How to think and act like a Human in the age of AI and Automation

In late 2015, Klaus Schwab, Founder of World Economic Forum declared that humans are embarking on?4th Industrial Revolution.?This revolution is different from its predecessors, he noted in that it will disrupt without regard for industries and borders.?It will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before. The acceleration of innovation and the velocity of disruption will be hard to comprehend or anticipate and that these drivers constitute a source of constant surprise, even for the best connected and most well-informed.

McKinsey Global Institute and PwC have both independently projected that adoption of five powerful technologies (computer vision, natural language, virtual assistants, robotic process automation, and advanced machine learning) at the core of this latest Revolution could deliver about $15 trillion to global economy by 2030, averaging about 1.2% annual GDP growth.?

As predicted by Klaus, we are starting to see countless significant changes i.e. “use cases”?in our lives and our personal, work, social and cultural environments.?AI’s foothold appears finally to be gaining momentum after periods of hype followed by “AI winters”.?In recent years, we have witnessed quantum progress in Machine-learning algorithms, greater computing capacity and massive amounts of data being generated and now available to train AI algorithms.?AI appears to be enjoying a significant uptake in interest, investments and deployment, as barriers to entry have dissipated and a wide range of products, services, resources, and best practices have emerged.?

So how should we react to the 4th Industrial Revolution??

By thinking and acting like a ‘Human’.??

For millennia, being human has meant assimilating important experiences from different members of our tribe to elevate our collective intelligence so we could adapt effectively despite massive shifts in immediate environment and endure to become - the most dominant species on Earth.

As humans, we must now urgently assimilate some critical lessons to look beyond hype and harness real benefits from AI and Intelligent Automation to avoid another period of “AI Winter”.

Image Credit: Gerd Altmann / Pixabay

1. Digital Transformation is not about Technology, it is about People and Culture

Today’s Customer is empowered like never before and prefers to spend money on memorable experiences over things.?When we look at innovation through this human lens, everything becomes a lot clearer.?Manual work is not the enemy, poor customer or employee experience (EX) is. Millennials will constitute 75 percent of the workforce by 2030, according to the U.S. Bureau of Labor Statistics. Customers and Employees trust companies they feel understand them and have an emotional connection with. Make empathy, a human trait the centerpiece of your transformation design to win in the digital era. It has helped savvy market leaders like Amazon and Apple increase brand loyalty and ‘stickiness’. The same thought extends to building a thoughtful culture towards employees where they feel valued and have the tools and gamified immersive learning opportunities they need to do their jobs, now and in the future. The roles of Chief Employee Experience Officer and Transformer CLO have been born because companies have realized they cannot move the needle on their CX scores unless they take care of their employees first.

It is a lingering misconception that Intelligent Automation or AI ‘tools’ can fix broken or bad processes and improve CX. Understanding how each individual process can impact customer experience before aligning investments in AI and Automation is how digital innovators and fast movers have been so successful in driving consistency across channels.?

Companies that focus on creating memorable experiences will win the war for scarce talent and wallet-share of customers. According to Forrester, customers are 4.5 times more likely to pay a premium for an excellent experience. As they say, margins are in the memories!

Image Credit: Gerd Altmann / Pixabay

2. Digital Workforce will Complement Human Workforce, not Replace it

There is some consternation about how AI will peel away “human jobs” with every successive wave of new technological advancement. Potential for disruption to labor market from new technology is not a new phenomenon and certainly not unique to the digital era. In fact, previous technological advancements replaced livestock and humans with machines in manufacturing. Will the new smart machines armed with advanced sensors and deep learning capabilities take humans out of the loop not just in manufacturing and routine service sector jobs, but more broadly???What will humans do in the future if machines can perform all or most of the tasks better??

Once we get past the hype though we begin to see that AI and smart automation are less of a threat to human workforce and more a force multiplier to augment humans as partners. Among firms investing in AI and Automation, the ones that are generating most performance improvements are those where machines and humans are working collaboratively. Business requires both complementary strengths available in machines (speed, scalability and quantitative abilities) and humans (leadership, creativity, empathy, social skills).?

According to McKinsey Global Institute fewer than 5 percent of occupations will be automated in their entirety in the near or medium term. Rather, certain?activities?of most occupations are more likely to be automated, requiring business processes to be transformed, and jobs performed by people to be redefined.?

To take full advantage of this ‘man-machine collaboration’, companies must understand how humans and machines can augment each other’s strengths, and how to redesign business processes to support the partnership. It is critical that business and technology leaders articulate their digital vision for the future and sense of purpose clearly to the human workforce. The role of the ‘transformer CLO’ is becoming ubiquitous in companies seeking to personalize and atomize digital learning.??The key is to encourage human curiosity, leverage peer teaching and develop experiential capabilities for the future. If employees believe that AI will help them do their jobs better, they will be less resistant, and more effective in making the collaboration successful. Since it takes time for employees and clients to adapt to significant change,?the progress will be evolutionary and early adopters will likely need to be incented for their loyalty.

It is safe to conclude that some jobs will be lost, many jobs will be created and most jobs that exist today will be changed. There is general consensus, however that low-skill low-wage activities will be more susceptible to automation.

3. Humans are Essential to Build Responsible or Ethical AI

It has not been very long since Amazon was forced to scrap its secret?AI Recruiting Tool that showed bias against women. (Source: Reuters).??Machine learning specialists found a big problem: their new recruiting engine did not like women. The system had taught itself that male candidates were preferable based on patterns in resumes submitted over 10-year period. Since most came from men, a reflection of male dominance in the tech industry, AI simply exacerbated the inherent bias based on underlying data. In November 2019, New York Department of Financial Services looked into allegations of gender discrimination against users of the Apple Card. Some Apple Card customers including Apple co-founder, Steve Wozniak said the credit card's issuer, Goldman Sachs, was?giving women far lower credit limits, even if they shared assets and accounts with their spouse. How do we know the machine learning algorithm is not biased if no one can explain how it works?

Activists are also concerned about the adjacent issue of transparency in AI. The opaque nature of deep learning techniques creates the problem of “explainability”, of how the machine arrived at a decision or prediction. This has significant implications in applications where trust matters. The role of “explainers” is now being contemplated to address the transparency issue. These are human experts who can explain AI behavior to others. For instance, law enforcement official will want to understand why an autonomous car took actions that led to an accident or failed to avoid one.?

?“We are increasingly focusing on algorithmic fairness as an issue,” noted Rachel Goodman, an attorney with the Racial Justice Program at the ACLU. As AI deployments become ubiquitous and proliferate, the sensitivity to potential bias will increase. For instance, the European Union’s General Data Protection Regulation (GDPR) gives consumers the right to receive an explanation for any algorithm-based decision, such as the rate offer on a credit card or mortgage. It also protects consumer privacy and data rights.

Eliminating bias from AI is not easy. In fact, it is very hard. For AI to be trusted, we must effectively mitigate against data scientists and engineers inadvertently introducing human prejudice by guarding against diversity deficit in training set, using demographic data that is unbiased and auditing the algorithms. According to a research report from NYU, women made up only 10% of AI research staff at Google and only 2.5% of it’s workforce is black. This diversity-deficit representation can lead to biased datasets and eventually algorithms that are much more likely to propagate biases. Like humans, AI systems learn from their environments which makes it imperative that we remain continually vigilant as a society and species to who’s teaching algorithms to the machines.?

In collaboration with the Ethics Institute at Northeastern University, Accenture has released a report on Building Data & AI Ethics Committees that provides a roadmap for ethical stewardship of AI. Minimizing bias will be key for AI to reach its potential and imbibe trust. But this is a topic that requires much more research to elevate our common understanding of potentially existential implications for society and companies. A combination of ‘human-in-the-loop’ internal (ethics committees) and external (regulators) governance will become critical part of the ecosystem as AI proliferates. Even though AI applications can get a lot of things right the first time, humans are going to be needed to evaluate a set of decisions and look for any gaps in the datasets or oversight that led to a mistake.

Image Credit : Gerd Leonhard/Flickr

In Summary

The need for ‘academic validation’ is rapidly declining as AI's value to business becomes increasingly irrefutable. Nearly 80 percent of executives at companies that are deploying AI recently told McKinsey that they are already seeing value from it.?We are on the cusp of rapidly scaling AI technologies and cognitive agents commercially that until recently were only being exploited by fast movers and innovators.

As our focus shifts away from AI as a technology to the impact that AI can have on businesses and lives, the question is no longer how it works, but what AI can do for you. And as humans we should be prepared to ask how we will measure success as machines become less artificial and much more intelligent.

About the Author:?Anurag Darooka is a Digital Transformation Market Leader with over 25 years of experience in Management Consulting working with C-suite. As Managing Partner with Slalom's Banking & Financial Services Practice and former Global Vice President for Cognizant's Digital Business, he has successfully scaled AI-based Platforms, developed new Intelligent Automation Practice and led business-aligned digital strategies for many leading B2B and B2C companies.?

He has spoken extensively on a range of topics including How to Drive?Strategic Client Engagement in Digital Economy?at CMO Conference. Anurag is an Executive Coach and has delivered many thought leadership seminars on?Elevating Leadership?and?Exceptional Client Experience?with McKinsey & Co.

Anurag has a Business degree with honors in Economics from Sydenham College, University of Mumbai and multiple Post Graduate Executive Certifications from MIT Sloan School of Management, Columbia Business School and Carnegie Mellon University’s Tepper School of Business. Anurag resides outside New York City, in Madison, New Jersey and works around the globe.

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