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Eighteen months into the generative AI boom, some may wonder if the shine is wearing off.
In April, Axios called gen AI a solution in search of a problem.
1 form of deployment.
Meanwhile,Appleand Meta are reportedly withholding key AI features from Europe over compliance concerns.
But the problem isnt the technology; its the mindset.
What we need is an alternative approach.
Not all AI is the same.
We have a bandwagon problem with companies jumping on the AI train, particularly for generative use cases.
Then, and only then, can we build a roadmap for concrete, long-term value.
Chief Sales Officer, Virtusa.
Not all AI is the same
Broadly speaking, Enterprise AI splits into generative and analytical applications.
AI for analytics meanwhile, has been commercialized for far longer.
Analytical and generative AI can overlap, of course.
Still, the two sides are fundamentally different.
Analytics AI helps you operate.
Generative AI helps you create.
Too many stakeholders gloss over this bifurcation, but it matters in the all-important value conversation.
AI-powered analytics have long proven their ROI.
Thats a different ballgame.
We see lots of experimentation and capex, but not necessarily commensurate output.
Business leaders are trying to force AI and generative solutions especially onto problems they dont have.
Its time to take a step back.
Leaders need to remember two things.
First, its important to separate the use cases.
Second, its just as important to integrate AI only where it makes sense.
It should solve acute problems that thebusinesscan realize value by solving.
Otherwise, it represents a solution without a problem.
You gave the orchestra drums for an arrangement with no percussion.
The firm needs to invest significant resources into training its latest resident.
After all, its there to solve an acute problem, not to just go knocking on every door.
Done correctly, generative models can deliver substantive long-term value.
Thats why its essential to have the discipline to invest in this domain knowledge from the outset.
Leaders need to build that into any AI investment plan if they want useful, long-term results.
But the generative intelligence must have proper guidelines and industry-specific training, lest the implementations stray from their lanes.
Approached this way, gen AI wont go the way of the metaverse.
If not, the cost of failure is already becoming clear.
But for those who adopt an engineering mindset and dont take shortcuts, this alternative approach can indeed deliver.
A pragmatic approach to AI starts by asking the right questions and committing to an investment of domain knowledge.
It ends with targeted solutions that deliver quantifiable long-term value.
We list the best Large Language Models (LLMs) for coding.
The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc.
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