Bots are making their presence felt, but a new approach is needed to unlock their full potential.
The year 2016 will be remembered in technology circles as the year that the bot craze reached maximum volume. Not surprisingly, Facebook had a big hand in building the momentum. Since the tech behemoth announced a bot developer framework and distribution platform in April, the market has been flooded with upstart companies (and stalwarts such as Microsoft and Oracle) looking to ride the wave. And what a wave it is: The artificial intelligence (AI) market — of which bots are a major subset — is expected to become a multibillion-dollar industry in the next few years, and this very site published an overview of the bots market that found 170+ companies, $4 billion in funding, and thousands of bots currently in the space.
But as it stands today, bots are nascent. As with any technology, bots will need to mature before we can gain a clearer view of the role they’ll ultimately play and their lasting impact on both our everyday lives and as a tool for business. Most bots in use today aren’t demonstrating any meaningful form of AI. They are instead carrying out a very narrow range of specialized, niche tasks — ordering pizza, checking the status on a shipment of new shoes, or determining whether a flight is delayed. It’s worth noting that within this narrow scope the bots tend to get the job done, but their potential is so much greater. So the question becomes: What will it take for bots to take the next step in their maturity process and reach that full potential?
As Apple, Microsoft, and others have learned the hard way, one of the major challenges in building an all-knowing, monolithic bot that can handle a wide range of tasks is that it’s massively complex. This shouldn’t come as a surprise, of course. The ultimate goal of these bots is to interact with us as naturally and effortlessly as a human would, but that means they need a deep understanding of human language and interactions — one of the most daunting tasks we can ask of a machine. And if we ever can get there, it would take longer than today’s businesses are willing to wait. What we need, then, is a different approach that can help make bots perform better and faster.
The solution: bot colonies. If bots are expected to interact like humans, doesn’t it make sense to organize them in an occupational structure similar to how we (and bees) have worked throughout the ages — by assigning specific roles to groups of bots, in the name of having those groups work together as a collective team?
We’d start with Worker Bots, whose specific focus would be on individual, rote tasks that can be executed quickly and simply. They would have a simple and well-structured language that they use to communicate. Within this group there could be the Returns Bot, the Order Bot, the FAQ Bot, and so on. These bots all would have very limited syntax and use card deck-style or quick response messages to solicit input. The scope of responses they deliver is deliberately narrow so that there’s no risk of them getting confused or wandering outside of their knowledge domains.
Overseeing the Worker Bots would be the Queen Bots, the controller bots that direct traffic to other bots and issue commands. The Queen Bots’ primary function is to interpret every customer query so they’re taking the higher order language and figuring out which other bots to bring into play. For example, a Queen Bot wouldn’t know how to process a “return” query, but can determine when the conversation is about “returns” and can direct the customer to the Returns Bot.
Finally, at the top of the chain is a human, who can step in and handle any queries that fall beyond the scope of ability (or knowledge domain) of the Worker Bots or Queen Bots. In many systems bots can appropriately handle a large percentage of customer queries; for example, in the customer support world this can be at least 80 percent, leaving the remaining and more complex 20 percent of queries to humans. By assigning the work this way, there is a clear chain of command and every member of the bot colony is playing to their strengths and skillsets, all without the customer seeing the machinations taking place behind the scenes.
This tiered approach to bot deployment not only streamlines the customer query response process — thereby delivering optimal customer service — but also serves as a valuable blueprint for businesses that want to implement bots quickly and add functionality as needed. With this approach, adding functionality is as simple as incorporating a new tier or type of bot, once it’s equipped with the appropriate level of AI for its task, to deliver a top-notch customer experience.
The answer to unlocking the potential of bots to interact like humans? Have them work like humans: as a team, with each one playing to their strengths.
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