MAY 2018CIOAPPLICATIONS.COM9· Be naive to the risks/rewards of an automation strategyDo:·Start small and move fast· Benchmark the use cases you are hoping to improve before you launch your bot· A/B test interactions with and without the bot in the mix· Make sure your chat/messaging/service agents know what the bot is designed for and choreograph the handoffs from bot to people· Find an external partner to help facilitate consensus. Make sure they are directly accountable for successCould you elaborate on some interesting project/initiatives that you're currently overseeing?We are engaged in many different chat-bot and bot related initiatives across both support and marketing/acquisition use cases. One of our most interesting initiatives today is our work on our own IP called the Bot Trainer Platform. Accuracy is the key driver of a good chatbot experience and one of the biggest failings of chat-bots today is the degree to which NLU can accurately understand user input. Many solutions use leading open source NLU engines like Stanford NLU which do an increasingly good job of understanding and parsing input across many languages. This works well for common phrases like "I need to pay my bill", or "my TV isn't working!"The challenge for brands is that those NLU data sets aren't proprietary and don't solve for years developing memorable product names, tones of voice, or unique brand attributes. So if a customer says "Can I get InstantInk for my OfficeJetPro?" an off the shelf NLU engine might not know what to do. Compound that times the number of products and services a brand offers and it's easy to see how an end customer might get frustrated.While brands are starting to solve for these challenges by developing better internal knowledge bases and metadata around their products, too often the catalog of products and services and unique brand language makes its way into a developer's hands for integration into the NLU data. It's not a big stretch to say that developers aren't necessarily the best curators or keepers of this data.The Bot Trainer Platform was built for this dynamic; it turns the building of the proprietary NLU data set over to a brand's front line agents. These are the same people who are taking calls about the very same issues and who typically go through regular and intensive training on a brand's products and services. They are also trained in the language of the brand, so if a luxury retailer wants to refer to everyone as their "treasured friend" (I'm making that up), the agents are usually in the loop in real time. Agents use the platform during their daily routine to review, qualify, or reclassify end-customer input and feed that new data back into the chat-bot. The result is an increasingly smarter bot who learns to model the very people it was built to exist alongside. That accuracy pays off with higher accuracy, better resolution rates, and better satisfied end-customers.How would you see the evolution a few years from now with regard to disruptions and transformations within the arena?We are still in the very early days of chat-bots but there are already some interesting things to watch. Voice, as a share of user input will grow very quickly. Brands that figure out how best to use that channel to serve and solve customers' queries will benefit. Data Security will continue to grow in importance as public awareness increases through stories like Facebook's current imbroglio. Eventually platforms that achieve scale and adoption will start to squeeze out their peers. Eventually we'll see consolidation of platforms and mainlining of bots into even larger platforms to become core functionality.Customers will be increasingly tolerant of bots in the mix and we're already being trained through search queries and even Alexa requests to structure our language to be more consumable by bots. No matter what the future holds for bots, we will always have humans in the mix. We need each other for high emotional, high touch, and high value interactions. The choreography between the two - humans and bots - is the space to watch, when and where they make each other smarter, more contextually relevant, and more...human, is ultimately where the true magic happens. Gordon White
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