Like everyone else, I’ve been pondering what generative AI means for martech, marketing, and the world in general. My crystal ball is no clearer than yours but I’ll share my thoughts anyway.
Let’s start by looking how past technology changes have played out. My template is the transition from steam to electric power in factories. This happened in stages: first, the new technology was used in exactly the same way as the old technology (in factories, this meant powering the shafts and belts that previously were powered by waterwheels or steam engines). Then, the devices were modified to make better use of the new technology’s capabilities (by attaching motors directly to machine tools). Finally, the surrounding architecture was changed to take advantage of the new possibilities (freed from the need to connect to a central mechanical energy source, factories went from being small, vertical structures to large horizontal ones, which allowed greater scale and efficiency). We should probably add one more stage, when the factories started to produce new products that were made possible by the technology, such as washing machines with electric motors.
During the earliest stages of the transition, attention focused on the new technology itself: factories had “chief electricians” and companies had “chief electricity officers”, whose main advantage was they were among the few people who understood new technology. Those roles faded as the technology became more widely adopted. The exact analogy today is “prompt engineer” in AI, and will likely be even shorter-lived as a profession.
Most of the discussion I see today about generative AI is very much stuck in the first phase: vendors are furiously releasing tools that replace this worker or that worker, or even promising a suite of tools that replace pretty much everyone in the marketing department. (See, for example, this announcement from Zeta Global https://zetaglobal.com/press-releases/zeta-introduces-generative-ai-agents-powered-by-zoe/ .) Much debate is devoted to whether AI will make workers in those jobs more productive (hurrah!) or replace them entirely (boo!) I don’t find this particular topic terribly engaging since the answer is so obviously “both”: first the machines will help, and, as they gradually get better at helping, they will eventually take over. Or, to put it in other terms: as humans become more productive, companies will need fewer of them to get their work done. Either way, lots of marketers lose their jobs.
(I don’t buy the wishful alternative that the number of marketers will stay the same and they’ll produce vastly more, increasingly targeted materials. The returns on the increasing personalization are surely diminishing, and it’s unrealistic to expect company managers to pass up an opportunity to reduce headcount.)
While the exact details of the near future are important – especially if your job is at stake – this discussion is still about the first stage of technology adoption, a one-for-one replacement of the old technology (human workers) with the new technology (AI workers). The much more interesting question is what happens in the second and third stages, when the workplace is restructured to take full advantage of the new technology’s capabilities.
I believe the fundamental change will be to do away with the separate tasks that are now done by specialized individuals (copywriters, graphic designers, data analysts, campaign builders, etc.). Those jobs have evolved because each requires complex skills that take full time study and practice to master. The division of labor has seemed natural, if not inevitable, because it mirrors the specialization and linear flow of a factory production line – the archetype for industry organization for more than a century.
But AI isn’t subject to the same constraints as humans. There’s no reason a single AI cannot master all the tasks that require different human specialists. And, critically, this change would bring a huge efficiency advantage because it would do away with the vast amount of time now spent coordinating the separate human workers, teams, and departments. There would be other advantages in greater agility and easier data access. Imagine that the AI can get what it needs by scanning the enterprise data lake, without the effort now needed to transform and load it into warehouses, CDPs, predictive modeling tools, and other systems. Maintaining those systems takes another set of specialists whose jobs likely to vanish, along with all the martech managers who spend their time connecting the different tools.
Of course, the vision of “citizen developers” using AI to create sophisticated personal applications on the fly is entirely irrelevant when the citizen developers themselves no more have jobs. Thousands of independent applications that make up today’s martech industry may vanish, unless the marketing Ais build and trade components among themselves – which could happen.
So far, I’ve predicted that monolithic AI systems rather than teams of (human or robotic) specialists will create marketing programs similar to today’s campaigns and interactions. But that assumes there’s still a demand for today’s types of campaign and interactions. This brings us to the final type of change: in the outputs themselves.
Again, we may be in for a very fundamental transformation. The output of a marketing department is ultimately determined by the how people buy things. It’s a safe bet that AI will change that dramatically, although we don’t know exactly how. For sake of argument, let’s assume that people adopt AI agents to manage more of their personal lives for them (pretty likely) and that they delegate most purchasing decisions to those agents (less certain but plausible, and again already happening to a limited degree). If that happens, our AI marketing brains will be selling to other Ais, not to people. To imagine what that looks like, we again have to move beyond expecting the AI to do what people do now, and look at the best way for an AI to achieve the same results.
If you think about marketing today – and for all the yesterdays that ever were – it’s based on the fundamental fact that humans have a limited amount of attention. Every aspect of marketing is ultimately aimed to capturing that attention and feeding the most effective information to the human during the time available.
But AIs have unlimited attention.
If an AI wants to buy a product, it can look at every option on the market and collect all the information available about each one. Capturing the AI’s attention isn’t an issue; presenting it with the right information is the challenge. This means the goal of the marketing department is to ensure product information is available everyplace the AI might look, or maybe in just one place, if you can be sure the AI will look there. Imagine the world as a giant market with an infinite number of sellers but also buyers who can instantly and simultaneously visit every seller and gather all the information they provide. The classical economist’s fantasy – perfect market, perfect information, no friction – might finally come true.
And, also as the classical economists dream, the buyers will be entirely rational, not swayed by emotional appeals, brand identity, or personal loyalties. (At least, we assume that AI buyers are rational and objective, although it won’t be easy to ensure that’s the case. That relates to trust, which is a topic for another day.)
If the role of marketing is to lay out virtual products on a virtual table in a virtual market stall, there’s no need for advertising: every buyer will pass by every stall and decide whether to engage. With no need for advertising, there’s no need for targeting or personalization and no need for personal data to drive that targeting or personalization. Privacy will be preserved simply because advertisers will no longer have any reason to violate it.
The key to business success in this world of omniscient, rational buyers is having a superior product, and, to a lesser extent, presenting product information in the most effective way possible. There’s still some room for puffery and creativity in the presentation, although presumably mechanisms such as consumer reviews and independent research will keep marketers reasonably honest. (Trust, again.) There’s probably more room for creativity in developing the products themselves and constructing a superior experience that extends beyond the product to the full package including pricing, service, and support.
We can expect the AIs to play a major role in developing those new and optimal products and experiences, although I suspect the pro-human romantic in everyone reading this (except you, Q-2X7Y) hopes that people will still have something special to contribute. But, wherever the products themselves come from, it will be up to the marketing AI to present them effective to the AI shoppers.
(Side note: today’s programmatic ad buying marketplace comes fairly close to the model I’m proposing. The obvious difference is the auction model, where buyers bid for a limited supply of ad impressions. It’s conceivable that the consumer marketplace would also use an auction. Again, just because most of today’s shopping is based on a fixed price model, we shouldn’t assume that model will continue in the future. Come to think of it, an auction would probably be the best approach, since buyers could adjust their bids based on their current needs and preferences, and sellers could adjust them based on inventory and current demand. In the traditional marketplace, this would be called haggling, or negotiating, and it’s the way buying has been done for most of history. With perfect information on both sides, the classical economists would be pleased yet again. It could be fruitful to explore other analogies with the programmatic marketplace when trying to predict how the AI-to-AI marketplace will play out.)
(You could also argue that Amazon, Expedia, and similar online marketplaces already offer a place for virtual sellers to offer their virtual wares to all comers. Indeed they do, but the exact difference is that searching on Amazon requires a painfully inefficient use of human time. If Amazon evolves a really good AI-based search method, and can convince users to share enough data to make the searches fully personalized, it could indeed become the basis for what I’m proposing. The biggest barrier to this is more likely to be trust than technology. It’s also worth noting that traditional marketing barely exists on those marketplaces. The travel industry, where most marketing is centered on loyalty programs, may be an early indicator of where this leads.)
So what role, exactly, do humans play in this vision?
As consumers, humans are no longer buyers. Instead, they receive what’s purchased on their behalf. So, their main role is to pick an AI and train it to understand their needs. Of course, most of that training will happen without any direct human effort, as the AI watches what its owner does. (I almost wrote “master”, but it’s not clear who’s really in charge.) For people with disposable income, purchases are likely to move away from basic goods to luxury goods and experiences. Those are inherently less susceptible to purely rational buying decisions, so there’s a good chance that conventional buying and attention-based marketing will still apply.
As workers, humans are in trouble. Farming and manufacturing have been shrinking for decades and AI is likely to take over many of the remaining service jobs. Some conventional jobs will remain to do research and supervise the AI-driven machines, and there may more jobs where it matters that the worker is human, such as sports and handcrafts, and where human interaction is part of the value, such as healthcare. But total employment seems likely to decrease and income inequalities to grow. It’s possible that wealthy nations will provide a guaranteed annual income to the under-employed. But even if that happens, meaningful work will become harder to find.
I’ll admit this isn’t a terribly pleasant prospect. The good news is, predictions are hard, so the odds are slim that I’m right. I’m also aware that we’re at the peak of the hype cycle for ChatGPT and perhaps for AI in general. Maybe what I’ve described above isn’t technically possible. But, given how quickly AI and the underlying technologies evolve, I wouldn’t bet on technology bottlenecks blocking these changes indefinitely. Quantum AI, anyone?
All that said, of the three major predictions, I’m most confident about the first. It’s pretty likely that a monolithic marketing AI will emerge from the specialized AI bots that are being offered today. The potential benefits are huge, the path from separate bots to an integrated system is an incremental progression, and some people are already moving in that direction. (Pro tip: it’s easier to predict things that have already happened.)
The emergence of an AI-driven marketplace to replace conventional human buying is much less certain. If it does happen, the delegation will emerge in stages. The first will cover markets where the stakes are low and buying is boring. Groceries are a likely example. How quickly it spreads to other sectors will depend on how much time people have and how much intrinsic enjoyment they derive from the shopping itself.
The role of humans is least predictable of all. Mass under-employment probably isn’t sustainable in the long run, although you could argue it’s already the reality in some parts of the world. The range of possible long-term outcomes runs from delightful to horrific. Where we end up with depend on many factors other than the development of AI. The best we can do is try to understand developments as they happen and try to steer things in the best directions possible.