Big Data

Overloaded? Digital assistants to the rescue

Data is empowering us like never before. But there’s a flip side to having access to so much valuable data: information overload. That’s when data causes more pain than gain. It looks something like this:

It all feels like a bit too much.

The catch-22 of “knowledge work”

The term “knowledge workers” generally describes anyone with a desk job. That’s tens of millions of people across the globe. Each day, these workers must process, analyze and manage information to solve problems and innovate. Already, information overload is hurting workplace productivity.

IDC reports that digital data will surge to a trillion gigabytes by 2025. That’s 10 times the 16.1 zettabytes of data generated in 2016. On the other hand, the number of knowledge workers is shrinking. McKinsey Global Institute predicts a shortage of 80 million knowledge workers worldwide. So while workloads are increasing, the workforce is decreasing.

What’s the solution to this dilemma? Intelligent digital assistants.

Digital assistants

Bring on the robots

Meet the next disruptor: the intelligent digital assistant. Enabled by artificial intelligence, these digital helpers can automate complex data work, helping employees do higher-value work. Sifting through mounds of data, prioritizing projects and managing tedious tasks are just a few of the many activities digital assistants can do.

Digital assistants in action: A usage scenario

How might a digital assistant make knowledge work easier? Let’s walk through a scenario.

Imagine Rob, a software account rep with more than 40 accounts, is having trouble staying on top of them. He’s overloaded with information and tools, including Salesforce, Gmail, Slack, Google Sheets, and LeadLander, among others. Rob needs to constantly check these disparate systems and synthesize information to get the insights he needs to effectively serve his customers.

A digital assistant could do a lot of this work for him. For example, Rob could have his assistant monitor product usage and Salesforce to detect customers who are up for renewals in the next three months, but haven’t been actively using the product. The assistant can send Rob notifications, enabling Rob to resolve any issues that may be occurring and increase the customer’s chances of renewing.

He could also have his assistant watch for new job postings from his clients on and LinkedIn. If the assistant finds a job ad from one of his clients, it can notify Rob that the customer may need additional software licenses for the new employees. The digital assistant can even proactively recommend additional tasks to offload to the assistant, enabling him to be more proactive. Collectively, these actions could add hours back to Rob’s work week while helping Rob better meet his goals.

Rob frequently works with Alice in customer support, who could also benefit from a digital assistant. To provide proactive service to customers, she could train her assistant to monitor new support tickets. If three or more customers report the same problem with the same product within a week, the assistant can automatically send the engineering team a high-priority ticket that includes a summary of the related tickets. The assistant can also send ongoing status updates to management, saving the support team significant time.

As these scenarios illustrate, there are three key capabilities that can make or break the effectiveness of a digital assistant app:


Many digital assistants require IT intervention because of complexity. That’s not exactly a motivator for adoption. Workers should look for a digital assistant that’s intuitive to set up and use—allowing them to easily create their own complex situations to detect and actions to take. The technology should also include a catalog of pre-built skills that can be personalized—to help users get started quickly.


Employees should look for a digital assistant that can work with the systems they use and accommodate their unique key performance indicators (KPIs) and work processes. In other words, the digital assistant should be as useful as a human assistant that they might train.



Detecting situations that matter, delivering context-driven notifications at the right times and automating actions are all intelligent capabilities. But after a while, the digital assistant should be able to learn from how workers use it, and make proactive recommendations. That’s what users would expect their human assistants to do. So employees should look for a digital assistant that they don’t have to micromanage.

To see how you can start optimizing your productivity with help from an intelligent digital assistant, check out this video.

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