The following discussion explores the continuous stream of reinvention for best practices, business models, and strategies that will occur between now and 2010 and beyond. It provides greater detail on the nature of the reinventions and how practitioners will experience such changes.

Reinventing Best Practices

What will it be like for an e-knowledge practitioner to experience the past, present, and future all at once? To witness cascading cycles of best practices in e-learning and knowledge management? To hear of interesting new practices and approaches from institutions and enterprises half way around the globe? And to try to make sense of the many choices and options in attempting to reinvent and innovate one’s own e-knowledge initiatives?

How do companies spot and manage promising opportunities? They do it as surfers ride waves or scientists conduct research. They observe selected environments systematically and scan ripples of opportunity on multiple horizons. They learn to recognize patterns of impending change, anomalies, or promising interactions, then monitor, reinforce, and exploit them.

James Brian Quinn, 2002

Making Innovation Easier—Yesterday, Today and Tomorrow. Even today, mass systems for education and training offer little more by way of personalization than did Henry Ford’s pioneering system for mass production of vehicles: “You can have any color you like as long as it’s black.” Ford’s system, remarkable for its day, had its R&D equivalent in Thomas Edison’s laboratories at Menlo Park. Edison is famously credited with the insight that invention was 99% perspiration and 1% inspiration. He needed thousands of experiments before he was able to devise a long-lasting electric light. His studies of other inventors convinced him that sudden flashes of inspiration — ‘Eureka’ moments — were the exception. Most of the time, small steps were all that was needed to make valuable advances. He isolated the processes that were common to the mass of inventive steps, and established the world's first production line for inventions and innovations.

What was admirable about that approach was the way in which people who would not have considered themselves to be creative were enabled to be creative and to come up with ideas for products that changed their society. Today, comparable vistas are emerging. The difference between now and Edison’s time is that today “we have the technology,” such as computer-supported ways to develop personalized knowledge bases specific to our needs, which can be combined with databases of processes that are proven to be helpful. As an early illustration, consider the success of NASA.

They were an early user of a system for managing invention and innovation, developed in the former Soviet Union under its Russian acronym TRIZ. The inventor of TRIZ looked for patterns in many thousands of patents, and determined the physical effects that were the basis for each invention. From this, he was able to come up with generalized processes that anyone could use to create new inventions.

In the West, the system is now available commercially under such names as the Invention Machine. It provides a decision-support system that incorporates links to relevant databases, covering such areas as constraints (e.g., physical and chemical properties of materials) and possibilities (e.g., known physical effects and phenomena). We see it as a precursor of the kinds of tools that will become widely available in the knowledge economy for managing knowledge of all kinds and seeing patterns that can be put to use in new contexts.

The key to success is to bake specialized knowledge into the jobs of highly skilled workers—to make knowledge so readily accessible that it can’t be avoided.

Thomas H. Davenport and
John Glaser, July 2002

Such tools will need to be embedded into people’s knowledge space in a highly amenable manner.

Emergent Best Practices. Two benchmarks guide our vision of emerging best practices for e-knowledge: 1) current e-knowledge practices that are gestating in different settings across the globe, and 2) the impact of impending changes in standards, technologies, marketplaces, infrastructures, and knowledge ecologies. The leading edges of many of these best practices are evident today.