LIVE FROM MWC LAS VEGAS 2024: Generative AI (genAI) is ready for prime time across a range of enterprise use cases, but a Qualcomm executive noted businesses need to fully embrace them.

“We’ve reached a point where for enterprises generative AI is not a luxury, not an option to be considered, but it’s a necessity at this point in time to move forwards,” said Durga Malladi, (pictured, right) SVP and GM, technology planning and edge solutions at Qualcomm.

On the consumer front, he stated genAI is changing how users interact with their devices through voice commands. It can also translate languages to enable users to speak to one another across their devices.

“Now, let’s actually shift towards enterprise use cases,” he said. “This is where there is a massive opportunity for productivity increases.”

For enterprises, Malladi noted genAI can translate documents from one language to another or summarise Word documents to create detailed, short notes.

“These are pretty good tools in the sense that you’re about 90 per cent there, and so it really simplifies the amount of work that you need to do,” he explained.

Another area where genAI is getting tuned up is across software organisations. Software engineers can ask large language models (LLMs) to make code for a specific topic, which saves time and reduces the need for debugging.

“These productivity tools are clearly transforming a lot of the enterprises, and it’s important to actually embrace them so that employees get to use them as much as possible,” he noted.

In addition to productivity tools, genAI can also benefit enterprises’ bottom lines by improving efficiency.

It also enables better collaboration between enterprise teams, or with employees in remote locations.

“The ability to communicate in a very easy way makes it bigger,” he said. “The other part that I think is essential is that we are just at the beginning of generative AI running into workforces at this point in time.”

While most LLMs are currently trained on open-source information, enterprises will benefit from using their own data for specific LLMs.

“Once you have these fine-tuned models for this enterprise-specific data, then you start seeing a very different kind of use case that will come in,” he noted.