The human capability to under-or overestimate
The opposite can be true also, where we overestimate humans. Indeed, the history of KM technologies is a case study in overestimating humans. For example, expecting workers to add essential metadata to documents manually and then file and sort them in the right place at the right time. No humans, other than the most pedantic and obsessive, will ever do that work willingly and, even under duress, will seldom do it regularly or accurately. It’s little wonder that KM systems today, from Microsoft Syntex, Sinequa, Guru, and beyond, try to automate the addition of metadata to files through AI. It’s not simply the fact that the sheer volumes of information that flow through organizations today are too large for humans to deal with; it’s equally the fact that they would not do the work even if they were able to.
Yet maybe the most glaring example of underestimating humans we encounter in our work is in the world of AI. It’s partly the term “intelligence” in AI that misleads so many, as AI is not intelligent in the same way that humans are intelligent. Though powerful, AI ultimately matches patterns it has learned, and even the smartest of AI systems is limited in how many patterns it can match and make sense of. An AI system that can read the words in a contract is of zero use when spotting patterns that indicate a common cold. On the flip side, humans have a near-unlimited ability to spot patterns.
Moreover, humans can select and pick which patterns to use effectively in whatever situation. In practical KM and information and automation management terms, we should always be aware that there is more going on when a human is involved in a work activity than meets the eye. Increasingly we see that reality clash when AI is implemented in organizations. Super smart AI is brought in to automate, or even in the popular parlance, “hyper automate,” a business process only to discover later an activity that seemed straightforward was incredibly complex and poorly understood. The complex processing in the business activity was not revealed in the business analysis of the tools, procedures, and data; rather, the complex processing was undertaken in the head of an experienced human worker.
You might be surprised how often we meet with a well-funded, technically advanced startup that has developed impressive technology and is set to change the world. There are a lot of them about; even in a recession there is a mountain of money in the tech world being spent on developing technology to automate work. However, all too often, we spot the Achilles’ heel of these startups: the fact that they have under-or occasionally overes- timated the value of humans in the workplace. What can seem near perfect in a development environment or on a whiteboard can hit a wall the moment it enters an actual workplace.