative AI in your marketing campaigns. Demonstrate your commitment to data privacy and security. Proactively address potential biases. And consistently deliver accurate and valuable personalized experiences.

Among Your Marketers

Lack of trust among your marketing team members stands in the way of end user adoption. Here, it’s essential that you ​​maintain human agency, autonomy, authority, and accountability in all key marketing decisions and in every marketing workflow.

By Your Company’s Leaders

Lack of trust by the leaders in your marketing organization and across the enterprise slows progress, kills your credibility as an AI changemaker, and makes it harder to sell in and scale AI implementations. Here, it’s important to build confidence in using AI systems for high stakes decision-making and market-facing communications.

10 Ways to Build Trust in AI Systems

Now, with a basic understanding of why trust is important to each of these three stakeholder communities, let’s explore 10 steps you can take to establish trust as you embed AI throughout your marketing operations.

• Assemble a diverse and multidisciplinary team to build, evaluate, and buy AI systems for your organization.  This helps make sure that a wide range of perspectives and experiences are considered, reducing the risk of unconscious bias in AI training and output, promoting fairness, and increasing the system’s relevance and usability for a broader audience.

• Be intentional about the creation of inclusive and appropriate data sets. It may be necessary to collect additional data about underrepresented and marginalized groups to promote responsible and inclusive use of AI. Bear in mind that you won’t always control or have good visibility into the core data set — for example, when you buy or build systems that use popular generative AI foundation models like OpenAI’s GPT-4. ‌In these cases, it’s important to conduct adequate diligence before you determine your level of comfort with a potential vendor’s data practices. Publicly available vendor risk profiles like the ones published by Credo.ai can be helpful. And our own discussion guide for technology partner evaluation provides a practical framework for asking the right data questions. And when you’re fine-tuning these third-party models or applications with your own proprietary data, pay attention to any unintentional bias that may have seeped in over time.

• Clearly explain the ways in which data is being combined and used within AI algorithms to validate the output of AI systems and correct for possible biases which may come up later. Here again, the burden of explainability may fall mainly on the developers of the underlying foundation models or the third party application companies that embed those models into the marketing AI systems that you buy. If you train or fine-tune any of these systems with your organization’s proprietary customer or campaign data, ensure that you have a solid understanding of how the addition of your own data affects the performance of the model and influences its outputs.

• Ask which groups will benefit from using the AI system, which will be harmed, and if the data being used is appropriate or fit for the intended purpose. Keep in mind that potential harms might be internal (for example, when a productivity-enhancing AI system might result in the elimination of headcount or the de-skilling of substantial marketing workstreams). Or they might be external. Consider inappropriate or even unethical uses of personal data, predatory or discriminatory marketing practices, or even low quality or inaccurate content produced by generative AI systems without adequate human oversight.

• Consider establishing an ethics board to provide holistic oversight on the ethical and responsible development of AI systems. Many organizations recruit outside advisors for their ability to lend diverse viewpoints to ethics discussions.

• Build in appropriate monitoring and validation mechanisms as the AI system is used over time. Maintain a registry of all active AI projects. Establish a robust AI governance program to continually evaluate and address potential organizational, regulatory, and reputational risks before they become problematic. As real issues inevitably arise, address them promptly and own up to any errors.

• Enlist independent third parties to conduct periodic audits. Doing so will help make sure that your AI systems are performing as intended and are producing accurate, fair, and unbiased outcomes. Independent outsiders can also evaluate the sufficiency and effectiveness of your organization’s overall AI governance model.

• Track both the performance of AI systems and the impact of decisions suggested by them. These are critical steps toward aligning intentions with outcomes, an early warning for any variations or degradations in performance over time, and a basis for ongoing discussions around risk and mitigation. When you clearly communicate performance issues to your internal leadership and key stakeholders, you create the kind of transparency that fosters trust and credibility. If you encourage others in your organization to report any performance issues that arise in their own experience with your AI systems, you create deeper engagement around responsible AI.

• Create standards to govern the development, purchase, and usage of AI systems. At CognitivePath, we tend to view these essential guardrails as ​​“freedom within a frame” – a set of policies and practices that mitigate the most common and most egregious risks to protect your employees, company, customers, and brand, while empowering your marketing end users to experience the productivity gains, creativity boost, and engine for innovation that AI-powered workflows can offer.

• Safeguard users by following social norms, along with applicable laws and regulations. It’s equally important to safeguard internal users and external constituents. At the same time, marketing leaders should acknowledge ‌the complexity inherent in adhering to norms and even regulations. Norms and even the notion of what constitutes bias or fairness vary by country and culture. Regulations and laws are nascent, open to interpretation, and vary by region. In other words, there’s no “perfect,” but your brand’s purpose and values can (and must) be your guide for responsible AI.

The Effort is Essential

Trust is the key to unlocking AI’s true potential in marketing. By proactively building trust with consumers, marketers, and leadership, we lay the foundation for responsible AI adoption and innovation. This demands inclusive data practices, explainable systems, governance, and auditing that center on human values. While complex, the effort is essential.

When our AI reflects our brand values and commitment to fairness and agency, it becomes a powerful engine of trust, strengthening customer connections and fueling organizational growth. Trusted AI allows us to transform marketing through more relevant, ethical experiences. The future of our profession depends on the trust we consciously build in AI today. With focus and care, we can drive responsible AI innovation that propels marketing forward.

First things first: I’m pumped to announce that No Brainer is now part of the Marketing Podcast Network, the premier network of top-quality audio programming by marketers, for marketers. We’re looking forward to growing alongside 40 or so of our favorite marketing thought leaders — and we’re glad you’re taking the ride with us.

And so, without further adieu…

In today’s power-packed episode, Geoff and I rant about “X” — Elon Musk’s entry into the generative AI race — just weeks after he signed the controversial “AI pause letter” that called for a six-month moratorium on training advanced large language models. What are his plans for his so-called TruthGPT? Will he build an LLM that’s trained on tweets? And could X become the “everything app” we never even knew we needed?

Then, we switch gears to talk about the implications of AI in ad world. Inspired by a great article at FastCompany.com, I share some real (and potential) ways that the big advertising agencies are integrating generative AI into their creative workflows. Geoff talks the truth about the long-term love affair between advertising and AI. And we ponder whether agencies will rise to the challenge and become indispensable AI advisors to big brands… or sink to the depths of pointless hype (as many did with Web3 and the metaverse in 2022.)

Be sure to visit the No Brainer website for the complete show notes, including links to the articles mentioned in this episode.

Today’s episode is packed with power as Geoff Livingston and I dive deep into AI ethics. With ChatGPT churning out fake news and Midjourney attempting to pump the brakes on deep fakes, it’s no wonder companies and countries are looking to rein in the use of generative AI.

We explore all of this and more, pondering whether humans are striking back against runaway AI productization. Geoff shares his thoughts on the seven essential elements for corporate responsible AI policy, we discuss some practical ways to keep AI from going off the rails (and taking your brand reputation with it) and tackle some tough questions that were submitted in advance by show listeners.

The show wraps with some thoughts on why it’s important to involve your employees in your AI ethics decisions, and even more important to understand what your consumers expect when it comes to how your brand uses AI in your marketing programs.

For full show notes including time stamps and links to all the sites, programs, and resources mentioned during today’s show, visit NoBrainerPodcast.com. You can find our episodes on Apple, Spotify, iHeart Radio, and just about any app you use to list to your favorite pods. Speaking of favorites: If you like what you hear, be sure to subscribe, rate, review, like, and recommend us to all your friends.

By now, it’s pretty well known that ChatGPT has a tendency to invent facts, given that it has no basis to “know” true vs false. It’s just using probabilities to string together words and phrases in a way that mimics humanlike writing. It’s also likely to invent fake sources for those fake facts. (Literally, fake news…) In fact, as anyone who has spent a fair amount of time using the hot bot knows, imaginary sources are a prevalent problem.

So prevalent that Chris Moran, the head of editorial innovation at online news site The Guardian, took to his own Opinion page to write about two inquiries from two researchers looking to verify reporting that ChatGPT had attributed to named reporters working for the publication. Pretty typical stuff for researchers and reporters. Except that neither article exists.

The same day Moran published his Opinion piece, news broke that ChatGPT had invented a sexual harassment scandal and named real George Washington University law professor Jonathan Turley as the perpetrator. Its source? An imaginary Washington Post article from 2018. In fact, when WaPo did its own investigation into the incident, reporters there found that Microsoft’s Bing — which also incorporates GPT technology — repeated the accusation.

Where a couple of odd calls to an editorial desk might be a nuisance (for both the desk and the researcher who likely assumed they were looking for nothing more than a check-the-box verification), false accusations of criminal activity are something far more dangerous.

“Hallucinations” like these are likely to become less prevalent over time, as the large language models that underly chatbots like ChatGPT, Bing, and Google’s Bard are trained on even larger data sets, refined through reinforcement learning, and fine-tuned based on the millions upon millions of prompts from and interactions with early users. But incorrect and illogical errors may remain a fixture of GPTs for a long time — possibly forever.

I should pause here to point out that, inflammatory headline aside (damn you, tempting clickbait!), ChatGPT does not, in fact, “lie” any more than it tells the “truth.” It does neither. It has no understanding of the words it strings together. It has no moral compass, no motives, does no reasoning, and never pauses to reflect. Again, it merely strings together words in a manner that often turns out to be true, but sometimes doesn’t. ChatGPT doesn’t know the difference. But you, dear human, do…

And that’s where you come in. What’s a user to do?

The lesson for any consumer or business end user who employs ChatGPT (or any of the many applications built on top of OpenAI’s GPTs) as a research or writing assistant is a simple one: Verify. Verify. Verify.

If you thought prompt engineering would be an important job skill in the age of AI, wait until I tell you how important fact-checking will be. It’s important to keep in mind that an AI like ChatGPT hasn’t been trained to tell the truth or get things right; It has (essentially) been trained to respond to user queries with information that sounds plausibly accurate, to make connections between words without understanding the meaning behind those words as a means of generating believably human-like text.

If I sound a bit like a broken record, so be it. It’s far too easy to anthropomorphize this technology and assume it has more agency over its actions than it does. And this is precisely where any human user is bound to get in trouble. Ultimately, you are both the arbiter of the truth you tell and the person who is accountable for it — whether or not you incorporate AI into your writing workflow.

There are 100 million+ people using ChatGPT today. And that’s a lot. But bear in mind that this or Google’s GPT model will be coming to your Microsoft Office and Google Workspace apps – that’s BILLIONS of users with access to what is arguably the most powerful — and still highly flawed! — generative AI models in the world. And then there’s GPT-powered search, which Microsoft already offers as part of Bing inside its Edge browser and Google is still testing before rolling it out to the general population.

So even if you’re not a ChatGPT power user today, you’ll be using technology like this (perhaps without even thinking twice about it, as is the case with so many AI applications today) before you know it.

If you’re a generative AI decisionmaker at a media company, marketing agency, brand, or really any company of any kind, the lessons are: Cautious will beat crazy. Editorial (or content) standards are more important than ever. Clear, written guidelines (even policies) and effective communication, roll-out, and enforcement of those guidelines will keep you from looking like idiots (at best) and landing in hot water (at worst).

In this episode of No Brainer, Geoff Livingston leads off with Adobe’s foray into the world of generative image-making with a proprietary model based on rights-cleared images. How does it stack up against Midjourney and the rest?

With Adobe being the latest move by an incumbent technology company, we ponder how the market may shake out between Big Tech and a crowded AI startup scene, leading into a deeper conversation about why marketers need to move beyond AI tools and tactics to make strategic decisions about the right AI stack and the best AI use cases that deliver real benefits for their organization.

Next, I talk about ChatGPT PlugIns, which I see as OpenAI’s ‘App Store Moment.’ Find out why, then listen as we discuss the implications for the future of search marketing, content marketing, the media industry, and brands. Will every brand need to build a custom Plugin to remain relevant to consumers?

Finally, Geoff returns to the importance of strategic thinking for this week’s Brainer (key takeaway) and my No Brainer (tip or trick) highlights some LLMs (beyond ChatGPT) that you can try today.

Episodes are embedded below. Get the full show notes including all the episode links on the No Brainer Podcast site. And check us out, subscribe, and drop a rating or review on Apple Podcasts, Spotify, or anywhere else you enjoy podcasts.