One of the most notable aspects of Gartner Symposium is the wide range of future-gazing sessions on offer. The Gartner Hype Cycle is a well-used benchmark for judging the state of emerging technologies, and Gartner analysts are amongst the best at exploring the potential impact of new technologies and business models on established industries.
At Gartner Symposium Barcelona, analyst Hung LeHong spoke about how emerging technologies may disrupt the fundamental constituencies of the business relationship – those of manufacturer, distributor and customer. For example, he outlined how smart machines can buy their own spare parts and maintenance, how 3D printing means that designers, customers, distributors or retailers can be manufacturers, and how new platforms mean that banks, technology platforms, citizens or third-parties can be credit providers. These are all sectors in which established players are at risk of being disintermediated by other, more agile competitors and new technologies.
The point at which these innovations become the norm in established industries is probably some way off. But the moment at which they have an appreciable effect on established business models is much closer. In industries where margins are typically on the slim side, even a small leakage of revenue to competing models can make the difference between growth and decline. However, there is no reason why it cannot be established companies that take advantage of the opportunities on offer.
To implement emerging technologies at the scale of a large organisation, requires investment and a certain amount of risk. Identifying the right technologies, the right time to implement them, and the right model with which to do so, is only possible if a business understands the true drivers of value and performance within its finances and operations.
Without that understanding, any attempt to embrace transformative new technologies is a shot in the dark. But, if a business is able to accurately model how its operations create value, and attach a predictive capability to that model, then it becomes possible to simulate the consequences of running some of the business according to a new business model. (For example, if a manufacturer was to target having five percent of its product 3D printed by distributors.) It is also possible to predict the likelihood of various scenarios that may result from that, and the impact if it should succeed or fail.
This level of predictive analysis may sound like it is firmly in the realms of big data or artificial intelligence – but technology of that type is not suited to what must be a highly precise and disciplined process. Predicting the impact of industry disruption essentially means analysing network effects within a complex and not wholly-understood system (i.e. the market economy). That can only be done by building a mathematical model of a company and its market from first principles. That is a lot more likely to yield credible results than simply plugging all the available industry data into a set of pre-configured algorithms. It is therefore a great deal more useful for companies trying to predict the impact of disruptive technologies on their industry.