We are investing substantial time and effort in helping people to better understand what all the hype around AI and conversational technologies like chatbots and intelligent digital assistants is about. It can be rather overwhelming in our experience. And we don’t blame you. We are overwhelmed at times as well, by all this news on AI induced jobless futures and fully automated and self-learning machines. It looks as if AI will change the world before we can even say “what?”. But we know better.
Why use statistics if we can apply math?
We work with AI technology (like Natural Language Processing and machine learning algorithms) for over a decade now. We are convinced it can do great things, and will be able to do even greater things in the future. But it will take some time and many learnings before we get there. Progress has to be made, and will be made for sure. Just not through claiming ever greater disruptive breakthroughs that are based on assumptions, not evidence. Progress will also not be made by seeing AI, and machine learning in specific, as the panacea to all problems. As our CTO says: “Why use statistics if we can apply math?”.
And that’s exactly right. Stop asking the machine to find the most probable answer if you know it with certainty yourself. Just provide the machine with the answer and instruct it to provide it when the question pops up. In most cases this will be a faster fix than asking the machine to learn the answer by comparing 100k+ inputs (Q’s) and outputs (A’s) to come up with the best estimate for the answer. This is specifically true since machine learning algorithms tend to make mistakes and therefor require human supervision.
Invest in relevance, not long tail understanding.
Also, NLP/NLU has intent recognition rates of 85% to 95%, depending a bit on the width of the application and the time spent on improving it (should be on the high end after a year). You can of course spend hundred thousands of dollars into getting it to 99,8% or better. But should you? In our experience the return does not justify the investment. What does justify the investment, is making the answers more relevant to the customer/user by using context and personalisation.
Think of it, what is the reason your customer still makes a call after he’s read the airlines luggage policy? Right, he wants to know what that means to him as an occasional flyer with a last minute discounted ticket on a transatlantic flight. Better NLP/NLU will not provide him with the answer he’s looking for. A more personally relevant answer will. And of course the threshold for your company could be at 96% or 84% intent recognition. The exact rate is not the point, the point is you should know when to apply other strategies to improve the customer’s experience and get their jobs done.
AI has reached the peak of inflated expectations
Gartner believes that AI has now passed the peak of inflated expectations. That’s good news for all of us, but mostly for you. Because now you can start using AI, without the BS. And you can start using it to solve your problems and those of your customers, not the problem of a company that lacks experience in the field and has a ‘feeling close to certainty’ that conversational artificial intelligence will change the way the world spins.
Time to move forward, without the BS
Thus, now is the time that we make some sense of it all and help you move forward. And this is why we asked Martin Hill-Wilson, a well known expert in the field of customer experience and digital service transformation, to help us better explain what can and should be done today, with existing AI powered technology and proven strategies that deliver real results. Results as in improved customer experience, reduced costs and better conversion rates. Results that matter to a business.
Get started with our hosted webinar & white paper series
We designed a three part series of webinars and white papers in which we try to get to the bone to get you started. This is not about technology per se. This is about how to apply it in your omni channel strategy. Its helps you understand the differences between all the bot-types out there, see the use cases that work and get a good understanding on where to begin, how to quickly implement and learn from experience.
But I’m typing too many words here. Let Martin explain for himself in these three short video’s:
The first part sets the scene for why conversational self service is now right for mass deployment and provides tips on making your first deployment a success.
This middle part explores how conversational self service fits into a broader omni-channel strategy and some key advice on how to get up and running fast.
This last part considers what’s next once you are up and running with your first intelligent assistant. How do you grow its value?
If you want to subscribe to any of the webinars we organise or see the webinars we already did, please visit our event page and subscribe to our YouTube channel. You can download the latest version of the white paper here.