The Challenges of Information Overload
The information overload phenomenon besieging knowledge workers is one of the more palpable consequences of today’s information age. In a world in which there are nearly instantaneous updates about business objectives pertaining to financial trading, healthcare patient outcomes, and other metrics, it’s become increasingly difficult to discern at what point there is too much information.
Moreover, there’s a fair amount of evidence indicating that, as they currently exist, the relatively recent gains in information retrieval are actually aggravating, rather than ameliorating, overload. Instances of summarization, curation, content and context generation, and other outputs of sophisticated machine learning (ML) models are simply producing more information for users to sift through.
But when properly implemented, these same constructs may very well provide a salve for information overload—if not the means of its elimination. The crux of doing so is to first acknowledge, then overcome, the fact that, as Laserfiche CTO Michael Allen termed it, “A lot of information is irrelevant. It’s not that it’s useless, but it’s irrelevant to your task.”
Drivers
The premier challenge of information overload is that, as Allen implied, the vast majority of information encountered by knowledge workers is not as meaningful as it could be and doesn’t align with what they’re trying to do. There are several reasons for this. Most prominent is the fragmented technology landscape organizations are contending with, which has multiple systems and resources that are vying for an employee’s attention. Matt Healy, Pega senior director of product marketing, notes that users—particularly new employees—contend with an overwhelming reality: “Ten different systems you must log into, SOPs [standard operating procedures] you must refer back to and follow, with legacy applications like terminal green screens, Lotus Notes mixed in, as well as dozens of document types you have to navigate, acronyms, and codes. That’s a lot to take in all at once.”
Additionally, the dubious quality of information generated from systems rooted in advanced ML also contributes to information that’s not directly relevant, or trustworthy, for a particular task. There are other facets of information created by generative models that compound this challenge. “With the rise of generative AI, more content can be produced quicker, and not necessarily of a higher quality, by knowledge workers. Finding and managing what matters, what is true ‘knowledge,’ what are the right insights, or an organization’s critical institutional memory, gets harder to find” was an insight from Alex Smith, iManage’s global product lead–knowledge, search, AI. Finally, the surplus of real-time and near real-time systems, which are continually updated to produce new information with each moment, also adds to the grand total of information readily available to contemporary knowledge workers.