66 million years ago, an asteroid 10 KM in diameter struck earth near what is now the town of Chicxulub in the Yucatan. In its wake, a mass extinction event killed off 75% of all life on earth. Fast forward to present time, scientists are actively debating whether we are now in the Anthropocene Epoch, where the predominant impact on the planet’s climate and ecosystems are due to human activity. Some even pin the date the epoch began as the year 1950.
If the volume of digital ink spilled since the release of ChatGPT in November 2022 has a voice in the debate, perhaps a case can be made for that date being the starting year of the Anthropocene Epoch.
The collective consciousness of the permanently online has a habit of over interpreting era-defining shifts in technology. Afterall, crypto was the darling of Silicon Valley only a year ago. This feels different. It doesn’t seem to be a question of whether the language models that underpin ChatGPT transform how people interact with tech. Rather it’s the immediacy, ubiquity and impact that’s causing luminaries to either ask for a pause or really hit the panic button and shut it all down.
Whether AI is the tech equivalent to Chicxulub or not, it has already permeated critical areas of civic life, redefining our content consumption, social interaction and entertainment channels. Soon it will permeate critical areas of how we work.
Consumer experiences condition enterprise expectations. Era-defining tech, emblematic of the zeitgeist of time-periods of big change become clear only in the fullness of time. It’s because the way we work evolves in response to the changes in the way we play. People want better user experiences at work because they can see what’s possible at home, with their friends, and in their personal lives. As with the iPhone before, this will happen with AI in the enterprise — as companies figure out new ways to transform productivity.
IT isn’t as sexy — yet — as generative avatars for social media. Nevertheless, this Boring AI is where most of the useful work will be done. Right now most of that Boring AI is used for automating low value work (image recognition, customer support chatbots, automatically writing sales prospecting emails) etc.
The obvious next step is overhauling higher value workflows in the enterprise by making them do things which were not possible to do before without language models. Of course, most B2B tech providers have also caught on to this thoroughly predictable insight and have generously sprinkled some GPT fairy dust on their existing stacks, with generative AI capabilities for text and image generation across the sales and marketing tech stacks.
Whether the millions of marketing, sales and support personnel want to hand over their jobs to generative AI right now remains to be seen.
No matter, generative AI for sales and marketing, while interesting, isn’t the most useful stuff possible. Though still new, the real value is a bit further up the stack. There lies the possibility of inventing whole new categories of software that don’t look and act like anything we have out there now.
An example is the process mining company, Celonis. Celonis analyzes business processes at large companies to suggest ways for optimizing everything from purchasing to procurement. Recently, the buzzy startup, Numbers Station wants to bring AI to the decidedly unsexy — but also decidedly critical — world of data extraction and cleansing. There will be plenty more like these in the future, and this is where the real value and the real transformation is likely to lie.
Enterprise adoption of language models will happen when enterprises are both bought into the transformative, value-creating power of the technology, but also aware of the risks and consequences of it — and have a way to deal with it. While the buzz and promise is palpable, the risks and impact are unclear. Thus, large companies are adopting the stuff that automates the most boring of Boring AI — tasks which cause expense but may not have discernable strategic advantage.
A world with more companies like Celonis and Numbers Station requires corporations to have well formed strategies around trust and safety, security and data protection to limit exposure from data privacy, as well as the really boring stuff like extensibility, integrability, observability and ease of use. Until that happens, we’re looking a lot less like Chicxulub than Clubhouse — a big deal for a lot of people, but a non-event for virtually everyone else.
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