Technology’s impact on knowledge management

The future of work is being shaped by three key forces. As technological advances of cognitive AI, machine learning, and everything-as-a-service become more integrated into the fabric of business the very nature of the workforce, work and workplace are rapidly evolving. It is enabling a new generation of workers who will be largely “gig” and digitally savvy. The best talent may no longer be where companies exist meaning that managing a more broadly dispersed workforce will be a competitive differentiator. When compounded with a significant number of pending retirements and a complex global regulatory environment it is clear that the topic of knowledge management is top of mind to leaders.

Becoming a smart, adaptable business is not a one-dimensional issue. As high-tech becomes an integral part of our daily work, changing manpower or IT on one hand or replacing machinery on the other is not sufficient to keep an organization competitive. On the contrary, many startups and boutique companies with limited experience and resources are outsmarting and disrupting established companies. Understanding the reasons for this phenomenon can shed light on the essential transformations needed to compete in this dynamic market. Namely, better knowledge management and clever implementation of digital workplace tools can make businesses smarter than ever.

 

Developing and using new technology has always been a transformational part of human work, starting with the earliest wood and stone hunting tools and, of course, the first wheel. Similarly, Artificial Intelligence (AI) is another milestone in the evolution of technology. Surprisingly, AI algorithms were introduced back in the 1950s, although they were not widely used. So what’s different now? What makes today’s technologies so disruptive and why AI has suddenly become the last word? The answer lies in the availability of data and how it is used.

In looking into what makes these technologies so exceptional, we find that the larger, more relevant, and diverse the data sets they are based on, the more efficient and accurate they are. Although non-AI technologies can perform tasks over large amounts of data, they are not based on the data itself. Advanced AI technologies, however, can set their own commands, which derive from a learning phase, based on the data given to them. Said another way, non-AI technologies perform tasks in response to commands, while advanced AI technologies can perform tasks according to the data they receive. This type of advanced AI, which learns from data, is called Machine Learning (ML).

Today, unlike in the 1950s, strong computing power exists and the access to information is broader. These developments make it relatively simple to analyze, search, scan, and extract information from incredible amounts of data in a relatively short amount of time. Combining the capability to utilize massive data sources with AI learning algorithms is what really differentiates and makes today’s technologies extraordinary.

Today, unlike in the 1950s, strong computing power exists and the access to information is broader. These developments make it relatively simple to analyze, search, scan, and extract information from incredible amounts of data in a relatively short amount of time. Combining the capability to utilize massive data sources with AI learning algorithms is what really differentiates and makes today’s technologies extraordinary.

Eyal CahanaEyal Cahana is a senior manager with Deloitte Consulting LLP’s Knowledge Management practice in Israel, specializing in planning and executing organizational knowledge management strategies and tools.

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