Presented by Bold360 by LogMeIn
When it comes to most things business, AI is making its mark as the must-have technology. Whether we are talking about customer-facing chatbots to help with engagement and conversion or AI working in the background to help make critical business decisions, AI is everywhere. And the expectations of what it can and should be able to do is often sky-high. When those expectations aren’t met, however, it’s not always 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 there is a critical difference between the two. 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 technology. If, on the other hand, 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, you may be using AI to deflect routine inquiries so your agents can spend more time with customers who need them, but leadership might look at what’s happening and wonder why average 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 touchpoints along the path. If you use AI in pre-sales to create a great experience for potential customers, what happens when they’re at the support stage of the journey? Will support agents be able to provide an equally good experience? Will they have the historical information to make the interaction seamless? 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, despite 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 the business changing results you’re looking for.
Ryan Lester is Senior Director, Customer Engagement Technologies at LogMeIn.
Go deeper: Learn more about the “The 5 biggest mistakes companies make when implementing AI” at the upcoming webinar hosted by VentureBeat and LogMeIn.
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