Digital decisioning – the automation of decision-making using some combination of decision modeling, business rules, machine learning, predictive analytics and AI algorithms – is relevant to every industry and any organization of reasonable size. In fact, there are several distinct use case areas for military (and supporting infrastructure) digital decisioning.
Perhaps the most widely discussed is the automation of decisions to deliver autonomous systems. Just as the Mayflower Autonomous Ship (MAS 400) showed how digital decisioning could create an autonomous vessel, the same approach could be used to build autonomous supply trucks, autonomous refueling, autonomous reconnaissance drones and much more. The issue of autonomous systems using weapons is a separate issue, one of ethics not technology, but the use cases for autonomous vehicles are many.
The military is also a very large organization. Like all large organizations it has a whole raft of back-office processes around its supply chain, training, and force management. Digitizing decision-making in these sorts of processes has proven able to make them more effective, quicker to execute and less wasteful while also reducing fraud risk. Applying digital decisioning here would allow the military to devote more of its resources to combat operations while sustaining the advanced logistical infrastructure that a modern military needs.
It is also possible to apply digital decisioning in the realm of decision support for long term, “big” decisions. At first glance, this seems contradictory – surely decision support is about helping people make decisions while digital decisioning is about building systems that “replace” human decision-makers?
In fact, let’s look at three ways digital decisioning can augment human decision-makers for these broader and more strategic decisions.
First, when we model these kinds of decisions out, we find that the big planning decision can be broken down. The top-level planning decision is dependent on many other decisions which in turn depend on more granular decisions. It turns out that a surprising number of these decisions can be automated, as they involve calculations, projections and classifications based on the available data. Using digital decisioning for this means that the human decision maker is presented with the answers they need to these decisions, not just more data.
Second, some of these more strategic decisions are informed by results aggregated from many more operational decisions. If these decisions are made consistently and in a way that captures good data about how the decision was made, then the planning decision will be well informed. Digital decisioning enables these decisions to be automated transparently so they can generate the data needed for planning.
Finally, these strategic decisions require a lot of what-if analysis. What if an autocratic leader decides to invade a neighboring country? What if we need to supply a significant percentage of our stocks of this equipment to someone else? What does this branch of the military need to do differently based on choices made by other ones? Automation of decisions about the consequences of choices enables effective simulation of those choices. New parameters or choices can be specified, and the automated engine is used to re-run all the downstream decisions to see how they play out. These automated decisions are not to be used in real life but instead represent “digital twins” of the decisions made by military officers to enable systematic assessment of the consequences of strategic decisions.
In each of these cases the use of digital decisioning is shifting the perspective from one of passive decision support to one of more active decision augmentation.
There are many other tailored use cases applicable to the military and government agencies – reach out today to discuss further.
Also if you are in the Mid-Atlantic region, we invite you to join us for an in-person event on this topic, click here to learn more.