Shhh! The robots are moving back home to software! (Not that they ever left.) Concepts forged in the IoT will become part of every other system. Digital components are easily connected and share a multitude of underlying principles, so concepts quickly move between unrelated disciplines, and all related technologies tend to converge.

While considering the IoT we have looked at autonomy, and what is required for devices such as automobiles and industrial robots to operate safely on their own, coordinated with other devices and working with humans (Autonomy INFOGRAPHIC, Challenges of Autonomy: The Video, Where the AI Rubber Hits the NHTSA Road: Letter to GM Describes State of Autonomy). We have reviewed the special challenges of autonomy, and how they are being solved to create efficient and effective systems. These capabilities are now destined to enrich other areas.

A key issue is how to apply this learning to business processes themselves. Autonomous robots and vehicles are extensions of digital processes. These processes are defined by software and engage numerous other systems toward the performance of a given end.

The development of an autonomous device requires layer upon layer of intelligence performance (see The Composite Nature of Embedded Cognition). Devices must be able to sense the activities surrounding them; they must have the ability to interact with their surroundings; and they must be able to provide a wide variety of actions that can be flexibly fitted to the accomplishment of a mission. All of these details might also be applied to strictly software systems.

A cognitive approach does not simply glue artificial intelligence to existing processes with the assumption that AI will provide the required result. Instead, we will see multiple AI systems used in conjunction with each other to perfect and deliver software solutions. Each routine will access a range of sensor and processing capabilities which will offer autonomy at the process level. Autonomous agents—software systems capable of specific actions in a larger whole—will then perform their functions as needed to achieve a desired end result. This is in line with a growing understanding of the composite nature of artificial intelligence. It also demands new forms of orchestration and new ways of providing AI capability.

An autonomous agent business process management solution will be able to sense when processes are required rather responding to a fixed call within another software program. This means that processes will anticipate requirements and act early to create an efficient solution. They will act with project managers understanding of when specific data or tasks need to be accomplished. Autonomous agents will be able to interact with other programs and bring a catalog of analytic, machine learning, predictive, and sensor-driven capabilities. This range of functional autonomy will need brokerage, data sharing, and orchestration. A collaborative framework will be required to ensure that the components do not block each other and that the priority of specific tasks is respected.

With an autonomous agent-based process management solution, response in a complex environment will be much faster and more effective than with a fixed system. Similarly, the cognitive capabilities of such a composite system would likely create new possibilities in overall management and furtherance of larger goals. It would become possible to orchestrate all business processes and micromanage them on an atomic level through ability to immediately activate an autonomous response from coordinated process components.

The further development of digital business, AI, autonomy, and cloud computing all tend in the direction of componentized autonomous agents. However, if we look for a timeframe, this will occur well in the future. We are now at the stage of integrating tiny amounts of AI in small and disparate processes. Robotics are merely at the edge of achieving true autonomy. And the processes of orchestration and synchronization of vast independence and coordinated autonomous systems is at the moment beyond our grasp.

However, it is important to understand that in a digital business environment, all of the advances that are made in one field filter with little delay into all other sectors. As we develop cognition and autonomy for robotics and vehicles, these same processes become available to programs of recruitment, sales, finance, manufacturing, medicine, and everything else. We are moving into not only a artificial intelligence – driven world, but a composite artificial intelligence driven world. The capability of developing layer upon layer of such cognition will create field effects that will ultimately change the nature of the combined process. Just as the human mind is entirely different from the neural mechanism of a single cell, the enormous multilayered possibilities of a galaxy of autonomous agents creates a subtly new system whose capabilities cannot yet be adequately explored.

We are just at the beginning of this change, and the marketers are fierce in describing their products as the apex of this evolution. But we are nowhere near the ability to fully comprehend the requirements, capabilities, and consequences of such a cognitive software environment.

For business, taking a more complex view of AI in the enterprise is mandatory. The effects will require a shift in strategy. Software vendors will need to understand the subtle ways in which their programs will need to interact. This is a long term movement, but preparing for it must begin now.