Are AI powered smart chatbots at workplaces gaining traction?3 min read . Updated: 30 Jul 2018, 09:04 AM IST
The need to reduce operating costs, coupled with fear of losing sales, is pushing companies to invest in chatbots
Mumbai: When Spanish start-up Landbot recently won a $2.2 million seed round for a chatbot that doesn’t use artificial intelligence (AI), it did raise some eyebrows.
Landbot, though, is an outlier in the AI chatbot market. The fact is that firms are already embracing enterprise AI-powered chatbots on the back of changing customer expectations—especially from millennials, the need to reduce operating costs, fear of losing sales and falling customer satisfaction.
HDFC Bank Ltd, for instance, has an AI chatbot called Eva built by Bengaluru-based Senseforth AI Research. AI banking platform Payjo launched an AI-powered chat assistant for State Bank of India (SBI) called SBI Intelligent Assistant (SIA). Axis Bank Ltd has its Axis Aha chatbot.
Similarly, other banks and financial institutions, such as DBS Bank Ltd, ICICI Bank Ltd, Yes Bank Ltd and Tata Capital Ltd, have their smart chatbots.
The human resources (HR) sector is another case in point. Gurugram-based HR tech start-up, Leena AI, is building HR bots that can be integrated into Slack or Workplace by Facebook. They are built and trained using information in policy documents and by mining data from various back-end systems like Oracle and SAP. On its part, Kwench Global technologies—an HR technology firm—is also enabling Workplace by Facebook with PULSE Bot that captures an employee’s mood in real time.
Other smart bots like Zoho Corp. Pvt. Ltd’s chat software called Cliq blend instant messaging with video, audio, and group conferencing. Unilever’s oral care brand Signal Pepsodent created a Facebook chatbot called ‘Little Brush Big Brush’ in Indonesia and Vietnam to “provide cute animated stories to motivate kids to brush their teeth and build their emotional connection with the brand". Many real estate firms across the world have chatbots that engage with customers. This allows to reduce the cost of servicing customers.
Chatbots, according to a 24 May 2017 note by research firm Forrester, can not only help customers retrieve information such as weather reports, bus schedules, package statuses, and currency exchange rates much faster and easier than by navigating websites and apps or searching though large pools of data such as frequently asked questions or product inventories but also improve efficiency in completing simple tasks like pushing coupons and redeeming loyalty points, and also to build emotional connections between customers and brands and increase engagement.
Chatbots have the potential to grow from $700 million in 2016 to $3 billion market in 2021, according to Mumbai-based chatbot platform, Haptik Infotech Pvt. Ltd. Companies are looking at $8 billion in annual savings by using chatbots, according to Juniper Research that referenced over 50 firms across eight regions and assessed the impact of chatbots in four industries—banking, healthcare, social, e-commerce and retail.
Chatbots, though, if not properly implemented can prove to be simply gimmicks rather than add value to businesses. Researcher Forrester believes that to truly meet a need for enterprise-grade customer service, chatbots—also called virtual agents and cognitive agents—must be able to understand what a customer speaks or types, discern their intent, respond in a conversational manner, and act on the customer’s behalf.
All of this must take place in a secure environment, and the chatbot must seamlessly hand off the interaction to a live agent when required, according to Forrester researchers. Chatbots can also fail, for instance, due to common mistakes like not clearing defining their purpose; setting goals that are too ambitious for existing tech capabilities; and launching them before they are ready, Forrester cautions.
AI-enabled enterprise chatbots may also find it difficult to scale up especially when you compare them to Apple Inc.’s Siri, Amazon Inc.’s Alexa, Google Inc.’s Allo and Duo, or Samung Electronic Co.’s Bixby due to relatively limited learning data sets.