Sticker shock: Are businesses increasingly disillusioned with AI?

Artificial intelligence (AI) is remarkable — at least on a smaller scale, as seen in personal assistants, robots and mobile devices. However, the jury is still out on large enterprise projects. Executives and experts may find their hopes for AI may be more complicated than intended. AI technology is increasingly expensive, businesses are not prepared and return on investment (ROI) is still a big question mark.

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That is warning from David Linthicum, a highly regarded analyst, the real deal wrote the book on enterprise integration, cloud and more. But now he is no longer optimistic about the success of AI projects – at least for now. He believes a “recession” in enterprise AI buying is imminent, as companies realize reality doesn’t live up to the hype and fall into disillusionment. However, what will emerge in a year or two will be solid AI use cases and implementations that are more relevant to the business.

Linthicum explains there are four reasons why businesses lose faith in AI:

• Tap into the “data wall”: The main problem businesses are facing is “not because Innovative AI their technology sucks, but because their data sucks,” he explains. The challenge is “there’s no easy fix for this, you’re going to have to stop what you’re doing, iterate and fix the data yours. For many of these organizations, that particular problem has not been addressed in the last 20 or 30 years. [Moreover]That’s a significant cost and risk, and someone has to go to a board meeting and tell them we’re going to spend $30 million to fix our data before we can use genetic AI. Those are difficult conversations.”
• Financial sticker shock: Building, deploying, and maintaining AI requires more resources than previous waves of technology such as cloud or mobile. “These things are very expensive,” he said. “They cost at least two to three times more than traditional environments, they need specialized processors like GPUs, they need a lot of resources, they need a lot of ecosystem-based components, they need mining data. created to adjust data and models.” training, model tuning, all the things that come with AI.”
• Lack of strategic direction: “Businesses need to plan better,” Linthicum stated. “Don’t understand the state of your data until you work on a generative AI project, [that’s] is not the way to do it. It’s looking strategically at how your data needs to fit into your use of this new technology.”
• Lack of skills: AI success requires well-trained people — “and I’m not talking about certification training around learning a cloud provider’s AI platform,” Linthicum said. “I’m talking about understanding architecture, understanding data science, understanding AI ethics, understanding model tuning, understanding performance benchmarking, and understanding aggregated data.” This is “very different from traditional software development.”

There is no historical technology parallel to the effort needed to support AI, “which would be much more complex, much more expensive,” Linthicum\ details.

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This requires “cleansing and managing their data, equipping them with the skills they need, planning their strategy, mapping out use cases, and mapping out ROI.” Then, “you’ll get to the point where you’re using AI as a strategic differentiator for your business. You can do something your competitors can’t – provide a better customer experience.” , higher productivity, lower price and better efficiency.”