- To enable true knowledge collaboration and connect individuals to the information they need, we must begin concluding a broad scale from enterprises’ collected data.
- Two more essential properties of AI assist businesses in accomplishing this and overcoming the obstacles associated with legacy knowledge management to date.
- AI can develop a network of knowledge and experience in real-time by leveraging data.
Individuals who work in huge organizations spend excessive time seeking solutions. That may be unsurprising given that 90% of the world’s data was created alone in the last two years. Each day, 2.5 quintillion bytes of data are made, and the number is growing. Even though the amount of data we generate has increased rapidly, our understanding of handling it has not kept pace.
The extent to which this has harmed employees and businesses is beginning to become apparent. Employees are inundated with information and unable to access the knowledge they require. This has a detrimental effect on business productivity, staff collaboration, the efficiency of project completion, and innovation. Businesses must address data overload before the divide between data generation and management becomes unmanageable. We need to expand access to knowledge and improve the speed and precision with we solve problems to increase employee engagement and productivity. This is how we begin.
AI-assisted identification of redundant or out-of-date data
As data pools grow, it becomes increasingly difficult to locate what we need. The useful and the trivial, pertinent and the irrelevant, cohabit in perfect harmony. Manually sorting through this data consumes necessary employee time with pointless searches, resulting in low productivity and a stressful work environment.
Consider how the majority of firms handle their data. You complete a piece of work, whether a spreadsheet with sales targets or a status update for a project, and save it to several databases. What occurs next? In most cases, nothing. It is merely stored and becomes obsolete with time. When a colleague comes across this information later, it is devoid of context, making it difficult to determine whether or not it is functional.
However, we are not discussing a few documents here. More bytes of data are generated each year than stars visible in the observable universe to put things into perspective. At this scale, it’s unsurprising that when we fail to manage our data effectively, the distance between us and the information we seek feels impenetrable. According to an IDC poll, organizations are struggling to manage the complexities of their data.
In short, despite its transformational promise, obsolete data is detrimental to individual workers and the productivity of entire enterprises. Today’s enterprises’ utilization of data and provision of necessary knowledge will have a make or break’ effect on the organization. So, how do we improve?
Artificial intelligence is intended to supplement, not to replace, human expertise
The data overload difficulty is also a collaboration difficulty. When people cannot readily locate what they require, they become overwhelmed. Improving access to knowledge by strengthening the connections between experts inside an organization helps address this. It is where the benefits of contextualizing data and applying AI to contextualize data become apparent. When left unmanaged, all that information can become the key to unlocking knowledge.
To enable true knowledge collaboration and to connect people to the information they need, we must begin concluding the data we already have in businesses on a large scale. Thus, we can connect individuals with questions to the appropriate colleague(s) who answers. Two more critical characteristics of artificial intelligence assist firms in accomplishing this and overcoming the challenges associated with legacy knowledge management to date.
To begin, AI can be trained to forget. This means that AI can not only determine who knows what about a subject, but it can also contextualize that information and determine when it becomes obsolete or redundant, allowing it to ‘forget’ unusable facts as needed. Second, by utilizing non-sensitive data extracted from existing technologies, AI may see through silos. It may draw inferences at scale using various data sources, resulting in creating a live map or ‘knowledge network’ of who knows what within an organization on a single integrated platform.
In brief, AI can create a network of knowledge and experience in real-time by utilizing data. When searching for answers, everyone has fast access to the most accurate, up-to-date information or the most excellent expert available at the time.
Before zettabytes of data become yottabytes, it’s time to accept artificial intelligence’s role in addressing data overload. With AI, we can begin exploiting data in the manner businesses and employees expect: to enable connection, problem-solving, collaboration, and the discovery of answers.