A lot of blogs and writers are discussing the role of machines vs. humans and their relationship, especially in the context of supply chain management. Scenarios are being painted of computers operating in a real-time environment, using blockchain to process transactions, relying on Internet of Things to order goods and services, which are delivered by drones to consumers homes. The reality of this scenario is far fetched indeed, primarily because futurists are dramatically underestimating the degree to which people organizations must change to deal with these technologies. A similar level of euphoria existed 17 years ago, when people started playing against computers in chess. Some readers may recall the level of discomfort when computers were playing against and beating Grand Chess Masters in the 1990-2000 period, described by Gary Kasparov in this book “Deep Thinking”.
“The growth of machines from chess beginners to Grandmasters is also a progression that is being repeated by countless AI projects around the world. AI products tend to evolve from laughably weak to interesting but feeble, then to artificial but useful, and finally to transcendent and superior to humans…Overestimating the potential upside of every new sign of tech progress is as common as downplaying the downsides. It’s easy to let our imaginations run wild with how any new development is going to change everything practically overnight. Human nature is simply out of sync with the nature of technological development. We see progress as linear, a straight line of improvement. In reality, this is only true with mature technologies that have been developed and deployed. We expect linear progress, but what we get are years of setbacks and maturation. Then the right technologies combine or a critical mass is reached and boom, it takes off vertically for a while until it reaches the mature phase and levels off.”
Kasparov also notes that it is important to recognize the role that machines have in AI, and the importance of humans “in the loop”, through the use of “Moravec’s paradox.
“In 1988 the roboticist Hans Moravec wrote, “It is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility. Computers are very good at chess calculation, which is the part humans have the most trouble with. Computers are poor at recognizing patterns and making analogical evaluations, a human strength.”
Or as Bill Gates stated in his axiom:
“We alway overestimate the change that will occur in the next two years and underestimate the change that will occur in the next ten.”
These insights suggest that change management is not just option, but truly imperative in a period when so many new technologies are coming on-line. Waiting for something to happen will be result in failure to adapt, and ultimately, to extinction.
A central issue that emerged from discussions I’ve had with executives in the last month is the importance of the three big shifts occurring in terms of defining organizational governance, establishing the talent requirements for the change, and building the right level of trust between enterprises to share data. Concern by executives is expressed around how to socialize these changes, the communication strategy for the digital economy, and getting people to adapt. The intent is to engage people to share “what decisions could you make if you had this specific information?” as a means to identifying how to design not only the user interfaces, but the timeliness and specific types of data required for these decisions. Being able to see what “experts” are doing in terms of “best practices” can help establish the essential features of the data platforms and interface mechanisms.
Another important element is how to incent suppliers to be honest and share their issues. How can you trust them to trigger orders, without having to ask them and check on them? How to reward people who go the extra mile, and using smart contracts through emerging technologies such as block chain? These and many other questions are more interesting to consider then how fast the technology will be adopted.
The cultural transformation that will accompany these changes will be significant. Using the example of driving and “trusting” the Waze app to take one down the right street to avoid traffic, participants noted that Deere managers will have a hard time trusting new systems and follow their guidance. The change management portion of analytics will be a bigger inhibitor than the software and hardware changes. This needs to be explicitly accounted for in developing talent strategies.