Next Generation Conversational AI: From Conversation to Action

How Conversational AI Tech Goes Beyond Chatbots to Bring Real Business Value

Conversational AI is a group of technologies that mimics human communication through automated speech-enabled applications that offer interactions between people and technology that feel like human-to-human conversations. Bolstered by machine learning, next generation conversational AI tools can communicate like a human by responding in a way that seems “real” by recognizing text and speech and, perhaps most importantly, understanding its intent.

While traditional chatbots and FAQ bots (early conversational AI technologies that communicate via pre-programed messages) are available from a dime a dozen companies, true next generation conversational AI solutions that can support voice, text and SMS across multiple channels and devices are still in short supply. Even more rare are advanced conversational AI tools, which offer conversations and solutions that are indistinguishable from speaking to a real human, along with the integrated applications that make their human-like reactions a reality.

As conversational AI technologies continue to evolve and grow, innovative enterprise organizations are asking an important question: “What’s next?” Here are a few improvements we predict next generation conversational AI will see in the near future.

1. Next Generation Conversational AI will get continually more “human:”

Just like when an individual is learning a new language (for example, an English speaker learning Spanish), conversational AI tools have a hard time understanding nuances in language.  From accents to slang to sarcasm, there are many ways individuals can differentiate humans from machines. However, we know conversational AI systems will continue to grow more intelligent over time, as we’ve already seen them advance significantly over the past few years.

2. There will be a focus on removing discrimination/bias:

Unfortunately, as with all technologies, conversational AI and machine learning tools are rife with bias. Take Amazon’s facial recognition software as a quick example. Why? Because the human programmers who build them are themselves biased. This has been a major sticking point in conversational AI tools in the past. But with the tech industry and the world at large putting added focus on issues of inclusivity, conversational AI and machine learning tools will also be evaluated, tweaked and improved.

3. We will see an Increase in multimodal experiences:

Multimodal AI is when various data types (image, numbers, text, speech) are combined with multiple intelligence processing algorithms to achieve increased performance. Multimodal AI often outperforms single modal AI against many real-world problems. As conversational AI gets smarter and organizations expect more from their conversational AI solutions, multimodal options will become standard in this industry.

4. Companies will leverage more data for optimization and personalization:

One big part of conversational AI tools becoming more intelligent is better personalization. Organizations will begin to find new ways to leverage the information they gather in conversational AI tools to drive better, more customized customer experiences (and ultimately increase profits). By tying back-end systems together and leveraging this data (see the next section), everyone wins.

5. Organizations will create seamless integrations with enterprise systems:

To truly impact a business’s overall operations, next generation conversational AI tools must move from simply supporting conversations into driving real action. Organizations that want to truly embrace AI and machine learning will build integrations between their conversational AI tools and existing enterprise systems to improve customer experiences and reduce operational costs.

For example, organizations could pull data about a customer’s payment history from its payment processing systems to block a debit card that’s received too many chargebacks and have an AI assistant convey that information to the customer. Or, it could pull past customer behavior and information from various sources to cross-sell and upsell new tools and services that are most applicable. These are simple examples, but the possibilities are endless with the right tools, people and technology in place.