How Moveworks employed Conversational AI to facilitate hybrid work

Conversational AI, which enables chatbots to have human-like interactions, has been a hotly discussed issue in business IT. Some believe it is the future of how businesses will interact with their personnel and consumers. Others argue that the technology behind conversational AI aren’t developed enough to recognise English language subtleties, much alone other languages.

Although opinions differ, the facts continue to demonstrate that conversational AI is on course for mainstream deployment. According to a recent Replicant poll, approximately 80% of customers are eager to converse with conversational AI. According to Gartner, enterprise-level chatbot deployment will more than double in the next two to five years. This spike in demand has been noticed by Moveworks in recent years. The California-based firm, which uses conversational AI to provide end-to-end employee assistance, has witnessed a huge increase in demand, notably during the pandemic, when the need for hybrid and remote work increased considerably.

“Three secular developments are driving the way to a brand-new age of conversational AI — SaaS [software-as-a-service] connectors, corporate messaging, and NLU breakthroughs,” Bhavin Shah, the founder and CEO of Moveworks, said during a panel at VentureBeat’s Future of Work Summit.

Often, the reaction to conversational solutions such as chatbots is unimpressive, as they fail to comprehend the meaning and subtleties of a user’s speech and respond incorrectly. This, according to Shah, is due to the tools being hard-coded with inflexible logic flows (if this then that kind of system) and may be eliminated with the efficient use of sophisticated ML models, enabling the tools to be more seamless.

“By applying machine learning, new approaches, and ensembles of techniques – from spell checker models to statistical grammar models – you can really respond to the discussion as it occurs with the employee rather than predetermine it,” he said.

Conversational AI enables enterprises to handle hybrid, remote work

Moveworks’ advanced conversational AI has already resulted in great business results for firms operating remotely, Shah said, citing the example of Palo Alto Networks, one of North America’s leading cybersecurity companies.

Palo Alto envisioned Flexwork, an ecosystem connecting together Uber, Box, Splunk, and Zoom for seamless remote working, during the height of the pandemic in April 2020. However, in order to realise the goal, the organisation required a digital centre to assure tailored (based on location, position, and working habits) and frictionless employee assistance. That’s when Moveworks stepped in and created Sheldon, a conversational AI chatbot that enables Palo Alto staff to request IT assistance, HR assistance, and more.

“More than 90% of workers now utilise Sheldon on a regular basis.” And Sheldon solves over 4,000 problems totally autonomously end-to-end, saving Palo Alto Networks over 180,ooo hours of productivity,” the creator stated, adding that the company’s stock has increased 252 percent since then.

The CEO went on to identify numerous success examples in which chatbot solutions not only assisted organisations in thriving in a hybrid work environment, but also fueled the broader progress of conversational AI technology.

For example, Hearst Media, which has been in business for 130 years, employs a chatbot called Herbie to give hybrid staff with support information and resources from systems spread across over 360 subsidiary firms. Herbie, according to Shah, addresses this mammoth task by using an Enterprise Cache system, which searches accessible resources every four hours, to ensure staff get a single, accurate fragment of information as the response to every enquiry.

Moveworks also improved an ALBot chatbot for the chemical company Albemarle. This approach differs from others in that it not only supports English-only inquiries, but also those in other languages. This allows the corporation to treat its whole worldwide workforce as first-class citizens while saving money on multilingual support agents. However, it was not a simple process since supporting other languages necessitated starting from scratch and developing new machine learning models utilising language data (examples of queries/use cases) that was not as publicly accessible as English language data.

“As a result, we found a mechanism called communal learning,” Shah said. “We can abstract all of these various phrases that are spoken, regardless of language, and then take that notion and permute all of these different instances into millions of use cases that we can use to train our machine learning models, making them more robust and precise.”

Conversational AI has a market potential

With firms like these emerging and employing NLU and AI to power remote employee experiences through chatbots, conversational AI is projected to become more ubiquitous in the long term. According to a Markets and Markets analysis, the technology’s market size is predicted to expand 22 percent to approximately $19 billion by 2026.

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