Venture capitalists aren’t the only ones who are banking on generative artificial intelligence (AI) to be the next big thing in tech. So too are data center leaders who see chatbots as more than an ultra-niche area of generative AIs poised to make operations leaner while hitting employment and sustainability metrics.
Chatbots have made steady and vast improvements since the first wave in 2016 when the tool led to frustrating interfaces. Microsoft released its chatbot Tay to Twitter. Tay quickly made headlines. Within 16 hours of its deployment, the chatbot tweeted 95,000 times with a high percentage of tweets involving abusive and offensive messages.
Today’s chatbot, however, is capable of more than canned customer service and biased responses. Heavy investments into generative AI and machine learning (ML) mean chatbots can do more than imitate human interaction and spit out artificial responses. Beerud Sheth, founder and CEO of Gupshup, a service that allows companies to build and deploy chatbots for various messaging applications, says “there are some broader opportunities” for data centers.
“Now it can answer very specific questions of ‘what happened to my server or my service?’ Or ‘when will it come back?’” Sheth said. “(Chatbot) has the language capability of GPT-3, but it also has accurate information coming from the data center there to respond to those questions.”
The Chatbot Gold Rush
Thanks to utilizing natural language processing (NLP) — the automatic manipulation of natural language — most modern chatbots can map user input and intent, classifying the message and preparing a fitting, human response. NPL opens up tons of possibilities for using chatbots in data centers, especially now that chatbots are multipurpose pieces of AI-powered software that enable a machine not just to react but to comprehend.
In a new market research report published by Global Market Estimates, the chatbot market is expected to grow at a CAGR of 25.2% from 2023 to 2028 while hitting $10.5 billion by 2026. The NLP industry? A promising $26.4 billion is expected by 2024. Success stories involving chatbots across industries are no longer predictions; they’re a reality.
Sheth adds that conversational AI can dramatically reduce data center operating costs because chatbots can be articulate and accurate.
“Whenever there's a crisis or something happens you need to have a whole bunch of things like remote hands, you need to have people responding very quickly, being available and I think a lot of those could be fully or entirely automated and expanded with AI,” Sheth told Data Center Knowledge.
Data centers don’t even have to rely on major players like Amazon, Google, Acuvate, or OpenAI to create their own chatbot. They can build their own further reducing the dependence on a dedicated workforce. Companies can either use an existing platform to create a chatbot, or they can build a bot from scratch.
Furthermore, chatbots in the data center can be used to simulate real-world scenarios, enabling data center operators to identify potential problems and proactively address them before they occur. As a result, there is a growing interest in using generative AI in the data center industry, and there will likely be more research and development in this area in the future.
“AI models can be very good at detecting these once they're trained for it,” Sheth says.
But chatbots aren’t an end-all for data centers even if teams have adopted chatbots to optimize work and shorten the time and effort it takes to get feedback. Though chatbots can help data centers with superior efficiency, it’s only better at synthesizing information than humans.
The technology can be “both underestimated and overestimated at the same time,” writes California-based Companies-as-a-Product (CaaP) builder ZeMing M. Gao.
“AI is going to vastly accelerate the synthesis part of human knowledge. There is no denying that this is coming and also useful,” says Gao before adding that AI applications are fundamentally knowledge synthesizers, not knowledge creators.
Tell that to venture capitalists.
According to Pitchbook, investors have increased investment in chatbots, virtual assistants, and voice bots capturing 57.8% of VC investments in natural language interfaces in 2022. By 2025, the volume of data generated worldwide is expected to exceed 180 zettabytes — a key metric in understanding operation costs for a modern cloud or hyper-scale data center. That amounts to an annual growth of 40%. This much data center needs equates to more employees to handle technical work.
Data centers will have to support the growth ring more people but employment trends indicate a shortage, not a glut of capable workers.
The Data Centers Lean and Staffed
Sheth points to the promising side of how chatbots can help data center operators leverage AI capabilities as IT staffing issues continue to affect the sector.
Specialized chatbots data centers can use predictive analytics to identify potential talent retention risks by analyzing factors such as employee satisfaction, performance, and behavior patterns, says Sheth. This information can be used to develop targeted retention strategies and ensure that employees are engaged, productive, and motivated.
Generative AI can help also match employees into the right jobs by analyzing their skills and experience, as well as the requirements of specific job roles. This can help ensure that employees are placed in roles that suit their strengths and interests, increasing job satisfaction and reducing turnover.
In the context of data centers, chatbots are more than necessary. A Gartner report states that by 2025, half of cloud data centers will deploy advanced robots with AI and ML capabilities, resulting in 30% higher operating efficiency. Additionally, 30% of outbound marketing messages from large organizations will be "synthetically generated” by chatbots; up from about 2% in 2022
