Changing Labor Dynamics and the Future of Technology

US based companies continue to grapple with labor costs and scarce skills.  This challenge does not seem to be abating but rather accelerating.  US workforce population is also aging now averaging 42.6 years old in 2012 as opposed to 36 in 1992.  The knowledge these workers accumulate over a lifetime can be difficult to replace.  The resources to replace them are increasing in cost and scarcity.  While the most recent quarter showed an increase in productivity, US productivity has been flat to declining over the previous several quarters.  The challenge of today’s leaders is managing decreasing productivity and higher cost with a skilled labor shortage in the most disruptive time since the steam engine.

The knowledge worker is critical and we have used automation as a key tool to reduce costs and increase their productivity.  Compute power has been deployed liberally with great gains.  Outsourcing has been another strategy but much of what can be outsourced has and some of that labor has returned to the US to improve service.  There is a technology shift from on-premises to Cloud, PaaS, and SaaS.  Leading companies have executed these strategies already.  The search for productivity and cost reduction must begin elsewhere. The knowledge has to be preserved, the costs lowered, and the performance increased.  Better, Faster, and Cheaper – Possible?  It is time for a new approach leveraging new technologies.

The next inflection point of technology is now in the Singularity phase of Big Bang Evolution (Larry Downes and Paul Nunes, 2014, Big Bang Disruption, HBR).  Cognitive Computing is coming of age.  IBM’s Watson is a key player in this evolution but numerous upstarts may initiate the Big Bang phase of cognitive computing.  The technology will mature faster than any other technology has before, however the deployment of this technology will be as challenging as ERP deployments.

Business leaders are experimenting with cognitive technologies such as Attensity’s Natural Language Processing engine.  Insurance fraud costs the industry billions every year.  Detecting fraud requires a human to read and interpret claim documents leveraging years of experience.  However, only a fraction of claims are even reviewed due to the unstructured text that constitutes them and barely 10% of fraud is detected.  The analysts who do this work use their years of experience but are nearing retirement.  An inefficient process that is losing the knowledge  Imagine hiring enough resources to read every document included in every auto insurance claim an insurer processes.  This would be financially impossible and too time consuming.  But what if a machine could read them all?  What if it could capture the knowledge of these highly experienced resources?  What if it, working with a human, could learn and become more accurate?  This is the future of knowledge work: Highly experienced workers preparing for retirement training technology to do their job alongside an operator.  Think Kroger self-checkout as a conceptual example.

Leaders across industries realize that the next inflection point in business is cognitive computing.  This will be a merger of people, process, and technology that will have a lasting impact on cost and productivity in areas of business you cannot imagine.  It is time to think Lean Agile Start-up: develop your hypothesis, conduct your tests, and identify how your company will benefit from this step function improvement in productivity.

Industry leaders are seeing the future of this technology.  Tests are being conducted.  Technologies are maturing.  Begin learning more about this rapidly growing space and planning how you will leverage it.  This is the next strategic inflection point in technology and it will change everything.

#CognitiveComputing #Disruption #FrankDiana #NextGen

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