What are the disadvantages of generative AI?
On the flip side, the rise of generative AI could affect jobs as machines begin to supplant human workers. “AI could lead to further job displacement for those engaged in the processes impacted, and in some cases companies and business models may become obsolete,” said Hariharan.
For instance, generative AI’s ability to crunch numbers and write code could impact programmers in the tech industry, where Big Tech firms are already downsizing their workforces in a bid to optimize costs. “However, there’s currently a shortage of software developers, so if you could have generative AI help write code, that’s solving a major economic bottleneck,” noted Murphy.
Likewise, the technology has proven proficient in generating text, which could put jobs such as copywriting and customer service at risk. For example, Microsoft is launching a new AI assistant called Dynamics 365 Copilot, which will be able to draft contextual responses to customer queries, write product listings for e-commerce websites and more.
But generative AI tools are not 100% accurate — at least, for the time being. ChatGPT is prone to “hallucinations,” or output that deviates from its training data. “Because of this, generative AI will not yet replace jobs entirely. Instead, it will augment existing jobs by automating repetitive tasks, freeing up time for workers to do other things,” said Hariharan.
Generative AI tools also run the risk of plagiarism and copyright infringement, as they often repeat or paraphrase data sourced from elsewhere on the Internet. Then there are the data security risks involved, especially where client confidentiality is concerned. When new information is inputted into a generative AI system, it becomes part of its data repository and is made publicly available to other users. “Companies will understandably be guarded about this, so generative AI providers will need to create tools that are ringfenced — ensuring that all information is self-contained to each organization and isn’t comingled with the rest of the world,” said Murphy.
In addition, generative AI is expensive to deploy, which could limit its uptake. “Right now, a lot of the buzz surrounding generative AI is fueled by curiosity. But the challenge is that the tools are quite expensive as a lot of cloud computing and hardware are required to run the algorithms,” noted Hariharan. “Generative AI won’t be easy to commercialize unless you have very strong demand drivers that are monetizable. The results have to justify the cost of usage.”
Overall, despite these hurdles, generative AI could well be a game changer for business, dramatically redefining the way companies work. “Generative AI is the most important technological development of the last several decades. It is rapidly enabling use cases and scenarios that people once said would be impossible to achieve, and it’s only going to get smarter,” said Murphy. “It’s critical that generative AI is used responsibly and governed properly, so that it can amplify human potential instead of becoming too disruptive.”
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