Pragmatic Business Transformation with AI

I interact with numerous data scientists and people in the data science space on LinkedIn on a daily basis. Many of these have insightful things to say, about how data and artificial intelligence are transforming the business landscape. There is a certain alarmism in the context of the automation of business processes, that accompanies every discussion on artificial intelligence, and with good reason. One of these is Vin Vashishta, whose posts often address pressing challenges in data and AI. Here is a recent post by Vin and my comment. This blog post was originally on Medium, and is an expansion of the ideas represented by the comment.

Traditional Thinking Couches

Traditional thinking about how work gets done, in general has the following elements. Traditional work and time based thinking is based on scientific reductionism and paradigms such as linearity. In truth, this thinking has allowed us to come very far. The division of labour is the very basis of capitalism, for instance, and modern capitalism thrives on specialization and the management of work in this form.

  1. Linearity: The tendency to think of all work as ultimately reducible into linearly scalable chunks. Less of a task requires less resources, whereas more work requires more resources. To be fair, this kind of thinking has been around for millennia, since at least the time of human settlement and the neolithic age.
  2. Reducibility: This is a tendency to think of work as infinitely reducible, in such a way that if we complete each sub-task of a job in a certain sequence, we have the end result of completing the whole job. Systems engineers know better, and understand holism and reductionism in systems as analogies to the traditional view of reducibility and how it might affect the way we see work today
  3. Value-based Work and Tangibility: Another element of what seems to define work traditionally is the presence of tangible objectives, such as items shipped, or certain unambiguously measurable criteria met. In this world, giving a customer a good experience when they shop, or enabling customers or partners to better be served or serve us better, aren’t seen as value, but as non-value-added activities. For a long time, approaches to business transformation focused on the reduction of non-value-add activities from business process, with the view that this will improve process efficiency.

When we think about how businesses will take up AI and machine learning capabilities, we’re compelled to think in terms of the same above lenses. They’re comfortable couches that we cannot get out of, and as a result, possess and dominate our thinking about AI deployment in enterprises.

AI-Specific Cognitive Biases

Some dangers of thinking driven by the above principles are as follows:

  1. Zero-sum automation: The belief that there is a fixed pie of opportunity, and that when we give human jobs to machines, we deprive humans of opportunities. Naturally, this is not true, because general, self-organizing intelligences such as humans are more than capable of discovering and finding new opportunities. Fixed-pie thinking is probably one of the key reasons behind AI alarmism. I would additionally argue that at some level, AI alarmism is also the result of bogeyman thinking, a paradigm in which a strawman such as AI is assigned blame for large scale change. In the past, a lot technological progress and change happened without such bogeymen, even as other changes were being prevented because of such thinking. Another element of bogeyman thinking is the tendency to ignore complementarity, including situations where humans and AI tools could work alongside each other, resulting in higher process effectiveness.
  2. Value bias: While there is truth to the notion that processes have value-add steps and non-value-add steps, it is a feature typical of reductionism to assume that we don’t need the non-value-add steps at all, while they may be serving true purpose. For instance, all manufacturing processes that transform raw material to product have ended up requiring quality checks and assurance. As a feature of the evolution of industrial production processes, quality assurance and control have become part of nearly all manufacturing processes that operate at scale. QA and QC represent a non-linearity in the production system, or a feedback loop which provides downstream process performance information to upstream processes.
  3. Exclusivity: A flip side of bogeyman thinking, combined with value bias, is the phenomenon of exclusivity. For example, the interpretation of emotional expressions on a human face, has for long been a task that humans are great at — for long, we didn’t know of any higher animals, let alone technologies, that had this level of sophistication. Now, there’s a lot going on in the ML/AI space that has to do with the so-called soft aspects of human life — judging people’s expressions and understanding them, learning about their behavioural patterns, etc., and these capabilities are becoming more and more mature within AI systems on a regular basis. This contradicts traditional notions of human-exclusive capabilities in many areas. Naturally, this is seen as a threat, rather than a capability enhancer. The truth is that exclusivity is also to be considered a logical fallacy when discussing the development of AI systems.

It is common for one to fear he who seems to do everything that one can do, until that person becomes one’s friend. I’d say that the word is still out on what AI cannot do yet — and as a result, our approach to business transformation (as with transformation in other areas) should be humans + AI, and not AI in lieu of humans. This synergy is already visible in the manufacturing world, and perhaps we will see it make its way to other spheres as well. Fixed-pie thinking won’t get us anywhere when we have capability amplifiers like AI to assist humans.

Concluding Remarks

A key element of future human productivity is the discovery and exploitation of new opportunities in new frontiers. My suggestion to business leaders thinking about AI adoption for automation and process improvement, is to expand the pie first, by creating new opportunities to do more as a business, and enable your employees to take up and contribute more to your business. When you then enable them with AI, the humans+AI combination you will see as a result will take your organization to new heights.

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)