Three Ways To Use Artificial Intelligence To Improve The Employee Experience

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According to Ray Kurzweil, famed inventor and futurist: “Our technology, our machines, are part of our humanity. We created them to extend ourselves.”

Striving to extend ourselves is the essence of what it means to be human. We’ve been trained to fear bots without realizing it’s the augmentation of humans by machines that is really how artificial intelligence (AI) is disrupting work. That process is the “art” in artificial intelligence, and it’s time we embrace it.

I’ve traveled around the world and have been inspired by the ways AI is being used to help us become the best versions of ourselves. I’ve seen ordinary employees do extraordinary things when aided by augmented reality glasses on factory floors. I’ve seen novices become experts when given answers to questions before they’ve been asked. I’ve seen boomers outpace millennials with smart routing apps that categorize and prioritize work.

The “Musk vs. Zuck” AI debate and general ambivalence toward digital labor make me feel obligated to share why I’m enthusiastic about the future of work. What I’ve seen foreshadows a future where AI-driven automation is ubiquitous and we’re all extended by machines. Here are three ways you can take advantage of what’s ahead to deliver better employee experiences today:

1. Identify and automate repetitive tasks. 

Identify repetitive tasks and document how the smartest agents complete them. Once you’ve identified candidates for AI-driven automation, locate data sources that document all the ways they’ve been completed in the past. For instance, HVAC issues are sometimes fixed by adding coolant and other times by replacing air filters. Fixing common issues is a data problem that can be solved more effectively when AI-driven recommendations add context for humans.

Here’s a real-world example: A large Australian media company that I work with has an AI-based dashboard that recommends the best solution to employee requests using a knowledge graph that aggregates data from historical cases, email threads, chat transcripts and wiki docs. Using contextual recommendations lowered training time for new call center agents from months to days.

AI-driven automation not only helps humans make better decisions but it allows them to be more productive. Time spent drudging through queues of repetitive tasks is now spent doing knowledge work that requires intuition and judgment. 

2. Detect patterns across incidents, machine events and changes.

Incidents that impact employees are typically the result of problems. For example, email connectivity issues are often caused by issues with the exchange server. Remediating problems is the most effective way to resolve incidents, and AI is the most effective way to remediate problems.

Feed change records and asset information to an AI-based system and it can learn what infrastructure is related to what services. When employees report related issues, it can map them to services and infrastructure changes. Over time, it can learn to remediate problems before they impact employees.

Consider the example of a hedge fund service desk that supports its traders with AI that tells call center agents what caused service outages by analyzing historical change records together with impacted services based on incidents. For instance, AI learned to identify that when 5% of the building reports email issues within a 30-minute window, it’s almost always the result of unplanned patches applied to the exchange server. Outages are resolved faster, and employees no longer hate calling the help desk.

3. Diagnose and resolve organizational policy violations. 

AI is exceptionally good at detecting patterns and learning which patterns are associated with positive and negative outcomes. Consider where in your organization policies are violated because data is incomplete, inconsistent or because there is too much of it for humans to identify anomalies. Those are opportunities to use AI-based tools to help humans diagnose and resolve policy violations before they result in penalties.

I’ve encountered organizations that make use of AI to protect employees — often those most vulnerable to policy violations like low-wage earners and foreign citizens. For example, during tax season, an HR call center supporting 750,000 shift workers receives about two-thirds of its annual cases. Expecting humans to detect IRS policy violations in millions of tax forms with billions of data rows is unrealistic. To minimize risk and reduce error rates, they use AI to detect policy violations and then automatically amend and reissue W-2 forms.

The Future Of Work

The “art” in “artificial intelligence” comes from smart managers who identify business problems and design data-driven, algorithm-backed solutions. The power of AI is hard to comprehend, but it’s far from intelligent without context and compassion that can only be provided by humans. We confuse “automation” with “task elimination” when, in fact, automation is used today to improve decision making, reduce downtime and deliver better answers.

My challenge to you: Pick a business problem, identify data that might solve it, feed it to an AI platform and use automated decision-making to make humans better. There’s nothing artificial about making employees more intelligent. Provide the art, combine it with science and you’ll define the future of work.

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