Artificial intelligence (AI) is a hot topic. Skim tech journals or sites, and you’ll undoubtedly see articles focused on how AI is the big technology for 2020. CIOs are discussing how to bring AI into their organizations, and CX leaders are listing AI as a must-have.
But here’s the funny thing: AI doesn’t really exist — not yet anyway. I know many will be surprised to hear this, but before you decide that I’m wrong, consider Merriam-Webster.com‘s definition: “The capability of a machine to imitate intelligent human behavior.”
If you believe this is the right definition of AI, then I ask you: Are there machines imitating intelligent human behavior today? The answer right now is no. If there is a machine that seems smart on its own, the truth is that AI isn’t the driver — machine learning (ML) is. ML is alive and thriving, yet AI gets all the credit.
It’s time to get familiar with ML.
ML powers programs and machines to take data, analyze it in real time, and then learn and adapt based on that information. This is happening today. Think of the recommendations you get for products on Amazon or the shows Netflix suggests you watch. This is all due to ML. It learns your preferences based on your browsing/purchasing/viewing behaviors and then makes intelligent recommendations. The ability to synthesize massive amounts of data in nanoseconds makes machines smart. There’s actually nothing artificial about it — it’s real and at play in our lives already.
Without a doubt, ML is a game-changer for many industries, including contact centers. Similar to the way that automation revolutionized manufacturing, ML can be the missing link to revolutionizing the customer service industry. When leveraged correctly, ML offers enormous productivity gains in customer-facing interactions, empowering contact centers to use bots to perform basic, repetitive tasks. By offloading straightforward work to bots, human agents are free to do work that requires empathy and thought that only they can deliver. This can create an exponentially scalable customer experience workforce — in other words, it could solve the industry’s oldest and most expensive problem.
ML’s potential is big.
Once you know how ML works, I’m sure you can think of ways it has touched your life. But ML’s potential is greater than how we’re using it. In fact, I don’t think we’ve scratched the surface of its benefits. I believe one of the biggest untapped possibilities for ML lies inside organizations around internal processes. I believe that in 2020, we’ll start seeing organizations using ML’s data and analysis capabilities to make more informed workforce management decisions.
Instead of contact center managers having to manually sort through data to find out which agents are doing well on a particular day, they can use the insight delivered via ML to see who is providing great service and is able to take on additional customers and issues and, conversely, who is struggling and might need a break. This is an effect of ML’s ability to use sentiment analysis and natural language process (NLP) to identify patterns, including patterns in an employee’s productivity. ML gives managers informative, real-time data to help them support their staff, which helps employees succeed and helps to deliver an exceptional experience to every customer. Win-win.
When you have machines that can learn about your processes, customers’ and employees’ needs, and goals, you have the knowledge to make iterative, positive changes to your business. This can lead to:
• Better employee experiences and a more engaged workforce with less turnover.
• Better, more personalized, lower-effort customer experiences.
• Reduced staffing expenses and higher revenue potential.
• Streamlined operations by partnering humans with bots.
If you’re not a computer science nerd, the concept of ML might feel unrealistic, expensive or difficult to deploy. In short, it seems risky. However, I believe this is a technology your business should be using. Here are some tips to make the transition to ML less intimidating:
1. Do your research. While you should feel a sense of urgency to integrate ML into your business, don’t make hasty decisions. Take the time to get a solid understanding of your customers’ needs. You don’t want to start using just any solution, but one that best matches your business needs.
2. Choose the right ML-powered bot. Just like any other technology, there are options. Make sure you find a bot that meets the needs of your business and offers the services that make life better for your customers and your employees. Not every bot is built alike.
3. Don’t forget about your people. Leveraging the right technology innovation is critical to your business, but so is investing in your people and ensuring that the tech and the humans are working together harmoniously.
4. Realize that you’re never done. It’s important for leaders across all businesses to realize that customer experience is constantly evolving and that we must always be watching, evaluating and tweaking. Don’t be afraid to make changes or modifications to your ML plans. If something isn’t producing the results you want, find the issue, and make a change. Learn, and keep going. If you have a win, isolate what worked, and replicate it. Similar to the first tip, this isn’t a race, so be thoughtful about what you’re doing, and ensure it resonates with your business objectives as well as your customers’ and employees’ needs.
ML isn’t the way of the future — it’s the way of the present, and I can’t think of one reason you would knowingly decide to be late to the game. Your business deserves to work smarter, and this is the power of ML. Are you ready?