Over the last several years, AI has been seeping into nearly every industry, changing how humans and machines perform and interact day-to-day. But in the coming months and years, the future of conversational AI will advance more realistic approaches for implementing it across organizations. As people awaken to the idea that AI can augment their natural abilities, AI will become more widely adopted in the workplace.
Find out more of what’s coming in the AI field from some of our very own Cogicians. The key to AI’s increasing success will center on enabling a technological solution to resolve a key business problem. Companies can no longer experiment, deploying AI for the sake of it, hoping it improves business operations.
Josh Feast | CEO of Cogito:
The Year of Demystifying AI
Society will push for the demystification of AI and demand a better understanding of what technology is being built, and greater transparency into how it is being used. In recent years, there has been a clear shift in mindset across our society with AI, especially regarding privacy concerns. As a result, technology creators will have to embrace full transparency and responsibility to ensure they respect privacy rights and the technology is being used in a valuable and ethical way.
As transparency increases, people will better understand that AI is not an all-encompassing term for machines that can replicate and act like a complete human. It is a more explicit set of functionalities that can better automate simple tasks and help augment people who are performing more complex activities.
Emotional Intelligence Will Become a Competitive Differentiator
Conversational AI trends in 2021 and beyond will focus on the further mainstream development of Emotional Intelligence (EQ). Certain applications of AI are handling mundane tasks and supporting humans trying to cope with the increasing pressures of a post-COVID world. EQ will become a more critical skill set for executing higher order tasks and innovative thinking.
While only 20 percent of survey respondents said EQ would be an important skill for the future before the pandemic, that number jumped to 69 percent post-pandemic, according to Verizon. It will be the key differentiator for organizations, leading to more companies actively fostering EQ amongst their employees. This emotional initiative will enhance workplace culture, improve productivity, drive innovation, and tighten the bond between an organization and its customers.
Every Professional Will Have an AI Coach
Today’s conversational AI platform solutions are already augmenting humans in areas previously not considered possible. Most organizations have leveraged AI to eliminate simple tasks rather than helping humans to be better humans. Through thoughtful integration, however, AI can become a friendly point of contact and change the way we interact in virtual space, ultimately making people better versions of themselves.
By 2022, 70% of white-collar workers will interact regularly with conversational platforms, according to Gartner. This makes the ability to take “humanness” — emotion, fatigue, stress, etc. — into account a priority for every conversational AI platform. Adopting these types of AI technologies will enable organizations to foster more empathetic and human-centric organizations. Implementing this conversational AI key differentiator will undoubtedly help companies quantify emotions, understand the nuances of human communication, and enable better, more “human,” business decisions.
Closed Loop Systems are Best Positioned to Move AI Forward
Closed-loop systems will become standard for improving AI’s capabilities. Systems that can measure, provide feedback, observe the results, and learn from the usage will help position AI to become smarter and more effective at achieving a desired result. If you disconnect the usage outputs from the capturing and learning mechanisms generated from AI, it creates a barrier for fast and effective learning model improvement.
John Kane | Distinguished Scientist, Machine Learning at Cogito:
Democratization of Machine Learning
While Google’s AdaNet autoML has done much to “democratize” machine learning, there will continue to be a growing demand for machine learning and deep learning specialists in the year ahead. Businesses will increasingly adopt these automated modeling systems, which represent modeling problems that are considered “solved.”
But for new modeling problems or for areas which are still very much “unsolved,” there will be an increasing need for skilled scientists and engineers. The industry (and academic institutions) should continue their focus on developing ML and AI course resources and continue to address the ongoing skills gap to meet growing labor demands.
Discrimination and Bias in ML/AI
Businesses and society are becoming increasingly concerned about discrimination and bias introduced by machine learning and AI. This is because there have been cases reported where credit rating algorithms have unfairly treated people from certain demographics. There have also been image processing models incorrectly classifying the color of people’s skin. As a result, there is an onus on technology companies to implement processes which mitigate bias in their AI systems.
The future of conversational AI will focus more on companies adopting and implementing a model development protocol, which follows the FAIR framework. This framework involves using data collection and machine learning techniques to reduce the effect of bias in models.
Failing to implement such a protocol will cause negative perceptions from the public and customers, as well as ineffective applications.
Machine Learning Models Will Become More Context-Aware
The future of conversational AI beyond 2021 will emphasize context awareness as an even more important aspect of machine learning. Machine learning models will need to know more about context (e.g., what device is being used, what prior information is known about the user) and adapt accordingly. There will be increased interest in understanding the emotional and health state of users of these technologies, along with the need for contextually appropriate responses in the year ahead. As the context improves, so will the specific assistance that AI can deliver towards achieving a positive outcome for a given situation.
Some conversational AI examples that emphasize contextual awareness AI are being seen in human to machine (H2M) conversations across sectors ranging from drive throughs to retail and ecommerce. Chatbots to virtual assistants are beginning to use Contextual AI that can recall historical data, user inputs, previous interactions, and the human’s emotional state to steer the conversation.
Ali Azarbayejani | CTO of Cogito:
The Human and Machine Relationship
By nature, and design, humans and computers have different strengths. We are already seeing the human-machine relationship develop to become more sophisticated and exist in a more symbiotic manner via human-aware technology. Conversational AI trends in 2021 and beyond will support every company’s ability to embrace the skills of their human workforce with the support of AI creators developing technology that bolster humans’ natural abilities.
4 Things You Can Expect in the Future of Conversational AI
Deloitte Insights recently covered a survey they conducted which was based on a search of conversational AI related patents. The article shows five trends to look for in the future of conversational AI that mirror the Cogito panel’s insights about what the future holds:
#1. Training conversational agents
Around 20% of patents focus on automating and speeding up the training process to better understand users’ inputs, improve the quality of responses, and eliminate bias in the model designs. The list includes everything from contextually aware virtual assistants to healthcare chatbots without need for supervised learning to answer questions about suitable healthcare providers.
#2. Handling complex conversations
Research found that 18% of patents focus on complex conversations involving multiple commands in a single utterance or multi-topic conversations. One conversational AI example for a patent uses concept lattices that enable linking chatbots to handle complex conversational needs.
#3. Improving voice assistants
There were 11% of patents focused on improving voice assistants by enabling them to filter out background noise in almost any environment to increase the accuracy of request fulfillment and improve customer experience.
#4. Virtual assistant
They primarily designed the conversational AI chatbot for a narrow purpose business function, but 7% of patents focus on specialized chatbot ensembles that can handle a range of tasks for a user by automatically inferring intent and routing the request to the appropriate specialist agent. This multi-bot architecture includes a patent for an enterprise assistant that operates a master interface to route users to virtual assistant specialists for CRM, ERP, and human capital management.
The conversational AI market is expected to grow to $15.7 billion globally by 2025, according to Markets and Markets. This shows that the conversational AI trends in 2021 have moved beyond the hype around the term AI. The future of conversational AI will be based on far more clarity about what AI means for both society and businesses.
Until this point, the fear around AI stems from miscommunication around its reality and exaggerated claims of a machine takeover. But the technology can actually help augment a person’s natural abilities and increase productivity, making them better versions of themselves.