Contextualizing “AI Alarmism” in Business Process Automation

Alarmist speculations about Artificial Intelligence are everywhere these days. Business managers in labour-intensive markets such as India and China have, in recent months, come to fear data-driven process automation, often unfairly and unnecessarily. In this post, I wish to discuss some of the AI alarmism we see in the general public at large – ranging from well-founded speculation to the truly ridiculous. I will also present two mental models that may illustrate the usefulness of AI in process automation, before we arrive at how to contextualize AI-based automation.

Some Contours of AI Alarmism

In the last several months, the media has been awash with articles about data-driven process automation made possible by artificial intelligence, that is said to be doing any of the following things (listed in order of increasing speculation craziness):

  1. Taking away our jobs and rendering vast sections of human society jobless
  2. Doing things that humans do better than humans do them, and thereby obviate the need for humans in certain very human activities
  3. As a panacea for all kinds of faults and frailties that make us human, and therefore a representation of the post-human world
  4. Killer robots that will wrest power from all of human society, thereby resulting in the standard-issue-technology-apocalypse that is the staple of Hollywood movies

It is important to assess the sources of these fears and speculations, if only to debunk some of this AI alarmism. It is also important to understand true challenges where they may exists and the threats in that context.

A Process View of AI-driven Automation

In the past several decades, we have seen numerous technology revolutions and their socio-cultural impact on human society. Whether the rise of computerised and robotic manufacturing processes, that led to the digitization of manufacturing, or the evolution of automation methods in the knowledge work space we’ve seen in the last decade or so, the fundamental drivers have been two fold – improved process performance, and increased process flexibility:

  1. A better process for delivering value
    1. Improved process quality and reduced variation
    2. Reduced process time and opportunities to continually improve
    3. Reduced process cost and opportunities to spread value within processes
  2. A more scalable and predictable process for delivering value

Given this broader process-based view of excellence for organizations and how managers look to new technology from an operational effectiveness standpoint, can we see automation driven by artificial intelligence in a new light?  For instance: how can we understand what AI specifically offers to the process automation ambit, and what this means for businesses? To understand this, let’s take a look at what AI solutions currently allow businesses to do:

  1. Automate embarrassingly simple processes in business processes that have true scale, that are based on well-defined rules, but which are subject to variation – and do so cost-effectively
  2. Automate somewhat complex processes which require some human intervention, but which are not mission-critical, and do so in businesses processes that have true scale

Now, let’s look at what AI based automation is not capable of accomplishing in its current state:

  1. Truly domain aware decision making, as an expert system that is aware of business context, and which can make holistic recommendations only possible by highly skilled experts
  2. Truly complex decision making that considers multiple factors in a non-formulaic or dynamic manner
  3. Tasks of moderate to high complexity to be performed in a business environment where the scale of the business isn’t large
Automation value add at scale
Automation and its effectiveness with business scale
Automation value add with complexity
Automation and its effectiveness with changing process complexity

As you can see above, cost-effective process automation is held back by the business case of it and its applicability at different business scales. This leads to an interesting cost-benefit value analysis. AI based process automation in businesses is most effective when there is true business scale, when the processes in question are either simple, or moderately complex.

Data and AI-Based Automation

There is yet another factor that could potentially affect how effective automation might be – and this is the availability of data from processes. The importance of data can be characterised in some key ways:

  1. A core enabler for artificially intelligent systems and applications is learning from data. Being able to learn from data implies that there is a need to use statistical techniques. This implies machine learning, statistical inference, time series modeling of data in real time, etc.
  2. Building domain-specific context and awareness within the application implies needing to use knowledge models, which are representations of the system’s domain, in the form of entities and relationships.
  3. A key consideration for an intelligent system is not only being able to learn from data in the domain, but also the ability to act on the domain. These domain actions can take many forms – from the machining and welding processes we see in robotic manufacturing systems, to computer programs that can generate instructions for writing other programs or instructions in data-intensive systems.
  4. A subset or enabling capability in this context, therefore, is the ability to collect and manage data of various kinds in scalable ways, and in real time.

Reasons for Alarmist Speculation

Given these mental models of process-centric and data-centric views of AI-driven automation, let’s take a step back, and look what what is fueling this speculation:

  1. Misunderstanding about what artificial intelligence is and what capabilities it entails, on the business process side or on the data analysis side
  2. The lack of an objective scale for measuring or understanding AI progress
  3. Oversimplification of even simple, old and established human-in-loop systems
  4. Gross oversimplification of complex, human-engineered, industrial systems
  5. Mass media speculation that rides on the latest and greatest technologies, and importantly,
  6. The unceasing tendency of tech reporters and media to both liken the future to science fiction, and to jump to visions of utterly glorious or utterly ghastly futures, rather than evaluating technologies and their impact realistically

Concluding Remarks: Contextualizing AI-based Automation

First off, it is important to recognize that not all AI-centric speculation is unfounded. I wish to call out not those who have legitimately raised alarms about the policies, economics or ethics implications surrounding AI-based process automation, but those who stretch the speculation to the realm of fantasy. It is near-impossible to replace humans for certain kinds of tasks, such as those explained above that are comprised of high complexity, and that are mission critical for businesses. It is also important to consider the true scale and business realities of enterprises when speculating on AI. To this end, we may have to ask questions around whether and how a firm may use AI, and whether they have a sufficiently strong business case. Not only should speculators, consultants and pundits use such thumb rules, but it behooves business leaders and managers to similarly understand their own businesses.

Further Reading

  1. “Impact of emerging technologies on employment and public policy”, by Darrell M. West, Brookings Institution (link)
  2. “How humans respond to robots: building public policy through good design”, by Heather Knight, Brookings Institution (link)
  3. “It is time to dispel the myths of automation”, Viktor Weber, on the World Economic Forum website (link)
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2 thoughts on “Contextualizing “AI Alarmism” in Business Process Automation

  1. I sense a definite gap between what AI/ML are and what the common person, worker or executive, understands them to be. It is absolutely important that that gap be closed to reduce irrational fear and enable rational decision making. That’s one task I’ve set for myself this year: to develop a functional understanding of AI/ML.

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    1. Shrikant, thanks for reading through and for your comment. Appreciate you visiting my blog.

      I believe all data-centric professions should understand the pros, cons and risks of AI and ML, especially the ones that involve product or service quality. Coming from a quality background myself, I believe that there is a good reason for quality professionals to skill up on AI/ML and also have perspectives relevant to their industries and businesses. There are many great resources out there on the subject, and I hope that in time, knowledge about AI/ML and wisdom about its use will spread through the quality community and other relevant communities too.

      Like

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