applications may emerge from a convergence of resources or from technologies where no correlation had previously been identified. Examples of this phenomenon include the modern Internet, smartphones, personal computers, improvised explosive devices (IEDs), portable digital music players, and digital photography.
The committee recognizes how quickly forecasts, especially long-term forecasts, become obsolete. New information, discoveries, and scientific breakthroughs can quickly change a prediction from unlikely to inevitable. If a forecast is to have value it needs to be kept as current as possible and as dynamic as the domains it is covering.
A useful forecast provides insights that lead to effective action in the present. A forecast user must have confidence in the quality of the underlying data and in the analysis that led to the forecast. Success is measured not by how many accurate predictions a forecast makes, but by the value of its insights. A useful disruptive forecast reduces surprise; it alerts decision makers and provides them with the tools needed to avoid unanticipated and perhaps catastrophic outcomes.
It is also important to make a distinction between a vision (a forecast of a potential future state of reality described in a vague way, e.g., elimination of the gas power combustion engine for passenger vehicles); a measurement of interest (e.g., the energy stored per unit mass); a signpost (a recognized and actionable potential future event, e.g., the commercial availability of a battery that simultaneously surpasses gasoline in energy stored per unit of mass, energy stored per unit volume, and the price per unit of energy stored); and a signal (a piece of data, sign, or event that is relevant to the identification of a potentially disruptive technology—for example, Apple, Inc., placing a large order for new touch capacitance screens from a Chinese supplier). These concepts are critical for being able to discuss the comprehensiveness of forecasts and what one might hope to accomplish with better techniques (Strong et al., 2007).
The appearance of enabling tools is an important signpost and signal. Technology is the result of engineering, and tools enable engineering. Often, the emergence of disruptive technologies is preceded by the appearance of enabling new tools. Examples of this include the following:
Tools that perform nanoscale manipulation are enabling the rapid development of nanotechnology.
Biological analytical tools built using microfluidic technologies enable the study of proteomics, genomics, and cellomics.
The World Wide Web and blogs are tools enabling online social networking.
It should be recognized that many enabling tools are, in and of themselves, disruptive technologies. A useful forecasting exercise is to ask what other technologies could be envisioned once a new tool is predicted.
Those forecasting a disruptive technology should use reasoned analysis and seek expert advice to understand what foundational technologies and tools are required to engineer a new innovation. Estimating the timing of disruptive technologies requires understanding the sequence of foundational technologies and enabling tools and estimating when they will emerge.
Tipping points, “the levels at which the momentum for change becomes unstoppable” (Walsh, 2007), are especially important to look for. Malcolm Gladwell, who had earlier coined the phrase, defined it then in sociological terms: “the moment of critical mass, the threshold, the boiling point” (Gladwell, 2000). “Tipping point” may refer to the point at which an adopted technology reaches the critical mass, to a time when the manufacturer’s cost drops