Home / Blog / The economics of the Digital Transformation – XVIII

The economics of the Digital Transformation – XVIII

A blueprint of leveraging knowledge turning it into revenues. A product, like a robot has a local knowledge to enable its operation, Through local AI the robot can learn and can share the acquired experience with the manufacturer. By accruing experiences from the field and from different application areas (oranfge and green) the manufacturer can expand its global knowledge, and learn through inference and correlation using AI. This enhanced knowledge can be returned as a service to the product instances, can be sold on the market as a service or as a product. It can also reduce investment needed to upgrade existing lines of products and to design new ones.

Machine Knowledge and Economic Value

IBM has possibly been the first to “sell” machine knowledge by using Watson. Watson is not like previous expert systems that were crafted to suit a very specific task through algorithms created for that purpose. It is an “almost” general purpose intelligence that can be used in a given field to support human activities and decisions as well as support industrial processes and tools (robots) and that learns and improves by doing, like a human would learn as experience increases.

Lucy is an AI Knowledge leveraging tool based on AI that as been designed as an enterprise resource flanking humans. It is able to acquire knowledge and to turn it into executable actions by capturing enterprise data, monitoring processes and human activities. It learns by living in the enterprise environment becoming a knowledge source similar to the knowledge source represented by human resources (watch the clip).

In this decade it is expected to see a significant growth in machine knowledge and in tools to share this knowledge within enterprise processes.

Machines are already –unknowingly- the main knowledge repository in the sense that most enterprises are turning to knowledge management tools for accessing and sharing knowledge throughout the enterprise. The focus for these years is to make these tools more effective (better search facilities, provide access through mobile devices, shift the representation to graphics providing a synthetic view of data/information and of relationships, improved customization).

The global Knowledge Management market is exceeding 400 B$ in 2020 and it is expected to keep growing at a 22% CAGR until at least 2025.

So far knowledge management systems have been mostly unware of the knowledge managed and of its use/potential use (reason for stating that machine are unknowingly the main knowledge repository…) but this is bound to change with the growing role of AI in knowledge management systems. First signs are already here, like the announcement of MS on project Cortex aiming at providing knowledge on a need-to-know bases to people collaborating through Teams.

A growing number of companies are embedding AI in knowledge management and this is seen as an essential ingredient in the Digital Transformation. You can see a good, articulated list here. Knowledge as a Service, KaaS, is a growing market area that makes heavy use of machine knowledge.

The increasing capabilities of machines to flank and participate in human teams, more and more using anthropomorphic forms of communications (voice interaction, gesture understanding, mood analyses) make it easier to exploit machine based knowledge. Robots, particularly their evolution in Industry 4.0, are no longer pure executers, they are able to learn through experience and interaction and can share their knowledge with other robots.

In the near future this acquired knowledge will be leveraged as a service, the knowledge of a company robot can be sold to another company.

This is also opening up the issue of knowledge ownership (much trickier when knowledge becomes shared among humans and machines –see next) . Robots manufacturers, as an example, are offering proactive maintenance services and fine tuning by acquiring operational data. In doing so they acquire operational and contextual knowledge from the robots that will be used beyond delivering enhanced services to (the company that acquired) the robot. It can be used for refining their offer, for building new products as well as for “selling” the acquired knowledge (e.g. providing a robot in another company the knowledge tools to tackle a problem already addressed in the other company).

All of this is made “simpler” (conceptually at least) through the use of Digital Twins and Cognitive Digital Twins.

About Roberto Saracco

Roberto Saracco fell in love with technology and its implications long time ago. His background is in math and computer science. Until April 2017 he led the EIT Digital Italian Node and then was head of the Industrial Doctoral School of EIT Digital up to September 2018. Previously, up to December 2011 he was the Director of the Telecom Italia Future Centre in Venice, looking at the interplay of technology evolution, economics and society. At the turn of the century he led a World Bank-Infodev project to stimulate entrepreneurship in Latin America. He is a senior member of IEEE where he leads the Industry Advisory Board within the Future Directions Committee and co-chairs the Digital Reality Initiative. He teaches a Master course on Technology Forecasting and Market impact at the University of Trento. He has published over 100 papers in journals and magazines and 14 books.