Much has been written about Human Capital, especially in the Information Age. More so in fact, as the Information Age has evolved into the Internet Age, in which the innovation in products and services has been tremendous and has disrupted tried and tested business models all around the world. Organizations universally hail their people as their number one asset, and in many progressive sectors, these organizations spend billions on the recruitment and nurturing of this asset. Talented humans, as the logic goes, are the source of innovation, advantage, and organizational greatness.
With this logic, many sectors of the economy have moved ahead with remarkable speed. Technology, Healthcare, and Finance are all areas in which the intensive investment in Human Capital has yielded enormous fruit in terms of growth, profit, and innovative advancement. Unwilling to be left behind, “traditional” sectors like Manufacturing, Energy, and Transportation, too, have adopted similar attitudes, programs, and processes to ensure that they optimize their benefit from the maximal use of Human Capital, both internal and external to the organization.
Productivity and Creativity are the keys to this connection between “people-power” and business success. Both of these elements are essential to the development of a progressive, “learning” organization that is able to create and capitalize on new ideas, new trends, and new offerings. It is here, in the combination of two seemingly disconnected or even antagonistic factors, that the conundrum lies: how does an organization manage to both maximize “output” and to pivot creatively? In the common conception, the former is about operational efficiency, and the latter is about the artistic license; these two ideas can be in conflict, but great organizations create a space in which both are possible, and—you guessed it—technology plays a huge role in this harmonization.
With regard to Productivity which, put in a different lexicon, implies the ability to produce outcomes at scale, the recent adoption of Machine Learning offers enormous potential. Beneath the technical surface, Machine Learning implies both intelligence and automation powered by data and prediction. In this way, ML allows for intelligent systems in which agility and automation create speed and scale at a heretofore unimaginable magnitude. Advances in both software and hardware allow organizations to avail of the benefits of ML at a manageable cost; as such ML is far more widespread than it was even a year ago and is growing exponentially.
While ML is undergirded by Human Capital insofar as brilliant engineers and scientists are required to create “agility by algorithm,” many believe that it will undercut Human Capital because, frankly, it is too powerful a tool for intelligence and automation. Why do we need humans, they ask, if machines are adaptive and smart? The debate about the extent that which ML will either produce or reduce human capabilities and jobs is far beyond the scope of this piece, but suffice it to say that whatever one’s views on the matter, it is clear that the combination of ML and human creativity is the most potent.
This is true for a variety of reasons that underpin, the idea espoused at the beginning of this piece, namely that Human Capital and creativity are core sources of advantage. The argument for this is simple-yet-powerful.
First, ML itself is based on human ingenuity. While there are arguments that machines will soon pass the famous “Turing Test,” we are still not at the stage in which human creativity, with its infinite permutations, is replaceable by machines.
Second, given the infinitude not only of ideation and creativity but of consumer behavior, the acts of “human understanding and empathy” are not matchable by machines. Given that most businesses require products and services that humans, with their seemingly infinite frailties, variations in taste, and temporal behavioral changes, have to buy, the human-to-human connection prevails as the most important source of inspiration. Creativity and empathy provide the motive force, while ML creates the scale and speed required in modern enterprise. Both are necessary while neither is sufficient.
Third, much of business is built on relationships and not optimal transactions. This point requires no explanation.
Fourth, since presumably, ML is an “equalizer” insofar as any relatively solvent organization can pay for platforms that are ML-enabled, people are the differentiator, not systems.
Great organizations emphasize and laud Human Capital but also provide humans with the tools required for massive operational success. ML is just such a tool and when put in the hands of great teams, is the source of sustainable advantage.
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