unhappy woman sitting in glow of computer
PHOTO: Niklas Hamann

Good science fiction excels at tapping contemporary anxieties to forecast the future fate of humanity. Consider the list of workforce automation fears showcased in the recent “Kerblam” episode of the TV series Doctor Who: Robotized megacorporations, computer-controlled commerce, ubiquitous unemployment, human irrelevance and destructive despair. There’s no spoiler in sharing that — aside from some teleportation and a sonic screwdriver — everything in the story line is already pretty plausible.

The Doctor may be sci-fi fantasy, but the issues are real.

Artificial intelligence (AI) technology is already reshaping many manufacturing and service industries, and it is rapidly disrupting other sectors as well — for both good and ill.

On the positive side, according to Gartner, “global business value derived from artificial intelligence is projected to total $1.2 trillion in 2018, an increase of 70 percent from 2017.”

On the negative side, AI is creating a lot of human anxiety. In a report titled “Robomageddon: The Skills for the Future Study,” MindEdge, a provider of online training, polled 1,000 US managers in 2018 and found that “52 percent of employees whose companies have already adopted robotics and automation are worried about their job security, and 42 percent of managers believe the impact of robotics and automation in the workplace will result in the elimination of jobs.”

The focus on AI either cutting costs and increasing efficiencies or displacing workers and eliminating job categories has left behind the more important conversation — how AI can help human employees.

AI is truly phenomenal technology. Algorithms can now accomplish once-unfathomable feats at lightning speed, saving untold millions of man-hours. But companies are not made of math, they’re made of humans doing human things together for other humans. (“Corporations are people, my friend!”) So what can AI do to help them out?

Tools Hindering Trade: The Customer Experience Example

Consider the customer service sector as one example of an area in which AI could be helpful if used correctly. In our increasingly connected and digitalized world, the rise of AI, decision support, automation and chatbots in customer service has exploded, driving the multichannel customer experience (CX) phenomena.

But adoption of those types of technologies for employee engagement has been slow. Contact centers have some of the highest employee turnover rates of any sector of the economy, and there’s been troubling analysis suggesting that our wonderful new technology is antithetically inhibiting employee performance, engagement and satisfaction in contact centers. 

Information Week reports that, in a recent Gartner study of 2,000 front-line contact center representatives, 65.8 percent of the respondents described their experiences with systems and tools as negative, using words like frustrating, difficult and inefficient. The same analysis noted that service representatives use a mind-boggling average of 8.2 different systems and tools during a customer interaction. Meanwhile, “talk time is up nearly 14 percent over the past several years — while call volume has remained the same.” No wonder! Have you ever had a conversation with someone who’s thumbing through apps on a smartphone or tapping away on a laptop? It’s not a fulfilling experience for either party. How are people supposed to communicate with anyone about anything while fiddling with all that technology?

So we have amazing systems driving less-than-amazing experiences for the people charged with using them. This is not progress. I propose that a primary source of the problem stems from something obvious: We’re measuring the wrong things.

Related Article: Stop Thinking AI vs. Human, Think AI With Human

AI in the Workplace: What We Need to Measure

In our rush to capitalize on the potential of AI technologies, we’re failing to evaluate the way they ultimately integrate into human workflows. When it comes to the customer service sector, technology can now better handle a lot of discrete tasks, but it does not replace human representatives, and that’s where we have to turn our attention.

For example, it’s becoming standard practice for companies to host always-on, automated and largely self-service interaction options for their customers. Digital account portals supply constant access and handy personalization capabilities to clientele, utilizing well-designed chatbots and virtual assistants that are excellent for handling basic functions like taking orders, processing payments, conducting status checks or responding to simple requests for information. AI applied toward those efforts has already delivered measurable benefits.

But when it comes to more complex requests or problematic issues that require human nuance and context, the mere existence of technology-enhanced services can complicate the situation. If human agents have no access to what transpired during those digital interactions, they are already at a loss when dealing with the customer. And even when human agents can access those systems, they shouldn’t be flipping back and forth through applications, scrolling through reports, and digging up records while simultaneously trying to engage in proper human interaction with the customer. It’s called “undivided attention” for a reason.

That problem is a perfect target for AI-driven solutions. Analytics engines that deliver historical and/or relevant customer information to support agents automatically and in real time can speed, rather than delay, productive conversation. Natural language processing (NLP) systems trained to recognize spoken keywords and supply agents with useful prompts or notes can spare employees from app fatigue and the need to switch tasks midsentence. Virtualized on-demand training systems that employees can self-direct to refresh or acquire new skills can keep workers stimulated and engaged.

These are the kinds of employee-centric AI tools that deserve more study and development. If your systems aren’t generating a positive employee experience, it’s going to reflect in the customer experience the employees deliver. Exploring the ways AI can better serve employees is the solution. And measuring how employees view these tools should be the first metric for success, not an afterthought.

Related Article: The Humanoid Touch: How AI Is Changing Customer Experience

How We Need to Measure

Applying AI to better serve the employee is crucial, but this must also be measured and managed with caution. Even with focused and well-integrated systems, we now have enormous amounts of data flying around the average employee, which can easily distract and overwhelm. There must be balance between assistance and intrusion.

One of the greatest struggles from an AI development perspective is determining the right level of aggressiveness. How often should a system prompt the employee? Should there be some sort of triggering mechanism? Can it be ranked? Do we allow the employee to turn off certain notifications because they’re annoying? Those old enough to remember Microsoft’s Clippy will know whereof I speak. It was an amazing tool and it could absolutely help you construct a Word document, but it was constantly in your face and it drove everyone mad.

As the Clippy example illustrates, one of the real risks of using AI to support and assist employees is overdoing it. Imagine you’re just sitting there doing your job. But with every move you make, there is someone right beside you saying, “Hey, do this! Have you remembered this? Did you see this? Hey, now do this!” It would get very frustrating very quickly, and you would not be a happy human.

The only way to arrive at a balanced employee-centric AI application is to collaborate with employees. The people using the technology should have representation at the development table — if you want the tools to succeed, you have to create mechanisms to make that possible. Luckily, having employees participate in shaping their roles within an organization is also an excellent way to increase engagement and job satisfaction.

Related Article: Where AI Customer Experience Investments Are Paying Off

Future AI Metrics

A final point about AI, measurement and employees: As AI technology becomes integrated into the enterprise, we will also need to adapt the way we gauge human performance. In CX management, technological innovation dictates that businesses have to start restructuring how they view customer contacts and the human employees who perform those jobs.