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Today were witnessing an interesting contradiction unfolding when it comes toAI it is both overhyped and underhyped.
Many remain in the experimentation phase for now.
But why is that?
And that introduces risk and missed opportunity for enterprises seeking to scale AI competitively and responsibly.
Simply accessing foundation models doesnt unlock business outcomes.
The real challenge lies in building robust enterpriseapplicationson top of them.
This is no small feat either.
And the industry is betting on a group of largely inexperienced professionals to deliver on this.
Armand Ruiz is Vice President for IBM’s AI Platform, watsonx.
The hype risk
Hype can be a threat to problem solving.
And in this case, hyping up the wrong things is a threat to solving AI complexity.
The AI industry is marketing well ahead of its current capabilities, blurring business focus and value.
Some are already touting timelines to achieve AGI, all while incidents ofchatbothallucinations and faulty outputs increase.
Id argue that those use cases are severely underhyped.
More importantly, were overlooking that enterprise adoption of gen AI is reliant on developers with limited AI expertise.
This is easier said than done for a few reasons.
There remains a significant knowledge gap preventing app developers from becoming proficient AI developers.
On top of that, these professionals are trailing constant innovation cycles.
Ultimately, this impedes production and has an upstream effect on costs and business growth.
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