Is Machine versus Human a suitable sense-making framework for AI?

AI (artificial intelligence) including derivatives such as RPA (robotic process automation) have become increasingly popular, at least within conversations by the growing and growing ‘in crowd’.

AI is a generic term with an incredibly broad and deep subject matter, representing a diverse range of capabilities and applications.

AI is so topical that it is now entrenched as a mainstream conversation. Everyone seems to have an opinion.

The AI in-crowd are often promoting digital transformation, which is another term that is now so overworked it is simply mixing mediocracy into the blend of transformation. This is not helping the genuine transformative opportunities for AI.   

Another nuance in the way AI is communicated is the discussion around Machine versus Human.

Not surprisingly, AI has been met with a growing momentum of cultural resistance and scepticism, sometimes with good justification. The arguments and concerns are growing, which are not being helped by increasing levels of miscommunication and misunderstandings. The Machine versus Human argument often leads to the belief that lots of jobs will disappear and a smaller amount of new jobs will appear. To make matters worse, often both sides agree on this point!

It is important we all consider whether the Machine versus Human sense making framework is fit for purpose.

Though the technology industry has not been around for a long time, the Machine versus Human argument has happened several times before as wave after wave of disruptive technologies emerge. Yet, more people are working today around the globe than ever before.

It is worth considering some lessons from the past that may help the way we look at things today.

During the 1970/80s, transformation was being led by mainframe-based enterprise applications, which used automation to materially reduce headcount, especially in the back-office.  Indeed, in those days the ROI for disruption through automation was easy to justify.

During this period, the ‘hype’ experts even predicted we humans would need to work less and less, and spend more time having fun and going on holidays. These well-paid futurists were so wrong as in retrospect we are working longer and longer hours ... so much so that time is now our most scare resource.

During this disruptive and transformative period with many jobs being lost something else was happening that created more and more jobs.  The automation enabled organisations to materially reduce the time and costs to bring new products to market.  This shift enabled more and more variety of products and started a journey from mass production to mass customisation, which is still underway.

One of the unintended consequences for reducing the time to market led to increasing number of deviations that needed to be tackled outside the boundaries of the applications. In turn, this has led to a growing number of niche Subject Matter Experts (SMEs). Niche is the operative word! The parallel advancements of personal computing enabled workarounds to handle the exceptions, which led to an explosive growth of knowledge workers.

AI applications bring new types of automation. Like their predecessors, each AI Application is bounded by data and processing capabilities. This bounded knowledge determines both the flexibility and limitations of the automation. Outside this bounded knowledge are humans, who will be dealing with the consequences of the AI automation. As in the past, as change accelerates, the more exceptions and opportunities are generated leading to more and more knowledge work.  

This means the AI sense making framework should shift from Machine versus Human to Machines and Humans working together in a world where constant change is the modus operandi. In this world Chatbots, will help humans with ‘instant upskilling’ to be truly agile and adaptive.   

A Machine and Human sense-making framework does require a holistic solution to be designed and not just focus on the AI automation.