Like any emerging technology, there is always an adoption curve. If a human can quickly solve a problem by using AI, then that’s a win for everyone. But the purpose of AI is to augment – not replace – human intelligence, so while the interactions will continue to get more seamless, the need for a human-in-the-loop will remain.
There is a lot of exciting work happening in the field of NLP that makes the experience more and more seamless. This includes advances that enable AI to achieve higher accuracy, from smaller data sets, as well as advancements so that systems can handle the complexity and nuance of human communication like misspellings or mispronunciations, idioms and industry specific phrases, topic changes and disambiguation. There have also been advances so that systems can support many different languages and dialects, without needing to be retrained every time they encounter something new. Advances in NLP models for document understanding help us uncover more precise insights from complex document types like PDFs, charts and tables, which results in more accurate answers ending up in the hands of end users. Ongoing research and development in this space will continue to result in even better experiences at a rapid pace.
Another factor that will lead to more seamless interactions are deeper integrations that enable companies to build systems that reflect the full context of the user’s journey. For example, if you discover a product on your bank’s website, fill out a form and go into your local bank branch bank to apply for that product, you’ll have a better customer service experience down the line if the virtual agent you end up interacting with has the context of your past interactions readily available. This will lead to more meaningful interactions and faster resolutions – regardless of what channel you are interacting on.