The Challenges of Information Overload
It’s possible to hurdle these impediments via tempered automation and by beginning with the specific business objective for which curation is performed. For instance, when processing a claims form, the content might include someone’s name, address, date of birth, and assets. After that data is extracted, “curation then identifies either the document, or the portion of the content within a larger document, that’s relevant to your objective,” Allen specified. “It gathers [that data] and then provides that information to the user with the purpose of helping the user achieve their goal.”
It’s important to temper such automation, which might involve language models and agents, with human oversight. “AI can support the management and discovery of good information, but human-in-the-loop processes will remain vital to validate content, preserve institutional knowledge, and ensure that trusted insights are not lost in an expanding volume of information,” Smith offered.
Goal-Oriented Automation
In general, sophisticated ML models are proffered as a panacea for all enterprise issues with information technology. They become much more so for overcoming information overload for KM when they’re attuned to particular tasks employees are working on. Instead of using general models for general-purpose information retrieval, conversational search, synopses, and curation, organizations can refine their outputs by tailoring these models to these respective constructs. For example, instead of building or buying an organization-wide system for implementing language models, agents, vector databases, and other facets of statistical AI, it may help to devise smaller “project-oriented systems with a specific data model and goal—an objective—which are driving this curation and compression,” Allen said.
The role of data quality in such systemic implementations is paramount. Not only does high data quality reduce the number of inaccuracies, hallucinations, and unsanctioned actions that can exacerbate information overload, it also helps prioritize sources and systems for certain applications. Doing so requires organizations to “identify the business systems they trust, understand the quality of the data within them, and document known weaknesses such as poor inputs, missing fields, or outdated records,” Smith explained. “This transparency allows people to interpret AI or agentic outputs appropriately and apply the right level of review.”
A pair of tenable outcomes produced from fostering such advanced ML systems can help to counteract information overload. The first is fewer quantities of information delivered accurately the first time employees ask for it, which reduces the overall burden of information overload. The second is a greater capacity to align the workforce with organizational priorities. According to Healy, “Enterprises are beginning to use AI to evaluate the characteristics of new assignments: scoring how complex it might be and then intelligently routing it to the right queue for specialists or generalists. This allows them to get the right work to the right team at the right time, while effectively utilizing their specialist teams.”