People seldom like repetitive work, but often we as leaders omit to prioritize optimization of processes and technology usage, to benefit both productivity and employee satisfaction. It looks like that is about to change in 2020 with Machine Learning and AI technologies on the rise.
I’ve had many jobs and worked many projects in my career. Always at the forefront of technology, always in fast-moving companies. I sometimes wondered why we as leaders talk productivity and efficiency, yet often fail to invest in processes optimization where there is a lot of repetitive tasks eating time from our valuable resources.
My first job after school was in Internal IT. I can’t stop smiling when I think about the times, I picked up the phone or answered an email from a frustrated user who complained about a malfunctioning keyboard or mouse. The solution? Always the same; “Try to follow the cable and see if the plug is still connected to the computer. It never was, I was a hero in that person’s mind for the next 10 seconds and I probably had that same phone call 5 times a day.
I am sure we have all been on the opposite side of things and struggled with internal support resources who had to run through a narrow-minded script. Or struggled to find the information we needed in a FAQ that just wasn’t designed for humans. Optimizing processes and time usage of support resources, should not come at the sacrifice of user satisfaction or make people spend twice the time.
That’s why I’m fascinated by the opportunities that conversational AI opens. We’ve probably all had an experience with a poorly designed chatbot a couple of years ago, but technology has been catching up and it’s time to look at optimizing your customer or employee-facing communication and helpdesk now.
I know of multiple customers who have successfully implemented conversational AI, and the stories are similar. “50% decrease in chat that required human interaction”, “25% fewer phone calls to helpdesk after just one month”, “30% fewer emails to customer support” are all examples from customer cases I’ve been following.
There are obvious cost-saving opportunities in this. I am fired up about the opportunity in minimizing ‘factory-work’ style repetitive tasks and re-investing those resources in revenue-generating activities, or improved citizen service if you are in the public sector. It’s not uncommon that customer success teams spend +50% of their time on fire drills and traditional support. What if we can cut away just 25% of that? The opportunity to give customers a much better and streamlined service where handover from conversational AI to humans happens seamlessly should be leveraged now.
There is a lot of impressive technology out there, what I like about Kindly.ai is that they have kept the complexity in the backend and created a simple yet effective frontend that doesn’t take a programmer or very technical resource to learn. On top of that their multilingual capabilities, and open API allows for advanced AI service cross-market, and integration with the backend systems needed. That allows you to quickly train your staff in becoming AI trainers, and leverage the people who truly understand customers’ issues and how to respond to them rather than technical resources. Lastly, it’s super important to make this an ongoing prioritization. Customer support staff should check in often with the fallback log, and prioritize the 10 minutes of AI training that will cut away 10 hours of support questions in the future.
I feel certain we are on the verge of seeing conversational AI taking off for real in 2020. As the computers start understanding humans, this opens up great opportunities for better customer service or employee support.
If you are interested in a dialogue around conversational AI or want to know more about what we do at LeapForward, please do reach out on firstname.lastname@example.org