Whether you’re excited by the potential of generative AI or terrified of its implications, you’re likely trying to parse how it will change your work.
Scott Belsky, who currently serves as Adobe’s chief strategy officer and EVP of design & emerging products, recently shared some of his predictions for how AI will reshape business (emphasis on the “Belsky”).
He sums up his “general thesis” as: “We need to value human ingenuity and free up the capacity of creative minds for higher order tasks.”
Beware of Too Much Optimization
“As AI gets really good at optimization, some industries and business models will need to change,” Belsky writes.
First, Belsky posits, businesses that use AI to optimize their bottom lines may inadvertently degrade the value of their products.
Dating apps and music streaming services are vulnerable to short-sighted decisions, he suggests. The former might try to keep customers on their app longer by offering less promising matches, while the latter might try to keep artist payouts low by offering the longest songs that match a user’s parameters. Both are recipes for “optimization” that would likely harm customer satisfaction.
New Pricing Models Are Coming
Next, Belsky joins the chorus of those predicting that AI will kill the billable hours model. “New pricing models are overdue to replace time-based and finger-in-the-wind pricing in the age of AI where time is magically compressed,” he writes.
Consider this, Belsky argues, “the ultimate SOURCE of the differentiating value delivered to a client: It is less ‘time’ and it is more ‘experience.’”
The individual factors (that will continue to matter) are “one’s years of experience, honed skills from formal education and practice, one’s taste and intuition, one’s creativity, one’s network of relationships, and even one’s proprietary data and algorithms honed through volume of past experiences,” according to Belsky.
So how will industries, like law, design, consulting, etc., charge clients? Perhaps they will move to a model more akin to that of medicine, in which there “is a new source-of-truth for the ‘value’ of tasks across professional trades via a third-party billing service that determines price.” Or (less ominously, for my money) “we enter an era of results-based compensation that is far more objective and measured?”
Data-Driven Purchasing
As humans become more comfortable with AI assistants, Belsky predicts, “you may start trusting the guidance of your agent more than any other signal.”
This would, theoretically, have a seismic impact. If this happens, “the best product at the best value may in fact win,” Belsky writes. “This is a win for buyers, but may be quite disruptive to sellers who fail to innovate and endlessly optimize” but have traditionally relied on brand loyalty.
To remain relevant, marketers will have to deploy a tandem-style approach.
Belsky argues, “If Macromarketing runs on a calendar, Agile Marketing runs on a stopwatch.” Brands must adapt to “tell their stories at the speed of social.” But it will not replace “Macromarketing,” which “requires a lot of coordination but helps a brand establish its identity and sets the tone for the rest of a company’s marketing.”
The “Core and Periphery” Model of Entertainment
As AI becomes more embedded in our workflows, Belsky writes, “the core (Hollywood — and all the players involved with original story creation) only gets stronger and more efficient, and the periphery (user-generated content, unsanctioned sequels, and long-tail spin-offs) grows by 100x.”
He also expects that “we will crave story, meaning, and originality more than ever before” as we are exposed to more AI-generated content. In part, that means “we can TAKE MORE CREATIVE RISK.” For example, maybe “Hollywood will spend less time replaying safe playbooks (sequels and familiar storylines) and more time developing NEW franchises and imaginative storylines?”
Also, Belsky writes, “Perhaps AI will help user-generated content not only improve in terms of quality, but also get exposure from a higher-signal network of curators? So far, social platforms have surfaced content based on what the “critical mass” thinks (number of likes) rather than what the “credible mass” thinks (WHO actually liked the content, and how credible they are as tastemakers).”
