As smart as today\u2019s conversational interfaces appear to be, when Alexa can\u2019t distinguish what the word \u201cit\u201d refers to in a sentence, how valuable is she really?\nChatbots should be more than a party trick; and at this stage, many of their use cases leave much to be desired. But repairing their conversational shortcomings could create value where we never thought possible, and potentially be the engine that drives the IoT landscape forward.\nThe Key to Conversation: Context\nMachines don\u2019t understand context the way humans do, and solving this problem is the next step towards creating an interface that\u2019s truly conversational. Luckily, progress in machine learning and natural language processing is giving new meaning to \u201cconversational\u201d devices. My colleague Katherine Bailey, Principal Data Scientist at Acquia, wrote an article recently about conversational AI and the road ahead. She talked about a machine learning technique called Word Embeddings, where vectors are used to represent words in 300+ dimensions, which introduces context into artificially intelligent systems. This technique is maturing, and conversational interfaces are getting smarter the more you speak to them. They can learn and categorize human language through experience, making them that much more useful for consumers.\nA human\u2019s time is much more valuable than a machine\u2019s, and companies like AWS are capitalizing on this reality. Amazon Lex (AWS\u2019s service for building conversational interfaces into apps), works to optimize chatbots around intent of use, helping companies build bots that can anticipate your next move. This model is bringing value to the chatbot, and it isn\u2019t one to be overlooked. Orienting our devices around the user is the sweet spot of AI innovation \u2014 enabling conversational UIs to serve as a catalyst for human efficiency.\nRegardless of whether a machine can disambiguate pronouns, there will always be something missing. The human touch can\u2019t be mimicked or mirrored, not even by the most advanced chatbots on the market. But learning to work within these limitations will lead us down a productive path towards a value-driven IoT landscape.\nThe Future of Chat(tier) Bots\nThe truly conversational chatbot will be seen as a north star. The IoT space is at an inflection point, with conversational UIs in the driver\u2019s seat. As machine learning teams and R&D labs continue to chip away at the \u201cparamount\u201d chatbot, their progress is slowly but steadily unifying a fragmented ecosystem. This process is becoming more achievable each day with services like API.ai, which help companies easily integrate natural language understanding into their products. When chatbots are primed for true conversations with their users, the value of artificially intelligent systems won\u2019t be questioned.\u00a0\nWhen I think about the potential for conversational UIs, there\u2019s a whole lot more room to grow. What\u2019s next is for us to think critically about where machine learning techniques make the most sense. Where will they save us the most time and allow people to focus on tasks of higher value? Once the IoT landscape reorients itself around the needs of the user \u2014 whether that\u2019s in the workplace, on vacation or in your living room \u2014 your devices are about to get smarter (and chattier) to hopefully make your life a whole lot easier.