Editorial
The Gist
- Predictive prevention. AI's predictive capabilities can prevent tech failures, improving customer and employee satisfaction.
- Data quality importance. Reliable AI outputs require broad, deep, well-structured, and real-time data.
- Workflow enhancement. AI-driven workflows boost productivity and customer satisfaction across industries.
We’ve all been there. You’re next in line to check out when the cash register goes down, or you’re on the phone with customer service only to have the agent tell you their computer is slow.
Most recently for me, it was on a flight. I had settled into my seat when the captain announced a delay. This time a handheld baggage scanner was broken, and they couldn’t scan and load all the checked bags.
As a senior director of product management at a software company, I couldn’t help but wonder: What if the airline could have known the scanner had an issue and prevented the problem before the baggage handler grabbed that device? How much happier would the employees and customers be? And can artificial intelligence (AI) help us achieve a better outcome?
Let's examine the impact of AI integration.
As businesses constantly seek new ways to enhance customer satisfaction and employee productivity, incorporating AI and machine learning (ML) into your workflows can deliver meaningful results. With AI integration, you can elevate your service delivery, improve operational efficiency, increase revenue and ultimately build stronger, longer-lasting relationships with your customers and employees. These are lofty promises, but for good reason.
Employee Satisfaction and Productivity Depends on Technology That Works
Your employees want to be happy and productive. But it’s hard to be either when your technology doesn’t work and you can’t keep up with your deadlines or provide the best service to your customers.
Think about my delayed flight. Suppose the airline had used AI to analyze historical telemetry data and perform anomaly detection on its ground-side operational devices. In that case, the airline may have seen that the problematic scanner had been dropped twice in the past week and was likely to experience a connection failure. The company could have told its employees, “Don’t use this device today.” The flight crew would have had a smoother boarding process — and a much better workday.
In the context of enterprises, AI-driven workforce analytics can give insight into the performance of employee devices such as laptops and how they use their technology. AI trained for IT can even facilitate personalized employee experiences by tailoring IT services and support to each employee’s preferences and job needs, enhancing employee satisfaction and retention.
Related Article: How AI Integration Can Power Better Human Experiences
Get Faster Access to Resolution With AI Integration
But the benefits of AI integration go further than keeping employees happy. When the checkout registers stop working at a fast-food restaurant or retail store, it’s a costly problem. The traditional solution is to put a lot of expensive people in a “war-room” scenario (virtually or in-person) to fix it, no matter the time of day. Is it the applications? Networking? Something else?
Now, with AI and advanced large language models (LLMs), a retailer’s IT staff (or MSP) can simply query the data; that means posing a real question in natural language to determine the cause of the outage and getting answers in a fraction of the time it would take for people to track down the problem on their own. With faster access to more data — and more answers — you can save your company money and let the IT team focus on other projects that add value to your organization.
Related Article: Effective AI Implementation Starts Here
Match Your Customers’ Expectations for a Frustration-Free Experience
Of course, the employee experience is inextricable from the customer experience. While those airline employees were trying to fix their broken scanner, the passengers on the plane were becoming increasingly frustrated as they anticipated missing an important client meeting or their daughter’s soccer game.
Your customers don’t care if you’re using AI to fix problems — they just expect technology to always work, wherever they are. AI integration empowers businesses to minimize the impact on their end users. AI integration can’t prevent every technology outage — but it can help you avoid many of them and lessen the impact of the ones that do occur.
Even in everyday usage, AI integration can improve customer satisfaction. Recently, a global information company used an AI-driven platform to measure customers’ digital experiences and enhance their productivity. By combining a wealth of metrics with subjective data, including end-user feedback from surveys and sentiment input, the company created experience level agreements (XLAs) for IT and other business functions such as onboarding and procurement.
They tracked approximately 50 metrics directly from endpoints, which allowed them to assess critical app performance, service reliability, number of user tickets,and feedback to establish baselines and monitor the overall digital experience. By establishing XLAs and proactively addressing issues before they impacted end users, the company drastically improved customers’ digital experiences.
Understanding the Recent Hype Around Generative AI — and the Importance of Quality Data
As more organizations implement AI, it’s become clear that the quality of the underlying data equates to the quality of the AI output. AI is fueled by data, which is why you’ll get better results when your data is broad, deep, well-structured, real-time and has minimal noise (so you can focus on the signals). These characteristics typically lead to more reliable results from AI, which is critical to long-term success.
As a recent report by Enterprise Strategy Group noted, “Without good data to feed into the AI, trust can never be achieved. Without trust, full adoption can’t happen. And without full adoption, the overall goals can’t be achieved.” This is one of the reasons why limited availability of quality data is the number one challenge for organizations, according to this same report.
Finally, recognize that there’s a lot of hype these days, especially around generative AI. We’ve certainly gone through boom and bust cycles with AI. But there are some critical differences in what we see now, including devices with more compute power, more comprehensive LLMs and more trustworthy iterations. You should certainly proceed with caution and take it one step at a time.
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The clear benefits of AI integration are here to stay, however, which is why more and more organizations are using AI-driven workflows to enhance the employee and customer experience. Thanks to AI, we can all look forward to more days without flight delays or long queues at the register.
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