Enterprises Losing Faith in Artificial Intelligence
Artificial intelligence (AI) has been remarkable in smaller-scale applications, such as personal assistants, robots, and mobile devices. However, the jury is still out on large enterprise projects. Executives and professionals may be waking up to the possibility that their hopes for AI may be more complicated than planned.
Four Reasons for the Disillusionment
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Hitting a “Data Wall”
The main issue enterprises are running up against is “not because the generative AI technology is bad, but because their data’s bad,” explained David Linthicum, a highly-regarded analyst. The challenge is “there’s no easy fix for this, you’re going to have to stop what you’re doing, loop back, and fix your data. For many of these organizations, that particular problem hasn’t been addressed for the last 20 or 30 years.”
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Financial Sticker Shock
Building, implementing, and sustaining AI requires more resources than previous tech waves such as cloud or mobile. “These things are very expensive,” said Linthicum. “They cost at least two to three times that of traditional environments, they need specialized processors like GPUs, they need a lot of resources, they need a lot of ecosystem-based components, they need the training data that’s the data tuning, the model training, the model tuning, all the things that come with AI.”
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A Lack of Strategic Direction
“Enterprises need to get better at planning,” Linthicum stated. “Not understanding the state of your data until you work on a gen AI project, [that’s] not the way to do it. It’s looking strategically at how your data needs to align with your utilization of this new technology.”
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Lack of Skills
AI success requires well-trained people — “and I’m not talking about the certification training around learning one 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 synthetic data. This is ‘very different than traditional software development.'”
A Glimmer of Hope
There is no historic technology parallel to the effort necessary to support AI, “which is going to be much more complex, much more expensive,” Linthicum detailed. However, what will emerge after a year or two will be solid AI use cases and implementations, aligned closer to the business. This requires “cleaning and managing their data, getting the skills they need, doing the strategic planning, mapping out the use cases, and mapping out to the ROI.” Then, “you’ll get to a state where you’re using AI as a strategic differentiator for your business. You’re able to do something your competitors can’t – providing a better customer experience, higher productivity, lower prices, and better efficiency.”
Conclusion
The warnings from David Linthicum are clear: enterprises are facing a period of disillusionment with AI due to the complexity and expense of implementing and sustaining AI technology. However, with the right approach and investment in data cleaning, skills, and strategic planning, the potential benefits of AI can be realized. It is essential for executives and professionals to understand the challenges and take a more strategic and realistic approach to AI adoption.
FAQs
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What is the main issue enterprises are facing with AI?
The main issue enterprises are facing is “hitting a data wall” – their data is not good enough to support the implementation of AI technology.
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Why is AI more expensive than previous tech waves?
AI is more expensive because it requires specialized processors like GPUs, a lot of resources, and a lot of ecosystem-based components, as well as training data and model tuning.
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What skills are required for AI success?
AI success requires understanding architecture, data science, AI ethics, model tuning, performance benchmarking, and synthetic data – skills that are very different from traditional software development.
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What is the potential outcome of a strategic and realistic approach to AI adoption?
The potential outcome is using AI as a strategic differentiator for the business, providing a better customer experience, higher productivity, lower prices, and better efficiency.
