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Origins of Innovations: Identifying Future Demand in Digital Farming

Innovations in digital farming come from nascent, and identified, needs. The processes by which needs are identified can be numerous, but, even more important, is that the validation of this need can be formalized. The processes used to validate need can serve to assist in the structuring of the need, the contextualization of the need, and the diffusion of the need. These aspects of the need will serve as a part of formal processes that intrinsically validate the need and simultaneously identify competitive solutions. These processes are important to the successful and economic launch, deployment, and exploitation of the developed solution: the innovation.


In this article, I will assist in this effort; to which we have attached the label, “The Commercialization of Technology”. Included are references to books, articles, and lectures on the identified subjects. These learning segments will further assist in providing formal context.

To start this initiative, even though there are numerous ways to identify needs in digital farming, a process for examining and identifying need is explored and familiar ways to look into the future and learn from the future are identified. In general, there are two ways to look into, or forecast, the future, but since the forecasting of the future is fundamentally impossible, what we will identify potential futures and form a method for identifying the required strategic decisions.

The first way to look into the future of digital farming is to take the past and to project it on its current trajectories into the future.  This is more often a tactical approach to forecasting because it tends to deal with incremental decisions about the present, and the here and now of the ecosystem. It is very much a supply side view that is based upon the ecosystem as it is and projecting the ecosystems ability to deliver this future.


The farther out into the future that we attempt to “see” the future, the less viable this vision of digital farming will be. This projection process says nothing in the ecosystem is changing and therefore tomorrow will be like today; or it will be more of the same. This process tends to be a very good forecaster of the near term and, therefore, the operational or tactical world. This future tends to be very supplier centric and is a supply side, or internal, view of the business and its markets. The innovations identified tend to be operational or transitional, and they only address the world of present markets and ecosystems.

The second method of “seeing the future” assumes the world is changing and it is impossible to predict the nature of that change. This method uses the world and all of the aspects of its ecosystem to examine the future in the form of scenarios of what might be. These scenarios are about the future ecosystem and the markets that this future represents; and based upon these new ecosystems, they are used to synthesize strategies that provide the most enabling markets and organizational responses.

Since there is no clear view of the future, this process strives to develop several potential futures. In the process of identifying these futures, the demand these futures reveal will provide a plethora of planning challenges. In some cases these futures are called scenarios, and in others they are just potentials; they can be full scenarios or just cameos of that future. This approach enables the organization to identify the immutables of its market and not only contextualize the future but look back on that future to identify the myriad of mutable milestones that lead to these futures. All of these mutable milestones have potential but none is being “predicted”. This approach enables the organization to planfully articulate potential responses from which the organization can make strategic decisions that trade off future resiliencies for future efficiencies.

Once the milestones of these futures are identified and alarmed, they will serve as warnings or alerts to the sets of pending futures identified in the scenarios. Most importantly these scenarios can be used to identify and point to strategic needs in the future world that will enable agility and resiliency. These strategic needs identify the future demands in the market, and these needs can be artfully developed to enable the organization to prepare and organize for some set of potentials in those futures. As a newer insight to this world, we can now see that this is a demand-side future; and, as such, it will pull us into the future. These strategic needs align with the emergence of the future, and the business can ensure those developments align with that emergence and enable the organization to be agile and flexible in planning and executing for that future.

These future scenarios assist in strategy and they provide the context for innovation and change. Once identified, the journey to innovation is more than half-way underway. A lot of time is spent in this initiative on the formulation and synthesis of the system as this step is the major contributor to the majority of the life cycle cost of the solution. The results of this front end effort will tend to lock in the costs for major portions of the solution.

Origin of Innovations

This section is shaped by the following books: “Proteus: Insights from 2020” by Michael Loescher, “Learning from the Future” by Fahey and Randall, “Art of the Long View” by Peter Schwartz, and “Paradigms: The Business of Discovering the Future” by Joel Arthur Barker.  This section also includes pieces of the “Knowtions” forecasting process and business model. This section is about exploring the origins of Innovations and the continuing evolution of the human need through scenario planning and development of problem insights. This segment looks into the future.

Forecasting, or future casting, is about the past pushing us into the future; and, simultaneously, it is about the future pulling us into the future. Often, forecasting builds the future out of the immutable insights, the drivers, in the market and develops the scenarios that enable these insights to highlight and illuminate the potentials for new innovations and markets. As often, the forecasts are about the tactical decisions required to efficiently execute the needs of today. Forecasts are never all right; but done properly and with sufficient breadth and diversity, they prepare us and assist us in planning and decision making. For our purposes, they also illuminate the human needs that drive innovation.

Historians have been able to place the past in some apparent logical constructs; putting the future in perspective has been done based upon our propensity to extrapolate the present and the past into the future. When we look at the future through the lens of past experiences and with the arrogance that the future is knowable, we set ourselves up not only for change, but also for surprise. However, we can use intangible but immutable metaphors, or drivers, to develop new observations about the future.

We can use these immutable metaphors and observations to articulate future scenarios that represent patterns in the future that we have not seen before; patterns that we cannot necessarily quantify. Logic which we use to analyze patterns is intrinsically reductionist, and it misses the synergy of an additive process that gives us new insights and scenarios; an intuition of the future. Metaphor the embodiment of intuition is often the first source of innovation. Our exploration of Innovation starts with identifying the metaphors of our problem area and mission.

In each of the cited references we develop the Insights for our problem set. In “Proteus, Learning from the Future” and “Art of the Long View”, these Insights are used to build scenarios. In “Knowtions” and “Paradigms”, these Insights are used directly as metaphors to identify new product ideation. These can then be evaluated, analyzed, and planned. One very exciting outcome, which gives this future cast its name, is that there is a gut level response to the ones that are real. These ideas, we know, are real. We know their value. And, the typical response is why didn’t we think of this before?