In the Lead Magazine, Fall 2023

THE FUTURE OF Survey Insight and Foresight

The 2024 edition of the survey will build on the success of the 2023 survey and capture cross-cultural insight and foresight on leadership from 18- to 30 -year-old professionals from around the world. We welcome institutions that would like to partner with us on the 2024 survey.

CONTACT: ruchin.kansal@shu.edu or karen.boroff@shu.edu

The 2022 survey contributed to the national discussion on the CROWN Act (Create a Respectful and Open World for Natural Hair), and provided actionable insights for organizations on the impact of remote work on leadership development.

FALL 2023

Special Issue: Navigating Artificial Intelligence

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Disruption, Innovation, Leadership and Building Our Future

Leading the charge by embracing disruption, driving innovation and guiding AI’s impact on our future.

By RUCHIN KANSAL, JACK SHANNON and ANDREW SIMON

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Datacentric Leadership

Leaders must adapt their mindset and skill set to embrace the potential of AI while managing its challenges and risks.

By KAITKI AGARWAL

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A Unified Vision Navigating resistance and embracing change in the age of AI: A multi-step approach for effective leadership.

By RAMA IYER

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In the Lead with … Polly Mitchell-Guthrie

The VP of Industry Outreach and Thought Leadership at Kinaxis discusses how humans and AI can work together to achieve more comprehensive outcomes.

By THE EDITORS

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The Future of Leadership Survey: 2023

Insight and foresight from the survey’s third-year findings.

By RUCHIN KANSAL, KAREN BOROFF and BIANCA JOHNSON

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Letter from the Editor

8

Leadership Lessons

The emergence of generative AI presents a transformative moment in history, and its impact is expected to be rapid and significant.

By RAJESH RADHAKRISHNAN

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In the Crucible

Leveraging technology and embracing change is the new paradigm for 21st century leadership.

By CLINT WALLACE

38 In Focus

Cristina Hill ’20 emphasizes that critical thinking is the most important skill to help young professionals be successful.

40 Case Study

In the AI era, leadership will require navigating a changing landscape with emotional intelligence and empathy.

By VISWA VISWANATHAN

44 Point | Counterpoint

In Competing in the Age of AI, leveraging data, analytics and AI can remove traditional constraints on business growth.

Reviewed By PAULA ALEXANDER and STEPHEN WOOD

COVER ILLUSTRATION

BY MICHAEL WARAKSA

PHOTO ELEMENTS PROVIDED

BY

GETTY IMAGES

Ruchin Kansal, M.B.A. (Editor) is a professor of practice at Seton Hall University and the founding editor of In The Lead. Prior, he led the Business Leadership Center at the Stillman School of Business. He also held senior leadership roles at Capgemini, Deloitte, Boehringer Ingelheim and Siemens Healthineers. He received his M.B.A. from NYU-Stern.

Kaitki Agarwal is the co-founder, president and CTO at A5G Networks — a leader in converged 4G, 5G and Wi-Fi autonomous mobile core software. In 2012, she co-founded Parallel Wireless, where she led the disruption of the traditional RAN market with the open RAN movement. She is also a member of the Harvard Business Review Advisory Council and Forbes Technology Council.

Paula Becker

Alexander, Ph.D., J.D. is an associate professor and chair of the Department of Management at the Stillman School of Business at Seton Hall University. She developed the curriculum for Corporate Social Responsibility, a core course in the school’s M.B.A. program. Her research focuses on firm financial performance, executive comp and socially responsible management.

Karen Boroff, Ph.D. is professor and dean emerita at the Stillman School of Business at Seton Hall University. Among other work as interim provost, she led the creation of the University-wide Leadership Development Program. She earned her Ph.D. in Business from Columbia University where she concentrated in Industrial Relations and Human Resources Management.

Rajesh Radhakrishnan is leading Automation

Anywhere’s rapid expansion into the GenAI +Automation market as GM/EVP of Solutions. Prior, he served as the CRO of GlobalLogic and brings deep business process knowledge from his experience running a $3.5B P&L for the apps and BPO unit at HP. In addition, he has built and led product organizations at Mercury, Veritas, Healtheon/WebMD and Oracle.

Jack Shannon, J.D. is professor of legal studies at the Stillman School of Business at Seton Hall University. His interests focus on the intersection of law, business and technology with a particular emphasis on the issues and challenges propelled by digital transformation and its impact on the established order in business, law and society more generally.

Andrew Simon, Ph.D., Psy.D. is professor in the Department of Psychology at Seton Hall University and specializes in leadership and organizational performance. He is a member of the Buccino Leadership Institute Advisory Board and has served as a senior staff member in executive education programs at The Wharton School. He is the president of the International Council of Psychologists.

Viswa Viswanathan, Ph.D. is an associate professor of computing and decision sciences at Seton Hall Univeristy. He dared to be an AI-nerd before it was cool by working on AI for his doctoral thesis in the 1980s. His long stints in academia and industry enable him to seamlessly blend theory and practice. He is currently researching ways to harness technology to make dramatic improvements in student learning outcomes.

Cristina Hill

graduated magna cum laude from Seton Hall University. A Girl Scout for 12 years, she received the Gold Award for her Seniors in Touch project, helping older adults learn technology. In her free time, she loves to read, learn how to cook, cheer on her pal’s rec softball team and enjoy all the social opportunities Manhattan has to offer. In the future, she would love to eat her way around the world and learn Spanish.

Rama Iyer, M.B.A. is an HR leader with 25 years of global experience across organizations such as GE Capital, Coca-Cola India, ICICI Prudential Life Insurance, The Economist Group and BoehringerIngelheim. She has deep expertise in organization effectiveness/development and HR functional leadership. She currently serves as senior directorleadership development at Spectrum.

Bianca Johnson is an accomplished CPA, employed as a data analyst at Ernst & Young. A graduate of CUNY College-Staten Island, she is pursuing her master’s in business analytics at Seton Hall University and set to graduate in 2024. Beyond her professional interests, she enjoys watching horror movies, solving jigsaw puzzles and seeking thrills on roller coasters.

Polly Mitchell, M.B.A. is the VP of Industry Outreach and Thought Leadership at Kinaxis, the leading provider of supply chain management solutions. Previously, she served as director of analytical consulting at the University of North Carolina Health Care System and in various roles at analytics leader SAS Institute. She has a B.A. and an M.B.A. from the University of North Carolina at Chapel Hill.

Clint Wallace, M.B.A. is the SVP of people, functions and organizations at Kenvue. Prior, he served as the SVP of HR, Sanofi North America, VP of North America HR for BASF and in leadership roles at GE Healthcare and Pratt & Whitney. He was a commissioned officer in the U.S. Air Force and retired in 2008 after 21 years of service. He holds an M.B.A. from Rennsalear Polytechnic Institute.

Stephen Wood, M.S. consults and writes on policy topics after 43 years on Wall Street and in governmental finance. He specializes in infrastructure and project finance, public-private partnerships, federal and state grant and finance programs. He is also an expert in financial modeling for large, complex capital programs. He teaches about corporate social responsibility at Seton Hall.

Embracing the AI Revolution

SITTING DOWN to write this editorial, I wondered what ChatGPT would write. I prompted it: “Write a 500-word letter from the editor for a magazine focused on leadership in the age of AI.”

I was impressed. The letter composed by artificial intelligence had eight paragraphs. The first two paragraphs introduced the readers to the magazine’s theme: leadership in the age of AI. The next three outlined four themes that the magazine covered: the role of leaders in fostering a culture of innovation, ethical dimensions and the transformational nature of leadership. The following paragraph mentioned articles that explore the industry-specific impact, and the last two welcomed the readers to read the pieces with a critical-thinking mindset and thanked them for reading the issue.

I would give ChatGPT a B- for its

work. The structure of the letter was coherent. Without knowing the content of the articles, it reasonably predicted the themes and compellingly presented them. The tone of the letter was factual and welcoming. It did not deserve an A+ because it exaggerated several claims about the magazine’s content.

And that is the challenge of AI. It is factual until it is not. That presents a big dilemma: how much to believe in it. As you read the perspectives presented in this issue, you will note that no one argues with the assistive power of AI or its disruptive power to change industries, societies and economies. The concern is trust and ethical considerations. Everyone realizes the risk of AI going rogue, yet everyone is optimistic that humans will manage it for the greater good.

My takeaway is that our goal should not be to reject but to embrace AI with the proper controls. Even though there

is some self-governance as innovators unleash AI on humanity, the role of the regulators has never been more critical. While there is always the need for a proper balance between innovation and regulation, in the case of AI, it does feel like the need for regulation is critical.

As you read the issue, here is what I’d like you to think about:

1. How is AI impacting me personally and the business I am in today?

2. What might be the long -term implications?

3. How do I prepare?

Welcome to leadership in the age of AI. Happy reading!

– Ruchin Kansal

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Students receive high-level training to apply analytical and technical methods required for enhancing supply chain operations.

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Students receive an experiential learning experience by analyzing actual data used to determine findings essential for decision-making in management.

Unleashing Transformation in the Digital Era

The emergence of generative AI presents a transformative moment in history, and its impact is expected to be rapid and significant.

BY RAJESH RADHAKRISHNAN

WE ARE at a transformative moment in history with generative AI. We have seen a transformation like this before, with the internet, the cloud, the smartphone and social media.

What is different now is the speed. When we went through the cloud revolution, companies took almost 20 years to fully embrace it. For the first 10 years, people questioned whether they should use the cloud. And then they decided to do it.

In the case of generative AI, it is not going to be years, it’s going to be months.

People will need to move very quickly. Generative AI is going to revolutionize how work gets done. The change will affect knowledge workers and eliminate a lot of the mundane work they do. It will not affect the nonrepetitive knowledge work, the kind, for example, that CEOs or other leaders do. But it will affect the repetitive knowledge work that a paralegal, junior banker or an actuarial analyst might do.

The interesting question is whether this means we don’t need junior bankers and paralegals anymore. Or if this is when the junior bankers and the paralegals learn how to use this tool (because

they are so much more adept than the more senior people when it comes to technology) that we don’t need the senior people anymore.

Also, if you are No. 2 or No. 3 in your industry, does adopting generative AI allow you to become No. 1? And if you are No. 1, this is the time when you should be afraid and careful that your position is not affected. Companies are adopting generative AI in service operations (everything from the service desk to the contact center), and to improve their core product (for example, anti-money laundering, doctorpatient communication, complaints

resolution and claims processing). It’s an opportunity for the core business to embrace AI, become more efficient and gain a competitive advantage.

ESTABLISH GUARDRAILS

Security is going to be very challenging. The technology now allows someone to impersonate your voice, call your kid, and say, this is your dad or mom and give me your Social Security number, or whatever. You can make deepfakes and videos, malware and even bombs.

Therefore, you must establish the proper guardrails quickly. You cannot afford to have your data showing up all over the place. Many companies are reacting by banning things like ChatGPT simply because they’re afraid that personal data and IP will get compromised.

As they say, the most secure email system is a nonexistent email system. The key is to establish some guardrails, but you must let people play — allow people to experiment.

EMBRACE INNOVATION

The most successful companies are not only allowing employees to innovate, but they are also crowdsourcing the innovation. They are encouraging employees to come back with their ideas on what should be done differently. They are asking them to challenge the status quo. They’re setting up internal competitions and finding what’s different because the best ideas will come from all over the organization, not necessarily just from the top.

Often, in fact, those at the top don’t know how to leverage this new technology because they simply don’t have the time to use it.

FLATTEN THE ORGANIZATION

The top-down hierarchical organizational model can often run counter to innovation. The critical thing

to realize is that innovation will come from different places in an organization and from outside the boardroom. If you take ideas through a hierarchical organization’s approval layers, they will never surface.

So you have to find ways to flatten the organization. A new group of people is likely to rise to the top, but it is hard to predict who that will be at the outset. You have to find ways to flatten the organization and allow everyone to participate.

THE RISE OF THE GENERALIST –CONNECTING THE DOTS

The specialist game is slightly more challenging now because the AI is your specialist. I see a need for talent and leaders who are as versatile as a Swiss Army knife. Moving forward, leaders will have to become good at connecting the dots. Traditionally, senior leaders have not had the time or the skills to get into the details. Unfortunately, this may be a case where they must get into the details and connect the dots across everything that gets done in their organizations. The product team will have great ideas for improving the product with generative AI.

But are those the most relevant ideas that will help you against the competition or generate value for your company? Leadership must find a way to connect and bridge those dots.

ESTABLISH PEER GROUPS

This is the time to talk to some peer groups and determine what else is happening because good ideas will come from more than just your organization.

work.

This is one of those times when you must see what others are doing, not just within your industry but across industries. That will give you a lot of ideas and a lot of information on what to look for.

I tell people in my company that they should have at least three or four conversations about generative AI every week to hear what others are doing and share their thinking. When they do, they will find out whether others agree. This is all good as it will give them different ways to approach and think about it, and that’s important.

RETHINK YOUR PORTFOLIO

It’s time to rethink your portfolio. Like offshoring, for example. Every time you move the needle on automation, you change the game on what can be reshored and offshored.

It is also one of those moments that can change the products you bring to market. It’s not just a question of efficiency and things of that nature but the nature of the product itself.

Can you embed generative AI in your product? First electronics and then software ate the world, and now AI will be next. You may not know what it means yet, but your product will have AI in it, whether it’s your car, book or whatever it is.

If you don’t move fast, the competition will move quickly in this space.

Leaders need to move quickly to establish guardrails, drive innovation, connect the dots and rethink their portfolio while flattening their organization to get ready for this new wave.  L

Generative AI is going to revolutionize how work gets done. The change will affect knowledge workers and eliminate much of their mundane

Evolving Leadership with Tech and Collaboration

Leveraging technology and embracing change is the new paradigm for 21st century leadership.

BY CLINT WALLACE

AS A senior executive who has retired from the military reserves with over 20 years of service and in parallel operated in the corporate world for the past 30 years, I have seen leadership transform. What’s most apparent to me now is we are more in tune with what drives peak performance in teams while leveraging technology hardware and software to enable the strategic imperatives of the business.

When I first started in business and the military, leadership was much more command and control. Today, however, leadership is centered more around leaders serving their teams and taking the time to influence more through their

emotional quotient (EQ) rather than titles or power.

Through decades of research, industrial psychologists have developed profiles for good leadership and the means to assess those people leaders. Technology has played a role in those assessments of leaders and capturing and archiving data for year-over-year and peer-to-peer comparison.

THE ENVIRONMENT

My experience in today’s business is that it’s very dynamic and challenging with greater needs and expectations of employees and customers. The past few years have been unprecedented with talent shortages continuing to rise, the

need to upskill and reskill the existing workforce and customer demand being unpredictable with higher expectations.

I am now having to make decisions more quickly with greater precision, delighting a more diverse customer base and ensuring valuable experiences for talent in the company.

THE TOOLS

The good news is that leaders today have more tools and assets than before. Software, machines/robots, artificial intelligence and people are the most important assets in any company.

Leveraging robots, software and artificial intelligence requires strategic thought on a foundation of the ethical

and moral context of the business while balancing the companies’ purpose, vision, values, commitment and behaviors.

I found this to be the solid foundation on which we operated — leading to an organizational culture that fosters trust, engagement and inclusiveness.

As these multitudes of tools cohabitate in one environment, I have had to invest in the continuous learning of my teams, focusing on digital communication, cybersecurity awareness, information management and data literacy.

We have put even extra focus on data literacy to ensure data is understood and interpreted correctly. This has enabled informed decision-making with greater precision.

Equally, we have also focused on ensuring our artificial intelligence systems address concerns about bias and privacy. It has been critical for our organization to put guardrails up in a transparent and balanced manner, as we know we will be held accountable for any inaccuracies and shortfalls. The team and I have invested time in understanding the depth and capabilities of our artificial intelligence system while managing its abilities effectively. Our implementation has been gamechanging, giving us speed and lowering our cost to serve the business.

LEVERAGING ASSETS EFFECTIVELY

At one of my former companies, I had growing pressures of lowering the cost to serve with higher demand for key critical skill hiring within a very niche environment. We were faced with tremendous headwinds in the market, and

our business was counting on us to deliver.

The team came together and participated in a “possibility thinking” exercise, generating ideas that would achieve the greatest value.

My strategy was to leverage technology by redirecting routine and mundane work that I call foundation work.

We needed to scan a high volume of resumes; this is where I made the investment in an applicant tracking system (ATS). Its software used algorithms to analyze resumes and match them against specific criteria such as skills, work experience, education and certifications. It enabled us to filter through large volumes of resumes efficiently and identify the most suitable candidates as part of our first-screen candidate selection process.

Internally we launched a campaign to collect skills and career-aspirational data from our workforce. All employees loaded their skills and short- to longterm career aspirations in our Human Resource Information Management System (HRIM). This data enabled the artificial intelligence tool built into the system to make learning and development recommendations and career move proposals that would enable their capability build.

The last piece of our strategy was to use “bots,” short for robots. Bots are software programs designed to automate tasks, and with speed and precision, our bots entered data into our HRIM system to enable the accurate onboarding of our new hires.

As a result, we were able to go to the market and find talent with the right capabilities and hire them quickly while having a good inventory of

internal capabilities with the right focus on the development required to close capability gaps.

SUMMARY

As I continue my leadership journey in the 21st century, I am expected to lead a broad network of stakeholders (employees, shareholders, customers, investors, suppliers/partners). To win in this dynamic marketplace, I have found that being a boundaryless collaborator across the stakeholder network serves the best interest of the business, given that I am able to promote teamwork while harvesting the collective intelligence around one purpose, one mission.

Leveraging technology, including artificial intelligence, has become so sensationalized that its often misunderstood. This can become “noise” and a distraction from creating value for the company. I believe this type of technology can enhance our work environment, but we must take the time to understand it and anchor ourselves on the values and principles of the company, while ensuring we have solid guardrails in place that will delight customers and deliver a great people experience.

As a leader, I have adopted these traits: external focus, imagination, inclusiveness, courage, clear thinking and continuous learning.

Leaders must be externally focused, ensuring what they are doing will leapfrog the competition. They also must be imaginative and have the courage to act on their ideas. Leaders must also embrace inclusiveness, listening to the diverse perspectives of those around them.

Leaders must also be clear thinkers to distill the most complex of problems. Continuous learning has also served me well, as I have immersed myself to new ideas and concepts. I will continue to be a lifelong learner — it is challenging and rewarding all in one.  L

Leaders must be externally focused ensuring what they are doing will leapfrog the competition. They must also have the courage to act on their ideas.

Disruption, Innovation, Leadership and Building Our Future

Leading the charge by embracing disruption, driving innovation and guiding AI’s impact on our future.

BY RUCHIN KANSAL, M.B.A., JACK SHANNON, J.D. and ANDREW SIMON, P h .D. P sy.D.

The need for effective leadership in the face of disruptive, large-scale change is fresh in our minds. COVID-19 took us from staying home for two weeks to a global pandemic overnight, launching us into a new future, ready or not.

Today, as the acute phase of the pandemic has passed, its effects linger well beyond health implications. Connecting remotely for work, school and socializing has become part of everyday life, with little reason to doubt this is the new normal. The core structures of society — government, business, education, nonprofits — have all been affected.

History provides endless examples of the natural, social and technological worlds imposing themselves on taken-for-granted routines and processes. Yet we need not look back any further than the past few years to appreciate the speed and force with which our lives can be flipped upside down. Will we learn from

this and take action to build our future, or remain focused on short-term horizons and allow the future to be imposed upon us?

Any domain in which disruptive, large-scale change is emerging provides us an opportunity to make a choice about building our future rather than having it imposed upon us. The impact of artificial intelligence is just such a disruptive change. Algorithms and machine intelligence have already entered our lives and are embedded in, for instance, social media, online shopping and commonly used apps like Uber and Waze.

AI’s presence has recently entered the common conversation, mostly due to the accessibility of ChatGPT, the application that’s as easy to use as a Google search. Questions regarding the impact of AI have proliferated rapidly. What will it do to education?

What facets of the workforce will be replaced by machines?

What about privacy, security, health care …?

Such questions only scratch the surface, though, when it comes to the potential for machine intelligence. It is possible

that no part of our lives will go untouched. The choices we are faced with today in relation to AI are comparable to the ones we were facing before the pandemic. We can either innovate and find ways to integrate AI into our lives, building our future, or allow its disruptive force to dictate our future for us.

The foundation for taking charge of our future comes through an understanding of and practical approach to the roles of disruption, innovation and leadership (DIL). Understanding these factors requires an appreciation of their interconnectedness and complexities.

Disruption

● Disruption sends seismic waves through society, shaking up norms and processes and leading to transformative change with broad technological, social and economic implications.

The power of disruption lies in its ability to drive evolution and progress. From COVID-19 response strategies to climate

change adaptation, synthetic biology and the proliferation of accessible AI platforms, these innovations are shaping our society at an exponential rate. The pandemic triggered a massive upheaval, but this should not be considered a threat. Instead, disruption should be seen as an opportunity to inspire innovation and advancement.

In the world of technology, disruption has played a crucial role in transforming paradigms. Digital technology, for example, has fundamentally altered how we communicate and exchange information, changing the media, retail and advertising industries. Now, AI stands on the brink of another revolution, capable of reshaping many fields by automating routine tasks, enhancing health care through predictive diagnostics and giving rise to new forms of engagement and service delivery in education, public services and transportation.

Besides technological changes, disruption also deeply affects our sociocultural fabric. The internet has redefined human

interaction, creating global connections and raising concerns about privacy and cyberbullying. As AI and automation integrate into societal structures, they bring change, reconfiguring employment dynamics, reshaping skill requirements, and provoking ethical debates about data privacy, algorithmic equity and the moral responsibility of autonomous systems.

Disruption profoundly affects the economy, offering a range of possibilities and dangers. For example, e-commerce has already shown its capacity to disrupt the conventional retail industry. At the same time, artificial intelligence is about to change the job market in a revolutionary way and reshape the distribution of wealth.

While predicting disruption accurately is challenging, we can prepare for it by cultivating key skills, adopting a strategic mindset, and fostering an adaptive organizational culture. The critical skills to prepare for disruption include adaptability and resilience, creativity and innovation, critical thinking, digital fluency, emotional intelligence and a commitment to lifelong learning. The same innovative spirit that guided us through the pandemic can now show us the potential disruptions AI poses. Rather than fearing job losses due to AI, we should envision a world where humans and machines work together harmoniously and create strong systems to protect data in an AI-dominated world.

We will increasingly see the expansion of the generalist versus specialist debate. Of course, as usually happens, the correct approach will depend on context. Specialists’ deep knowledge and skills in a specific area make them invaluable in situations where high expertise is needed. However, they risk becoming obsolete if their specialty is heavily disrupted and they cannot transfer their skills. With their broader range of skills and knowledge, generalists may find it easier to adapt to disruptions because the breadth of their experiences to generate creative solutions allows them to switch between different roles and tasks flexibly.

Leaders can foster this by promoting a culture of innovation, continuous learning and cross-functional collaboration, thereby preparing their workforce for any disruption that may come their way.

We are at a pivotal moment in history comparable to the invention of the printing press, modern agriculture and medical breakthroughs. The effects of climate change, synthetic biology and AI have the potential to alter our society drastically. We can either

deny these changes or take control of our future and lead the way through this disruption. We must embrace disruption as a driver of innovation, with leadership guiding us forward.

Innovation

● Here is the thing about innovation: It is both a cause of disruption and the key to combating it.

Innovation is the least understood and the most widely used word in the business context and is positioned as the panacea to all ills that face our society. However, most innovation efforts fail, not because they are lacking in intent, but because they lack a clear purpose, process, people and governance.

What is innovation? It is the introduction of a new idea, product, process, service or business model that adds new value.

Innovation can be of three kinds. Let us take the example of the telephone. When the phone was introduced, it was a disruptive innovation. It changed how we communicated, led to social, cultural, and economic structures, and created a new world order. Then came the smartphone. It is a phone, but it is also a multipurpose device: a camera, a torchlight and a music player.

This is an example of a transformational innovation, where an existing product was redefined and ended up upsetting the established world order and creating new industries. Now, we see incremental improvements in the capabilities of a smartphone: It comes in different sizes, has faster chips and has become a health-monitoring device.

Most innovation is incremental. We would argue that incremental innovation creates the engine for transformational and disruptive innovation.

Let us take the case of AI, which is a result of many incremental and transformational innovations over time: large language models, text, image and voice recognition, machine learning, robotics, high-speed networks, digital circuits, computer chips and more. What is also worth noting is that many of these innovations were considered disruptive at the time. The key takeaway is that the world continues the innovation cycle of improvement, transformation and disruption, and in the process, continues to advance the human race and create new value.

However, innovation is hard to manage, especially in organizations. Outside of the traditional R&D process, the innovation agenda is seldom clear, yet widely practiced. This results in failed initiatives,

frustrated employees and wasted money.

We are experiencing the same with AI. Absent a clear agenda and mandate, there is a hodgepodge of initiatives emerging all over organizations, some propelled by curiosity and good intentions, others by the hubris of business leaders, and more peddled by fearmongers. It is, therefore, important to manage innovation with a clear agenda and supporting process, people and governance. And that requires strong leadership.

It is the leadership that has the responsibility to create clarity of purpose around innovation, set a clear innovation agenda with pragmatic milestones and create teams to deliver on that agenda. And then become leader coaches. This requires our leaders to develop change agility and a learning and coaching mindset.

Leadership

● Effective leadership is always central to group or organizational success but takes on even greater importance during times of change, as we’re now seeing with the presence of AI. Leaders who recognize the inevitability of disruption and see AI as a technological development to be integrated into our lives will provide a service far greater than those who attempt to block or stop it, an effort as futile as attempting to stop the use of the internet.

Too often, efforts to identify successful leaders have produced little more than preferences for leadership styles or judgments about personal charisma. Yet, it is not hard to come up with examples of successful leaders who showed very different styles or who were not necessarily charismatic.

Think of the difference between President Kennedy’s inspiring declaration that the U.S. would land on the moon in less than 10 years and Gandhi’s peaceful but powerful stance in leading India’s independence from British rule. Other than their respective effectiveness and ultimate success, there was arguably little overlap in terms of personalities or leadership styles. It seems we are better served by embracing leaders who speak directly to a vision for building our future than to those who fit an image of what a successful leader looks like.

Providing a vision is more than simply imagining an endpoint, no matter how clear or convincing it appears. Competent leaders integrate both current and emerging environmental demands with the history, values and current practices of those they are leading. They present a future that

is innovative yet accessible; the collective needs to feel capable and motivated to build such a future for itself.

As part of providing direction that values the power of AI, effective leaders will need to facilitate broad-ranging connections and partnerships, sharing their vision with those outside their immediate organization or community. We need leaders who will recognize that collectives and networks hold the potential for far greater impact than will occur through the actions of any single organization. The integration of varying talents and resources will bring forth ideas and innovations that only occur via partnerships.

Of course, far-reaching interrelationships will also bring greater complexity than would be found within single groups or organizations.

Embracing this complexity and dealing with the accompanying challenges are the very spots where our most skilled and competent leaders will focus. Prioritizing connections will set the basis for innovative thought and action, and will characterize our next generation of leaders, those guiding us through the emergence of the AI era.

The DIL Triad

● Disruption, innovation and leadership have been a constant of human civilization. This triad is the wheel of human evolution and is more than the sum of its parts. At times, disruption has been forced upon us, whether it was the bubonic plague, the financial meltdown, the world wars or COVID-19. It took strong leadership and an innovative mindset to build a human condition that was better than what we were before.

Similarly, in times of peace, innovations such as the steam engine, the printing press, the personal computer, the internet and smart devices have disrupted the normal, creating a new normal, and leaders worldwide have maintained a world order where such innovation could thrive.

At the same time, Alexander the Great, Mahatma Gandhi, Nelson Mandela and many others have taken it on themselves to drive change that has disrupted existing world orders and unleashed new structures that have given rise to new sets of innovations in political and economic structures.

We can be confident that the disruption, innovation and leadership cycle will continue to shape the human future. AI is the latest one. Our choice: recognize, prepare and participate in shaping the future or allow it to impose itself upon us.  L

Datacentric Leadership

BY KAITKI AGARWAL

I heard a gentle, soft voice with music, filling me with positive energy. It gently woke me up on a weekday and greeted me.

Good morning Kaitki; it’s a gorgeous sunny day. I hope you are energized to start your day. I am here to help you start your day. Let me know when you are ready. Oh, BTW, I applaud you for your workout last evening and for exceeding your exercise goal.

Hearing positive words to start my morning routine pleased me, and I began to get ready. I told my virtual assistant to go ahead with my schedule. She responded and told me my breakfast menu, my car-servicing schedule and my plan for the day. She also mentioned my son’s school event that I was required to attend.

My virtual assistant takes care of many tasks of my daily activity, saving me time that I can use for essential activities. I took my auto-driving car to work, and during my ride, I started thinking about how leadership had evolved over the years and how it is changing in this era of AI.

AI has given us many tools, automated many manual aspects and presented us with enormous amounts of data. We have gathered many insights from the data to improve processes and workflows to improve organizational efficiency. We have also saved capital and operational expenses and are getting the expected return on investment in AI.

At the same time, AI has improved things, simplified our lives and removed low-key and mundane manual tasks. It created opportunities to upskill and train our staff. If you think it replaces the job functions, I don’t agree. It gives new tools to all the functions, and we need to train the staff on using those tools.

I was listening to a David Letterman interview with Bill Gates from 1995, where Gates tried persuading Letterman to get a computer. Letterman did not appreciate the value of the computer and asked Gates, “Do you know this internet thing?”

Gates responded with several ways the internet could be used, such as being able to watch and listen to a baseball game on your computer. Letterman said, so you can listen to a baseball game on your computer; does the radio ring a bell? Gates responded, well, Dave, in 27 years, you’ll publish this video clip on YouTube.

With the advent of the internet and personal computers, there was much concern about people losing jobs and many security-related issues. Now, with AI, we seem to be reliving that discussion. Computing transformed the world for everyone — think about the number of jobs created when computers came in, providing many new opportunities and industries. It did not take away jobs, it changed them. It brought in more jobs, and it was great for the economy and humankind.

Since AI is coming into our lives in a big way, it needs a new mindset, and we will have to see how to use its full potential.

In this transformative era, leaders must adapt their mindset and skill set to embrace the potential of AI while managing its challenges and risks.

It also brings several challenges, and we must proactively manage those aspects.

AI is becoming an integral part of our business, and large language models (LLMs) have emerged as the most versatile and powerful AI models available. Generative AI models — such as generative pretrained transformers, or GPTs — are a type of LLM, which are trained using self-supervised or semisupervised learning on large quantities of unlabeled text.

Some well-known LLMs are ChatGPT-4, Google’s Bard, BLOOM and LLaMA. Generative AI has several potential applications that we can think of today, and many will evolve. Some applications are in education, art, music and writing, as well as in health care, finance and gaming — essentially in every field we can think about.

Despite all the excitement, generative AI comes with significant challenges and risks. The models are trained on the unproven repository, basically the internet. Training data has a lot of biased and misleading information, so can we trust the output generated by generative AI models?

Generative AI models, such as ChatGPT-4 and Google Bard, are intriguing, but only as good as the data used to train them. They unfold many issues and concerns around governance, copyright, intellectual property, ownership rights, data sharing, security and data sovereignty.

Data used to train AI models shouldn’t infringe on intellectual property. AI algorithms should be unbiased and shouldn’t discriminate based on attributes like age, ethnicity, gender, etc. Organizations must disclose the data source they use to train the model. AI brings many questions and complexities around ethics, trust and the need for new policies. Data generated from machine-learning models can’t be trusted if the data used in AI algorithms are biased.

Organizations and governments intend to separate high-risk use cases of AI, like legal, hiring and financial applications, from low-risk use cases, such as AI-based itinerary generation or gaming. Cyber security and external AI-based threats are critical areas of focus for organizations.

AI transformation needs significant mindset change at all levels, like what we did when we entered the era of PCs. Change management will become critical to organizations.

Leaders must prepare and train themselves to make the best out of the AI opportunity while handling its challenges and risks. They should:

Become Datacentric Leaders

● Most leaders today focus on their function. Now, they must learn about other aspects that may influence the data. Organizations and leadership shall become more datacentric

Despite all the excitement, generative AI comes with significant challenges and risks.

than workflow-centric. Leadership must get more proactive by using the data and insights generated by AI tools.

In other words, leaders will have powerful tools to continuously improve. Data and insights will allow innovation at all levels and in many areas. Leaders should recognize and capitalize on that. Leadership will become more global than just functional. Leaders will have views and insights at every level beyond the functional aspects.

Become Adept at Change Management

● Today’s leaders do use change management, but they need to become much better at it. We already have many policies and regulations in place, and organizations are functioning based on those norms, so there is a level of comfort in everyone’s minds, e.g., legal structure, copyright laws, human resource policies and patent laws are all in place.

Organizations are mainly focusing on their product and business using these norms. With the changes called out by generative AI, these fundamental building blocks will change, and leaders will need to build that awareness. They must make sure that these new norms get into practice and, in some cases, contribute to evolving these changes.

They will be dealing with two dimensions of change management. One dimension enables change in the organization most effectively; another is participating in the change, keeping an open mind and collaborating industry-wide to define and refine that change.

Become Strategic and Proactive Leaders

● Leadership shall evolve to be more strategic and proactive to identify the transformations needed in the organization, both in bringing new AI tools and changing the roles and responsibilities of the individuals to get the best out of the AI transformation.

There shall also be a balance between technology and business leadership. Technology leaders will expand their perspective on business aspects, macro and microeconomic environments, business challenges and how technology and innovations impact and influence those aspects.

Similarly, business leaders shall become more technology conversant. This convergence will create more strategists with a broader perspective to connect the dots in different dimensions. It will allow a better cross-functional view and reduce silos within the organizations.

My thought process was interrupted by the voice in my car. You have reached the office, Kaitki. Have a great day! L

Leaders must prepare and train themselves to make the best out of the AI opportunity.

A Unified Vision

Navigating resistance and embracing change in the age of AI: A multi-step approach for effective leadership.

BY RAMA IYER

Artificial intelligence (AI) is not the first pathbreaking change we are navigating, nor will it be the last. As we think of leading through such a momentous change, a look at how we navigated past changes has valuable lessons for the present.

The role of a leader during such times of change is multifaceted and complex. This article posits that leaders need to lead in two dimensions.

First, leading people will be the primary focus of this article. This requires identifying three types of stakeholders and leading each of them very differently.

The second, leading the application of the change itself, will receive a brief overview here.

Leading People: Understanding the Different Stakeholder Sets

● We all perceive the impact of change differently. Calestous Juma wrote an enlightening paper for the World Economic Forum summarizing his book, Innovation and Its Enemies: Why People Resist Technologies. He analyzed past examples of groundbreaking innovations to show that society tends to reject new technologies when they substitute for, rather than augment, our humanity. On the other hand, we eagerly embrace them

when they support our desire for inclusion, purpose, challenge, meaning and alignment with nature.” 1

Let us break down Juma’s two responses to change using Maslow’s Hierarchy of Needs (See Figure 1).

Maslow’s theory posits that we all have five levels of needs. The most basic needs are physiological, followed by safety and security, then love and belonging, then self-esteem, and lastly at the apex is self-actualization.

Juma’s reference to “new technologies that substitute rather than augment” triggers a fear of unfulfilled “physiological and security” needs. These are basic needs, and any threat to them can be a source of immense anxiety.

FIGURE 1: MASLOW’S HIERARCHY OF NEEDS

Those experiencing a change from this lens show up as the “resistors” to change. They feel threatened that the upcoming change will replace them and their jobs. Hence, their response is largely emotional.

Now consider Juma’s reference to change that enhances purpose, challenge, meaning and inclusion. Such a view of change correlates with the self-esteem and self-actualization needs in Maslow’s hierarchy. If there are new ways of achieving one’s potential and elevating the work we do, that vision of self-fulfillment can be a strong motivator to embrace and drive change. Those experiencing a change in this manner tend to be the “change agents.” The immense optimism generated by this response also makes it primarily emotional.

Most individuals fall somewhere on that change continuum and can be classified as “fence sitters.” They are typically ambivalent toward the change and show some readiness to be persuaded by factual arguments as well as emotional ones.

Leading People: Influencing the Different Stakeholder Types

● First, let us focus on those likely to embrace change — the change agents. They see AI as the panacea to the drudgery of programmatic and predictable work, and they see the possibility of how AI will free us to apply our intellect to more

sophisticated problems and applications.

Communicating with these individuals involves articulating a two-to-five-year vision of the future accompanied by examples and details that make this future come alive. These are leaders’ change champions, so leaders should take the time to ask questions, engage these team members in reflection and push them to temper their sometimes-unbridled enthusiasm with reality checks. Leaders can leverage their expertise and energy as they define the strategy and plans to reach the vision.

The resistors, at the other end of the change spectrum, demand equal attention. The media is rife with opinion columns that predict how some jobs will be replaced by AI, and there is widespread fear of its negative impact. Hence, it is common to see team members fearing the impact of AI on the workforce and the personal implications for themselves.

These fears stem from a threat to the basic needs of physiology and security. One implication of Maslow’s theory is that it is very hard to focus on higher-level needs when basic needs like safety and security are not fulfilled. For this reason, trying to make “resistors” reimagine a change-adapted future at the outset will probably fall on deaf ears and only serve to escalate their anxieties.

As a first step, start by acknowledging their fears and understanding where they are coming from. Glossing over them prevents leaders from bringing their organizations along

and truly being authentic. It is in a leader’s best interest to pay attention to their points of view while also tempering their extremely negative reactions with reality checks and examples of how past changes have ultimately led to positive outcomes.

The second step is to articulate the cost of “not changing.” Borrowing Daniel Kahneman’s theory of loss aversion from his seminal book, Thinking, Fast and Slow, we can infer that people are more afraid to lose what they have, than seek pleasure in the possibility what they can gain. In other words, in situations where the status quo seems acceptable, people prefer to avoid losing what they have, than take a risk to gain what they do not currently have.

Hence, if change resistance is accompanied by a strong emotional response, the change message must start with articulating the loss or cost they will experience if they do not change.

The third step then is to clearly define the benefits of change itself.

To recap, acknowledge the fear, listen carefully to articulated risks, share the cost of not changing, and then find the right time to introduce possibilities. Better still, let their change-agent peers introduce them to the possibilities.

Let us not forget about the fence-sitters, who are open to the appeal of the future, with some acknowledgment of risks.

Unlike the other two groups, with fence sitters, leaders do not need to temper the extremes of enthusiasm or fear. However, they need to see a concrete reality, and this audience

may push you to polish the finer aspects of your message.

Leaders are also likely to face the most incisive and data-based questions from this interested-yet-skeptical audience. This group, in conjunction with the change agents, can also help you integrate AI into your work more effectively by shifting perspective from the “or” to the “and” as defined in a 2020 MIT research brief,2 a very comprehensive analysis of the prospects of AI.

Successful integration of AI into our work and lives is about finding how AI will do what it does best and truly complement human efforts so we can continue to elevate our human capabilities. We co-exist with AI by co-opting it for the right ends; we do not get replaced by it.

While these three strategies to engage different kinds of stakeholders are useful, they cannot be successful unless accompanied by a journey of self-awareness.

Before leaders engage with stakeholders, they need to understand their own mindset and how their own preferences and response to this change are being consciously and unconsciously reflected in their messages. Some self-reflection questions: What words, phrases and responses belie their own propensity for risk-taking? Are they able to balance their messages and listen keenly to stakeholders without being unduly influenced by their own ingrained biases regarding AI?

At such times, an impartial and honest voice can be a leader’s most challenging yet best friend. Some examples are getting a coach or a mentor, role-playing with peers, and writing and rewriting messages for clarity and succinctness.

Leading the Application of Technology

● We are all accountable to the people we lead and the customers we serve. So, we must take ownership of key decisions that will impact them. AI is a change whose impact will be felt across most jobs and all industries.

Whether you are a leader in health care or education or telecommunications, take the time to engage and learn about AI, how it is evolving, and what its application in your industry will be. Be prepared to make decisions on how it will show up in processes and products and how you will evaluate if it provides value for your organization. This means a leader must have the dexterity and judgment to be a change catalyst when an upside opportunity to leverage AI presents itself.

In addition, leaders need fast follower skills to scan the environment for use cases and quickly take action if they spot an opportunity they initially missed.

All this requires a leader to lean into curiosity and learn more

Take the time to engage and learn about AI, how it is

about AI and its impact on work and lives. An intentional exercise to understand the origins of AI, its evolution and its impact will pay itself forward many times over. It is imperative that every leader invests in it and gets ahead of this knowledge curve.

Leaders will also need to grasp the frailties of AI. Since there is widespread literature on the possibilities of AI in positively transforming our work and lives, I will focus on a few risks in this section. AI is simply a machine learning tool that teaches itself to process data and make decisions. Hence, it is imbued with the frailties of its creators and comes with ingrained biases. Leaders should understand how these would play out in their own functions and organizations and ensure there are guardrails against these biases becoming systemic. Rigorous testing, pilots across diverse customer segments and robust feedback are crucial in ensuring that AI truly makes us better instead of perpetuating our existing imperfections.

Let us consider some other risks of AI that are not well understood today. AI reflects the data that it uses to evolve itself. As a result, it is subject to the risks of data poisoning.3 This involves manipulating the data set that the machine learning model uses to train itself, by injecting polluted data. Even small pockets of bad data can control the behavior of the AI and deliver false or skewed results.

Additionally, AI is subject to “alignment” problems.4 When AI is designed to single-mindedly pursue a goal set by a user, it can also act in other unintended ways that are harmful.

For example, an AI trained to single-mindedly make efficient choices will find every single way to do so, but it may lose sight of thoroughness or let bias creep in, during its relentless drive to efficiency.

Another drawback leaders should be aware of is that machine learning models are not transparent.5 A human could be asked to explain the logic programmed into a machine. But when a machine evolves and learns, it is very hard to decipher its journey of thought progression.

Conclusion

● Making decisions while being faced with so many unknowns makes the role of a leader extremely complex at such times, and it demands the seamless integration of leading people and the application of technology.

The kind of industry a leader is operating in can have a big impact on how a leader leverages the three types of stakeholders. Consider first an industry like telecommunications, where technology is the engine of growth. Organizations in this

industry are likely to have more change agents who are eager to embrace AI’s possibilities. Leaders themselves may belong to this group. In such organizations, a leader must lean to the uncomfortable and seek out the resistors to balance the voices in the room and pay keen attention to the risks they surface.

Now take pharmaceuticals, where technology is an enabler. Organizations in such industries are likely to have a large proportion of fence sitters and resistors. Leaders in these organizations would do well to hire and empower change agents who will be provocative and catalyze change.

Taking controlled risks through small pilots, engaging in deep analysis of these pilot outcomes and flexibly changing course have become critical leadership skills.

While doing all the above, leaders must ask the right questions, keenly build their knowledge, and listen to vastly different viewpoints to understand their implications. The most important ask from leaders today is to meet their stakeholders where they are.

1 https://www.weforum.org/agenda/2016/07/ why-do-people-resist-new-technologies-history-has-answer

2 https://workofthefuture.mit.edu/research-post/artificial-intelligence-and-the-future-of-work/

3 https://themathcompany.com/blog/data-poisoning-and-its-impact-on-the-aiecosystem#:~:text=A%20form%20of%20adversarial%20attack,model%20and%20deliver%20 false%20results

4 https://www.economist.com/science-and-technology/2023/04/19/ how-generative-models-could-go-wrong

5 https://www.economist.com/science-and-technology/2023/04/19/ how-generative-models-could-go-wrong

evolving, and what its application in your industry will be.

In the Lead with... Polly Mitchell - Guthrie

“In the Lead” is a conversation with industry leaders on key trends and leadership challenges. In this issue, we spoke with Polly Mitchell-Guthrie, VP of Industry Outreach and Thought Leadership at Kinaxis. Here we discuss how humans and AI can work together to achieve more comprehensive outcomes.

Ruchin Kansal What is AI, what is generative AI, and will it take away our jobs?

Polly Mitchell-Guthrie Yes, these questions are certainly top of mind for many people these days. Yesterday, somebody was at my house installing blinds, and we were talking about AI. He is a golfer and said that the range of motion that a human has far exceeds that of any robotics we can build. I thought it was an interesting perspective.

In general, I like to start off by saying that AI is the science of how computers can mimic humans. A subset of AI is machine learning, which is most of what we see these days, despite the advent of generative AI. Machine learning means computers learn from data to predict something. If we know what has been done in the past, we can learn from the data and predict what will happen in the future.

What’s so different about generative AI is captured in the word “generative.” What I would like to point out is that it is still grounded in predictive modeling.

ChatGPT, the most well-known example of generative AI, is a probabilistic sentence completion machine. What I mean by that is if you input a question, something like “Can you give me a song about the supply chain in the voice of Bruce Springsteen?,” it will come up with something. It’s predicting something that hasn’t been done before, but it’s going to do it probabilistically. It will come up with a song that could likely be in Bruce’s voice.

And I say “likely” because one of the things people say about generative AI is that it lies. For example, when it comes up with the song in Bruce’s voice, even though there might not ever have

been such a song, what AI is saying is that probabilistically, this is a song Bruce Springsteen could have written, even if he never wrote it. Those are called hallucinations, a technical term for when it is coming up with something that could have existed but never did.

But to us humans, it feels like a lie. Because how can you say that? How can AI say Ruchin wrote a paper that you never actually wrote? Or you spoke at a conference when you never actually did? That behavior can be deceiving.

And in terms of whether it will take away our jobs, I find people have polarized thinking. Some are excited about what AI could do and want to pursue those possibilities, and some are worried it’s going to take away jobs. What I like to say about AI is that it still lacks what I call the three C’s: context, collaboration and conscience.

AI cannot create context from nothing. For example, AI can see a fork, knife, plate and glass on your table, but it won’t know that this thing is called a table setting. In my world of supply chain, it can tell you that something happened, but may not have the context of why it happened, the organizational history, etc.

Second, it cannot collaborate. You and I could come up with great ideas that we might not be able to come up with on our own individually, because of the richness of our complementary skills and thinking and experiences and diverse perspectives. AI cannot work in this way collaboratively.

Finally, AI does not have a conscience. It does not know right from wrong, and that’s one of the things that I’m sure we’ll talk about later. It just comes up with an answer based on probabilities. And that’s where humans come in to say this is an action we should take.

Given that it lacks those three C’s, what generative AI is going to do is increase our productivity, as it will allow us to do more things, different things, and automate the obvious things that are not really the best use of our time, giving us the chance to focus on the things that are higher value.

There are two examples I like to give on this. When ATMs were first installed, people worried that there would be mass layoffs of bank tellers because people were used to going into the bank to get cash or make a deposit, and now you could do that with an ATM. In fact, what banks did is open more branches and hire more tellers because tellers could now do things that they could not do before: cross-sell and up-sell.

The second example is radiology. They’ve been saying for years that radiologists would be going away as a profession because of predictive models. One of the things that AI is good at is image recognition. For a long time, it’s been able to recognize an image, classify it and predict whether it is a tumor or a fracture, etc., more accurately than humans.

But some economists did a study and found that there are 30 tasks in the radiology workflow and only two of those have to do with the actual image classification. The other 28 involve steps that a machine cannot do. So, what we may be doing is automating and predicting tasks, but humans will remain in the workflow and continue to matter.

RK That is a optimistic view on AI’s impact on jobs. With that in mind, how can leaders alleviate the concerns that are out there regarding AI and create secure and innovative cultures?

PMG It’s a great question and certainly a leadership challenge. One of the things I’d like to point out is that so much of what leads to job dissatisfaction is the enormous amount of time spent on tedious tasks that are mind-numbing and neither creative nor inspiring. With AI, we can automate what is not worth a person’s time.

What I really want to do as a leader is encourage people to see that AI will help take the tedious stuff off their plates and give them a chance to focus on what is important. And then redevelop them and

help them build the skills that they need in the contemporary world. That’s one way to think about what leaders need to do — to help employees see that AI is going to give them a chance to be more creative and operate at the top of their license.

RK I was going to say top of the license, and you caught me right there. That is certainly a positive implication of AI, and I think we as leaders must do a better job of communicating that. What I also hear in conversations today is that historically, organizations were built on workflows, and in the future, organizations will be built on data?

PMG I don’t imagine workflow is going to go away. However, our workflow is going to change. Another important point to make about AI is that it will complement and augment humans, and not replace them.

What that means is that we are going to automate workflows, flag exceptions and focus on solving those exceptions. Which ones we would flag will depend on the nature of the business we are in. We might care about a minor variation in one and allow for greater latitude in another. So, we set the parameters. We are in charge. That’s one thing.

Research conducted by the University of Chicago and the University of Pennsylvania found that people are unwilling to trust AI or machines if they don’t have a sense of control over them. They did a test and found that if people thought a forecast had been done by a machine or a model, they didn’t trust it. But if it was done by a human and had a mistake in it, they would be willing to say that “I can understand why this happened.”

Further, if people were given the ability to set some parameters, for example, they felt like they still have their hand at the wheel, even though they might have been on cruise control. They were more willing to trust the machine in that instance. So, we must give people a chance to see that they’re still overall in control.

Secondly, human control is important for validation and monitoring. These models will make mistakes that don’t make sense. When COVID-19 happened, mathematical models had to go out the window, because suddenly

AI cannot create context from nothing. For example, AI can see a fork, knife, plate and glass on your table, but it won’t know that this thing is called a table setting.

history was no longer a good predictor of the future. We had to have humans who had history and experience, who had domain expertise, who knew what they were doing. We had to validate and monitor.

I would say that we must be prepared to change the workflow, be adaptable, build new skills, seek new opportunities, and I’d say, we should think big. I’d like to think about what we can do differently that we couldn’t do before because we were so busy spending time, say, updating lead times.

The new workflow is humans and models working together in a complementary fashion, where models can do a lot on their own, and humans monitor and validate.

RK You brought up two interesting concepts. One, think differently and think big, and two, humans and robots come closer together as a team. How do leaders harness this and create a positive culture?

PMG I’ll step back for a second and put generative AI in context. We think language is something that is uniquely human. When we see AI-generated output, what’s underneath it is a mathematical model that can produce language or even images and voice. When we see what it can do, we’re amazed and may feel like, oh my gosh, it’s taking over the world.

I’d liken it to a magic trick in the sense that a magician can pull a rabbit out of the hat, but only under carefully controlled circumstances with a specific sequence of activities. A magician is doing things in a particular order and misdirecting your attention. If you were to see that same magician out at a restaurant that night and go up to her table and say, “Can you now pull a rabbit out of the hat,” without her tools, the sequence, the proper setting, she can’t do it.

Therefore, what I say to leaders is that they need to do three important things in the age of generative AI. One is to ask it the right questions. Two, to direct the right attention, and three, to exercise the right judgment.

In terms of asking the right question, what is important is to understand that the questions you ask make a difference. There’s a skill in asking what’s called the right prompt. The question you type into the chat box is a prompt. If you ask a vague, amorphous prompt, you’re going to get a vague, not helpful

answer. However, if you ask for a specific prompt and you can start iterating, you’re going to get better answers.

Similarly, a leader’s role is to ask the right questions about what’s going on in the organization. Who needs to be involved? Who are we not thinking about? Or what are we not thinking about related to what is going on here? What have we done before? Where are we going? Those are all examples of asking good questions. A leader’s role also is not to assume you have all the answers and to engage a broader team that can collectively do more together.

Second, is directing the right attention. As I said before, generative AI can misdirect your attention. It is easy to have our attention distracted. We all know in this age of smartphones, FOMO [fear of missing out], etc., it can be hard to hold our attention and to hold our attention on the right things.

I often tell my teams to remember that these days, our challenge is to say no to the things that are not worth our time. The question is, what’s the best use of your time? How do you operate at the top of your license? It can be hard to say no, so directing the right attention is saying what is most worthy of our attention.

How do I lead an organization to have my people focused on the important, not just the urgent? What’s the most value that we can add? What is the most aligned with our strategy?

So, asking the right questions, directing the right attention, and then exercising judgment are all things that the machines cannot do. Machines cannot look for bias on their own. They cannot know those complex trade-offs that can’t be weighed into a mathematical model — what’s the right thing to do in a situation, with a strategy, with an opportunity, with a particular challenge. Judgment is a unique human capacity. And that’s why leaders need to exercise that judgment.

RK So, if leaders can ask the right questions, direct the right attention and exercise judgment, it sounds like AI can help us become a lot more efficient and enable us to make smarter decisions. How do you make room for empathy and human connection in all of this?

PMG As we look at what AI models can do, automate or compute, we still want these models to complement humans.

Business acumen is also important, whether you’re a physician or a supply chain practitioner, or a marketing leader.

And if we’re going to have the models complement humans, we need to help the models do the best they can, and that involves data scientists who can help tune them. I know you’ve told me you’re into cars. Tuning a model is just like tuning a car, you know. We tune a model to do the best it can do.

We also need to tune humans. What humans need is compassion, leadership and vision. And empathy, which means saying, “I understand that you are here to do a job, but you’re also a human who’s got a lot of other things going on in your life. As a leader, I need to pay attention to when there’s a challenge going on in your life, or what opportunities I see for you. What are you best at? What really excites you and motivates you the most?”

Empathy is about recognizing that and helping my employees perform at the top of their license.

My company partnered with an organization headquartered in the U.K. called boom! — The Global Community for Women in Supply Chain. They conducted a survey last year on what supply chain practitioners need to thrive and survive. One of the top things respondents asked for is compassionate leaders.

Compassionate leaders are the ones that can recognize that I am more than a data point. That my child is sick today. My father just died. My spouse is undergoing chemotherapy for cancer. Or that I’m no longer excited and motivated at work, or this project has been long and hard. Or that I feel overwhelmed. Compassionate leaders pay attention to these things to help humans become high performing. A highperforming human is best for everyone because everyone feels most satisfied.

I’m not talking about pushing beyond limits. I’m saying most of us feel excited when we are at the top of our license. When we are getting joy out of our work or what we’re doing, we are going to give our best to the organization and be the happiest ourselves.

RK It was so well said. It is a leader’s job to unleash that passion and the potential for their teams to work at the top of their license. My question is, what do you foresee as the skills that will be needed to perform at the top of the license in the age of AI?

PMG Absolutely. What is critical to be able to maximize the efficacy of technical skills is our other skills as well.

The ability to communicate rises to the top. A physician wrote an interesting article about how he was having trouble with the family members of a patient because there was a particular treatment that the patient needed, but the family was concerned that it wasn’t right.

So, the physician asked ChatGPT, “How do I communicate what the family of the patient needs to do right now?” The answer he got gave him some words to use that he hadn’t thought of before. He was able to communicate the message in an empathetic and humane way when he literally showed the machine output to the family as the prompt for the discussion.

So that’s about communication. How do I communicate a difficult message and tell a story? How do I take an AI output and explain it to a business leader who may not have the mathematical training that I do? All these are important communication skills.

Critical thinking skills are equally important. They are about the ability to monitor the bias in the machine-generated output and saying I’m not going to trust the results on their own. It is saying I’m going to use my critical thinking skills when something doesn’t seem right.

And of course, judgment. I spoke about the importance of exercising judgment earlier. You can’t ask ChatGPT, “What should my business strategy be?” It’s a judgment call.

Change management is another critical skill needed moving forward, particularly in this age where change is happening so fast. We need to reflect on that and what we need to adapt our organizations. Even if you’re not a change-management expert yourself, you need to know when your team, a situation, a task force or a group needs change. You need to know who can help you figure out how to adapt.

Business acumen is also important, whether you’re a physician, a supply chain practitioner, or a marketing leader. Understanding the business you are in, what’s happening in your business or organization, and what’s the context around it. And then based on that, how to use judgment and make a good decision.

Asking the right questions, directing the right attention, and then exercising judgment are all things that the machines cannot do. Machines cannot look for bias on their own.

RK I fully agree with you. We know that it is the above skills that are a better predictor of long-term success, and we must double down on building those skills. And that brings me to the last question of our discussion. Are there ethical issues that we should be concerned about as we adapt to AI?

Also, I was watching The AI Dilemma. They said that 50 percent of AI researchers believe that there is a 10 percent chance that AI can disrupt humans as we know them. Then they ask if 50 percent of airplane engineers told you that if you get on this plane, there’s a 10 percent chance you will die, would you get on that plane? But with AI, it feels like we are just embracing it without fully understanding it. What do you think of that?

PMG I think the concerns about AI fall into four categories. One that you mentioned is the policy-alignment problem. The notion that AI could suddenly act on its own and work against what is in the best interest of humans, and therefore needs to be regulated. I think the most honest answer to any of this is I don’t know. I can’t tell you for sure that’s not going to happen. I can tell you based on the readings I’ve done and the people I talk to that at the present time and in the foreseeable future, I don’t see that alignment problem as a grave concern.

A second concern is arming bad actors. We must monitor and pay attention to that, to what could go wrong. I’ve heard examples of generative AI mimicking voice and bad actors using it for ransom. But bad actors have been around since the dawn of time, and I don’t think that’s a new threat. We’re just giving bad actors new tools. We certainly need to pay attention to that.

Third is jobs. I believe that there will be net job creation, but there will be some whose jobs are more impacted, and there will be some who will be out of work. So, we have to think as a society about how we invest in job training, reskilling and restructuring.

Professors at the University of Toronto — Ajay Agrawal, Joshua Gans and Avi Goldfarb — have written a fascinating book Power and Prediction: The Disruptive Economics of Artificial Intelligence. They point out that it has typically taken new technologies even like electricity decades to be adopted. I think the pace of change with AI in many ways will be slower than people think. I don’t think professors are going to lose their jobs overnight.

The fourth, and the one that I think is going to have the biggest and most widespread impact and therefore needs the most

attention, is bias and misinformation. I’ll give two examples.

ChatGPT was asked to write performance reviews for an engineer, a female engineer, an African American engineer and an Indian engineer. Given just that little level of information, it was remarkable how biased the results were. If that’s all you’re given, you would assume that they all get the same review, but in fact, the results were quite differentiated. Women were rated more harshly. People of color were rated more harshly, and this reflects the bias we have in society. It’s well known that this bias exists, but it’s remarkable that it comes out of a mathematical model that is given little information. We have to monitor and pay attention to that kind of bias.

A second example I’ll give is an earlier precursor of generative AI in image recognition. Joy Buolamwini, a leading researcher in AI, particularly in image recognition, conducted the Gender Shades Project, and what she showed is that the classification capabilities of AI were far worse on women than men and on people of color versus white people.

So literally, if you take a brown-skin person versus a blackskin person, the darker the skin, the worse that recognition was. To the degree that only 65.3 percent of the time could it get an African American female face recognized correctly whereas, for a white male, it was 99.02 percent accurate. Part of the challenge they found is that these models were trained by teams that were white male majority, so the human bias was built into the model.

I think that kind of bias is going to be sometimes widespread and sometimes subtle. We need to recognize that it is a problem in the first place and care about the problem. We need to have diverse teams building AI models, and we need to have monitoring and validation. That is where we as humans will need to play a critical role. That’s where critical thinking, judgment skills, compassion and empathy are all essential in being able to recognize, identify and act on that kind of bias. And misinformation.

RK That was a good framing of the four key risks that AI presents. We need to have a deliberate, measured approach to addressing each of those. Thank you so much for your time and for your unique insights.

PMG Thank you for taking the time to interview me and for the invitation in the first place.  L

What I really want to do as a leader is encourage people to see that AI will help take the tedious stuff off their plates and give them a chance to focus on what is important.

THE FUTURE OF LEADERSHIP SURVEY: 2023

The Seton Hall University Stillman School’s Department of Management research team presents the results of its third annual, Institutional Review Board-approved “The Future of Leadership Survey: Insight and Foresight About the Future of Leadership from the Leaders of the Future.”

By RUCHIN KANSAL, M.B.A., KAREN BOROFF, P h .D. and BIANCA JOHNSON, M.S.

EXECUTIVE SUMMARY

THE RESEARCH TEAM launched this longitudinal research in 2021 to pointedly examine the leadership insights and foresight of those entering their professional lives. Before that, leadership research tended to center on C-suite executives, either on their own behaviors or as observed by those who report to them or by other stakeholders. There was a significant gap in the literature on how the next generation viewed leadership and how best to develop the leaders of tomorrow.

This, we believed, was a serious omission in research for two reasons. First, organizations needed guidance on how they may want to respond to the leadership aspirations and expectations of tomorrow’s professionals. Second, there was a dearth of guidance on how to develop the leaders of tomorrow based on their own wants.

Recognizing the connectedness of organizations globally, this year we took an important and critical step in expanding our research worldwide. In addition to Seton Hall University students and alumni, the survey was distributed to the target segment across the United States, Canada, Mexico, Brazil, China, Japan, Singapore and the European Union 5 (France, Germany, Italy, Spain, United Kingdom). We undertook the first step in understanding

leadership from colleagues on the African continent by surveying a small subset of leaders in Nigeria. We received more than 5,300 valid responses, providing us with a robust data set to conduct our analysis.

The findings from the survey are intriguing. While mainstream news media are full of stories regarding the hesitation of employees to be back in the office and relative job dissatisfaction and leadership challenges, the survey findings present a different picture. The results indicate 70 percent of employees work remotely less than 50 percent of the time; which means 70 percent of the respondents are in the office at least three days a week. This is in stark contrast to what news reports seem to indicate.

Further, we find that according to the respondents, remote work has not impeded productivity, nor has it impacted their leadership development, nor is the lack of it a primary cause of job dissatisfaction. The primary cause of job dissatisfaction around the world is salary.

The results should give employers pause. The results indicate that the emerging workforce is keeping pace with and has adapted well to a hybrid work environment. This cohort believes that the employers are providing them with good development opportunities and want more. Rather than forcing workforce policies of the pre-pandemic era, employers will be well-served to meet the emergent workforce where is, and it can be a win-win situation for all.

RESEARCH DESIGN

In 2023, the research team decided to broaden the age group of target respondents from 18-25- to 18- to 30-yearolds. From the 2021 survey, we had preliminary data that signaled that work experience moderated insights and foresight on leadership. With the 2022 survey, we were not able to confirm

the impact, as our dataset did not have an adequate sample size of those with work experience to draw statistically valid conclusions.

To enhance the insights, in the third survey we expanded the respondent base to include 18- to 30-year-olds to get an adequate sample of the generation getting ready to enter the workforce and also to obtain insights from those who have already gained some work experience.

The overall objectives of the survey remained consistent:

• To better understand the expectations of future leaders — college students and recent graduates — regarding the leaders they seek to work with or want to become.

• To develop insight and foresight on values, competencies and preferred models of leadership development for the future.

The survey started with a selfreflection on traits noted in leadership literature. We again asked about the relevant physical traits of leaders, nested in the prior traits research, some of which is over 50 years old.

The next set of questions focused on the values, character and competencies of mid-level leaders and the challenges they face. These questions were posed to obtain a form of leadership trajectory for young leaders. In other words, we would learn the competencies and values that would be important in a leadership position they envisioned 10 years or so into their own future, as well as the competencies and values they would be seeking from their own boss.

There was also a series of questions posed on how best to develop leaders of tomorrow. The survey again probed the impact of remote work on leadership development. The team also surveyed the respondents on their job satisfaction to understand what leadership dimensions affect this the most.

Finally, respondents replied to a set of demographic and work experience questions.

In February 2023, survey respondents across the globe were sent electronic copies of the survey, translated into local languages. For Nigeria, 10 interviews were conducted over the phone using the same survey instrument.

Seton Hall University’s social media accounts were leveraged to invite responses, and several reminders were also sent. Over 5,300 completed surveys were returned, with the results tabulated in aggregate at global and regional levels. (While respondents from Nigeria had the highest scores overall, we have excluded them from the overall analysis, given the very small sample size.)

INSIGHT #1

Personality self-reflection seems to bode well for nurturing the leaders of tomorrow

The self-refection questions that we asked are based on the Big 5 profile model proposed by L.R. Goldberg, J.A. Johmon, H.W. Eber, R. Hogan, M.C. Ashton, C.R. Cloninger and H.C. Gough in “The International Personality Item Pool and the Future of Public domain Personality Measures,” Journal of Research in Personality 40 (2004), pp 84-96 1

At the global level, the respondents exhibit a high level (score above 8) of openness to new experiences — 8.20 (I enjoy hearing about new ideas, and I enjoy thinking about new things). The respondents exhibit a moderate level (score between 6-8) of agreeableness — 7.94 (I sympathize with other people’s feelings, and I take time out for others), conscientiousness — 7.75 (I am always prepared, I pay attention to details), emotional stability — 6.72 (I am relaxed most of the time, I am not easily bothered by things) and extraversion — 6.67 (I talk

to many different people at parties, and I don’t mind being the center of attention).

Respondents whose primary work location is China, Japan and Singapore scored highest on extraversion and emotional stability. Respondents from Mexico and Brazil scored the highest on openness to new experiences and conscientiousness, while respondents from the United States and Canada scored highest on agreeableness. (See Figure 1)

Analyzing the data by gender, both males and females scored equally on conscientiousness. Males scored higher on extraversion and emotional stability (moderate levels), while females scored higher on agreeableness and openness (high levels). Respondents with experience greater than one year scored higher on all dimensions compared to respondents with less than one year of work experience. On the other hand, supervisory experience increased the scores on extraversion, conscientiousness and emotional stability, while reducing the scores on agreeableness and openness. Remote work did not seem to affect the profile traits.

The literature suggests that leadership

success has been known to correlate with high levels of openness and conscientiousness. Further, “extraversion has proven to have strong predictive validity of leadership in a position following a job interview.” (Salgado, 1997) The survey data suggests that the leaders of the future have the raw materials to be successful leaders — especially as conscientiousness has scored high across regions, gender, years of work experience and supervisory experience, while the personality traits of openness and extraversion are present in healthy doses.

INSIGHT #2

Attitudes toward physical traits converge globally. The 2021 and 2022 survey results indicated acceptance of diversity in the ranks of future leaders, with an expectation that leaders are well-attired, healthy and with well-groomed hair. Gender, race and ethnicity-related physical characteristics did not rank as high for this generation. The findings from the survey allowed the team to contribute to the national discussion on the CROWN Act of 2022 (Creating a Respectful and Open World for Natural Hair Act of 2022) and the Future of Work.

Yet again, the respondents overwhelmingly ranked controllable attributes of physical appearance — attire, health and hair — as important attributes of a leader. Genetic traits such as height and voice are not emphasized as important. The results are consistent globally and at regional levels, with Asia putting a little bit more emphasis on attractiveness over well-groomed hair.

Based on the responses, we infer that employees still view their leaders as figureheads of the firm and are the face of it to the public. That figurehead role demands that leaders convey a presence regardless of whether the respondent is a man or woman and regardless of race.

We are heartened to see that leaders of tomorrow have moved beyond the superficial physical traits of leaders, such as those centered on height and more. Nonetheless, wearing the leadership mantle carries with it expectations about how those leaders present themselves to their many stakeholders. Our message to future leaders remains loud and clear: invest in apprwopriate attire, stay wellgroomed, and please, take care of your health. Invest in diet and exercise.

(See Figure 2)

INSIGHT #3

Future leaders face higher volatility and uncertainty.

When surveyed on what the respondents look for when hiring their immediate supervisor, we notice convergence in the values and character traits expected from a leader at the global level. The most desired values and character traits are “admits mistakes,” “recognizes the dignity of employees,” “is a continuous learner” and “is ethical.” Additionally, European respondents want leaders to “encourage open dialog.”

REFLECTIONS

Asians also want leaders to “seek diverse perspectives” and “pursue excellence.”

(See Figure 3)

Surprisingly, “driven by higher purpose” has been ranked the lowest across all geographies, except for Asia. This is in stark contrast to the significant emphasis on purpose in leadership literature today. Our hypothesis is that the responses are indicative of the volatile business environment where rapid change, shifting priorities and high employee turnover require leaders to be continuously learning, treating employees with respect and remaining ethical.

A review of key challenges faced by leaders confirms our hypothesis — that “dealing with crisis and failure,” “managing work-life balance” and “creating an environment where employee work is valued” are the key challenges facing leaders today. (See Figure 4)

An examination of competencies required of future leaders affirms the hypothesis that future leaders need to be prepared for leading in a rapidly changing world. A view of the pattern that emerges at a global level indicates that future leaders need to be able “to cope with and adapt to stressful situations,” and be critical thinkers, collaborators and communicators, innovators and change agents. (See Figure 5)

INSIGHT #4

Early data suggests that leadership development is normalizing in hybrid work arrangements.

We surveyed the respondents to understand what percent of their work was remote over the past 12 months. Despite the impression created by the news stories that employees are hesitant to come back to offices, 43 percent of the survey respondents reported less than 25 percent remote work — which translates to approximately one day of remote work. Another 31 percent reported remote work

to be between 26-50 percent, which translates to about two days of remote work. Only 26 percent of the respondents reported more than 50 percent of their work being remote. It is, therefore, safe to conclude that 75 percent of the survey respondents are in the office three or four days a week. (See Figure 6)

Looking across regions, we see consistency in a high percentage of respondents reporting less than 50 percent of their work being remote, except for the respondents in Asia, where the percentage is slightly lower but still predominantly less than 50 percent of the work being remote. (See Figure 7)

In the 2022 survey, 60 percent of respondents based primarily in the United States reported less than 50 percent of their work being remote. The 2023 data suggests that the percentage of remote work has decreased overall, as 75 percent of the respondents based in North America now report less than 50 percent of their work being remote.

What is interesting is that when asked whether remote work has affected their leadership development, in 2023, 59 percent of the respondents said yes and 41 percent said no.

This is in contrast to the results from the 2022 survey, where 76 percent of the respondents indicated that they experienced a lack of leadership development because of the pandemic — which was characterized by a higher percentage of work being remote, with the caveat that the 2022 survey respondents were primarily from the United States.

The 2022 survey respondents also indicated that if firms manage to keep remote work to less than 50 percent, there was a slight positive uptick in certain leadership growth dimensions. However, if employees experience more than 50 percent of their work remotely, a negative impact on leadership development was observed. (See Figure 8)

FIGURE 3

VALUES AND CHARACTER TRAITS OF A LEADER, BY REGION

FIGURE 4 CHALLENGES OF A LEADER (TOP 3), BY REGION

COMPETENCIES OF A LEADER (TOP 3), BY REGION

STAKEHOLDERS COPES WITH AND ADAPTS TO STRESSFUL SITUATIONS CAN BRING DIVERSE GROUPS TOGETHER TOACHIEVEORGANIZATIONALOUTCOMES CAN DIFFERENTIATE AMONG FACTS, PERCEPTIONS, ASSUMPTIONS AND INTERPRETATIONS CAN ATTRACT, DEVELOP AND RETAIN THE RIGHT TALENT USES OBSERVATIONS TO FIND AND IMPLEMENT SOLUTIONS TO PROBLEMS

What becomes very interesting is that when asked to rank how valuable the various approaches have been in their ability to advance their leadership competencies in this work environment, looking at the pattern across the globe, “the ability to be exposed to leadership communication,” “the ability to be exposed to collaborative work” and “the ability to be exposed to opportunities that extend beyond one’s comfort zone” ranked the highest.

In 2022, the respondents (albeit primarily U.S. based) noted that the above three approaches to leadership development were negatively affected by the pandemic.

The conclusion that can be drawn from this observation is that while it may have taken employers a couple of years to adjust to the new post-pandemic reality of hybrid work, they have adapted well and are doing many things right — the leaders are communicating effectively, there is enough collaborative work, and people are getting enough work opportunities to stretch beyond their

comfort zones. This bodes well for employer-based leadership development.

(See Figure 9)

INSIGHT #5

70 percent of the employee base is at risk of attrition at any time. At the same time, when asked “to what extent have you considered leaving your job in 2022 versus 2021,” an astonishingly high 30 percent responded, “More.” What is also interesting to note is that only 30 percent said “Less,” which means 70 percent of the employee base of a company is at risk of attrition at any given time. This cannot be good for any company, its morale, its productivity or its competitiveness in the market.

(See Figure 10)

What is confounding is that when asked how much the respondents agreed with various statements that were presented, the respondents moderately agreed with the following: “my work can make an impact,” “I am loyal to my employer,” “I have control over my

FIGURE 5

work,” “I am satisfied with the feedback that I receive,” and “I am satisfied with the time required to be physically present in the office.”

The three statements where the respondents are least agreeable are: “I am satisfied with my salary,” “I feel my employer is concerned about my mental health,” and “I feel my employer takes steps so I don’t feel burnt out.” Further analysis reveals that salary is the primary factor for respondents considering changing jobs. (See Figure 11)

Based on the data at this point in time, we can make the following inferences about the current work environment as viewed from the perspective of our target respondents — 18- to 30-year-olds:

• The workforce is less loyal, with 70 percent of respondents reporting that they have considered leaving their job the same or more than the previous year.

• Dissatisfaction with salary is the primary factor that is driving this sentiment.

On one side, this is indicative of a growth economy that is offering workers multiple choices. On the

other, it does not behoove well for organizations if they must face a high rate of churn as a result. It might be beneficial for employers to consider increased salary incentives to retain the current workforce, balancing it against the cost of constant hiring, workforce development, leadership development and succession planning.

INSIGHT #6

Employers must take center stage as the locus of leadership development.

When it comes to preferences on methods of leadership development, the employer takes center stage. At a global level, respondents have ranked internships and structured undergraduate and graduate-level leadership programs as the top three preferred choices. This affirms our recommendation from prior surveys that universities have a critical role to play in developing leaders of the future and will be well-served in developing and offering such programs.

Many U.S.-based universities already do that, including Seton Hall through its Buccino Leadership Institute. However, a closer look at the pattern where we picked the preferences most ranked in the top five, “internships/ externships,” “assigned mentors from industry or employer,” “constructive, just-in-time multistakeholder feedback,” “on-the-job leadership coaching sponsored by employers” and “structured leadership development programs sponsored by the employers” emerge as the top preferences.

Employers therefore will be well-served to continue to invest in the leadership development of their employees. (See Figure 12)

CONCLUSION AND PATH FORWARD

We are delighted that we have the benefit of more than 5,300 global data points in this year’s survey. Based on the findings described above, it is fair to conclude that we are looking

FIGURE 9

CURRENT PRACTICES RANKED BY THEIR VALUE TO ADVANCE LEADERSHIP DEVELOPMENT (TOP 3), BY REGION

CURRENT SENTIMENT ABOUT JOBS (TOP 3), BY REGION

to remote work, which is not excessive in the first place based on the respondent set. It does feel that their employers are doing many of the right things — providing both meaningful, impactful work and development opportunities.

The employees also see their employers as the loci of leadership development and want the employers to invest more.

MOST

VALUABLE LEADERSHIP DEVELOPMENT APPROACHES (TOP 3), BY REGION

STRUCTURED UNDERGRADUATELEVELLEADERSHIPDEVELOPMENTPROGRAMS STRUCTURED GRADUATELEVELLEADERSHIPDEVELOPMENTPROGRAMS CONSTRUCTIVE,JUST-IN-TIMEMULTI-STAKEHOLDERFEEDBACK

The fact that 70 percent of respondents reported that they considered leaving their job the same or more in the last year is an area of concern because an unhealthy, disengaged workforce cannot be good for any organization. The good or bad news is that the primary factor driving this behavior is salary. Companies need to take this to heart, conduct a cost-benefit analysis on offering higher salaries for talent retention and orient their human capital strategies accordingly.

While we have reported only globallevel findings in this report, the data also gives us insights into trends by region, gender, race, work and supervisory experience. 0Our intent is to uncover significant insights at the regional level that build upon the global level insights presented here.  L

at a global workforce that is more similar than not. At a macro level, the dominant personality traits of the respondents include their openness to new ideas and their agreeableness. They score lower on the traits of extraversion and emotional stability.

The workforce wants its leaders to represent them, so, in turn, has expectations that leaders will be well-groomed, well-attired and healthy. It wants leaders who have a learning mindset and can deal with constant change with empathy. It seems to have adapted well

Acknowledgment: We are grateful to our partners Awais Majeed, Steven Scott and Alex McAuley at Atheneum for making the global survey a reality for us. Their global outreach was key to collecting global data at such a large scale in such a short time. We would also like to thank Samah Alshrief, Ph.D., for her work in supporting the data analysis related to this research.

1 Also see O.P. John, L. P. Naumann, and C.J. Soto, “Paradigm Shift to the Integrative Big Five Trait Taxonomy: History, Measurement, and Conceptual Issues,” in O. P. John, R. W. Robins, and L.A. Pervin (eds.), Handbook of Personality: Theory and Research, 3rd ed. (New York, NY: Guilford, 2008): 114-58; and M. R. Barrick and M. K. Mount, “Yes, Personality Matters: Moving On to More Important Matters,” Human Performance 18, no. 4 (2005): 359-72.

FIGURE 11

IN THE LEAD Thanks for taking the time with us, Cristina. Can you describe your current position?

CRISTINA HILL I’m a senior technology auditor in the internal audit department at a financial services company. When I tell people I’m in IA, their eyes tend to glaze over, so generally I say that I make sure everything works the way it’s supposed to. More specifically, I work on a team that specializes in IT application testing, and we support our main audit teams that focus on the different business units.

Cristina Hill ’20

Critical thinking is the most important skill to being successful. It enables you to question assumptions, evaluate evidence, and mitigate risks.

HOMETOWN

San Ramon, California

CAREER

Senior Technology Auditor, American Express

SETON HALL STATS

B.S. Finance and Information Technology Management double major; Buccino Scholar; Servant Leader Scholar; Delta Phi Epsilon; internships at Major League Baseball Advanced Media, EY and Davines North America

ITL What attracted you to the field?

HILL Honestly, I never wanted to be an auditor. I assumed auditing would be boring, but really, it’s like a more bureaucratic version of investigative journalism. It’s impossible to get bored; I am constantly learning about our diverse business processes, nuances among global markets, developing technology and the new risks that emerge with it. Each work paper is not just a piece of paper filed away and never looked at again until it’s time to roll it forward in the next audit; it’s an opportunity to tell

a story and objectively evaluate how we are meeting our obligations to our customers and regulators.

ITL How did the Buccino Leadership program prepare you to lead after graduation?

HILL I think I entered the workforce years ahead of my peers in terms of soft skills. Through the array of projects and opportunities offered by the Leadership program, I learned to be agile, to work with different types of people and take on various roles. Balancing my involvement with the program with my classwork and other extracurriculars gave me time management and organization skills I use every day. Most importantly, the Leadership program pushed me far out of my comfort zone.

ITL What moment or activity sticks out as the turning point in your leadership development at Seton Hall?

HILL In my senior year, I worked three jobs, was involved in two Leadership initiatives and held a position in my Greek organization as a full-time student. And it was the most fun I had in college. I loved everything I was involved in and wasn’t willing to sacrifice any of my activities, so I had to make it work. That year tested all the skills I had been building, and it was incredibly difficult but completely worth it.

ITL What specific skills allow you to be successful in your position?

HILL The most important skill to being successful in my position is critical thinking. We have a saying in audit, “Trust but verify.” We must question assumptions, consider different perspectives, evaluate

the validity and reliability of evidence and assess the risks that face our company and how we can mitigate them.

ITL AI posits significant disruption to work as we know it today. Do you feel prepared to tackle the challenges that are forthcoming?

HILL Absolutely: This is something that has been on my mind for years since our sophomore Ideas and Trends project that focused on AI. I know how important it is to be curious and push myself to stay ahead of the curve. When I graduated, I knew I had to enter a profession that does the kind of high-value work that AI can’t replace. As I said previously, critical thinking is paramount to success in my position. I think that within the coming years, AI will do wonders for productivity within my profession, but it will not be able to make the same informed analysis or conclusions that I make daily.

ITL What leadership skills do you think recent graduates often don’t have but will need to be successful?

HILL Being open and proactive to coaching. I didn’t fully understand how valuable Peer 360 was when I was in the Leadership program, but now I recognize the importance of being self-aware, internalizing feedback, and again, critical thinking. It is so important when you enter a new job and receive feedback to understand why you’re getting those comments and how to apply them to your future work. Managers can’t always give you that full picture in the moment, so don’t be afraid to set up a time to discuss feedback.

ITL What advice would you have given to your younger self at Seton Hall?

HILL If you knew me in college, you would know that I was notorious for having a messy room (let me be clear: messy, not dirty). My schedule was so packed that I rarely had time to fully clean my room, and when I did, I found the task so overwhelming I would do anything else to avoid it. I was so hard on myself because I couldn’t do what I thought I should do.

In my post-grad life, I learned there is no one way you “should” go through life; do what works for you. I didn’t magically learn how to dedicate a chunk of time to cleaning my room from top to bottom. I discovered the most effective strategy for me is to set a 10-minute timer and get as much done in that time limit as possible. I don’t see cleaning as an overwhelming task anymore, and often those 10 minutes give me the momentum to keep going.

Similarly, I learned to use the dread of doing one task to motivate me to cross everything else off my to-do list. I don’t deep clean my room every April because I should be spring cleaning, I do it because I don’t want to do my taxes.  L

When I graduated, I knew I had to enter a profession that does the kind of high-value work that AI can’t replace.

Leading Among Knowledge Equals

BY VISWA VISWANATHAN , Ph.D.

ARTIFICIAL INTELLIGENCE (AI)

refers to the simulation of human thought processes by machines, especially computer systems. People have applied AI successfully in numerous areas, including continuous speech recognition, optical character and handwriting recognition, automated language translation, object recognition, text generation, game playing (chess, Go) and automated driving.

Although AI has been with us for more than seven decades, a steady cycle

of over-promises and under-delivery by researchers characterized much of the history of AI between 1950 and 2000. Consequently, progress went through a few “AI winters,” during which government research funding dried up.

In stark contrast, the past two decades have witnessed giant leaps in AI to yield genuinely useful applications that have found their way into the daily lives of most people. Most recently, ChatGPT — a chatbot that generates human-like responses to questions — has captivated the public. In fact, I used ChatGPT as a

reference tool for this article!

Naturally, we wonder how all this will affect our lives. Many people worry that AI will take away jobs. Will it take away one class of jobs and open opportunities for others? Will it dumb down society by reducing the need for people to think for themselves? Or will it increase creativity and innovation by taking care of routine thinking tasks and free us up to be more creative?

Will people slowly lose basic skills like writing? One could argue that people do not need to be taught to write

In the AI era, leadership will require navigating a changing landscape with emotional Intelligence and empathy.

as AI tools can complete that step. All we need is to be able to ask the right questions of AI systems.

Will AI reduce the need for teachers? After all, people can learn a lot just by chatting with a bot. Or will it free up teachers to provide more personalized education for those who need it, while letting it take care of the first or early levels of teaching?

Foreseeing which way the impact will go is challenging.

The great American philosopher Yogi Berra famously said, “It’s tough to make predictions, especially about the future.” Many visionary leaders got it all wrong when predicting the impact of new technologies on society.

➤ “There is no reason anyone would want a computer in their home.”

Ken Olsen, the founder of Digital Equipment Corporation, an iconic computer hardware and software company in the 1980s and 1990s.

➤ “I think there is a world market for maybe five computers.” Thomas Watson, chairman of IBM, 1943. There now are more than 2 billion personal computers in use worldwide.

➤ “Television won’t last. It’s a flash in the pan.” Mary Somerville, pioneering radio broadcaster, 1948.

➤ “The internet will catastrophically collapse in 1996.” Robert Metcalfe, founder of 3Com and inventor of ethernet, in 1995.

➤ When the World Wide Web arrived, people only thought about it as an infrastructure to facilitate informationsharing. Nobody predicted the staggering impact that it would have on commerce.

➤ When social media platforms took shape initially, people only saw their benefits. Their potential for spreading misinformation and the impact this would have on society flew safely under the radar.

Why do we seem to be so bad at foreseeing the societal impacts of technological changes? Invariably, people make forecasts based on initial versions of new technologies. Almost always, scientists and engineers quickly enhance the technologies and iron out flaws to make the technologies much more widely usable.

Even more significantly, though, when many creative minds encounter new technologies, they try to apply them to problems quite different from the original purpose envisioned by the creators, and the innovation starts having impacts in many unanticipated areas.

Inevitably, the people who try to assess the societal impacts of new technologies fall woefully short because they cannot possibly imagine where creative minds could take them. These improvements in many areas then interact with each other

incomplete our analysis is, we must make the effort to look at least at possible firstorder effects.

Even before the emergence of AI, the imperatives and characteristics of leadership had evolved in response to the evolution of human values of equality.

Starting with leadership flowing from birthright, power and force in traditional societies, early industrial societies saw the emergence of hierarchical structures characterized by mostly centralized decision-making. This suited a relentless focus on efficiency and productivity.

Based on research findings of the era, the post-war practice of leadership gave importance to psychological and social factors. Rewards and punishments served as prominent levers of leadership.

Later, leadership styles that motivated people to exceed their own performance goals for the greater good of the

to produce second, third and higherorder impacts as the original innovation produces waves of change.

To complicate matters even more, consider that multiple technological advances occur concurrently, and their impacts interact with each other as well.

With potential path-breaking applications in many domains, we should expect AI to have far-reaching impacts on many aspects of society. The constant stream of improvements in computing and communication technologies will only serve to amplify this impact.

One could almost say that only fools will venture to make definitive predictions on the societal impacts of new technologies. Nevertheless, however

organization started to gather momentum. The latter part of the 20th century heralded democratic and participative styles of leadership. The idea of servant leadership — prioritizing the needs of the team much more than those of the leader — also gained currency.

The new century, with its rapid technological advances and globalization, has led to the emergence of diverse, dispersed and digital teams.

At a very broad level, we have seen a shift away from the primacy of the leader as the wisdom of team members began to be trusted more. From a model where the leader was assumed to possess all the knowledge and the team members were to simply obey orders, leadership models

Inevitably, the people who try to assess the societal impacts of new technologies fall woefully short because they cannot possibly imagine where creative minds could take them.

that recognize that melding the diverse views of team members can lead to greater team performance now thrive.

When a leader possesses vastly more knowledge and experience than team members, then the imperative of educating team members on the basics of the task or setting up mechanisms and practices to ensure that team members blindly obey rigid instructions will consume much of a leader’s time and energy. Consequently, the leader will not be able to devote time and energy to other nontangible aspects.

Historically, with the increasing quality of education and widespread availability of information, the knowledge gap between the leader and the team members has constantly decreased. This has freed up leaders’ time to focus on other — mostly psychological — aspects not directly related to the team’s task, but crucial to its success.

Like the information revolution fostered by the internet, AI will empower individuals, but to a much greater extent. As a result, we can expect the knowledge gap between leaders and team members to shrink even further and even more rapidly than before. To be successful, leaders in the AI era need to excel in managing the psychological and

social aspects of leadership.

We often tend to consider decisionmaking and strategizing as the prominent strengths of a good leader. These will continue to be important. However, successful leaders also play the roles of coach, mentor, communicator and role model. We can expect the importance of the following skills to increase as AI becomes more and more pervasive. While being thoroughly knowledgeable in the kinds of things that AI can and cannot do well, aspiring leaders should focus on improving their skills in these areas:

➤ Motivating and Inspiring Creating a compelling vision and generating shared ownership for that vision. Motivating team members to exceed their own expectations and to feel a sense of accomplishment from the team’s achievements rather than only from their individual achievements.

➤ Emotional Mastery Leaders need to be able to manage their own emotions and help team members manage theirs. They need to foster a positive work environment and help the team navigate challenges.

➤ Empathy As leadership practice involves managing the needs of

the whole team more and more, good leaders show empathy and understanding and display a sharp awareness of the feelings and perspectives of team members to build trust, resolve and/or minimize conflicts, and earn the legitimacy to lead.

➤ Building Relationships Strong relationships play a key role in a team’s success. Leaders need to build strong relationships with team members and facilitate the creation of strong relationships among them. This requires networking, collaboration, negotiation and conflict-resolution skills.

➤ Self-Awareness Responsible leaders are aware of their own strengths and weaknesses. They seek feedback and learn from personal experience to constantly grow and develop.

➤ Develop Team Members Good leaders cultivate new leaders. They identify and nurture the skills of team members through mentoring and coaching and providing feedback.

CONCLUSION

Artificial intelligence is set to explode and transform many aspects of our lives, and we can expect AI to induce dramatic changes in leadership as well. Very broadly speaking, AI will continually shrink the knowledge gap between leaders and team members, and the ability of leaders to handle the various psychological and social aspects of leadership will rise in prominence.

Aspiring leaders should strive to enhance their ability to inspire and motivate team members. In addition, they should work on developing emotional mastery and empathy, being self-aware, building relationships with team members, helping team members build strong bonds among themselves and developing team members.  L

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A Battle of Wits in the Digital Era

Leveraging data, analytics and AI can remove traditional constraints on business growth.

REVIEWED BY PAULA ALEXANDER, Ph.D., J.D., and STEPHEN WOOD, M.S.

IT IS GAME seven of the World Series: bottom of the ninth; winning run on third.

The batter due up is a .280 hitter, but the manager substitutes Skinny Joe, sporting a .279 average, as a pinchhitter. The TV commentator wonders aloud, “Why bother?”

The pitcher throws a hanging curve, and Skinny Joe hits it into the gap to win the game. In the postgame interview, the manager explains: Even though his batting average is only .279, Skinny Joe bats .600 when the pitcher throws a hanging curve, and this pitcher’s curveball will hang 95 percent of the time when the humidity is high and his pitch count is over 90 pitches, both conditions prevailing during the key at-bat.

We can imagine artificial intelligence (AI) at work in this scenario. Armed with sufficient data, an algorithm can offer decision support by analyzing a myriad of

conditions and circumstances far beyond our own ability to sift through the data.

This simple illustration shows how AI might improve our decision-making: Better game-time decisions lead to winning more games and enhancing fan engagement, which drives ticket sales.

There are only so many players, teams, games and stadiums before it becomes prohibitive or impossible to expand the production of baseball entertainment. And to stop here is to miss the point of Competing in the Age of AI, which delves into the characteristics of digital systems that make them so powerful.

In another illustration, author Karim Lakhani compares in a recent webinar the beginning of AI to the introduction of paved streets, citing Boston (a chaotic pattern) and Manhattan (an orderly grid). Boston used the new (at the time) “technology” of pavement to embed the pattern of the existing, already trodden

cow paths, whereas Manhattan took the moment of technological transition to ask, in today’s language, “What would be a better operating system?”

And so it is with AI. It would be one thing to view AI as a way to improve the functioning of every system inside the silos where operations and data are organized, such as general ledgers, customer lists, distributors and marketing automation.

Lakhani and Marco Iansiti’s book goes much further. How can AI transform all business and operating models to fundamentally alter a company’s growth? And why would you want to do this? The accepted wisdom among business leaders is to stick with businesses they know, in industries they understand. But opportunities now available through algorithms and data flows are quickly erasing industry boundaries.

Back to the baseball analogy: An MLB team is not merely a manufacturer of

baseball games. It is an entertainment company that creates a locus of activities around a happy pastime — the crowd, the concessions, the pageantry, the TV experience, the merchandise sales and more.

Major professional sports leagues have already made this conceptual leap from vertical focus (better baseball drives more ticket sales) to horizontal value creation (monetizing the ancillary values connected to the game). Baseball teams and other sports have already harnessed the power of data analytics. The additional leap for AI is found in understanding the power of digital systems and algorithms.

With the collection of massive data, initially collected into silos of “team and player stats,” “food sales,” and so on, and combined with data on location, affiliated activities, and electronic apps, we discover the keys to an exponential growth horizon:

➤ Scalability Digital systems are highly scalable, with virtually no marginal cost to expansion.

➤ Scope Digital systems can widen across any type of product line.

➤ Learning Digital systems do not deteriorate with time but rather spur increasing insights and analytic power the larger they become.

➤ Networking Connectivity Network value increases as nodes are added. After reaching critical mass, everyone uses a network because everyone uses it, similar to the phenomenon of Facebook and Instagram. This further reduces friction across products, scope and scale.

Iansiti and Lakhani encourage leaders to “rearchitect” their firms along lines that will create the mentality necessary to go from the traditional to the digital. To punctuate the importance of making

Competing in the Age of AI

by Marco Ianiti and Karim R. Lakhani

Harvard Business Review Press January 7, 2020

great evolution from a handmade “fit and finish” craftsman model to the scale and efficiencies of standardization and specialization.

This book explores its subject using a wealth of case studies, among them Ant Financial, Tencent, Google, Amazon, Kodak and Nokia. For those who want to get up to speed on AI, its importance and its impact on planning, Competing is a must-read. Readers only mildly interested in AI will find the book offers a superb conceptual analysis of operating models, product strategy and overall business design.

THE ETHICAL CHALLENGE

the necessary conceptual leap, the chapter on rearchitecting the firm begins with a memorandum from Jeff Bezos to his development staff, establishing a development mandate:

“All teams will henceforth expose their data and functionality through service interfaces. … All service interfaces, without exception, must be designed from the ground up to be externalizable. That is to say, the team must plan and design to be able to expose the interface to developers in the outside world. No exceptions. Anyone who doesn’t do this will be fired …”

The directive above contrasts sharply with an approach that considers a firm’s systems to be proprietary, like its “secret sauce.” Such a policy would lead any leader to ask, “Who knows what this is going to connect to? Who knows how this is going to affect our proprietary ‘value proposition’?”

Competing in the Age of AI contains many excellent discussions of business models through history to put the current AI disruption into context. Using Amazon as a case study, the chapter on architecting sets the stage by recalling the

We recommend reading the Age of Surveillance Capitalism, by Shoshana Zuboff, reviewed by us in the initial issue of In the Lead, in tandem with Competing. Zuboff lays out the existential questions regarding the invasion of privacy and the massive assembly of billions of dossiers on every member of our population.

She asks, “Who knows? Who decides? Who decides who decides?”

We might be complacent because this surveillance is conducted by companies, but we are blind to the violation of our privacy rights.

Interestingly, Competing raises numerous ethical questions, and it raises these questions in the practical context of how to approach data. But in Lakhani and Iansiti’s world, it is presumed that the surveillance data exists, it is readily accessible, it is not by the individuals surveyed but by the companies that collect it, by right of conquest.

Some critics raise the concern of AI becoming so powerful that it develops its own agency, an out-of-control enemy, like the computer HAL in the movie 2001: A Space Odyssey or Skynet in the Terminator movies.1

The New York Times asked several

experts, “Is there something about AI that keeps you up at night?”

Roman Yampolskiy said, in part, “Everyone dying … and that’s not even the worst. You have suffering risks where AI learns how to make us immortal and then forever you have a very unhappy existence.”2

In our present phase, Competing lays out multiple ethical issues leaders face:

➤ Digital Amplification Algorithms can be misused to tailor, optimize and amplify inaccurate and harmful information. Competing cites the example of Airbnb, where people with distinctly African American names were 16 percent less likely than those with European-sounding names to be accepted as guests. “With no organized effort to discriminate, the digital systems simply amplified the impact of the implicit … bias of homeowners.”3

➤ Algorithmic Bias Both selection bias and labeling bias are virtually unavoidable. The algorithmic process itself is a form of collapsing an otherwise incomprehensible data set into a “representative” sample. Algorithmic “choices” of what is representative are subject to both myopia and malintent.

➤ Cybersecurity “Every day Alibaba Cloud blocks 200 million brute force attacks, 20 million web hacking attacks, and 1,000 DDoS (distributed denial-of-service) attacks.”4 Competing asserts, “Leaders have a fundamental legal and ethical duty to protect the information obtain from customers, employees, and partners.”5

➤ Platform Control As Mark Zuckerberg said during U.S. Senate hearings in 2018, “Across the board we have a responsibility to not just build tools,

but to make sure that they are used for good.”6 But because the power of digital scale, scope and learning derives from the openness and connectedness of platforms, the potential for abuse is beyond managers’ imagination and eludes mitigation.

Competing echoes the concerns raised by Zuboff in the Age of Surveillance Capitalism concerning the potential for violating privacy and the thorny constitutional protections of free speech.

“For many content platforms that are open to anyone, the question of control and curation gets uncomfortably close to censorship. Executives and company stakeholders will increasingly face the issue of private actors governing public action, and few are equipped to deal with these questions or generate appropriate solutions.”7

➤ Fairness and Equity As firms like Apple and Amazon expand and prosper, we can think of their platforms as ecosystems, where app creators or online retailers join in, giving the platform many characteristics of a public square. The robust health of these platforms becomes a collective good, not only available to all who enter the “square” but increasingly important and necessary to wide constituencies of consumers and vendors. The potential for excessive and abusive market power is great.

Iansiti and Lakhani write, “The leaders of modern firms cannot afford to ignore this generation of ethical challenges.”8 They compare key digital platform companies to “keystone species”

in an ecosystem — those whose presence, behaviors and health create effects on the entire ecosystem, beyond the specific business “space” they occupy.

Competing endorses an approach proposed by Jack Balkin and Jonathan Zittrain called information fiduciary.9 Companies are bound to act as “information fiduciaries, an innovative approach in the face of the new realities stemming from the incidental generation of data by social media and information technologies: There is an opportunity for a new, grand bargain organized around the idea of fiduciary responsibility. Companies … would agree to a set of fair information practices, including security and privacy guarantees, … promise not to leverage personal data to unfairly discriminate against or abuse the trust of end users …”10

In conclusion, the authors urge a broad mandate upon business leaders: “As digital firms increasingly shape our global economy, their management will be held accountable to a different standard. Despite competing as individual businesses, each will benefit or suffer from collective accomplishments (emphasis added) such as improving privacy, removing news bias and manipulation, or even creating effective systems to encourage and retain displaced labor.”11 L

1 Critics of AI systems and their potential dangers include Stephen Hawking, “The Future of Artificial Intelligence,” (TED Talk, 2014); Nick Bostrom, Superintelligence: Paths, Dangers, Strategies (Oxford University Press, 2014); Max Tegmark, Life 3.0: Being Human in the Age of Artificial Intelligence (Knopf, 2017) and Elon Musk, “AI is More Dangerous than Nuclear Weapons” (New York Times, 2017).

2 New York Times, “8 Big Questions About A.I.”, June 1, 2023

3 Page 180

4 Page 184

5 Page 186

6 Page 189

7 Page 193

8 Page 196

9 Page 197. See A Grand Bargain to Make Tech Companies Trustworthy by Jack Balkin and Jonathan Zittrain, The Atlantic, October 3, 2016. Available at www.theatlantic.com/ technology/archive/2016/10/information-fiduciary/502346/

10 Page 198

11 Page 227

Iansiti and Lakhani encourage leaders to ‘rearchitect’ their firms along lines that will create the mentality necessary to go from the traditional to the digital.

edition, April 30, 2001)

Originally published 1943

“We shall not cease from exploration And the end of all our exploring Will be to arrive where we started And know the place for the first time.”

— T.S. Eliot, from “Little Gidding,” Four Quartets (Gardners Books; Main

SHU-174-23