Bad AI isn’t a tech problem, its a human problem. Join this VB Live event to learn about the five biggest mistakes companies make when they bring cutting-edge customer service technology to their workflows, and how to leap over these pitfalls and into real results.
AI is quickly becoming a must-have when it comes to customer service technology, and companies have sky-high expectations when they add it to their CX mix. When their expectations aren’t met, however, it’s not necessarily the tech that’s to blame. More likely, it’s the humans who brought it on board. Here are some of the most common human errors when it comes to implementing AI.
Mistake #1: Confusing automation with AI
Using AI and automation interchangeably is a common and understandable mistake. Both can do “human-like,” work and improve both productivity and customer experience. But automation follows predetermined “rules,” while AI is designed to simulate human thinking. If your goal is to reproduce a simple, repetitive task normally performed by humans, for example, filling in forms, resetting passwords, or routing inquiries, then you’re probably in the market for automation. If you’re looking for a solution that’s able to do more complex things, including conducting actual conversations with customers, analyzing customer data, and offering up relevant answers and recommendations, you’ll need AI with analytical and natural language processing capabilities. Choose the wrong one for your situation, and you’ll either spend a lot more than you need to or get much less than you expect.
Mistake #2: Not determining success factors
If you don’t define up front what success will look like, what it will take to achieve it, and how you’ll measure it, you’ll never know if you’re getting a return on your investment. Attempting to do everything at once, or choosing a broad, undefined goal (“Improve customer service”), is a set-up for failure. Instead, target a few specific KPIs. Then think about which teams need to be involved and what processes need to be implemented or changed to ensure success.
More important, make sure there’s internal alignment on goals. Otherwise, while you’re using your AI solution to deflect routine inquiries so your agents are free to focus on high level inquiries, leadership might look at what’s happening and wonder why call handle time is staying the same or even going up. Get consensus up front, and the tech won’t get blamed for failing at something it was never intended to do.
Mistake #3: Not getting organizational buy-in
Even the best AI solution won’t make a dent unless everyone affected by it is informed and on board. Customer service employees may hear the word “AI” and assume they’re going to lose their jobs. Be transparent about the ramifications of the new technology: Will employees be shifted to new roles or learn new skill sets? Will processes and procedures change? Will the AI, in fact, free employees to do more interesting, high-level work?
Meanwhile, leadership needs to understand that there will be ramp-up time to realize the value of the new solution. There’s a learning curve with any new technology or change in duties, and teams will need time to get up to speed. You’ll also need to fine tune and adjust the tech as you start using it in the real world. Set expectations up front.
Mistake #4: Not considering the impact on the entire customer journey
When you alter one stage in the customer journey, there’s a ripple effect throughout the entire experience. You’ll need a holistic view, so you can anticipate and address issues that could arise when you plug AI into one or more touchpoint along the path. If you use AI in pre-sale to create a great experience for potential customers, what happens when they’re at the support stage of the journey? Will customer support agents have the training and/or tools to provide an equally good experience? Look at the big picture and do what it takes to keep the journey coherent and consistent.
Mistake #5: Not understanding the cause of the problems you’re trying to solve
If, in spite of your best efforts, your AI solution still isn’t moving the dial, it’s possible that you didn’t adequately investigate the root causes of the problems you were trying to solve. If, for example, your goal is to improve your NPS (Net Promoter Score), you’ll first need to dig in and understand what’s keeping your scores down. If it’s because your customers are frustrated with wait times or the time it takes to resolve issues, AI might help. But even the best AI solution in the world won’t work if what customers are actually unhappy with is your shipping and return policy.
The potential of AI for customer experience is undeniable. Get the human factor right, and you’re far more likely to get results.