Unless you’ve been hiding under a rock, you’ve no doubt heard about chatbots. However, what’s not commonly known is that chatbots have been around for years. What differentiates today’s bots is the integration of back-end artificial intelligence, enabling them to do more than simply respond with the basic logic of yesterday.
I find it’s important to make distinctions between a bot and today’s A.I.-powered intelligent assistants. Chatbots just happen to be conversational. Intelligent assistants go beyond bots and perform tasks that assist the user. The future isn’t bots, it’s intelligent assistants.
Intelligent assistants are being developed extensively in both the consumer space (Amazon Alexa, Apple Siri) and enterprise (Nuance Nina and IpSoft Amelia). What remains to be seen, however, is how successful these assistants will be in the long run.
My advice as someone who follows the app and bot market closely is to think about how to make a product that goes well behind chat.
What makes a good intelligent assistant?
The key measure of success for an intelligent assistant, whether in the enterprise or consumer space, is how much value the assistant adds. This is usually in one of two categories, either performing a task a person would find hard to perform themselves or saving a person time by performing tasks that would take them a long time to do.
Let me give two not-so-obvious examples of successful intelligent assistants. The best example in the first category is the search engine. It’s clearly impossible for the average person to search for content on the internet without using a search engine. No wonder search remains Google’s primary business! For the second category, a great example is Kayak, which seamlessly searches for flight prices across many sites and saves time. Note that neither of these examples are new or would even be considered ‘bots’ by anyone. However, the value they deliver to users is clear.
The defining characteristic of success for an intelligent assistant is the value it delivers to users. In some cases the conversational interface (e.g., the ‘chat’) adds value, but in most cases the true value is what happens behind the scenes. Chat may (or may not) make it easier to ask the bot to do something, but it’s never useful when a bot does something that’s easy for us.
How smart do intelligent assistants need to be?
If you’re developing a bot, you need to first understand the value your users expect — and then make an intelligent assistant, not just a chatbot. Focus on ensuring your intelligent assistant can actually perform the tasks expected.
Another dimension to consider when creating intelligent assistants is how well they can perform those tasks and how much intelligence is needed to perform those tasks. For example, if the goal is to create a first-level support agent whose task is to simply route cases to the right skill group in the enterprise upon initial contact, a chatbot with some conversational capability and basic understanding about your field could do that task in a reasonable amount of the time. However, if the goal is to create a second-level support agent, who can actually troubleshoot issues, you’ll will need an intelligent assistant designed to understand your field with deep reasoning and learning capabilities. In either case, regardless of how much A.I. is used to build this “more than a chatbot” idea, the key criterion for success is how well it does the job it needs to do, how much time it saves, and the ROI it delivers to the firm.
A successful example is x.ai. A chatbot would simply ask when you’d like to schedule a meeting and then add it to your calendar. Not much value over just adding it yourself. However, what x.ai does is to take your input (such as attendees and other preferences) and then finds the optimal time and place for the meeting. The bot schedules it with all parties to save you time. Also, it’s much more efficient at that task.
The only ‘chat’ is the response letting you know it’s been added to your calendar (or if there are irreconcilable conflicts in scheduling it). The conversational interface makes it easier to interact with the intelligent assistant, but the true value is the behind the scenes work the Assistant actually performs.
The future of intelligent assistants
The brightest future for A.I. and intelligent assistants is in areas that augment human capabilities and in performing tasks a person finds difficult or time consuming. In many instances these use cases aren’t ‘sexy’ (like shopping, travel, or dating), but they’re the types of activities necessary for business and government to function effectively, albeit hidden from public view.
A good example is using A.I. is to analyze x-rays of cargo shipping containers to identify smuggled cars. It’s faster and more accurate than any human. And this will enable cargo inspectors to spend more time on important areas like smuggling prevention and enforcement.
To use a personal example from AppZen, our A.I. is able to verify data at rates and levels of accuracy that humans simply can’t match. Humans are still required for the inevitable ‘judgement calls’ on flagged expenses, but now they can focus their time addressing the truly important items, instead of slogging through the vast majority of expenses with no concerns.
Conclusion
We’re in the early stages in the rise of intelligent assistants. Tomorrow’s winners in this space will be the companies who focus on utility over flashy gimmicks. By focusing on augmenting human capabilities, these companies will unlock the true value of intelligent assistants and provide benefits for both their customers and users.
