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Conversational AI, which allows chatbots to engage in human-like conversations, is a hotly debated topic in enterprise IT. Some say companies have a future of how they will work with their employees and customers. Others claim that the technologies behind the AI of communication fail to understand English language noise, leaving other languages and not fully mature.
Perspectives may vary, but numbers continue to show that interactive AI is on track for widespread adoption. A recent survey conducted by Replicant found that about 80% of consumers are willing to talk to conversational AI. Gartner predicts that the next two to five years will see a 100% increase in enterprise-level chatbot implementation. This surge in demand is what Moveworks has witnessed in recent years. The California-based company that leverages communicative AI to offer end-to-end employee support has seen a surge, especially during the entire epidemic when the need for hybrid and remote work has increased significantly.
“There are three secular mutations that are paving the way for the creation of a whole new era of communicative AI – SaaS. [software-as-a-service] Integration, enterprise messaging and NLU advances, ”said Bhavin Shah, founder and CEO of Moveworks, during a panel at VentureBeat’s Future of Work Summit.
More often than not, conversation solutions like chatbots are less responsive, as they fail to understand the meaning and noise of the user’s sentence and come up with incorrect responses. “This is the result of hard-coding tools with strong logic trends (if it is such a system) and can be eliminated with effective employment of advanced ML models, allowing the tools to be more seamless,” Shah said.
“Using machine learning, combinations of new techniques and techniques – from spelling correction models to statistical grammar models – you can actually respond to a conversation as it emerges with the employee rather than predefining,” he said.
Hybrid with interactive AI, mastering remote work
The state-of-the-art communications AI offered by Moveworks has already led to positive business results for remotely operating enterprises, Shah noted, noting the case of Palo Alto Networks, one of the largest cyber security companies in North America.
At the height of the epidemic during April 2020, Palo Alto envisioned Flexwork, an ecosystem that brings Uber, Box, Splink and Zoom together for seamless remote working. However, to make the vision come alive, the company needed a digital hub to ensure personal (based on location, role, working habits) and friction-free employee support. That’s where Moveworks came from and developed Sheldon, a communicative AI chatbot that allowed Palo Alto employees to get IT help, HR help and more.
“More than 90% of employees now use Sheldon on a regular basis. And with more than 4,000 issues being resolved by Sheldon through fully autonomous end-to-end, Palo Alto saves Networks more than 180, ooo hours of productivity, “the founder said, adding that the company’s stock has risen 252% since then.
The CEO cited other success stories where chatbot solutions not only help the enterprise prosper in the work environment, but also advance the overall advancement of communication AI technology.
For example, Hearst Media, which has been around for over 130 years, uses a chatbot called Herbie to provide information and resources to hybrid employees from systems spread across more than 360 subsidiary organizations. Herbie, Shah said, faces this big challenge by using an enterprise cash system, which indexes the resources available every four hours, to ensure that employees receive a single, accurate snippet of information as the answer to each question.
Moveworks has also added a chatbot called ALBot for the chemical giant Albemarle. This solution is different from others because it supports not only English-only questions, but also other languages. This allows the company to treat its entire global workforce as first-class citizens and save on the cost of hiring multilingual support agents. However, the task was not easy as new machine learning models had to be developed to support foreign languages, starting from the ground up and using language data (examples of questions / use cases) that were not as widely available as English language data.
“So we invented a technique called mass learning,” Shah said. “We can abstract all these different sentences that are spoken, no matter what the language, and from there we can take that idea and turn all these different examples into the millions of use cases we use in our machine learning. We can do this to train the models, to make them stronger by making them more accurate. “
Market Opportunity for Conversational AI
Companies like this are coming forward and using NLU and AI to power remote employee experiences through chatbots, communicative AI is expected to become a common denominator in the long run. According to a study of markets and markets, the market size for technology is expected to grow by 22% to about $ 19 billion by 2026.
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