In this installment of the Cutting Edge, I’m going to suggest a big picture hypothesis for why edge computing will matter for businesses in the future while attempting to avoid hyperbolizing the possibilities that often are the outcome of such exercises. Hopefully, I can get your creative juices flowing with some inspirational perspectives on edge computing and how it can help you rethink process automation and operations optimization.
Extending the Digital Business Maturity Curve
In business circles, it is common to hear strategy consultants, analysts, and academics pontificate that companies need to become a “digital business” to compete and survive in this age of “digital”.
So, when and how did all this digital business mania begin? Gartner postulated back in 2013 that the Nexus of Forces (mobile computing, big data, cloud computing, and social networking) was enabling organizations to develop powerful mobile online channels to reach new customers, drive deeper relationships, and optimize interactions with the insights provided by big data analytics performed in the cloud.
Digital business was proposed as a state of maturity achieved in a post Nexus of Forces era. The thinking was hot technology topics of the day such as the Internet of Things (IoT) and “smart machines” would enable enterprises to build new business models that create “new value and new nonhuman customers”.
Arguably, Gartner’s customer-centric view on the evolution of digital businesses didn’t quite pan out as expected. Instead of relationships with things that are customers, we have things that are constantly marketing and selling to us. Let’s face it, replenishment orders are still old-school ERP supply chain functions of what Gartner dubbed the “analog” era. Consumer and enterprise purchasing is still predominantly done by people. Rarely, if ever, do we sell to a robot or a smart thing.
If we were to flashback to 2013, artificial intelligence (AI) was not yet a thing. Gartner took a conservative stance on AI given all the past predictions that it would go mainstream that didn’t pan out. Rather than hype the prospects of AI, Gartner coined the term “smart machines”. At the time, it represented their cautious acknowledgment that AI applications continued to progress but not outside of the realm of high-performance computing (HPC) in data centers.
In the last four years, AI at the “edge” has reached an inflection point with Edge AI across many endpoint device categories thanks largely to the smartphone and the introduction of “neural engines” on SoC, and ML frameworks and tools. Today, we can find AI applications on the smallest of consumer and industrial devices. A great and relatable example is the adaptive noise canceling featured in many true wireless earbuds.
Moreover, the mainstream adoption of containers and serverless computing in conjunction with 5G connectivity and MEC (Multi-Access Edge Computing) is poised to bring about a continuum of compute between the endpoint devices that populate the edge and hyperscale data centers. This is the edge cloud that I always harp about.
These are technology inflection points that were not so apparent back in 2013.
The Autonomous Enterprise
Beyond the “digital business” phase, Gartner suggested an aspirational state which they called “autonomous”. I would argue that we are at the cusp of the era of autonomous today, but not exactly as Gartner saw it back in 2013. I have dubbed the phase beyond digital business the “autonomous enterprise” which is coming about due to the post-Nexus of Forces technology megatrend forming at the intersect of AI, cloud computing, and 5G/6G, and other advanced wireless communications technologies of today and the future. I call this new Nexus of Forces the “5G Autonomous Edge”.
So, what are the implications of the 5G Autonomous Edge? I see the convergence of 5G, cloud computing, and AI to bring about edge cloud computing, the ubiquity of AI computing resources across the edge cloud continuum and endpoint devices, and an autonomous compute and network (converge IT/CT) infrastructure that will support new edge computing models that were not possible before technically or economically.
Simply put, businesses and consumers will be able to build intelligent control and orchestration systems and applications that reduce or eliminate the manual execution and management of processes and functions in an operating environment. For enterprises, these environments can be a store location, a factory floor, a distribution center, or a delivery truck. For consumers, the environment can be the home, the car, or one’s own body.
The primary outcome of the operations of an autonomous enterprise is adaptable automation instead of the hardwired automation of processes and functions of the past. This means autonomous systems in the field (the edge) can adjust their operation in an optimal and/or self-correcting way in response to changes in the business environment. This is the aspirational benefit of intelligent closed-loop automation powered by AI that I talked about in my previous article on critical automation.
The Autonomous Mindset Shift
For business constituents, nurturing an autonomous enterprise involves viewing process automation in a more progressive way that assumes AI will enable autonomous operations across the edge of your business. Even before this first shift in perspective toward autonomous-at-the-edge thinking, business leaders need to think of AI as more than analytics and insights about the business. The autonomous enterprise takes insights and automates a business process or operational function through the decision-making and actuation or control cycles. In other words, you are going closed loop with your automation.
Much of the new thinking regarding autonomous process automation will stem from real-time critical automation in mobile and remote contexts made possible with edge computing and 5G. For this reason, business leaders should contemplate the implications of the 5G Autonomous Edge technologies in enabling new customer experiences and business capabilities that are high performance, and situationally and contextually aware. While this kind of thinking about automation may not be entirely novel in some industries such as auto manufacturing, it will be a significant exploration for many others, most notably retail.
With all this talk of automation, the notion of an autonomous enterprise might seem dystopian. What will the role of people be in such an organization? Will there be a role for people at all?
Autonomous automation can also mean workforce augmentation. In other words, enabling autonomous endpoint devices, whether they are intelligent connected robots or a field service chatbot, to support people in your organization in the execution of their job roles and functions. Business leaders can also consider how autonomous systems at the edge can help in the coordination of people around tasks and projects to enhance productivity and outcomes, and continuously optimize orchestration of operational activities and tasks with and around people and smart machines.
Hopefully, that wasn’t too fluffy. At the least, I hope the article helped a few pioneering business leaders contemplate what comes after digital business as they chart out the next phase of their digital transformation journey. As always, I welcome your thoughts and reaction to this concept of autonomous enterprise and its implications on the way we need to think about business automation.
Looking for real-world insights into AI? Subscribe to the Enterprise AI Impact channel: