Is AI Really The Answer?
Earlier this week we shared some of the pitfalls of implementing self-service and highlighted the importance of strategic and empathetic implementation. AI (artificial intelligence) is one of the self-service tools in the customer service professional toolbox today. It is also one of the new buzzwords, together with blockchain (for the curious ones – my favorite explanations of blockchain are this video and this article).
Artificial Intelligence in Contact Centers
The primary current positioning of AI is in call centers. The value proposition is that with AI, companies will empower customer service employees to make better decisions/recommendations, thus increasing employee engagement.
Additionally, through AI, organizations will achieve significant ROI by automating the role of the customer agent (in JetBlue’s case, the crewmember) and scaling customer support without incremental headcount.
What is Artificial Intelligence?
So, what exactly is AI? Do you really understand what this technology can and cannot do? If you do not, keep reading as all working professionals today should understand at least the basics of AI. If you are like me, you probably get 100 sales emails every week telling you that you are running late and must leverage AI in your call centers.
But do you really need AI? What problem do YOU need to solve? And is AI the best way to do that for your company?
Asking the Right Questions
Last month I was invited by Execs In the Know to join their AI Advisory Committee with the below mandate:
“The final group output will come in the form of a report. The exact nature of the report will be determined and framed by the group, but may include areas such as:
- A summary of ways AI is enhancing CX channels
- Best practices on where and how to start
- Trends and technology in AI to improve service
- ROI of current AI customer service initiatives
- Perspectives and predictions about the future of AI and customer service”
This is no small mandate. And it is encouraging that we have a group of professionals examining these questions before we all get ahead of ourselves with AI and compromise the ROI we all want so much.
What is Machine Learning?
The most common use of AI is ML (machine learning). Basically, this is data mining and predictive learning on steroids. It enables a computer to make decisions and interact with a human. With that basic understanding I already have a few questions to all the companies that are calling, emailing, in-mailing etc., to offer me AI enabled solutions.
Who, or rather what, is enabling those solutions? Is it my company’s data? Because if it is, I have a lot more work to do internally before I respond to those sales pitches.
How Ready is AI for Business Implementation?
Erik Brynjolfsson and Andrew Mcafee provide a comprehensive explanation of what AI is and what it is not in their publication The Business of Artificial Intelligence. In it, they state that AI technology is ready for implementation in the business world and that “[t]he bottleneck now is in management, implementation, and business imagination.”
I do have the business imagination. But I also am taking my time to know exactly what capability I am buying with AI. It might be cheaper to streamline processes and fix existing software tools. It may be more cost effective to invest in integrations that enable my employees to deliver excellent customer service, instead of paying for yet another software integration that makes decisions based on my bad data. Beware of the danger of prioritizing the purchase of the expensive AI solution over cleaning existing data.
Don’t Wait Too Long
True, it is clear that AI is a solution that needs demonstrated ROI and employee adoption for success while it is learning. However, it is also clear that we cannot wait too long to get comfortable with this new technology. As Erik and Andrew say, one thing is pretty certain: “[o]ver the next decade, AI won’t replace managers, but managers who use AI will replace those who don’t”.
And that is the exact reason I chose to join the AI Advisory Committee.
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