It’s fair to say that chatbots are no longer a thing of sci-fi dreams and television shows. The bot market is growing faster than ever, and chatbots are on the rise. While these systems have seen a lot of early successes, the industry is still searching for the perfect bot, and that means a great deal of additional development and research.
For instance, we’ve seen a few major trends in the way that chatbots are growing within the industry, including:
- Natural Language Understanding: While natural language processing was a serious buzzword for a while there, companies are beginning to recognise that processing conversations isn’t enough. Today’s bots need to use machine learning to “understand” the context of conversations.
- Voice Interface: Rather than having to interact with a bot through text and typing, many businesses are exploring the ability to use voice commands to communicate more seamlessly with AI. This is something we’ve already seen frequently in the consumer world, with Alexa, Siri, and more.
- Hybrid growth: Conversational Interfaces allow for a more dynamic user experience, but it’s going to take time for the technology to develop. There’s still a long way to go with Natural Language Understanding (NLU).
The Rise of the Conversational Interface
In the chatbot world, the purpose of the Conversational Interface (CI) is linked to the ability to move away from natural language processing, and start truly understanding what’s going on in the world of the user. CI allows companies to personalise each user interaction by consistently using the most accurate and relevant data.
While there’s still a long way to go with CI, some of the benefits of this process might include:
- Better user attention spans: Most companies know that their chatbot users have a pretty limited attention span. The conversational interface helps to reduce the risk of distracted users by providing information progressively according to the needs and commands of a user. There’s a clear call to action available every time the user interacts with the system, which helps to improve engagement overall.
- Reduced frustration: While NLP programs are still in their starting stages (and not particularly reliable), users have a high expectation of what they want to get out of AI. This means that people have a low thresh-hold for errors they’re willing to accept from a chatbot. CI solves this problem by limiting user inputs to fewer options. This reduces the risk of inaccuracy and improves the overall experience for the user. Because the bot has fewer data segments to work with, it can use the information it has more effectively.
- Improved cost-effectiveness: One of the biggest benefits of CI is that it’s incredibly cost-effective. Even though NLP is largely underwhelming for most people, the cost of implementing it is incredibly high. In CI, the prices are usually quite a bit lower, as the system is based on web technology. Additionally, once a CI deployment is complete, the interface can work automatically from day one with very little human assistance. This means maintenance is kept to a minimum.
Where is CI Headed Next?
By reducing errors, limiting user frustration and improving simplicity in the chatbot era, conversational interfaces could be the next step forward in our interaction with AI.
Of course, like many things in this sector, CI is still in its youth, so we’ll have to wait and see how things will evolve going forward. The development of new technologies will help us to determine how we can improve the existing CI framework to develop bigger and better chatbot solutions for companies of the future.
