Software Eats World: Autonomous Agents in Business Process Management (BPM)

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.

The Composite Nature of Embedded Cognition

As cognitive approaches become embedded in business processes and devices they will become indispensable. Processes modeled upon thought will simply become part of the landscape of helper applications expected in the digital environment. We have already seen discrete intelligent processes becoming commonplace and even expected. Examples include spell checking and grammar checking in documents; machine translation in webpages and search results; packet filtering in communications; and personal photo enhancement. These technologies offer useful services. The underlying mechanism is often complex but, as with an automobile or any other advanced machine, the actual mechanics need not be known to the user.

Artificial intelligence even in its most profound forms is likely to follow this same path. We can expect smart processes to be brought together to make operations more efficient, friendlier, or easier to use. Embedded artificial intelligence is likely to become increasingly important.

Local intelligence, or the ability to make choices automatically at the individual operation level will become normal. When intelligence is embedded at the process level, many functions that might have required expert intervention, or paused for user selection, will be routinely and automatically performed. Leading the way will be further development of the user interface. This is the most visible element of IBM’s Watson Analytics program; the ability to understand and respond to natural language requests, framing those requests in a way that they can be submitted to databases and analytics to produce a guided result. The most advanced intelligence need not be in the assembly, analysis, or predictive capabilities of big data; but, rather, in the understanding of the request itself and ability to formulate an adequate response.

Embedded cognition provides a new range of challenges and opportunities. Its most visible impact is in robotics and in Industry 4.0. It will be inherently important to all autonomous systems. Embedded intelligence will not be online all the time, nor will it be capable of being adapted or updated on a real-time basis. This means it must be secure. Autonomous intelligence must also cooperate and communicate with other systems. Standardization will make the routines and operations in this category into commodity parts to be assembled in creating a greater whole. Any machine which has complex parts today demonstrates how this will work. Automobiles have transmissions whose technology may be swapped between vehicles. Principles of operations remain the same. Multiple complex parts may be assembled to create a machine of the next level of complexity. People will interact with this environment through personal devices as well as through monitoring of their own behavior.

In looking at embedded cognition it is already apparent that there are vast differences among the various smart processes being embedded in processes and devices. Some are for visual recognition and perception; some are for speech recognition; some are for decision-making under uncertainty; and some are for translation between languages. While similar principles are involved their specific requirements are not the same. Each defines a particular utility which may be brought together with other smart components to create a more complex machine. Embedded natural language processing can be linked to big data analysis to provide answers to questions; machine learning and pattern recognition can be applied separately to issues such as fraud. Pattern recognition capabilities can be embedded in equipment used to search for cellular components, or symptoms of a disease. They may become a part of an appliance which includes artificial intelligence in the same way that it includes the use of electricity. The AI capability is simply applied at the level where it is required.

While artificial intelligence does not presume to replace the human brain, it does provide a next level of flexibility and machine control, making it possible to respond to an evolving matrix of real time data. This can provide a finer and more efficient guidance of individual processes than might be available with manual or semi automated controls.

The applications of embedded cognition in industry and robotics are patently obvious. But it is significant that this technology will be available piecemeal and often taken for granted. Already we see elements of this appearing around the home with intelligent control mechanisms and voice-actuated modules capable of understanding and responding to limited commands. These commands might go out to a mechanism such as a multimedia device which itself contains intelligence for responding to the mood of the user. Or, it might query the refrigerator for information on user behavior that can be used to produce shopping lists or provide guidance in the purchase of food.

All of this is in a nascent state of development. The key issue is that artificial intelligence will develop for specific tasks and these tasks can then be combined in a modular fashion to create a broader and more effective process or mechanism based upon cognition in all components. Assembling such a composite appliance or process becomes a matter of orchestration—which may itself be handled by an intelligent machine.

The ability to create innumerable separate “smart” processes will act like algorithms in programming. Once established, they will become part of the language of a cognitive device universe. Combining these components will lead to the ability to create unique mechanisms with specific intelligence that will bring machine capability to a higher level of complexity and effectiveness. The beginnings of this are visible in autonomous social robots.

Companies need to monitor development of the building blocks of cognitive processes, and understand the growing capabilities. There will be no artificial brain, but there will be many tiny interactive minds that could create unforeseeable consequences as different capacities are drawn together. This will have implications for Security,a s well as for the future development of the IoT.

AI in the Enterprise: New Skills Required

As we move into the post big data era of cognitive computing and artificial intelligence, the shortage of high level analytics skills is likely to become more pronounced. (See The Big Data Skills Gap INFOGRAPHIC.) While some of the shortage issues are now being resolved, the world of artificial intelligence demands new skills and greater availability as AI becomes integrated into the process environment.

In a world in which processes of all types have some level of AI attached, whether as part of the user interface or as part of the operation itself, it becomes necessary to have more employees who understand the underpinnings of this technology. When the autonomous car breaks down, who are you going to call? While big data provides complex analysis of extraordinarily large data sets, the predictive nature and model creation requirements of deep learning and other machine learning techniques will make many operations appear like “black boxes.” Software could make and act on unforeseen decisions that could be detrimental to the business. Understanding the mechanics of artificial intelligence will become as important as understanding the basic operation an automobile engine was in the 20th century.

Unforseen Consequences of AI: A Car Hacking
Unforeseen Consequences of AI: A Car Hacking

AI training and education, must become a priority. The demand for skills is likely to be explosive due to ubiquitous use of these new technologies. Luckily, this is matched by significant changes in corporate training and external education programs. We are moving to a time of continuous learning. New courses related to analytics and advanced artificial intelligence are appearing almost daily. These range from specialty courses provided by vendors of analytics software such as IBM and SAS, to MOOCs and online courses supplementing what is available from conventional universities and academic institutions.

While courses are becoming available, training decisions are not as simple as they might appear. The level of prerequisite knowledge for advancing into machine learning and predictive analytics can present a taller hurdle than entering into conventional big data analytics or expanded use of statistics. Moreover, the prerequisites can easily change and the concentration of classes and courses can alter as the development of these disciplines continues to advance.

Courses are available in programs for certificates and degrees, as well as on a course by course basis. Examples include Deep Learning, from; the Machine Learning Engineer Nanodegree from Udacity and the Stanford Machine Learning course on Coursera, with Andrew Ng. Note that many analytics courses deal with the machine learning side of things as part of a wider discussion of analytics and programming. This is natural due to the close integration between these topics and the need to know and understand the basics of other approaches before being able to grasp the essentials of a cognitive approach. (See Machine Learning: The Video for a few examples.)

For enterprises and HR departments, there will always be the daunting question of whether to hire new workers in these skills with freshly minted degrees and specialty subjects; or to train employees already at work in related disciplines. Hiring provides immediate skills without experience with the enterprise and its systems; training takes an individual off-line for the duration of the training, but can provide a better quality result by adding skills to existing understandings of the workplace in which the new learning is employed.

What is clear is that the upcoming age out of next generation artificial intelligence requires a next generation of artificial intelligence.