Many organizations will be faced with change or die scenarios and many workers will become unemployed. This will be exemplified where control and respective cognition and services have been the norm and where there is a resistance to adopting new ways of implementing business processes. Agile workplaces promise to be more people focused, more flexible and more unique. This AI revolution is already here. The pace of adoptions it accelerating and the availability of opportunity to use and deploy technologies to support the workplace is speeding up. Agile paradigms have found their way in to a multitude of workplaces alongside new supportive technologies changing roles and processes. From hospital emergency rooms to educational assessments, to budgeting and banking, sales calls and food processing, AI and new technologies are causing organizations to reinvent themselves. Big shifts have started to play out in industries as diverse as energy, healthcare, manufacturing, and apparel and more upheaval can be anticipated. Innovation and responsiveness will thrive while redundancy and predictive process will be replaced, at least in part by new ways of working (Narayan, 2015).

 

Infrastructure and Obstacles

The pace of transformation is quickening and the speed at which new technologies are being developed and deployed is also accelerating. It is imperative the organization get a plan for adopting and adapting to new technologies including redefining business processes and employee roles. There are and will continue to be considerable concerns in the new workplace including privacy and security because of the enormous amount of data collected and shared. The quality and safety risks are still largely undefined. The legal and regulatory prospects could be substantial. Transformation to the agile workplace will not be easy. There will be tradeoffs and the cost and benefits of automating activities will vacillate between augmenting and replacing different activities. The impact of intelligent machines will have implications for the development of human skills and training. The pace of change requires organizations to embrace these priorities. It will determine the competitive position of the companies moving forward (Michael Chui, 2015).

Some of the changes needing to be considered involve embedded technology within an individual’s clothing which is already present as Wellness programs take off.  AI has already helped individuals keep track and benchmark their personal progress against others in their same age bracket or lifestyle.  But the immense information available across the dimensions of health claims and research may allow us to not only assist a person in their wellness programs, but make continuing suggestions in nutrition, medications, relaying real-time information to doctors or wellness experts.  All these culminate in a future of a healthier workforce which translates in to a more productive and more cost effective business environment.

We also see monitoring of keystrokes and applications used when performing tasks or solving problems.  The sources being used continue to be assessed as to their usefulness and whether they were instrumental in providing the information needed.  AI will gather that information and may even direct someone to a particular source as a reliable and useful knowledge base.

But these improvements using technology do not come without consideration of the privacy of the individual, the ability to “hold back” on information (opt out) and the need to have that information provided to the business.  Legislation will be challenged across a number of fronts to help business to forge into new territory and the rights of the individual, be they employee or contingent worker.  And it will not be a simple solution to simply aggregate the information and eliminate the individual identifiers, because it is that individualized information that will allow AI to identify, assist, and recommend actions at the micro-levels of the business to create improvement. (Knight, 2018)

AI, if not carefully programmed and monitored has the ability to exasperate inequalities in the workplace, home, legal and judicial systems. Sexism, racism and other unrecognized biases can be built into machine-learning algorithms underlying the intelligence and shape the way people are categorized and addressed. This risks perpetuating an already vicious cycle of bias that supports systematic inequality among poorer and nonwhite, population. The truth is that most of the programming and data analytics are being created globally by white males. Research by CMU (Carnegie Mellon University) has shown that women are less likely than men to be shown ads on Google for executive jobs. Correlations to unrecognized biases can be supple and it can be dangerous. From pay scales, to types of observation and surveillance, these algorithmic flaws are not easy to detect. Ingrained bias could easily be passed on to machine-learning systems and be built into the future. Intelligent machines could learn to think in ways that mirror a male dominated, narrow, privileged society which supports familiar prejudices and stereotypes (Crawford, 2016).

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