What’s in store for the future of conversational AI

In a previous blog, we dug deep into the roots of conversational AI, taking you on a journey back to the early days of chatbots and voice recognition. We’ve come a long way from the primitive models back then, and we’re (not-so-slowly) creeping into a new era of conversational AI where computers can really begin to feel human.

And we’re saying that in the least scary way possible. This actually brings up incredible new possibilities that may have previously seemed improbable outside of being a detail from a utopian sci-fi movie. 

What we’re saying is that the future of conversational AI has the potential to be bright, and can positively impact the way that we interact and work across industries. 

The current state of conversational AI

Technically, conversational AI is insanely more advanced than even 2 years ago. Thanks to the introduction of LLMs, whose algorithms are rooted in the way that the actual human brain works, tools are now more able than ever to use deep learning to understand and communicate in an increasingly human way. 

NLP split into NLU and NLG

Although the field of Natural Language Processing (NLP) has been around since the 1940’s, it’s now branched into two distinct directions: Natural Language Understanding (NLU) and Natural Language Generation (NLG). The specific focus on both of these branches is what empowers modern conversational AI systems to effectively interpret and mimic human language.

Using LNU, systems are able to parse out even the most complex sentences, recognize intent, and extract relevant information. LNG, on the other hand, uses that information to construct responses that are contextually appropriate and syntactically correct. This leads to conversations that flow smoothly and, most importantly, naturally. 

Contextual awareness

Context is one of the most important components of communication. That’s why the same exact phrase could have very different meanings to different people in different places at different moments. Today’s AI has made major bounds in taking this context into account. 

Many of the advanced models of today can maintain context over the course of a conversation, remembering previous exchanges and using this to make responses more relevant. They can also feed off of other indicators, like which features you’ve interacted with in a SaaS product, your location, or your preference settings, for example, to craft the best responses. 

Integration with data systems

Now, AI models are able to pull from more data than just the datasets they were trained on. There are many interactive relationships between these large language models and various databases and APIs. This means that the conversational AI models can assist with more specific tasks, like appointment scheduling, for example, where real-time, accurate data access is critical. 

Perhaps more excitingly, this data integration can also make data analysis possible in a more conversational environment. People are now able to interact with their data in ways that wasn’t possible before. Previously, someone may have needed to have extensive data analysis knowledge to dig into the data, interpret it, and make predictions. Now, in some cases, getting highly accurate predictions is as simple as asking the question in plain language. 

How it’s being used today

Many of us interact with conversational AI tools nearly every day, whether you’re aware of it or not. 

In the past decade or so, we’ve gotten cozy with AI-powered virtual assistants like Alexa or Siri, but in just the last year, conversational AI has begun to work its way into more advanced use cases. It’s not uncommon to come across this technology in many of the products or websites we interact with on a day-to-day basis. 

Customer service/support

This is where conversational AI really began its journey in the mainstream world all the way back in the 80’s and 90’s with the early phone assistants like Amtrak’s Julie. Now, the abilities to automate a lot of the most tedious tasks away from human staff have grown a lot. From AI chatbots online or in products to phone assistants, consumers are more able than ever to get the 24/7 assistance they need without having to get a human involved. 

This is being used in a variety of ways, from booking confirmations to technical support to FAQs. When it comes to speech to text, best apps in the market allow customer service representatives to talk to their customers and have the conversation live transcribed for any future references. Also, the company can collect that data, analyze it for the most frequent customer complaints, and use that as a guide for further product development.

SaaS user assistance

SaaS is arguably the industry that is most primed to be positively impacted by the future of conversational AI. Users are (hopefully) spending a significant amount of time in these SaaS platforms, and AI tools help to provide in-the-moment assistance without getting humans involved.

Whether it’s during onboarding, after a recent upgrade, or just during a regular session in the product, conversational AI tools like our Copilot can act like your users right-hand man. They’re able to ask questions and have conversations just like they would with a person and get accurate answers immediately. 

The main value of this tool is boosting users’ productivity and engagement. If they are able to ask questions, get clarification, and explore their full range of curiosities in the platform, they’re much more likely to get more value out of the product and, in turn, stick around longer. 

Marketing and sales

Conversational AI also has begun to help marketing and sales teams scale their operations.

The most common way that we’re seeing marketing-focused conversational AI now is the little chatbots that pop up in the corner of pages of a website, powered by tools like IBM wastsonx Assistant. The best of these kick off the conversation with something that is highly relevant to the content on the page and/or the exact user who’s browsing. This is a good way to immediately grab the attention of a fresh visitor. 

In sales, conversational AI tools can shift some of the early grunt work off of salespeople’s plates. They can engage cold leads and warm them up through human-like interactions, only handing the lead off to a salesperson when they’ve been sufficiently nurtured. 

Both marketing and sales teams are also able to collect all of the conversational data that’s being exchanged and use it to make strategic decisions about how they approach and nurture leads. 

Innovations in the works

There’s a lot to be excited about with the current capabilities of conversational AI, but when you take a peek at what the future of conversational AI may hold, things get a lot more interesting. There are several innovations on the horizon, but here’s what’s really getting people buzzing. 

Emotional Intelligence

If we go back to the original goal of conversational AI, which is mimicking and scaling human interactions, you can’t understate the value of emotional intelligence. EQ, as it’s sometimes called, is one of the main things that sets us humans apart from machines. 

But that gap is getting smaller now. Many LLMs are beginning to make major strides in integrating this emotional understanding into conversational AI. Emerging models will be able to detect even the subtlest emotional nuances in both text and voice communications. The models analyze differences in tone, word choice, or even speech patterns to identify relatively complicated emotions like sarcasm or frustration. This understanding is critical to effective communication, because the “robots” can adjust their behavior to reflect what they need in that moment, whether it’s empathy, support, or joy. 

This emotional intelligence that’s emerging in the models should help us see major bounds in both AI tools’ ability to understand as well as communicate effectively. 

Multilingual support

People are living in an increasingly international world. It’s not uncommon to be in contact with people on the other side of the globe in some respect every day. Multilingual AI advancements that are currently in development will help people to leap over the issues that language barriers cause, which is especially valuable for companies with an international customer base.

AI innovations in the language space will allow tools to seamlessly switch back and forth between different languages, helping teams bridge the gap between them and their customers without having to hire bi-tri-or quadrilingual team members. 

Cross-platform continuity

One of the major goals for the future of conversational AI is to allow for users to switch seamlessly from one device to another. 

As of now, conversations are typically bound to a single device, meaning they often have to start from square one if they want to continue probing into the topic with another device. Users want to be able to start a conversation on one device and continue it on another without losing context. 

One way that AI engineers are attempting to conjure a solution is through persistent memory, which means that these tools remember past interactions and can refer back to them when relevant. Now, this is beneficial for convenience and continuity in new conversations on a single device, but it shows promising potential for continuity across multiple devices in the future. 

The future of conversational AI across industries

Conversational AI has only tapped into a fraction of the potential for how it can be applied to existing industries. As technology continues to evolve and more business leaders begin to open their eyes to the potential in front of them, we’ll start seeing conversational AI make major leaps in many industries. 

Here are a few that show the most promise. 

Healthcare

With doctor burnout becoming a bigger issue than ever, hitting an all-time high of 63% in 2022, conversational AI has the potential to bring about a lot of positive change. The healthcare industry is beginning to look for ways to use conversational AI to relieve some pressure from both their practitioners and administrative staff.

In the future, we may see more virtual diagnostic visits where patients describe their symptoms and the AI spits out a preliminary diagnosis. Other areas where AI may be integrated into healthcare are patient data management as well as ongoing, 24/7 support for people with chronic illnesses.

The mental health space has also been brought up in popular discourse around conversational AI in healthcare. Although everyone may not yet want a fully robot therapist, conversational AI tools can help to bridge the gap in mental health services by providing a cheaper, more low-touch option. Conversational AI tools can also help to make a therapist’s job easier by conducting initial assessments, monitoring mood changes, etc. 

So far, it seems promising; AI therapy has demonstrated high satisfaction, engagement, and retention rates across most studies.  

Education

The global AI education market is projected to expand at a rapid rate over the next decade, with most estimates pointing to a 36% growth rate from 2022 to 2030. As of now, almost 50% of educational organizations want to invest in AI solutions to help close the employee skills gap. 

One promising area is the creation of personalized learning assistants, whether in the classroom or at home. Because every student in the class may respond to different learning styles, need different support, and struggle with different concepts, these conversational AI assistants can adapt to provide the support they need best. This can ensure that no student gets left behind in the classroom. 

Conversational AI can also help with administrative tasks like scheduling and grading assignments, which gives teachers more time to focus on meaningful engagements with their students. 

Finance

Financial institutions are already using conversational AI for customer support, but we’ll be seeing more advanced use cases in the coming years. 

Virtual financial advisors are one possibility that’s exciting to consumers. The advanced algorithms can analyze market trends and consumer behavior, in combination with the conversational data they collect, to offer real-time, personalized investment advice. 

Institutions like Schwab, J.P. Morgan and E*Trade have already begun to incorporate conversational interfaces that allow consumers to chat with AI-powered agents about things like their goals, risk tolerance, questions about loan agreements, and the state of their current investments. The possibilities here will only continue to deepen and expand. 

The gap is closing between machines and humans

Although we’re still a long way off from conversational AI tools feeling truly, 100% human, it’s clear that every year, or perhaps even every month, we’re getting closer and closer. And although that can be a scary prospect for some, all it takes is a quick scan of the new use cases we just listed above to see that it should genuinely help us create a better world. 

The future of conversational AI, in my eyes, is all about convenience, understanding, and the ability to lean into what you do best, once the machines can take all of the other pesky tasks off your hands.