What will applied AI look like in 2022?
AI adoption has skyrocketed throughout the last 18 months. Besides Joe McKendrick, who wrote the foundational piece on HBR, professionals who work on AI would readily attest to this statement. Google search seems to be in on this not-so-secret too: When prompted with “AI adoption,” its auto-complete spurts out “skyrocketed over the last 18 months”.
Both anecdotal evidence and surveys we are aware of seem to point in this same direction. Case in point: The AI Adoption in the Enterprise 2021 survey by O’Reilly, conducted in early 2021, had three times more responses than in 2020, and company culture is no longer the most significant barrier to adoption.
In other words, more people are working with AI, it’s now being taken seriously, and maturity is increasing. That’s all good news. It means AI is no longer a game that researchers play — it’s becoming applied, taking center stage for the likes of Microsoft and Amazon and beyond.
The following examines the pillars we expect applied AI to build on in 2022.
Typically, when discussing AI, people think about models and data — and for good reason. Those are the parts most practitioners feel they can exert some control over, while hardware remains mostly unseen and its capabilities seen as being fixed. But is that the case?