Most strategic plans assume a static future where the basic costs and use of labor remain the same. That is a critically false assumption.
Machine learning started in the 1990s. Anthony Goldbloom states that machines were trained to look at credit applications and determine a score. Since then the trend line was arithmetic but recently has climbed more steeply. Now we actually have cars on the road which are one step from being self driving.
The rapid dislocation of labor from human to machine is one major factor in the unusual American election. It’s a global challenge on how to make all humans necessary. At this point, the economy does not need grocery checkers, cab drivers, subway drivers, live telephone reception, farm workers, and a host of other tasks which have been human based for time immemorial.
Will your Strategic Plan survive the onslaught? Only those that account for machine learning and avoid that competition will be here in 15 years.
Machine learning requires tasks which are high volume and have limited causal factors.
- High Volume – Machine learning thrives in high volume. For example, one study had volume reports on police in Philadelphia and teachers. The goal was to hire police officers who are productive and not violent and to promote only worthy teachers (Chalfin et al. 2016). Since the variability is significant between exceptional performing teachers and future failures, the machine learning was able to discern patterns which would predict correctly in both scenarios – better, more productive police and teachers.
- Limited Causation – checking out groceries involves a group of products with bar codes, passing the scanner, settling the price and payment. Presumable bagging will be computerized too. The causation for the checkout system is simple – a consumer wants to process items one at a time and pay for the results and take them from the store.
Machine learning is not effective in novel situations, or with creativity, or with relationships
- Novel – Novel situations occur where original reasoning trumps learning. For example, some of the best American scholarship looks at problems from the perspective of 2 disciplines. Sociobiology looks at sociological problems and traces evolutionary causes. It’s a terrible discipline for machine learning at this stage. There is not a high volume of reports from which to learn.
- Creative – Machine learning will never produce the creativity of Mozart, bell hooks, or Monet. Creativity is a unique human fountain that never runs dry, While machine learning can produce copies of the Mona Lisa, it cannot jump ahead and create a new Mona Lisa. It’s work will always be derivative.
- Relational – So much of business success depends on 8 socio emotional skills
- Goal persistence
- Awareness of others
- Awareness of Self
- Optimistic Thinking
- Decision Making
- Relationship skills
- Self Management
- Personal Responsibility
Those skills work together to build teams, invent new solutions to business problems, and consider unique value propositions based on available resources.
So how does your Strategic Plan match up?
Test your vision based on the strengths and weakness of machine learning.
- Are you planning to manufacture a product without investing in robotics? It’s likely that robotics locally with lower transportation costs will be cheaper than cheap human labor in other locations.
- Is there new infrastructure planned in an area that you exploited? NYC plans to go green in 14 years. Any business that moves quickly can compete with old energy guzzlers that don’t see the problem coming.
- Did you create a unique value proposition that uses relationship skills for success? Edith Penrose says that effective teams become the real competitive edge of a company.
The coming and current dislocation will be a time for some Strategic Plans to gain the advantage and many others will collapse. It may be an opportunity for a start up to move shrewdly where a larger company could not change.
Is your Strategic Plan going to be terminated?
Bloom, Anthony. “Machine+learning+ted+talk”. TED Talks, n.d. Web. 14 Aug. 2016.
Chalfin, Aaron, Oren Danieli, Andrew Hillis, Zubin Jelveh, Michael Luca, Jens Ludwig, and Sendhil Mullainathan. “Productivity and Selection of Human Capital with Machine Learning†.” American Economic Review 106.5 (2016): 124-27. Web. <https://econ.tau.ac.il/sites/economy.tau.ac.il/files/media_server/Economics/PDF/seminars2015/AER%20picking%20people%20ML%2020151228_final%20complete.pdf>.
Nickerson, Amanda B., and Callen Fishman. “Convergent and Divergent Validity of the Devereux Student Strengths Assessment.” School Psychology Quarterly 24.1 (2009): 48-59. Web.
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