In a rapidly evolving digital landscape, keeping up with technological advancements is no longer a luxury but a necessity for contemporary business leaders. These innovations, especially in the field of artificial intelligence, are drastically redefining the way businesses operate. They steer businesses towards an efficient, sustainable, and profitable future. However, while AI has the potential to reshape business models, it also comes with its unique set of challenges.
1. Addressing Ethical Challenges in AI Adoption
The evolution of AI has brought with it challenges that organizations must face, chief among them being ethical considerations. The protection of consumer data, the need for unbiased algorithms, and the imperative for responsible use of this technology are issues that cannot be downplayed or overlooked. The solution lies in adhering strictly to ethical guidelines, implementing stringent security measures, and fostering transparency within organizations. Introducing rigorous stability checks and setting robust standards is also key as we navigate the landscape of an ever-evolving AI vendor ecosystem. By doing so, we are laying the foundation for an AI future that is not only innovative but also accountable.
AI, as a tool, is powerful enough to disrupt established systems and processes, and its infusion into business models necessitates the formulation of a new code of ethics. At this intersection of innovation and responsibility, enterprises have the opportunity to affect positive change at an unprecedented scale. By addressing the ethical challenges that come with AI adoption, companies can ensure that they are not only delivering greater value to customers but also safeguarding their interests. The real potential of AI lies not just in its ability to streamline operations and offer new services but in revolutionizing the very fabric of business conduct by fostering a culture of accountability and trustworthiness.
2. Harnessing AI for Business Model Innovation
The implementation of AI has drastically reshaped the business landscape, offering unprecedented opportunities for innovation and entirely new possibilities for value creation. AI is not merely a tool for automation or increasing efficiency; its most significant potential lies in its capacity to offer unique insights from large unstructured data sets. These insights can empower companies to develop new products or services, fundamentally disrupting traditional business models in the process. Instead of selling raw data, companies are now able to deliver ready-to-consume insights, heralding a shift in the value chain and opening doors to entirely new commercial and billing models. Enterprises have been gathering data for years, hoping to obtain a return on investment. AI offers a significantly faster path to ROI by leveraging this data. Instead of just selling data, businesses can now deliver ready-to-consume insights and reports tailored to the specific needs of their clients.
This opportunity represents a paradigm shift in how we approach value creation in the business world. It places the emphasis on a more client-centric model, where businesses move from selling raw materials or data to providing deep, actionable insights that clients can readily consume and implement. In this sense, AI facilitates a more nuanced understanding of consumer needs, allowing businesses to create more tailored, high-value offers. Moreover, it allows businesses to optimize their operations, making them more efficient and environmentally friendly. The potential is enormous, and we’re only just beginning to scratch the surface. By harnessing the full potential of AI, businesses can not just improve their offerings but disrupt their own business models, paving the way for future growth and success.
3. Mitigating Biases in AI Models for Accuracy
While AI brings automation and efficiency, managing bias in AI models is extremely important. Our reliance on these models for crucial insights and decision-making creates an imperative need to ensure they’re not swayed or affected by inherent historical or human biases. They should represent a fair and balanced interpretation of the world. Suchi Srinivasan suggests that AI models with a capacity ranging between 10 billion to 50 billion parameters are more adaptable to nuances and contexts of these fields. They offer a more focused mitigation of bias and can incorporate explainability better, producing trustworthy insights. As we rely more and more upon these models in guiding critical business decisions and strategies, it is paramount to continuously focus on refining them, actively seek out ways to mitigate biases and strive for accuracy. Remember, AI isn’t a substitute for human intelligence but a tool to augment it. So, the responsibility of ensuring it accurately represents our world lies with us as well.
ABOUT THE GUEST:
Suchi (Suchita) Srinivasan has worked at Boston Consulting Group since October of 2010. She is a core member of BCG’s Health Care practice and has significant experience in biopharma—specifically focused on market access, pricing, commercial strategy, and innovative growth. She is the lead for global value, access, and pricing in biopharmaceuticals.
Suchi helps clients with new product planning by supporting investment decisions and ensuring careful consideration of the relevant market and development drivers.
She supports due diligence efforts by quickly understanding new markets, unmet needs, and how to win. In her recent casework at BCG, Suchi also developed end-to-end disease area strategies to identify key areas for investment, led the strategy development for an imminent competitive threat, supported the US salesforce effectiveness effort to identify HCP value drivers, developed the brand positioning strategy for a launch drug entering a competitive class, and developed creative pricing, reimbursement, and access strategies for a drug entering a highly competitive class of drugs by building the narrative around value derived.
Thank you for reading Innovative Leadership Insights, where we bring you thought leaders and innovative ideas on leadership topics each week.
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Meet Lynn, a Customer Service Representative at ACME Corporation. Lynn’s manager is Riley, an AI (artificial intelligence) bot. Riley listens to all of Lynn’s calls in real time and provides instantaneous guidance and performance feedback. Lynn is part of the next-generation workforce and appreciates Riley’s management style because Riley provides the deep engagement and constant interaction that Lynn craves to feel as a contributing member of the team. Now, meet Pat, the human manager of Riley and a team comprised of humans and AI bots. As a second-line manager, Pat has management and leadership responsibilities to the downline reports to direct their work and inspire them towards ACME corporate goals. It is not easy when people have a variety of motivators and AI bots, well, none save what they have been trained to do.
Sound like an exciting future?
Surprise, surprise, this is very much the present. Everything described already exists. And, yes, AI systems are directing human work…and yes, human managers are directing AI “employees.”
We live in a time of rapid change, with tools that can cause a massive impact (both positive and negative). As a result, traditional leadership styles are too slow and disengaging for the workforce. Moreover, with the increasing pressure to build innovative teams and intrapreneurial cultures, leaders face the dual challenge of honing their skills and teams without many proven models to rely upon. To make life even more complicated, today’s leadership is faced with learning to manage and lead in a post-COVID world that requires managers to assess performance and steward employee well-being. Furthermore, leaders are also expected to be JEDI champions: facilitators of justice, equity, diversity, and inclusion in the workplace.
This is why we need books like this one on Innovative Leadership. Historically, people were thrust into management and leadership roles based on their non-supervisory work performance. More rigorous leadership programs were developed as business advanced to help prepare people for this transition. However, change has outpaced the curriculum development. Today’s leaders must understand the ever-evolving workforce and new transformative technological tools like artificial intelligence. Like it or not, the demands of today’s employees expect you to be ready (and that includes those AI bots), and there’s not a lot of time or patience for leadership to adjust and be effective.
Thankfully, while you may not need to worry about AI employees in the very near future, we do have some powerful AI and other emerging technology tools to support us as we shift into Innovative Leadership. From a data perspective, 2 leaders and managers have many data points to assess work performance. In the case of Lynn, we have our traditional metrics of wait time, resolution time, the number of levels supported, and so forth. However, thanks to AI tools, we can also better assess customer satisfaction throughout the entire call, using the science of psychographics (psychology and personality assessment) and neurolinguistics (science of language and word choice). More importantly, AI can assess the real-time performance of Lynn (or any other employee) as they interact with the customer. Depending on how the call progresses, the AI will provide instantaneous feedback and coaching to the employee to maximize the opportunity of a beneficial outcome for the call. That’s a level most managers cannot do with a single employee, let alone an entire team. More importantly, the insight the AI provides into employee performance is the most important for a leader to gauge employee performance and maximize customer engagement.
This is just the tip of the iceberg for leadership regarding tooling. The ability for introspection and honest, constructive feedback for leaders is critical. Getting this input, though, is challenging. This is where AI presents another boon for leaders: the honest assessment of leadership skills. As we move forward, there isn’t a single prototypical leader. We have different archetypes (the nine types of leaders as you will read in this book) based upon the ten core skills (also shared in this book). AI tools help leaders fairly and accurately assess these capabilities. This is crucial to help us understand our strengths and weaknesses and which type of leader we are to maximize our strengths. As we understand what an innovative leader means, we also see how difficult it is to get honest feedback from our staff and colleagues. Artificial intelligence provides leadership with another trusted source of information (beyond employee performance) to help us become innovative leaders.
Moreover, as we look to the future of work, it’s not only enterprises wondering what the jobs and leaders of tomorrow must bring to the table. Government agencies are investing heavily to adjust their workforce development programs accordingly. Singapore has created the TechSkills Accelerator (TeSA) initiative and AI Apprenticeship Program (AIAP) to provide the existing and future workforce with hands-on experience for these jobs of tomorrow. Canada has adopted Canada’s AI Augmented Workforce for their future work plan. The State of California has adopted an AI roadmap. The core tenants require full integration of AI skill development in K-12 and higher education curricula and a mandate to integrate AI tools to provide public services, including labor management and workforce development. That’s why management and leadership must be the first to understand and adapt to these changes because they will be the ones to lead the upcoming transformation of work.
To start leading your human and machine workforce soon, you must master ten critical skills. This book will share how to do that and essential frameworks to factor in contextual understanding and situational analysis. In essence, this book will serve as your sherpa as you enter the new world of Innovative Leadership. AI will be your leadership concierge so that you can maximize your effectiveness and support your employees in realizing their peak performance.
ABOUT THE AUTHOR:
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution, and a sought-after speaker. With 20+ years of business experience, he works to inspire clients and business partners to foster innovation and develop next-generation products/solutions powered by AI.
Thank you for reading Innovative Leadership Insights, where we bring you thought leaders and innovative ideas on leadership topics each week.
ADDITIONAL RESOURCES:
Ready to measure your leadership skills? Complete your complimentary assessment through the Innovative Leadership Institute. Learn the 7 leadership skills required to succeed during disruption and innovation.
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Chris Nolan, a multi-award-winning writer and director, brings an in-depth understanding of the complex interplay between humans and AI technology, and Michael Schindler, a veteran, writer, and podcast host, adds his transformative voice to the discussions on integrating AI with human qualities, share this article as a companion to their podcast The Rise of Humanness (The Role of People in an AI World).
Podcast intro from “Faux Mo:” and ILI AI experiment:
We find ourselves at a pivotal moment in human history — we are at the elbow of the exponential curve, poised at a “fork in the road” moment. We are either on the brink of a technological renaissance that will unfurl a new tapestry of human evolution or we are facing a dystopian future wrought by inaction, irresponsibility, and lack of foresight.
The trials and tribulations of recent years — the COVID pandemic, geopolitical turbulence, and anxiety over livelihoods in the face of AI — have shaken our collective psyche to its core, impacting our well-being and faith in the future.
Society, the workplace, and the economy all stand at the precipice of transformation or disintegration.
As recent stats from the World Economic Forum inform us, since COVID, the crises, uncertainty, and risks have only accelerated.
It’s a vast list from geopolitical, economic, social and climate upheaval, to threats to democracy and mental health deterioration. It is not hard to fathom why studies show people all over the world are terrified of what’s next.
Over 70% of the world, especially those between 18 and 40, are fearful of the future. And over half think humanity is doomed.
We are now at a crucial moment, where change will challenge and push the envelope of our linear-biased minds to master this new exponential world.
The futurists among us compare the present era to the convergence of twenty Gutenberg printing presses.
Overnight, the specter of artificial intelligence disrupting jobs and livelihoods whipped the unsuspecting teeming masses into a frenzied state of collective hysteria.
We’ve all been caught off guard with the seemingly sudden arrival of so much change. And we’re behind the curve. But we cannot allow ourselves to be caught off guard again. Or fall even further behind the exponential curve. Or become consumed with mass hysteria, despair, or apathy.
The pace of change is breathtaking, terrifying, and exhilarating all at once. And the acceleration is only accelerating.
Far more revolutionary, earth-shaking technologies and changes are coming at us down the pike — beyond just chatty GPTs and other AIs. Here’s how the hockey stick graph translates to AI’s exponentially accelerating computer power.
In 2050, it’s on track to have 1,000,000 times more intelligence than today.
Indeed, ChatGPT 4 has astoundingly leapfrogged beyond the projections that were once thought to be years away. We are now riding the crest of a double exponential curve, accelerating at a dizzying, doubling, doubling pace.
The arrival of artificial general intelligence (AGI, a machine possessing the full cognitive abilities of an adult human) is now estimated to emerge in a mere decade or two.
Thus, we find ourselves grappling with a world that is more uncertain, more enigmatic, and more unpredictable than literally any human can comprehend.
We are at an urgent crossroads, a time of decisions and actions for you, society, and all of humanity.
For, along with monumental challenges, we must not lose sight of the colossal opportunities and the potential abundance that this era offers.
We must turn this pervasive fearful, pessimistic narrative on its head, for despair, apathy, and apprehension serve only to hinder positive progress.
It is the optimists who shall save the world.
ABOUT THE AUTHORS:
Chris Nolan is a multi-award-winning writer and director, bringing in-depth understanding to the complex interplay between humans and AI technology. His insights, gained from a distinguished career, aid in comprehending and balancing the intricate dynamics of this digital era. His thought-provoking work on VUCA: The Secret to Living in the 21st Century, showcases his adeptness in decoding exponentially accelerating technological changes. Michael Schindler, a veteran, writer and podcast host, adds his transformative voice to the discussions on integrating AI with human qualities. His inquisitive approach breaks down the complexities of blending business and AI. Mike’s expertise, shaped by hosting the Military Wire podcast, frames his insightful outlook on the future of work.
Together, Michael & Chris consult with companies – and coach their leaders – on navigating VUCA in the business world…and understanding humanness in the process.
Thank you for reading the Innovative Leadership Insights, where we bring you thought leaders and innovative ideas on leadership topics each week.
ADDITIONAL RESOURCES:
Ready to measure your leadership skills? Complete your complimentary assessment through the Innovative Leadership Institute. Learn the 7 leadership skills required to succeed during disruption and innovation.
https://www.innovativeleadershipinstitute.com/wp-content/uploads/2023/09/Nolan-Image.png260470Jenna Reikhttps://www.innovativeleadershipinstitute.com/wp-content/uploads/2022/03/ILI-Anniversary-Logo.pngJenna Reik2023-09-11 20:54:582024-11-27 09:39:20The Rise of Humanness (The Role of People in an AI World)
The difference between an average leader and an outstanding one is deceptively simple. The secret to driving exceptional performance is being a human leader.
Human Leadership is the way of the future because it places the human element of business at the forefront—where it should have been the entire time—instead of focusing on performance, projects, or profit. Human Leaders support the people doing the work that brings in revenue. This in turn supports the customers, the organization, and the leaders themselves. Everyone thrives under Human Leadership.
A leader’s position is unique because it is both a directive and a supportive role. Leaders are directive because they determine direction through vision and hold their teams accountable for implementation. But more importantly, they’re supportive because it’s the leader’s job to ensure everyone has what they need to succeed. That’s why good human and relational skills are critical to leading well.
Leaders create value by proactively leading themselves, their team, and the business to drive profitable growth for all stakeholders. They create culture through connecting people with their work and with each other so that every role is more than just a job.
Today, I want to discuss 10 principles that effective Human Leaders believe and practice to drive outstanding performance.
1. People are the most important value creators in organizations.
I’ve said it before, but it always bears repeating. The human element is the most important aspect of any organization. Goods or services don’t magically appear without people to make it happen.
Human Leaders recognize this truth and use it to create value for all stakeholders. They know that when you take care of your people, your people will take care of the work. Around that strong foundation, everything else falls into place.
2. Organizations must be places where people come to grow, not just work.
Because Human Leaders recognize the value of people, they prioritize helping people grow and evolve on both personal and professional dimensions. This means creating a workplace culture that offers employee-directed development opportunities and making the work about more than producing deliverables.
Research shows shifting the workplace to a growth environment positively affects engagement, well-being, and relationships. Team members become more skilled, produce better quality work, and are happier in their roles.
The key to success, then, is to shape work roles around people, not the other way around. Craft work around more than what needs to get done. Use it as a tool to spark and grow your team members’ internal flames. Let people’s interests, strengths, and purpose inform job crafting and design.
Which leads me to the next principle:
3. Personal identity shapes professional persona.
Human Leaders know there is no such thing as separating your “personal” self from your “professional” self. Sure, someone might dress differently in and outside the workplace, but their personality and interests don’t change when they walk through the office door—and they shouldn’t.
The idea that there is one persona that leaders should project is old-fashioned and useless. Leading with a “chief problem-solver” identity doesn’t benefit anyone. This is especially true if that persona doesn’t align with your true personality.
Who you are as a person influences who you are as a leader. In other words, your leadership is influenced by:
The beliefs you hold.
The actions you take.
The behaviors you demonstrate.
The values that guide your decision-making.
Your personality.
Your energy source.
Your empathy (or lack thereof).
Every one of these aspects translates one for one into who you are and how you show up as a leader. Just as who your team members are in these aspects influences how they show up as employees. That’s the whole reason jobs suit different people.
4. We must be technically proficient in the human element, not just functional areas.
Human Leaders understand that it takes more than technical skills in a functional area to successfully manage a team. A leader lacking good social skills isn’t going to get far with their team members, even if they previously excelled in the exact same role as the people they’re now leading.
Being technically proficient in the human element means understanding people—as human beings, not just employees. Human Leaders get to know each member of their team so that they can understand them enough to answer these fundamental questions:
What do they struggle with?
What’s important to them?
Who are they at their core?
Which values guide their decision making?
What motivates them?
What do they need to do their best work?
How do they want to learn and grow while at work?
What is their why?
This is why emotional intelligence and good social skills are necessary qualifications for leadership roles. They’re the key to better connecting with the people it’s your job to lead. This is critical to driving performance and engagement.
5. To lead others, lead yourself first.
If you can’t lead yourself, no one will follow, listen to, or respect you. Human Leaders practice reflection to build a relationship with themselves—a key foundation to building human and relational skills to connect with others.
Studies suggest leaders who make time to reflect perform 23% higher than those who don’t. Reflection helps clarify your values, beliefs, and purpose. This empowers you to better understand your actions and make informed, intentional decisions about your behavior going forward.
Effective leadership involves crafting vision, using intuition, and adaptability. All of these require being in tune with and leading yourself well. Leading yourself first helps you discover your authentic leader signature.
6. Leaders lead people and hearts, not projects and tasks.
It’s common for leaders to earn a promotion as a reward for delivering great task-based work. But as I said above, you need more than the ability to complete tasks to lead. You can’t lead humans the way you lead projects.
Humans require motivation and inspiration to perform well. No amount of “hard skills” is going to help you achieve this. Human Leaders understand that while they can’t control others’ thoughts and emotions, they can elicit motivation and inspiration through their actions and behaviors.
Inspiring internal motivation in employees increases retention and raises performance and engagement.
7. Relationships power connection.
In a people-first operating model, the quality of people’s relationships determines work quality, employee experience and engagement, and perceptions of leadership.
Notice how everything in this list so far has relied on human and relational skills to connect with people? That’s because the connections are where the magic happens!
People who know they’re valued and are treated as such are free to explore, take risks, and stretch farther than they ever thought possible. All of this creates a positive reinforcing loop that elevates self-efficacy, self-confidence, and organizational performance.
That’s why Human Leaders prioritize connection through relationships.
8. Give trust first to get trust.
Human Leaders reject the notion that people have to earn trust before you can trust them. I know how that sentence sounds, but hear me out. For what exactly do people have to earn trust in the workplace? It’s not to do their job, since you’re choosing to trust them to do that the day they’re hired–right?
And yet, somehow we still expect people to “prove themselves” through multiple interactions over time before we grant them trust. This delays relationship-building and connection. It’s counterintuitive.
Human Leaders understand that teams connect faster and more deeply when there’s no secret threshold a person must meet before they’re worthy of trust. So there’s no reason to withhold it.
It’s a leader’s job to build a relationship with everyone on their team, so naturally, it’s important to develop trust with them. And contrary to popular belief, it’s not on employees to earn a leader’s trust, but rather on leaders to earn employee trust. The best way to do that: give trust first.
9. Leading is a partnership.
I wrote earlier about the dual nature of leadership roles. Being a leader means being both directive and supportive. Human Leaders understand that this dynamic makes leading an act of powerful partnership, not power-ship.
Human Leaders lead through coaching, collaborating, and connecting people. They drive engagement through honoring universal human needs rather than dictating how work should be done. They create value for all by listening, participating, and facilitating. They partner with team members to drive outcomes rather than distancing themselves through title, organizational hierarchy, or processes.
10. People want to be seen, heard, understood, appreciated, inspired, connected and feel they matter.
Human Leaders understand these basic desires are fundamental human needs. And as Maslow’s Hierarchy of Needs illustrates, we must meet certain needs before we can function beyond trying to survive. So, if a leader wants a high-performing team, they must meet these needs for each team member.
The needs to be and feel heard, understood, mattered, appreciated, inspired, seen, and relationally connected are just as important as those traditionally listed in the famous pyramid. Mapping these specific needs according to Maslow’s expanded hierarchy model would look like this:
Maslow’s Expanded Hierarchy adapted by Dr. Jennifer Nash
Typically, financial security falls under safety needs. Notice how the needs listed in principle #10 land in categories other than safety? This is a good visual to demonstrate why money alone is not enough to make work worthwhile.
While recent studies show that increased income does improve happiness levels up to a point, researchers found that money’s effect pales in comparison to the happiness brought on by relationships, hobbies, and meaningful work.
Once people earn enough to meet their basic needs, work and life satisfaction become far more dependent on purpose, vision, and impact. Human Leaders understand this, which is why they prioritize seeing, hearing, understanding, appreciating, and inspiring their employees. They know this is what ultimately drives engagement and retains valuable team members.
Don’t let the long list of needs intimidate you. There is a lot of overlap in the behaviors that meet the needs of this principle. That’s what I created the Human Leadership operating model for!
Everything you need to know to better see, hear, understand, appreciate, inspire and connect with people is in my book, Be Human, Lead Human: How to Connect People and Performance. All of this is key to helping people know and feel that they matter.
Want to assess your skills in helping others meet these needs through their work? Take the complimentary Human Leader Index™ to get a personalized report. I created this assessment to help you measure your current abilities and create a strategic development plan to elevate your leadership.
ABOUT THE AUTHOR:
Jennifer Nash, PhD, MBA, PCC is a leadership expert and consultant to Fortune 50 organizations such as Google, Ford, Exxon Mobil, JP Morgan, IBM, The Boeing Company, and Verizon. She is Founder & CEO of Jennifer Nash Coaching & Consulting, a leadership advisory firm helping successful leaders connect people and performance to deliver exceptional results.
Jennifer’s 25-year resume includes serving in executive and leadership roles at Deloitte Consulting and Ford Motor Company and as adjunct professor at the University of Michigan. She contributes to Harvard Business Review, has presented her research at Columbia University, and is a Harvard/McLean Institute of Coaching Fellow.
Thank you for reading the Innovative Leadership Insights, where we bring you thought leaders and innovative ideas on leadership topics each week.
ADDITIONAL RESOURCES:
Ready to measure your leadership skills? Complete your complimentary assessment through the Innovative Leadership Institute. Learn the 7 leadership skills required to succeed during disruption and innovation.
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10 Characteristics Business Leaders Will Need In The Age Of AI
Most of us interact with AI in some way during our daily tasks. With all the discussions about artificial intelligence, one of the biggest questions is what leaders must do to prepare. To answer this question, I used my personal experience experimenting with ChatGPT-4; read the World Economic Forum Future of Jobs 2023 report; read the Harvard Business Review article draft “Navigating Complexity and Learning with Agility: Keys for the Future of Work” by David Dinwoodie, Suzie Lewis, and Jim Ritchie-Dunham (currently only available in Spanish); and interviewed Neil Sahoto, AI advisor to the U.N., on my company podcast (read his accompanying article here).
Here is my top ten list of items leaders will need to do well in the AI-powered future. Most are skills leaders already need to have mastered. From that vantage point, part of the question becomes: what should we stop doing so that we can delegate tasks to AI while refining the skills that require human wisdom and contact?
1. Communication
Leaders must continue to focus on basic leadership skills involving relating to others—communication, collaboration, negotiation, facilitation, social influence, and active listening.
2. Growth Mindset
During the massive opportunities created by change, we must continue cultivating our growth mindset, curiosity, lifelong learning, and ability to unlearn and stop doing things that no longer serve our mission.
3. Adaptiveness
Using what we learn through our learning and growth mindset, we need to enhance our ability to anticipate change and proactively initiate aligned change initiatives. To do this, we need to build our adaptiveness, resilience, and business agility.
4. Emotional Intelligence
Because we will ask our teams to make changes that often feel unfamiliar and uncomfortable, we need to amplify our emotional intelligence, including self-awareness, self-management, relationship awareness, empathy, building trust and psychological safety, and other skills that help us relate to and inspire others.
5. Abundance Mindset
It is easy for each of us to struggle with uncertainty and fall into fear during uncertain times. Developing an abundance mindset instead of a scarcity mindset allows us to reframe uncomfortable situations into opportunities. Collectively, we have the knowledge, wisdom, resources, skills, and attitude to meet the challenges ahead.
6. Domain Expertise
We continue to need to excel in our areas of domain expertise and understand the latest technological developments in those areas. We must remain current in our domains, whether that is finance, HR, or medicine.
7. AI Skills
As we move from focusing solely on human skills to requiring the ability to leverage AI successfully, we need to know what questions to ask AI to get the results we want. Leaders will need to know (and have teams who know) how to leverage AI to get useful information and get work done. The better we are at asking the right questions, the better our AI assistants will be at providing useful information.
8. Analytical Skills
We will need to leverage what we learn in partnership with AI-generated information. To do this, we will need strong analytical and problem-solving skills, as well as systems thinking ability and the associated mindset, 360-degree thinking.
9. Creativity
Take a creative perspective to identify areas AI will not consider or to create solutions AI would not come up with. Know where to experiment with possible solutions.
10. Risk Awareness
Understand the risks AI presents and continually evolve governance processes to address these risks.
Final Thoughts: Unlearn And Delegate
In addition to these skills, we will need to develop the ability to unlearn and delegate. AI can now do many analytical tasks people have been doing. We are currently in the early stages of AI usage, and I like to frame AI digital workers as interns. We are responsible for the actions of our interns. Likewise, with AI, we are responsible for the accuracy of our work.
AI can process data much faster than humans. When prompted properly, AI programs produce reliable information much more quickly and efficiently than humans. The results are reliable for specific applications when the AI has been programmed with specific tasks and tested.
With that in mind, ChatGPT-4 can “hallucinate” (state false facts and cite false sources). It is crucial to set parameters around assigning and checking the AI’s work. I conducted several experiments, specifically with ChatGPT-4. It provided a strong starting point for writing. But in every instance, I asked it to cite and verify sources. In other words, I treated it like an intern; I knew I needed to verify its work until it had proven itself.
My company is currently experimenting with an AI “digital twin” that our team uses for teaching videos and social media. I encourage other leaders to look into ways they can use AI to increase their productivity and enhance their impact. At the same time, there are inherent risks with any new technology. Leaders who understand the strengths and weaknesses of AI and prepare themselves and their workforces have the greatest opportunity to smartly mitigate risks while still experimenting and learning in the process.
ABOUT THE AUTHOR:
Maureen Metcalf is the founder and CEO of the Innovative Leadership Institute. She is an expert in anticipating and leveraging future business trends. Ms. Metcalf helps leaders elevate their leadership quality and transform their organizations to create sustainable impact and results. She captures 30 years of experience and success in an award-winning series of books used by public, private, and academic organizations to align company-wide strategy, systems, and culture using Innovative Leadership techniques. Ms. Metcalf is a Fellow of the International Leadership Association. She also serves on the advisory boards of the School of Strategic Leadership at James Madison University and the Mason Leadership Center at Franklin University. Ms. Metcalf earned an MBA from Virginia Tech. She can be reached at mmetcalf@innovativeleadership.com.
ABOUT THE GUESTS:
James L. Ritchie-Dunham, Ph.D. is president of the Institute for Strategic Clarity, affiliated with Boston College, Harvard, and author/co-author of Leadership for Flourishing (forthcoming), Agreements (2023), Ecosynomics (2014), Managing from Clarity (2001), and many chapters and articles. He blogs regularly at Jlrd.me. His global research, surveying over 132,000 groups in 126 countries, shows (1) that you prefer abundance-based agreements to scarcity-based ones, (2) lots of people have figured out how to live this way, for decades, with far better results and experiences, and (3) you can choose to shift your agreements, experiences, and outcomes to abundance-based.
David Dinwoodie has collaborated with the Center for Creative Leadership (CCL) for over 15 years as a researcher, author, educator, and coach. As Vice-President of Global Leadership Solutions, managed CCL’s global portfolio of Open Enrolment Programs, Corporate Learning Solutions, Coaching, and Assessment Services across 12 campuses worldwide, servicing 3,000 client organizations and over 30,000 individuals each year. David is co-author of the book Becoming a Strategic Leader: Your Role in Your Organization’s Enduring Success. He is an Advisory Board member for the Penn State School of Graduate Education (SMEAL) and Developing Leaders Quarterly.
Suzie Lewis is the founder and managing director of Transform for Value, and an executive fellow at the Centre for the Future of Organisation, an independent think tank at the Drucker School of Management. Suzie is a global business leader, speaker, podcast host, and executive coach with extensive experience in driving international transformation projects, preparing business leaders and employees for change, and bridging the gap between human and digital ecosystems. Her quest is to build more inclusive & collaborative environments, placing the onus on how to drive value through people as well as data and processes to drive sustainable change.
Thank you for reading the Innovative Leadership Insights by the Innovative Leadership Institute, where we bring you thought leaders and innovative ideas on leadership topics each week.
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OpenAI is at the forefront of AI research and innovation, leaving the tech community eager to see what is next after their remarkable developments like DALL-E and ChatGPT.
But with emerging technologies becoming increasingly similar, it begs the question: what ties them all together?
The answer is Generative Artificial Intelligence (AI), a technology that can create brand-new content, from images to audio files, based on patterns an AI model identifies.
This blog post will explore the world of generative AI, covering everything from A to Z, including definition, techniques, benefits and more.
What is Generative AI?
Generative AI, also referred to as generative adversarial networks (GANs), is a subset of artificial intelligence (AI) and machine learning (ML) that enables machines to create new content from scratch based on patterns identified in existing data. In simpler terms, generative AI is an exciting technology that allows AI systems to generate original data like text, audio, images, or videos without relying on pre-existing information. Generative AI models learn patterns and relationships within the input data to create entirely new content.
While generative AI is a powerful tool for creating innovative content, it is also highly complex and requires extensive trainingand computational resources. In the past, working with generative AI was a complex and time-consuming process that required submitting data via an API or using specialized tools and programming languages like Python. However, recent advancements in this field have made the technology more accessible and user-friendly. Today, pioneers in generative AI are developing new and improved user experiences that allow for simpler and more intuitive interactions. Users can now describe their requests in plain language and even provide feedback on the style, tone, and elements to include in the generated content.
How Does Generative AI Work?
Generative AI uses a prompt, anything from a chat message to a picture, to generate new content similar in style or format.
To illustrate, if you want your AI to paint like Picasso, you need to feed it with as many paintings by the artist as possible. The neural network behind generative AI can learn the unique traits or characteristics of Picasso’s style and then apply them as needed.
This approach applies to models that write texts and books, create interior and fashion designs, non-existent landscapes, music, and many other applications. It achieves this through a range of techniques, which include:
1. GANs (Generative Adversarial Networks)
The Generative Adversarial Network (GAN) is a frequently utilized technique in generative AI. Consisting of two neural networks:
Generator
Discriminator
The generator produces new data that looks like the original data, and the discriminator distinguishes between the generated data and the source data. Based on the discriminator’s feedback, this feedback loop enables the generator to learn and improve over time.
2. VAEs (Variational Autoencoders)
In the realm of generative AI, a Variational Autoencoders (VAEs) method allows for the encoding of input into a compressed code into a smaller dimensional representation, which is then duplicated and stored by the decoder. In other words, VAEs are a type of neural network that can compress data into a smaller representation while retaining important features of the original data. This compression is done in a way that allows the data to be manipulated and generated in new ways, making it a powerful tool for data analysis, image and audio generation, and more. This compressed code preserves the original data’s important information while capturing its distribution in a much more compact form.
Essentially, encoding allows for the efficient representation of large datasets in a condensed form, which can be useful for various applications such as data storage, transfer, and analysis. Furthermore, encoding data in a compressed form also enables the generation of new data that share similar patterns and structures to the original dataset.
3. Transformers
Initially developed to understand images and languages, transformers have evolved to perform classification tasks and generate content. One of the most well-known transformer models is GPT-3, which uses cognitive attention to measure the significance of input data parts.
Transformers use a data sequence to process the input into the output, making them highly efficient in contexts where the data’s context matters. This technology is commonly used to translate or generate texts, where individual words cannot convey the intended meaning without the surrounding context. Additionally, transformers play a significant role in creating foundation models that can transform natural language requests into commands such as generating images or text based on user descriptions.
Google first introduced the concept of transformers in a 2017 research paper. The paper outlined a deep neural network that learns context, following relationships in sequential input, such as the words in a sentence. As a result, transformers have become a vital component in Natural Language Processing (NLP) applications.
What is Generative Modeling?
Generative modeling is an AI-driven approach that leverages statistics and probability to create a virtual representation or abstraction of real-world phenomena. Its purpose is to allow computers to comprehend the world around us, leading to predictions about the probabilities of certain subjects based on modeled data. By processing vast amounts of training data, unsupervised machine learning algorithms make deductions about the data and distill it down to its fundamental digital essence. This can then be used to model similar or indistinguishable data from the real world. For example, a generative model might be trained on a dataset of images of rabbits to generate new images that have never existed before but look realistic.
Generative modeling is distinct from discriminative modeling, which identifies and categorizes existing data. While a generative model creates something new, a discriminative model recognizes tags and sorts data. In practice, both models can be combined to enhance each other’s capabilities. A generative model can be trained to fool a discriminative model into thinking its generated data is real, and through successive training, both models become more sophisticated.
Why Do We Need Generative Models?
Generative models enable us to explore the possibilities of the world around us and imagine things that have never existed before. They are a powerful tool that can be used to generate new and diverse data, train machine learning models, and create new content in creative applications. Their ability to adapt to new data and perform well in a variety of applications makes them an essential tool for researchers, developers, and creatives alike.
One of the main advantages of generative models is their ability to produce large amounts of synthetic data, which can be utilized to train machine learning models in scenarios where real-world data is scarce or expensive to obtain. For instance, generative models can be used in the healthcare industry to generate synthetic medical images to train diagnostic models, reducing the need for invasive and expensive medical procedures.
How is Generative AI Used?
The potential of generative AI application goes far beyond its current use in social media avatars and text-to-image converters, as it can yield content that closely resembles human-generated one.
Let’s delve into how generative AI is used across various industries and contexts.
Image Generation
Image generation has become an essential tool for various industries, including fashion, architecture, and interior design, where it is used to create product designs and visualizations. The German sportswear brand Adidas, for instance, has utilized generative AI to design unique shoe patterns, while the Swedish furniture company IKEA has used the technology to create furniture and home décor products.
In addition, the film and video game industry also benefits from image generation, as it helps create realistic visual effects and virtual environments. Notably, Roblox, a popular online game platform, has recently integrated generative AI into its game development process, allowing developers to create more interactive and realistic player experiences. With generative AI, game developers can easily create complex 3D models, objects, and environments that populate the game world.
Moreover, generative AI has potential applications in medical imaging. For example, NVIDIA researchers have developed a generative model capable of generating synthetic medical images that can be used to train doctors and healthcare professionals in image analysis. By leveraging this technology, healthcare professionals can improve their diagnosis and training accuracy, ultimately leading to better patient outcomes.
Music Generation
Generative AI has opened up a new world of possibilities in the field of music by enabling the creation of new musical pieces by analyzing existing musical patterns and generating new ones based on the learned patterns.
This technology has a range of potential applications, such as:
Assisting composers in creating new music,
Generating background music for videos,
Creating personalized playlists for users.
One notable example is Amper MusicAI music generator, a cloud-based platform that content creators, including filmmakers, podcasters, and YouTubers, use to add original music to their productions. Users can input the type of instruments, mood, tempo, style, and other parameters, and the platform’s AI algorithm creates original music tracks based on those inputs in real time.
In classical music, researchers from the University of Surrey and the University of Kingston have developed a system called AIVA (Artificial Intelligence Virtual Artist) that can compose classical music. AIVA has been used to compose pieces for live orchestras and has gained recognition from prominent figures in the music industry.
Video Generation
Video generation using generative AI involves creating new videos by analyzing and learning from existing video data. This technology has several potential applications, such as creating personalized video content, generating visual effects for films and TV shows, and even creating training videos for industries such as healthcare and engineering. This generation found its application in RunwayML, a platform that generates realistic videos from text descriptions. Users can input a text description of a scene, and the platform’s algorithm generates a video of the scene, complete with realistic lighting, camera movements, and other visual effects.
DALL-E, a research project by OpenAI can generate images and even videos from textual descriptions. In one example, DALL-E generated a short video of a snail made out of a stack of staplers based on a textual description of the scene.
In the film industry, generative AI has been used to create visual effects for movies and TV shows. For instance, Industrial Light and Magic (ILM), a renowned visual effects studio, employed GANs to digitally rejuvenate Mark Hamill’s appearance to portray a younger Luke Skywalker in the TV series “The Book of Boba Fett.”
Another example is the use of AI to generate deepfake videos, which can manipulate existing videos to show individuals doing things they never did or saying things they never said.
Chatbot Generation
Chatbot generation enables conversational agents to interact with users in natural language, and many companies have incorporated this technology into their customer service and marketing strategies. In addition, advancements in speech recognition technology have enabled chatbots to interact with users through voice commands.
OpenAI’s GPT-3 is an excellent example of chatbot generation using generative AI. GPT-3 is an extensive language model that can generate human-like responses to text inputs.
Another language model that came out in 2021 is Google’s MUM (Multitask Unified Model). It was designed to understand multiple languages and contexts simultaneously, allowing for more complex and nuanced conversations with chatbots.
In the healthcare industry, chatbots and voice assistants are increasingly used to support mental health care. For instance, Woebot Health offers a chatbot that uses cognitive-behavioral therapy techniques to help users manage anxiety and depression.
Chatbots have also been used in the finance industry to improve customer service and support. Bank of America’s chatbot, Erica, can assist customers with account inquiries, money transfers, and other financial tasks.
Meanwhile, generative AI for e-commerce is becoming increasingly popular as it helps to create a more personalized and engaging shopping experience for customers. For illustration, Zara‘s chatbot allows customers to browse and shop for products through a conversational interface.
What are the Advantages of Generative AI?
In addition to the specific use cases mentioned earlier, another advantage of using generative AI is enhancing robotic control. Generative modeling in machine learning can greatly improve the control of robots. By using algorithms that learn from data and make unbiased decisions, we can reduce biases and ensure fairness and accuracy in decision-making processes.
Generative AI can also enhance the accuracy of robotic control systems by generating new data that improves the algorithm’s performance. This allows for physical experimentation and testing of theories, improving our understanding of complex concepts. Reinforcing machine learning models with generative modeling can make robots more efficient and effective in their tasks, leading to more reliable and consistent performance. These advancements have the potential to enable robots to perform increasingly complex tasks in various industries.
1. Creating Diverse Content with Automation
Generative AI offers an automated way to generate diverse content, from text and images to video and code. It can even provide answers to questions and create new content such as translations, summaries, and analyses. This is particularly beneficial for students and researchers, who can save time and easily access vast information and resources. The possibilities for creating unique and valuable content are virtually limitless.
2. Personalizing Content Creation
Generative AI models have a remarkable ability to create personalized content based on user preferences. Once trained, they can produce content tailored to the users’ preferences. This content is more likely to resonate with the intended audience, which can benefit businesses seeking more effective marketing campaigns. Using generative AI for marketing, they can produce content that better connects with their target customers.
The ability to create personalized content is a key benefit of using generative AI models that can make a significant difference in reaching and engaging with customers.
What are the Limitations of Generative AI?
Generative AI presents exciting data exploration and problem-solving possibilities, but its implementation and regulation pose unique challenges. Responsible and ethical use of Generative AI is crucial in today’s world.
Some of the main limitations include:
1. Errors and Biases in Generative AI Outputs
Generative AI has a drawback when it comes to the quality of generated outputs. Although these systems are capable of producing natural language and creative outputs, they may also contain errors and artifacts. This could be due to poor training, lack of data, or an overly complex model. For example, some generative AI models, like ChatGPT, may struggle with providing accurate and relevant responses to recent events. Meanwhile, Google Bard’s advertisement claimed that the James Webb Space Telescope was used to take the first pictures of a planet outside the Solar System, which was factually incorrect. It is necessary to note that the quality of outputs depends on the quality of datasets and training sets used. A biased training set can lead to biased results, ultimately affecting the reliability and accuracy of the generated outputs.
2. Intellectual Property and Privacy Issues
ChatGPT was launched in November 2022 to both applause and critique. As a chatbot, it has proven to be one of the most advanced, capable of understanding natural language and providing human-like responses. However, the public’s use of ChatGPT has revealed its potential for academic and workplace dishonesty. Moreover, the use of Generative AI models like ChatGPT raises concerns about intellectual property rights.
These models are trained using large datasets scraped from the internet, which means the generated content may be based on the works of other creators and artists. Using such a service exposes individuals and organizations to legal responsibilities, including infringement of intellectual property rights and privacy violations. For instance, personal or sensitive information may be generated by a particular service, posing a threat to privacy rights.
3. Intricacy and Technical Hurdles
Generative AI technology can be challenging to comprehend and utilize, which is a significant disadvantage. This can discourage some businesses from implementing it in their operations due to its complexity and unfamiliarity. Although free AI services like ChatGPT and Dall-E are available, they have their limitations. For instance, ChatGPT may experience downtimes during peak usage, and Dall-E restricts users to generate only 50 images for the first month, and 15 images per month thereafter.
Paid AI services offer more reliability and flexibility, but choosing the best one among the plethora of companies can be daunting. Furthermore, incorporating in-house Generative AI capabilities comes with technical challenges, as models can be computationally expensive and inefficient.
4. Adversarial Attacks
Adversarial attacks are a real threat to generative AI, where the inputs are intentionally manipulated to generate unexpected or harmful outcomes.
These attacks can take many forms, including:
Modifying input data,
Adding noise,
Generating new data.
Such attacks are especially worrisome in image and speech recognition applications, where manipulated outputs can have significant consequences. However, researchers and developers are taking several steps to prevent adversarial attacks. These include adding noise during training, creating more robust models, and using adversarial training to recognize and respond to these attacks.
The Effects of Generative AI on the Job Market
Generative AI has raised concerns about its impact on the job market, especially in creative industries. Past predictions about AI’s effect on jobs have been mixed, with some saying that low-skill jobs would be affected, while others argued that highly-skilled knowledge workers would bear the brunt of the changes. However, the tight labor market in recent years has somewhat suppressed these dire predictions. According to a recent Harris Poll, many workers are wary of generative AI, with 50% expressing distrust of the technology.
In contrast, The Atlantic has stated that predicting the exact impact of generative AI on the job market is difficult, but it is evident that it will have a significant effect on workers with a college education. Entrepreneurs create the future instead of predicting it, and in recent years, creative human intelligence has advanced the state of generative AI. Although it is challenging to predict precisely how generative AI will affect jobs, history has shown that technology’s impact on jobs can be unpredictable.
While many low-skill and high-skill jobs will be affected by the increased capabilities of computers, many current occupations will thrive, and new ones will emerge, driven by human creativity and imagination.
Generative AI: Key Takeaways
From art and design to healthcare and finance, the ability to generate new content based on existing data patterns can bring significant benefits to businesses and individuals alike.
Among others, these benefits include:
Enhancing robotic control,
Creating diverse content with automation,
Personalizing content creation.
As this field continues to evolve, we can expect to see even more impressive and innovative use cases emerge, making generative AI an exciting area to watch in the coming years.
ABOUT THE GUEST:
Neil Sahota (萨冠军) is an IBM Master Inventor, United Nations (UN) Artificial Intelligence (AI) Advisor, author of the best-seller Own the AI Revolution and sought-after speaker. With 20+ years of business experience, Neil works to inspire clients and business partners to foster innovation and develop next generation products/solutions powered by AI.
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Ready to measure your leadership skills? Complete your complimentary assessment through the Innovative Leadership Institute. Learn the 7 leadership skills required to succeed during disruption and innovation.
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Before you start the article, we have a treat for you: the debut of our newest team member, Faux Mo, Maureen’s digital twin! ILI is experimenting with AI tools. We will share some of our experiments as we learn more.
The rise of generative AI has opened new doors to improve your leadership skills. By leveraging state-of-the-art AI tools, you can augment your decision-making process, harness the power of data-driven insights, and stay agile in a rapidly-evolving world. In doing so, you not only gain a competitive edge, but also foster a culture of continuous learning, positioning you and your teams for long-term success.
Here are four ways to bring Artificial Intelligence into your leadership:
1. Understand the potential (and problems) of generative AI in leadership.
Remember the old programmer’s adage of GIGO: “Garbage In = Garbage Out?” That remains true even in this age of Chat-GPT! As generative AI continues to advance, it is imperative that leaders be cognizant of its ethical implications and potential biases. AI systems learn from the data they’re exposed to, and if the training data contains systemic biases, the AI may well reinforce or perpetuate these biases. Leaders should, therefore, do their best to ensure the AI’s information sources are as neutrally factual as possible. Staying actively aware minimizes bias and fosters trust among employees and customers.
2. Understand AI’s current limits
Ultimately, the integration of generative AI in leadership will be a transformative and mutually beneficial partnership. For the near future, at least, leaders should view AI as an augment for decision-making, balancing that with human intuition, creativity, and empathy. It’s vital to develop emotional intelligence, critical thinking, and adaptability to ensure that human ingenuity remains at the center of an organization’s progress and success.
3. Identify relevant AI applications for your organization
Like most tech, various AI options and apps are blossoming everywhere. Leaders should develop a strategic approach to AI adoption by selecting their AI version carefully. What fits best with current processes? What are the best areas for AI integration? Creating a roadmap for the implementation of the applications selected is vital. In addition to assessing the immediate benefits of AI, also long-term impacts are important to ponder. Above all else, constantly re-evaluating and adapting their strategy keeps leaders at the forefront of technological innovation.
4. Implementing AI within your organization
One crucial aspect of AI adoption leaders mustn’t overlook is fostering a culture that embraces AI technology and supports continuous learning among employees. As AI applications are integrated into various aspects of the business, employees have new roles and responsibilities that come with the use of any new tech. Training programs, for example, help employees develop new skills and leverage AI tools to complement their human expertise effectively. It’s just as important to create a collaborative environment that encourages open discussions, ideation, and experimentation with AI applications; it’s another way to build trust and confidence within a team. This approach also facilitates a smooth integration of AI into daily routines and processes, as well as enabling the team to collectively troubleshoot any issues that inevitably arise. Empowering employees with the necessary skills and knowledge will also help mitigate the fear of job displacement, fostering a positive outlook on AI as an essential tool to enhance, rather than replace, human potential.
By cultivating a strong culture of AI readiness, leaders harness the power of generative AI to unlock growth, increase competitiveness, and drive success. That success hinges on a proactive approach to identifying relevant AI applications, developing a strategic roadmap, and cultivating a culture of AI readiness within the organization. By tailoring AI solutions to meet specific organizational needs, leaders ensure that their business is well-positioned to thrive amidst the rapid advances in AI and other emerging technologies. Embracing generative AI will not only help companies make more informed decisions and optimize their performance, but also place them at the vanguard of the digital transformation journey, leading the way in their respective industries.
ABOUT THE GUEST:
Greg Moran is a C-level digital, strategy and change leadership executive with extensive global operations experience. He led corporate strategy for Ford and designed the plan that Alan Mullaly used to turn around the company. Greg held C-level IT positions in app dev, infrastructure and core banking applications at Ford, Nationwide Insurance and Bank One/JPMC, respectively. He began his career in consulting with Arthur Andersen Accenture, working across industries with 100 companies over the course of a decade. He is passionate about leadership and culture and teaches part-time on the topic at Ohio University.
RESOURCES:
Ready to measure your leadership skills? Complete your complimentary assessment through the Innovative Leadership Institute. Learn the 7 leadership skills required to succeed during disruption and innovation.
