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What is The Future Of Chatbots and Conversational AI? – AskSid

What can The World Expect From Chatbots and Conversational AI Technology 5 Years Later?

The most widespread use of AI is in chatbots – and it will only double in the next two to five years.

Technology-driven solutions for consumer engagement have assumed a pivotal role in shaping an organization’s operations. Artificial intelligence has penetrated the further reaches of the organizational matrix than you would like to imagine – the prime application being chatbots or conversational AI technology.

By replacing humans behind the chat screen with intelligent bots that learn with each interaction, it has become possible to economize customer engagement while delivering better brand value. However, conversational AI technology is still incipient and plagued with inexperience. So…what is the future of chatbots? Where is the world taking this technology, and what can the consumers (and businesses) expect from it a few years down the line?

To understand chatbot future trends, let’s first come abreast with the current situation of the fledgling conversational AI technology.

Current Scenario of Conversational AI Technology: 6 Critical Limitations

Scaling your conversational AI technology to the level that it generates exemplary ROI at the end begins by understanding where it needs to improve – the limitations that need to be overcome to assimilate this technology better into operations. With the chatbots in application today, these are the general limitations.

They are Very Basic

Apart from addressing general consumer queries and cracking an occasional joke, there isn’t particularly anything else that a chatbot can do today. It isn’t “intelligent” and “learning” until there is active involvement with a customer in a conversation – all that a chatbot does today is mechanically progress with a multiple-choice type question-answer format with a consumer. Any query outside of these preset responses just throws off the bot’s algorithms – it drives consumers away from a brand. Given that the technology is still new, if this is the extent of what a chatbot can do, then Houston has a problem.

They are…Bots

Conversational AI technology has been touted to be a thing “close to human interaction” – it is still quite far from holding an effective conversation with a consumer. Chatbots rely on algorithms to respond to consumer queries; if a consumer asks something that hasn’t hit its algorithms before (things that the bot hasn’t “learned” yet), it may return incorrect responses. This has the potential to frustrate consumers since there is a lack of human touch/interaction and an ineffective bot to top it off.

Many retail brands that run their apps employ chatbots that are mechanical and programmed to run on preset questions. Ask them something else, and you would be greeted with “Sorry, that isn’t a valid response” or something similar.

They Cause Redundancies

Conversing with a chatbot today often gets you redundant responses. This happens because of several reasons:

  • The algorithm of the chatbot doesn’t recognize or “understand” the user’s query
  • The chatbot hasn’t been programmed to respond to queries outside of a certain niche or category (for example, a retail assistance chatbot that cannot help you track your package)
  • The consumer is stuck in a conversational loop with a chatbot looking for answers (but not finding them)

Chatbots need to be more than just ornamental – an effective conversation that addresses all consumer queries is the least you can expect.

They Cannot be Empathetic

The base nature of machines is the lack of empathy. When businesses decide to deploy chatbots to handle consumer grievances, they need to be prepared to face the flak. Chatbots cannot gauge consumer sentiment – a characteristic that a human representative aptly judges and improvises his approach. A mechanical approach to appeasing an angry consumer may go wrong more than once. For a chatbot to gauge whether a response to emotion is wrong or right, a LOT of training and algorithm tuning would need to be done.

With these limitations, it is a path of relentless perseverance to achieve new heights in conversational AI technology. However, Deloitte has recognized 5 key parallels of progress that spell out a conversational AI future. Let’s see what they are.

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The 5 Pillars of Conversational AI’s Future

Because of COVID-19, there has been an accelerated adoption of chatbots, voice-based assistants, and virtual assistants among businesses. The key driver has been the enhancement of consumer experience and boosting consumer engagement with a brand. With that said, the limitations of the current conversational AI technology have dammed the immense potential of a great AI application.

The key areas where ceaseless research, experimentation, and finetuning have been continuing to create improved conversational assistants are as follows.

Training The Chatbot

A conversational agent needs to be constantly trained in the context of its niche and application, so that it may perform its duties well. For example, an inventory management bot would need access to product in- and out-scans, sales ledgers, bookings, and cancellations, etc.

With manually training a bot, there are inherent problems:

  • The process is long, tedious, and may incorporate errors. It may take up to 9 months to train a bot manually
  • The bot may not “learn” effectively, and may result in dissatisfactory performance

With these key problems in mind, innovators have set out to simplify the training process and make it more efficient. Deloitte data says that 20% of the patents in their survey pertained to improving the training processes of chatbots.

Preparing The Bot for Complexities

Conversations between humans are neither simple nor straightforward: if chatbots are to properly assimilate natural language processing, they need to be trained in handling complexities in conversations. Today, the bots struggle to understand the basic intentions behind a query. For example, it would be fantastic if you could ask your digital assistant to set a timer for your cookies in the oven and keep reminding you every 5 minutes to go check on them – bots today can’t accomplish this, because there are multiple tasks involved: setting a timer, and setting recurring reminders in a single utterance.

Many researchers have been working on this aspect, trying to imbue multitopic conversational capabilities, sequential-tasking, and multitasking in chatbots. The future of bots will be bright if they could come a little closer in their capabilities to Iron Man’s Jarvis.

Better and Personalized Interactions

Personalization is one of the key drivers of sales today, and all businesses wish to cash in on it. Training a chatbot to personalize messages on a superficial level is possible today, by programming it to recognize a returning customer, remembering order history and grievances. The future of conversational AI technology in personalization, though, lies in going a step further.

In the US, several patents exist which focus purely on chatbot personalization capabilities. One effective example is where a chatbot can gauge the patience of the consumer based on his typing and response speeds and can tune its responses accordingly; or if a user is dissatisfied, to bring in a human executive to carry the conversation forward.

Cutting Through The Noise

Most of the digital voice-based assistants today fail utterly when there is too much noise in the background. This is set to improve with the latest research and advancements in conversational AI technology that employs voice. Speech recognition refinement and noise filtering technologies are currently being worked on to make voice-based digital assistants understand their owners even when riding a bus or sitting at a cafe.

If voice-based assistants can be made to understand commands even in a noisy environment, that would be a giant leap into the conversational AI future.

The Multibots

Engineers have completely sidestepped the packaged deal of chatbot limitations. Today, chatbots are created with specific intentions: for example, a bot for the HR department, another bot for consumer queries, the third bot for inventory, etc.

By crafting a big bag based on multispecialty bot architecture, innovators are attempting to create a one-bot-for-all solution. This general-purpose bot would work by inferring the user query and routing it to the specialized bot in its system that is capable of handling it. This is a shining future for conversational AI technology where users can find their solutions in one place.

Conclusion

The global market for conversational AI technology and related applications is predicted to swell to $14 billion by the end of 2025: this figure speaks volumes of the obsession that businesses have with chatbots. While the number doesn’t spell out why conversational AI is so sought-after, it does tell you that innovations are making it worthy of investing in.

With the rate of trials-and-errors, experimentations, and innovations happening in the conversational AI technology niche, it is set to grow big time into a full-fledged system in the future.

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