Reduce information overload in the workplace with machine learning

In the digital workplace, information overload is a real problem—and it only seems to be getting worse.

The enterprise app market is poised to grow to $287.7 billion in the next eight years. Additionally, the average worker uses 9.39 apps to perform their daily tasks, yet all of these apps only add more data, processes, and silos to an organization. Rather than making life easier, they make it more difficult to find information, greatly hindering productivity. In fact, 25 percent of an employee’s working hours are often wasted solely on information searches.

In 1971, Herbert A. Simon, a Nobel Prize winning economist and pioneer in artificial intelligence said, “In an information-rich world, the wealth of information means a dearth of something else: a scarcity of whatever it is that information consumes. What information consumes is rather obvious: the attention of its recipients.” Knowledge is power, but too much of it can actually distract us from focusing on what’s important and reduce the quality of our decisions.

Today, enterprises have more data than they know what to do with and many business systems—whether they are legacy, on-premises, or even SaaS—aren’t structured in a way that enables users to get the data they need, when they need it. Employees simply cannot absorb all of the information in a meaningful way. It’s simply too disconnected.

For this reason, digital workplace vendors are applying (or planning to apply) artificial intelligence to improve collaboration and combat information overload. According to a recent Gartner survey, forty-one percent of organizations have already made progress in piloting or adopting AI solutions. Machine learning, along with other AI-powered solutions, are gaining popularity as a way to not only help find the information employees need, but also automate numerous tasks to enable them to be more productive.

This is why solutions, like Sapho Employee Experience Portal, which leverage machine learning to surface personalized data and information, are becoming an integral part of the digital workplace. For example, an organization may be using a learning management system (LMS) to manage, measure, and deliver its corporate education programs, but they often lack the personalization that would allow them to provide more targeted, relevant resources to employees. Machine learning can be used to inform employees about newly available courses similar to ones they have taken in the past — or in the future, they can receive notifications about courses that are relevant to areas they need improvement on based on their annual or quarterly reviews.

Sapho Employee Experience Portal uses machine learning to surface the most relevant data, information, system updates, and tasks to employees in a single portal that can be delivered to employees on any device or in any channel they’re currently using— whether it’s a messenger, email, or intranet.

Beyond improving what information is delivered and how it is delivered, machine learning enables more targeted context that allows people to be notified based on their personal preferences or habits. As employees tune their notifications according to what is relevant to their work and over time, Sapho will learn to recognize which updates or tasks are most important and automatically surface them.

As workplace tools continue to leverage more and more machine learning, the task of identifying signal from noise will become easier. Machine learning has the potential to decrease information overload and deliver information to employees that is directly relevant and actionable to their jobs, allowing them to be more engaged, effective, and productive.

Ready to learn more? Read how Sapho helped CBS Interactive transform employee experience, improve application adoption, and bring value back to employees.


Topic: Digital workplace

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