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From Exploration to Execution – Why Half Measures Fail

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Training Our Digital Reflection

How We Can Be Responsible Stewards of AI.

How We Can Be Responsible Stewards of AI

KEY POINTSWe are all teachers of AI—every digital action trains the future.Through our individual actions, we contribute to what AI will become, and this is an enormous power.Building better AI begins by building better versions of ourselves.

In my last post, I argued that AI systems function as mirrors to humanity. They reflect us back at ourselves, and they reflect all that we are – our weaknesses as well as our strengths, our shadows just as much as our light.

The reason AI functions as a mirror is that AI is trained on us – on vast datasets collected from human outputs. In a very real sense, we are all teachers now, because potentially any of our digital activities and outputs could be used to train AI systems.

And as we all know, teachers have an enormous power for making things better … or worse. As the American historian Henry Adams put it: “A teacher affects eternity. He can never tell where his influence stops.”

Now that we have that power, we had better think about how to use it wisely. In this post, I will draw on my recently published book, Transcend: Unlocking Humanity in the Age of AI, to show how we can be responsible stewards of our current and future AI systems. In particular, we’ll look at steps we can all take to ensure that AI develops in a safe and sane direction.

Teachers of AI

To think and act wisely in our role as teachers of AI, it’s helpful to understand how AI systems, and specifically large language models (LLMs) work. There are many other varieties of AI systems that are important and currently in use, but LLMs are the systems we have the most agency over in our use.

So how do LLMs actually work? When we type a question into ChatGPT or ask Claude to turn bullet points into a report, how do they manage it? The answer lies in patterns – patterns found in human language. LLMs are trained on enormous datasets made up of human-created content: books, articles, websites, reports, social media posts, and more. From these, the models learn statistical relationships between words and phrases. They don’t understand meaning in the way humans do, but they become highly skilled at predicting what kinds of language typically follow a given prompt.

When we ask an LLM to generate something – a summary, a story, a recommendation – it draws on these learned patterns, remixing and reassembling language in a way that seems fluent and relevant. It doesn’t “know” what a good report looks like, but it’s been exposed to countless examples, and it’s learned the structure and style from them.

Crucially, our interactions with AI systems can still influence their future development — but not in the way many people imagine. Most models don’t learn on the fly from individual users. They don’t “remember” conversations in the moment unless explicitly designed to, and they’re not being retrained in real time. However, companies often collect aggregated user inputs — including prompts, completions, and feedback — to help fine-tune future versions or to inform supervised updates. If you give a thumbs-up or thumbs-down, or if you opt in to share your data, you may be helping steer how future models behave. In that sense, our interactions aren’t just uses of the system — they’re contributions to its evolution.

Responsibility for Shaping AI

Let’s be clear – systemic issues require systemic solutions. The responsibility for shaping AI isn’t purely an individual responsibility. Companies developing AI models and applications have significant responsibilities, and so too do governments and regulatory bodies.

But let’s also be clear about something else – this doesn’t mean that we don’t have a role to play as individuals. We do, and it’s a crucial one. Through our individual actions, we contribute to what AI will become, and this is an enormous power. And with that power comes responsibility. Given that AI learns from us, given that we shape what it will become, we must acknowledge our power and we must be intentional about using it responsibly.

 What Can We Do?

To navigate these responsibilities effectively, we need to remember that AI stands as a mirror to humanity. And MIRROR, by fortunate coincidence, also provides a helpful acronym for the practical actions we can take to guide the future trajectory of AI. So, what does MIRROR stand for?

MindfulnessImpactResponsible ConsumptionReport ProblemsOngoing EducationReflective GrowthMindfulness

In an earlier post, I argued that we need to cultivate digital mindfulness. This carries over to our role in shaping AI. In this context, mindfulness means maintaining a constant awareness of the fact that our digital activities will shape the nature of AI and thus the future of the world. A mindless tweet, an exaggerated review, or a hasty comment might seem inconsequential in the moment. But collectively, this is the raw material that AI is learning from. Whenever we put our words out into the world in a form that will be preserved, we are inscribing them into the training data of future AI models.

Action Steps:

Practice conscious content creation – Before posting, ask: “Is this something I want AI to learn from?”Be aware of your digital footprint – Regularly review privacy settings and understand what data you are sharing with AI systemsChoose engagement thoughtfully – Be selective about which AI systems you interact with and how you engage with them

Example: Before posting a negative restaurant review after a disappointing meal, you pause and think, “If AI learns from this, I want it to understand the difference between helpful criticism and emotional venting.” Instead of lashing out, you focus your feedback on specific details – like long wait times or cold food – modeling the kind of clarity you’d want AI to reflect.

Impact

If mindfulness is primarily about being aware of the effects of our actions, impact is about deepening our understanding of these consequences. Understanding our impact also means recognizing that abstaining from engagement is itself a choice with consequences. When diverse voices opt out of AI interaction due to frustration or privacy concerns, the resulting systems can become skewed toward the preferences and perspectives of those who remain engaged.

Action Steps:

Consider potential harm – Ask who might be helped or harmed by particular AI outputs or applicationsConsider downstream effects – Think about how your AI interactions might influence future development and deployment of these systemsReflect on collective consequences – Remember that individual actions, when multiplied across millions of users, shape how AI evolvesEvaluate second-order impacts – Look beyond immediate results to consider how AI outputs might affect vulnerable communities or critical institutionsWeigh long-term implications – Balance short-term convenience against the long-term effects of normalized AI use in different domains

Example: Someone signing up for a healthcare app that uses AI to flag potential health issues might hesitate before opting in to share their data. But after reflecting on how anonymized data could help improve diagnostic accuracy for underrepresented communities, they choose to participate – recognizing that individual decisions can support more equitable AI outcomes.

Responsible Consumption

In a market-driven economy, nothing speaks more loudly than money. Our consumption choices send powerful signals to the companies building AI systems. When users flock to services that prioritize ethical considerations, transparency, and user control, the industry takes note. Conversely, when we embrace AI applications without regard for their ethical implications, we incentivize development patterns that show the same level of respect for our collective interests.

Responsible consumption means becoming a conscious consumer of artificial intelligence.

Action Steps:

Research company approaches – Before adopting an AI tool, investigate the company’s stance on ethics and responsibilitySupport ethical development – Choose products from companies that demonstrate commitment to responsible AI principlesValue transparency – Give preference to services that clearly explain how they train their systems and what data they usePrioritize privacy – Select tools that give you meaningful control over your personal informationAvoid harmful applications – Decline to use AI products with obvious potential for exploitation or abuse

Example: When an AI image generator fails to disclose how it trains on human-created work, some users choose to switch to alternatives that are more transparent – especially those that compensate artists whose styles have influenced the system. These market choices send a message that ethical sourcing matters.

Report Problems

David Morrison, former Chief of the Australian Army, once said: “The standard you walk past is the standard you become.” This is particularly important for the development of AI models. If we allow problematic outputs to pass by unchecked, we contribute to a future in which problematic outputs are the normal standard for AI systems. We need to be intentional and active about reporting problems so that we can help improve AI systems over time.

Action Steps:

Use reporting tools – Familiarize yourself with the feedback mechanisms available on AI platforms you useBe specific – When reporting issues, clearly describe the problem and why it concerns youDocument serious issues – For significant concerns, consider saving examples with appropriate contextShare with oversight groups – For systemic problems, consider sharing experiences with relevant consumer protection or advocacy organizationsFollow up when possible – If platforms offer case numbers or status updates for reports, check back on resolution

Example

When an AI assistant provides obviously biased information about a cultural topic, instead of simply moving on, you take a screenshot and submit it through the feedback form with a clear explanation of why the response was problematic, helping improve the system for everyone.

Ongoing Education

AI is evolving rapidly. Ongoing education means committing to continuous education about AI, because the better we understand it, the more able we are to influence its development intentionally. Learning about AI includes technical knowledge, ethical literacy, and making a conscious effort to seek diverse perspectives so that our understanding isn’t limited by our individual perspective or circumstances.

Action Steps:

Learn the AI basics – Develop a foundational understanding of how AI systems work and what they can and cannot doDiversify your information sources – Seek perspectives from different disciplines, cultures, and backgroundsJoin public conversations – Participate in discussions about AI governance, ethics, and future directionsShare knowledge – Help others understand AI concepts and implications in accessible waysStay curious – Recognize that AI is evolving rapidly and maintain an attitude of continuous learning

Example: Continuous learning doesn’t have to be overwhelming. Some people follow a podcast during their commute or set aside time once a week to read about AI trends and ethics. Even small efforts like these can build confidence and literacy – making it easier to engage with AI in informed and intentional ways.

 Reflective Growth

According to a recent article in the Harvard Business Review, therapy is the number one use case for generative AI in 2025. Once we understand that AI is a mirror, this makes a lot of sense. AI systems reflect back patterns in our individual and collective behavior – and many of these patterns might otherwise remain invisible to us. This mirroring effect offers a unique opportunity for reflection and growth – a chance to see ourselves more clearly through the lens of our technological creations.

Action Steps:

Notice your reactions – Pay attention to your responses when AI outputs surprise or concern youExamine revealed patterns – Consider what your interactions with AI reveal about your own habits, interests, and biasesIdentify growth edges – Leverage AI to identify weaknesses that you want to work on and strengths that you can lean intoCultivate self–compassion: Instead of using the insights of AI as another tool with which to beat yourself, approach them with kindness towards yourself and others – biases, blind spots, and other weaknesses are universal.

Example: After a few weeks with a fitness app that uses AI coaching, you notice it keeps recommending high-intensity workouts – even though you prefer a gentler pace. Rather than ignoring the suggestions or blindly following them, you pause to reflect. Is the app overreaching, or are there deeper reasons behind your resistance? You realize it’s both – some real physical limits, and some inner doubts you’d never quite named.

Conclusion

AI is already changing our world in profound ways, and its influence will only grow. None of us can shape the future of AI alone – not as users, not even as developers. Systemic safeguards and public accountability are essential. But each of us does have a role to play. AI learns from what we do, not just what we say. Through mindful online behavior, thoughtful interactions, and principled consumption, we help tip the balance – reinforcing the kinds of norms and values we want these systems to reflect.

But perhaps the most important thing we can do to make AI better is to work on ourselves. We have the opportunity to consciously shape AI to reflect who we are at our best rather than our worst. This means detaching from harmfulbehaviour patterns – distraction, division, shallow relationships, and convenience for its own sake – and devoting ourselves to what truly matters – freedom, connection, service, and love.

[Source Photo: Shutterstock]

A version of this article @ Psychology Today.  

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AI Isn’t the Problem, We Are

 When algorithms discriminate or polarize, they’re not malfunctioning—they’re mirroring the world we’ve built. AI exposes personal bias and inconsistency.

KEY POINTSAI systems mirror our values, biases, and contradictions.Bias in AI stems from historical data, not the code itself.What we click, share, and ignore teaches machines who we truly are.

Most recent public discussion about artificial intelligence frames it as a force that will reshape society, for better or worse. AI is presented as something external, a non-human presence that is inserting itself into our lives and threatening to change how we live and work. Understanding the impact AI will have on our day-to-day existence is important. But there is a crucial piece missing from this conversation. When we think about this technology, it isn’t enough to ask how it will change us. We also need to understand how we shape AI and what that process can tell us about ourselves.

Every AI system we create functions as a mirror, reflecting our values, priorities, and assumptions with startling clarity. When facial recognition technology struggles to identify darker skin tones, this is not a malfunction, it is a reflection of the assumptions and perspectives embedded in the data it was trained on. When content recommendation engines amplify outrage and division, this doesn’t mean that they are broken; they are successfully optimizing for engagement with how humans behave in reality. In many cases, the “threats” and “dangers” of AI have nothing to do with the technology itself. Instead, the things we have to worry about are reflections of qualities that are inescapably human.

Encoded Reflections

Consider hiring algorithms. In 2018, Amazon scrapped an AI-powered hiring tool after discovering it was biased against female candidates. The AI wasn’t programmed to discriminate, but it was trained on historical hiring data that favored men, and it learned to replicate those patterns. Similarly, research from UC Berkeley found that mortgage approval algorithms often offer less favorable terms to Black and Hispanic applicants, reinforcing longstanding inequalities in lending.

The use of AI systems in law enforcement, healthcare, and education reveals similar patterns. Predictive policing tools tend to focus on certain communitiesbecause they are trained on historical crime data. Algorithms in healthcare may be more likely to misdiagnose patients belonging to certain demographic groups. Automated grading systems in schools have sometimes been shown to favor students from wealthier economic backgrounds over others when the quality of the work was the same. In all these cases, AI isn’t creating new biases, it is reflecting existing ones.

This mirroring effect presents an important opportunity for self-examination. By making these issues more visible and more urgent, AI challenges us to acknowledge and address the sources of the data that cause algorithmic bias. This challenge will become increasingly personal. With the announcement of a new generation of AI-poweredrobots that will adapt to environmental conditions, we can expect the biases of individual owners to shape how these systems behave.

Our current approach to AI is filled with contradictions, and AI reflects those contradictions back at us. We value AI as a tool to increase the efficiency of our businesses, and yet we worry about it taking human jobs. We express concerns about AI-driven surveillance while willingly handing over our personal data in exchange for small conveniences (61 percent of adults acknowledge trading privacy for digital services). And while misinformation is a growing concern, engagement-driven AI models continue to favor viral content over accuracy.

Each Act Leaves a Trace

As AI continues to evolve, we must ask ourselves how we as individuals want to shape its role in society. This isn’t just about improving algorithms, it’s about ensuring that AI is developed and deployed responsibly.

Some organizations are already taking steps in this direction. Rather than simply refining AI models with the sole goal of increasing economic efficiency, they areevaluating the data, policies, and assumptions that shape the behavior of AI models. This could help mitigate unintended consequences.

Still, we cannot expect organizations and institutions to do all the work. As long as AI is trained on human data, it will reflect human behavior. That means we have to think carefully about the traces of ourselves we leave in the world. I may claim to value privacy, but if I give it up in a heartbeat to access a website, the algorithms may make a very different assessment of what I really want and what is good for me. If I claim to want meaningful human connections yet spend more time on social media and less time in the physical company of my friends, I am implicitly training AI models about the true nature of humanity. AI does not just expose systemic contradictions, it also highlights the internal conflicts of individuals. And as AI becomes more powerful, we need to take increasing care to read our principles into the record of our actions rather than allowing the two to diverge.

As we continue to integrate AI into our lives, we must recognize that these systems don’t just predict our behavior; they reflect our character. Reflecting on that reflection allows us to make better, more principled choices, but only if we’re willing to look closely and take responsibility for what we see.

[Source Photo: Pathdoc/Shutterstock]

Original article @ Psychology Today.  

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What the ‘Bhagavad Gita’ can teach us about AI

 2,000 years later, human choices still matter most. 

One recent rainy afternoon, I found myself in an unexpected role—philosophy teacher to a machine. I was explaining the story of the Bhagavad Gita to a leading large language model, curious to see if it could grasp the lessons at the heart of one of the world’s most profound philosophical texts. The LLM’s responses were impressively structured and fluent. They even sounded reflective at times, giving a sense that the AI model knew that it was itself part of this millennia-long conversation. 

Yet there was something fundamental that was missing from all the answers the machine gave me—the lived experience that gives wisdom its true weight. AI can analyze the Gita, but it does not feel Arjuna’s moral dilemma or the power of Krishna’s guidance. It does not struggle with duty, fear, or consequence, and it does not evolve through a process of personal growth. AI can simulate wisdom, but it cannot embody it.

The irony wasn’t lost on me. One of humanity’s oldest philosophical texts was testing the limits of our newest technology, just as that technology challenges us to rethink what it means to be human.

TECHNOLOGY IS JUST ONE PART OF THE STORY

As a founder of several technology companies and an author on innovation, I’ve followed AI’s evolution with both excitement and trepidation. But it was as a father that I first truly understood how important this technology will be for all of us. 

When my son was diagnosed with multiple myeloma, a rare blood cancer, I spent hundreds of hours using LLMs to find and analyze sources that might help me understand his condition. Every flash of insight I gained and every machine hallucination that steered me down the wrong path left a permanent mark on me as a person. I began to see that the technical challenges involved in implementing AI are just one part of the story. Much more important are the philosophical questions this technology raises when it leaves its imprint on our lives.

ARJUNA, KRISHNA, AND THE MORALITY OF INACTION

In the Bhagavad Gita, the warrior Arjuna faces an impossible choice. Seeing his family and teachers arrayed on the battlefield across from him in the opposing army, he lays down his weapons. Unwilling to harm those he loves, he believes that inaction will absolve him of responsibility for the deaths that will take place when the armies clash.

His charioteer, the god Krishna, disagrees, sharing an invaluable piece of wisdom that still resonates today: “No one exists for even an instant without performing action; however unwilling, every being is forced to act.”

Arjuna may think that his refusal to participate in the battle removes him from the moral fray just as it does from the physical conflict. But Krishna shows him that this is not so. Sitting out the battle will have consequences of its own. Krishna may not kill those he values on the other side, but without his protection, many on his own side will fall. His choice not to act is an action with consequences of its own.

DECISIONS (AND NONDECISIONS) HAVE CONSEQUENCES

This mirrors our predicament with AI. Many people today wish they could opt out of the AI revolution entirely—to disengage from a technology that writes essays, diagnoses diseases, powers weapons of war, and mimics human conversation with often unsettling accuracy. But as Krishna taught Arjuna, inaction is not an option. Those who want to wash their hands of the problem empower others to make decisions on their behalf. There is no way to rise above the fray. The only question is whether or not we will engage wisely with AI.

This wisdom extends beyond individual choices to organizational and societal responses. Every business decision about whether to adopt AI, every regulatory framework that governments consider, every educational curriculum that addresses (or ignores) AI literacy—all are actions with consequences. Even choosing not to implement AI is itself a significant action with far-reaching effects. As Krishna taught Arjuna, we cannot escape the responsibility of choice.

AI AS A MIRROR OF SOCIETY—AND BUSINESS

AI systems, and LLMs in particular, hold up a mirror to humanity. They reflect back at us all the human-created content they have been trained on, both the good and the bad. And this has ethical, social, and economic implications.

If AI-driven recommendations reinforce past trends, will innovation and sustainability suffer? If algorithms favor corporate giants over independent brands, will consumers be nudged toward choices that consolidate market power? AI doesn’t just reflect history—it is shaping the future of commerce. As such, it requires careful human oversight.

Recently, I conducted an experiment with a major retailer’s recommendation engine. The algorithm consistently steered me toward established brands with large advertising budgets, even when smaller companies offered better products or alternative options that might have interested me. This algorithmic preference wasn’t malicious—it simply optimized for historical purchasing patterns and profit margins. Yet its cumulative effect could make it harder for innovative, purpose-driven companies to gain visibility, potentially slowing the adoption of alternative business models.

AI AND PHILOSOPHY

AI-driven automation is also transforming the workforce, reshaping entire industries, from journalism to customer service to the creative arts. This transition is bringing new efficiencies but it also raises critical questions: How do we ensure that the economic displacement of human workers does not widen inequality? Can we create AI systems that augment human work rather than replace it? 

These are not just technical questions but questions with deeply philosophical ramifications. They demand that we think about issues such as the value of labor and the dignity of work. At a time when so much attention is being paid to bringing manufacturing jobs back to the United States, they also have an intensely political dimension. Will reshoring matter if these jobs, and many more, are automated within just a few years?

As AI becomes more capable, we must also ask whether our reliance on it weakens human creativity and problem-solving skills. If AI generates ideas, composes music, and writes literature, will human originality decline? If AI can complete complex tasks, will we become passive consumers of algorithmic output rather than active creators? The answers to these questions will depend not just on AI’s capabilities but on how we choose to integrate this technology into our lives.

THE MIDDLE WAY

Public sentiment toward AI swings between utopian optimism and dystopian dread, and I have witnessed this same polarization firsthand in boardrooms and policy discussions. Some see AI as a panacea for global problems—curing diseases, reversing climate change, creating prosperity. Others fear mass unemployment, autonomous weapons, and existential threats. I have seen senior leaders chasing the latest technology without thinking about how it can help deliver on the company’s mission while others reject out of hand the possibility that AI could do more than automate a small number of IT services.

The Buddha taught the virtue of the Middle Way: a path of balance that avoids extremes. Between the fascination of the AI maximalists and the fear of the AI Luddites lies a more balanced approach—one informed by both technological innovation and ethical reflection.

We can strike this balance only if we start by asking what values should guide the development and implementation of AI. Should efficiency always take precedence over human well-being? Should AI systems be allowed to make life-and-death decisions in healthcare, warfare, or criminal justice? These are ethical dilemmas we must confront now. We cannot afford to sit idle while these questions are answered in a piecemeal way depending on what seems to be most convenient at the moment. If we allow unreflective answers about AI usage to become deeply embedded in our social structures, it will be all but impossible to change course later.

THE PATH FORWARD

Jean-Paul Sartre, the influential French existentialist philosopher, argued that human beings are “condemned to be free”—our choices define us and we cannot escape the need to impose meaning on life through those choices. The AI revolution presents us with a new defining choice. We can use this technology to amplify distraction, division, and exploitation, or we can take it as a catalyst for human growth and development.

Transcending what we are now does not mean finding an escape from our humanity but rather finding a way to fulfill its potential at the highest possible level. It means embracing wisdom, compassion, and moral choice while acknowledging our limitations and biases. AI should not replace human judgment but rather complement it—embodying our highest values while compensating for our blind spots.

As we stand at this technological crossroads, the wisdom of ancient philosophical traditions offers valuable guidance, from the Bhagavad Gitaand Buddhist mindfulness to Aristotle’s virtue ethics and Socrates’s self-reflection. These traditions remind us that technological progress must be balanced with ethical development, that means and ends cannot be separated, and that true wisdom involves both knowledge and compassion.

Just as the alchemists of old sought the philosopher’s stone—a mythical substance capable of transforming base metals into gold—we now seek to transform our technological capabilities into true wisdom. The search for the philosopher’s stone was never merely about material transformation but about spiritual enlightenment. Similarly, AI’s greatest potential lies not in its technical capabilities but in how it might help us better understand ourselves and our place in the universe.

A MORE HUMAN FUTURE

This journey of philosophical reflection cannot be separated from technological development; it must be integral to it. We must cultivate what the ancient Greeks called phronesis—the practical wisdom that can guide action in complex situations. This wisdom enables us to navigate uncertainty, to accept that we cannot predict every outcome of technological change, and yet to move forward with both courage and caution.

By balancing innovation with caution, efficiency with meaning, and technological progress with human values, we can create a future that enhances rather than diminishes what is most valuable about being human. We can build AI systems that amplify our creativity rather than replacing it with mechanistic outputs, that expand our choices rather than constraining them, that deepen our human connections rather than substituting virtual alternatives.

In doing so, we may finally realize what philosophers have sought throughout history: not just mastery over nature, but wisdom about how to live well in an ever-changing and uncertain world.

[Source Photo: Julian/Adobe Stock]

Original article @ Fast Company. 

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Why AI Demands a New Breed of Leaders

[image error] Many CIOs lack the bandwidth and authority to solve the tough cultural and organizational change challenges that can block AI success. It’s time for an expanded leadership role.

Summary. Artificial intelligence is changing how humans and machines work together. But most organizations still focus on the technical aspect of AI implementation because their leadership structure does too. Companies need a new role, the chief innovation and transformation officer, to manage the profound cultural and organizational changes AI adoption brings. Here’s why forward-thinking organizations already have or plan to hire such leaders.

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ARTIFICIAL INTELLIGENCE is fundamentally transforming how organizations operate, but this transformation extends far beyond technical implementation. Modern AI systems are increasingly taking on roles that previously would have been filled by human workers. People working alongside these AI systems often need reskilling, upskilling, and training in behavioral traits such as critical thinking. To successfully manage this blend of AI tools and humans working together in new ways, leaders need to understand complex human and organizational factors, such as agility and cultural change, personality dynamics, and emotional intelligence.

Yet most organizations continue to treat the implementation of AI as a primarily technical challenge — and current technology leadership roles reflect this mindset. According to Foundry’s 2024 State of the CIO survey, 85% of IT leaders say that CIOs are increasingly becoming changemakers in their organizations, but only 28% call leading transformation their top priority. In another recent survey, 91% of large-company data leaders said “cultural challenges/change management” are impeding organizational efforts to become data-driven. Only 9% pointed to technology challenges.

But instead of focusing on the aspects of cultural and organizational change that are relevant to AI, much of the time and effort of IT and data leaders is spent on operational functions, long seen as the bread and butter of these roles. This operational focus seems to be increasing: Sixty-one percent of CIOs in the Foundry survey reported having less time available for strategic responsibilities over the past year than in previous years.

Although AI-enabled transformation clearly has enormous human and organizational implications, HR leaders have, for the most part, not stepped up to deal with such changes either.

When leaders fail to think through the strategic and organizational consequences of their AI plans, the results can be catastrophic. Zillow’s failed attempt to use AI-generated property valuations as the basis for its own homebuying division not only cost the company $300 million in losses but also saw its stock price fall by more than 20% as investors lost confidence in its ability to navigate the AI transformation. In another example, California State University had a clear strategic vision but failed to account for the human element. In early February 2025, the university announced a plan for integrating AI across all its systems and services, led by its newly formed AI Workforce Acceleration Board comprising representatives from 10 leading AI companies. Within a week, the initiative faced fierce opposition from staff members and students who objected to both the goal of the project and its implementation.

Sastry Durvasula, the chief operating, information, and digital officer at TIAA, views workforce transformation as a key part of his role.

The consequences of an excessively technical approach to AI implementation can also be seen on a more granular level. When Air Canada deployed a generative AI-based chatbot to assist travelers with booking flights, the goal was to create a more efficient and streamlined customer service experience. However, when the chatbot made mistakes about bereavement fares, passengers dealing with the loss of a loved one were faced with additional challenges to manage in an already difficult situation. This case underscores the need for organizations to address the human-facing personality of AI models, their decision-making authority, and the kinds of ethical boundaries and special circumstances in which it might be essential to have a human in the loop.

All of those examples highlight a crucial gap in organizational leadership. While CIOs and CTOs play critical roles in technical implementations and system maintenance, they sometimes lack both the bandwidth and the mandate to address the broader human and organizational implications of AI transformation. Organizations require a new kind of leader.

The new role we envision as being essential in the age of AI — which might be called the chief innovation and transformation officer (CITO) — combines technical expertise, behavioral insights, and strategic vision with a deep understanding of organizational psychology and culture change. This combination of skills ensures that organizations are properly equipped to manage the profound changes that the age of AI will bring. Companies are still debating the job title, but forward-thinking organizations are already employing, or hiring for, such leaders. Let’s explore the reasons.

Why AI Requires a New Leadership Model

The challenge of implementing AI effectively extends far beyond technical prowess. Leaders responsible for AI implementation must be able to do the following:

Navigate complex ethical landscapes. AI deployment requires careful consideration of ethical implications, bias mitigation, and alignment with organizational values.Foster cultural transformation. Successfully integrating AI — and, particularly, AI agents — means transforming an organization’s culture to embrace new ways of working and thinking.Manage human-AI collaboration. Leaders must understand both AI capabilities and human skills and psychology if they are to create effective partnerships between human workers and AI systems.Drive cross-functional integration. AI implementation touches every part of an organization, requiring leaders who can work across traditional silos.Deal with citizen development. AI is enabling businesspeople to develop systems and models that could previously only be created by IT professionals — a work trend requiring simultaneous encouragement and risk management.Ensure responsible innovation. Leaders must balance the drive for business innovation with careful consideration of the potential risks and societal impacts.

Some people serving in traditional IT and data leadership roles that focus on technical implementation and system maintenance may lack the skills and the bandwidth to address these broader challenges. Even for CIOs who have succeeded at driving revenue, the AI age raises the difficulty level. This misalignment is one reason why so many AI initiatives fail to achieve their goals.

New Hiring Patterns, Expanded Leadership Roles

Recent data shows that businesses are responding to the accelerating pace of technological change by broadening their C-suites to include roles dedicated to innovation, AI, and transformation. Increasing compensation levels and a surge in hiring reflect the strategic importance of innovation leadership, AI expertise, and transformation management.

Leaders with titles such as chief innovation officer, chief AI officer, and chief transformation officer are becoming increasingly common as companies wrestle with how best to meet these strategic needs. A study by Boston Consulting Group found that the number of companies hiring chief transformation officers increased by more than 140% from 2019 to 2021, and that those companies experienced a significant increase in total shareholder return in the year after the new appointment. This hiring trend has continued over the past three years.

The most effective leadership roles for managing AI will combine both technical and organizational change responsibilities.

Early Examples of CITO-Type Roles

A recent advertisement for a position at State Street Bank exemplifies the breadth of competencies required in these new leadership positions. The responsibilities of the role of chief transformation officer within the bank’s central Global Technology Services unit include “enterprise transformation leadership,” with specific accountabilities for driving “innovation and modernization initiatives, including automation, AI, blockchain, and cloud adoption,” as well as leading “cultural and organizational change efforts to embed agility, efficiency, and a customer-first mindset.” Other responsibilities involve business process optimization, customer experience and digital innovation, and change management. As of mid-March, the job had not yet been filled, and it may be difficult to find such a combination of skills. But the job description perfectly illustrates the responsibilities we envision as coming together in an office of innovation and transformation that is led by the equivalent of a CITO.

The CITO (or equivalent) role encompasses several critical functions. At the strategic level, these leaders align AI initiatives with organizational purpose while developing long-term transformation road maps. They ensure ethical alignment across implementations while creating frameworks for sustainable innovation that balance technical advances with human values.

An Emerging Requirement: AI Persona Management

AI persona management is an example of a new responsibility that requires new leadership from a CITO. Generative AI tools already can be asked to take on specific personalities — a teacher, a scientist, a lawyer, and so on. As agentic AI use grows, companies will need to manage AI agents with increased levels of autonomy and specific attributes and identities.

AI personas can be autonomous or collaborative with human workers, requiring leaders to manage the interplay between human psychology and intelligent machines.

You may think of AI personas as digital characters or workers with specific traits, priorities, and capabilities that are designed to interact with users and process information in customized ways. They perform defined tasks or serve as interfaces between humans and AI systems. This allows organizations to create distinct roles — such as strategic adviser and customer service agent — tailored to specific use cases. AI personas can be autonomous or collaborate with human workers, requiring leaders to understand and manage the delicate interplay between human psychology and intelligent machines.

Full article available @ MIT Sloan Management Review.

[Source Photo: Andrew Baker/Ikon Images]

ABOUT THE AUTHORS

Faisal Hoque is the founder of Shadoka, NextChapter, and other companies. His latest book is Transcend: Unlocking Humanity in the Age of AI (Post Hill Press, 2025). Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, the Bodily Bicentennial Professor of Analytics at the University of Virginia Darden School of Business, a fellow of the MIT Initiative on the Digital Economy, and senior adviser to the Deloitte Chief Data and Analytics Officer Program. His latest book is All Hands on Tech: The AI-Powered Citizen Revolution (Wiley, 2024). Erik Nelson is a senior vice president at CACI International, responsible for strategic vision and growth in the company’s Enterprise IT division.

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The Freedom to Be Human in the Age of Algorithms

 How to reclaim agency in a world increasingly shaped by AI.

KEY POINTSAI subtly shapes human decision-making.Algorithmic dependence erodes self-trust and autonomy over time.Digital mindfulness builds confidence in personal instincts over AI reliance.

Think about the last time you opened Netflix. Did you scroll through countless options or go with a recommended title? When you log into social media, do you decide what to see, or does an algorithm dictate your feed? When shopping online, do you browse freely, or focus on top-listed, AI-suggested items?

If you’re anything like me, most of the time, you go along with what the algorithm recommends. This is such common behavior that psychologists have given it a name: “algorithmic dependence.” As the name suggests, the term refers to our growing tendency to outsource decisions to systems designed to predict and steer our behavior.

Now, there’s nothing inherently wrong with accepting algorithmic suggestions. In many cases, they save time and reduce decision fatigue. But like water slowly carving stone, repeated reliance on AI recommendations can erode our capacity for independent judgment and even our sense of self. Over time, such reliance not only affects the quality of our choices but also undermines our ability to trust our own instincts.

When algorithms subtly shape our actions without our awareness, we risk drifting further from our authentic selves.

Reclaiming Our Agency

Let’s first define human agency: It is the capacity to make intentional, conscious choices and take purposeful action. It reflects autonomy, self-awareness, and the power to act in alignment with our values.

Exercising agency requires that we actively shape ourselves through deliberate choices, rather than being passively shaped by algorithms, social norms, or external pressures. Here are three practices that help preserve personal agency in the age of the algorithm:

Practice Intentional Pauses

Zen tradition calls it “ma”—the meaningful space between moments. Before accepting a recommendation, pause and ask, ‘Would I have made this choice on my own?’ This short reflection brings awareness to your decisions and strengthens your inner clarity.

Example: You’re on YouTube, and autoplay cues up another video. Instead of watching mindlessly, you pause and realize you’re not truly interested. You close the app and go for a walk instead, choosing awareness over automation.

Cultivate Digital Mindfulness

Inspired by the Buddhist practice of “sati” (mindfulness), digital mindfulnessinvolves intentionally choosing some decisions to make without technological assistance. Try picking a book, restaurant, or outfit without relying on reviews or ratings.

Example: Instead of checking Yelp, you walk into a neighborhood café that catches your eye. You scan the menu, feel the vibe, and decide to try it. The experience builds confidence in your own preferences and instincts.

Develop Strong Internal Values

Confucian “jen” (human-heartedness) encourages decisions rooted in compassion and ethics. When we reflect on our deeper values, our choices become more meaningful and less reactive to outside influence.

Example: A shopping app tempts you with fast-fashion deals. But you’ve committed to sustainable living. Rather than click impulsively, you take a moment to seek out a more ethical brand—one that aligns with your principles, even if it takes more effort.

Serendipity, Trust, and the Human Journey

The aim is not to reject AI but to relate to it consciously. This means:

Choosing when to use technology—and when to act independentlyRemaining aware of how algorithms influence our thinkingPracticing independent judgment regularlyEncouraging others to reflect on their choices, too.

Autonomy isn’t just about resisting automation. It’s also about staying open to life’s mystery. It’s worth recalling the ancient story of The Three Princes of Serendip. These young travelers, seeking glory, disguised themselves and journeyed without royal privilege. Along the way, through hardship and openness, they discovered beauty and wisdom they weren’t looking for. Their tale gave us the word “serendipity”—finding value in the unexpected.

Serendipity doesn’t belong just in old fables. When I left home at 17 for the U.S., I didn’t speak English fluently and knew no one. A woman I briefly met on the flight introduced me to her family, who offered me shelter and support.

No algorithm would have recommended either of our actions. An algorithm would not have told me to leave home at 19; an algorithm would not have told the woman to open her family’s doors to a youthful stranger. It was a moment of grace, a moment of humanity—and a defining moment in my own life. Utterly unplanned, totally unpredictable, it gave me the footing I needed in a land I did not know.

This story continually reminds me that some of life’s most defining moments can’t be predicted or optimized. They emerge when we allow uncertainty, listen to our intuition, and trust in the unfolding process.

Conclusion

To be human is to choose. It is to exercise the human capacity for intentional action, the ability we have been given to be the authors of our own lives. If we want to retain this distinctively human value, we need to be intentional about how algorithms figure in the choices we make. As AI becomes more embedded in our lives, we must actively preserve our decision-making capacities.

But to be human is also to recognize that there is a realm that is beyond the power of decisions. It’s the realm of mystery and wonder, of unpredictable blessings and fierce grace. It is the realm of the Princes of Serendip and the kind woman on the plane. To be human is to remain open to this realm—to wonder, to trust—and to remain open to the unexpected.

Let us move through this algorithmic era with clarity, compassion, and courage rooted in our values, guided by our wisdom, and ready for the beauty of the unknown. This is how we reclaim our freedom.

This is how we remain human.

[Source Photo: Shutterstock]

Original article @ Psychology Today.  

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Don’t move fast and break things—even with AI

Slowing down to speed up works better.

As digital technologies have become the leading engine of economic growth in America, we have become conditioned to equate speed with success. Major technological breakthroughs are reported in the news on a weekly basis, and the next paradigm-breaking revolution is always waiting just around the corner. Companies respond by rushing to implement new technologies and by striving for rapid, disruptive innovation. Those who can embrace this pace of change, we are told, will flourish, while those who lag behind will be left by the wayside.

There is, of course, a great deal of truth to these warnings. Companies that fail to innovate and change can quickly lose their market position and fade into history. Yet an overcommitment to this accelerated pace in both creating and implementing new technologies exposes businesses to a different set of dangers. And never has this risk been greater than with the advent of artificial intelligence. On the innovation front, new AI models are released at breakneck speeds, with researchers pushing back boundaries without fully understanding the future consequences of their work. Simultaneously, on the implementation side, corporate leaders face immense pressure to deploy these technologies as quickly as possible, often before their organizations are prepared to absorb them effectively.

Agentic AI offers a prime case study of this tension. Autonomous AI agents could deliver enormous value to companies that develop and deploy them thoughtfully. Yet rushing blindly ahead leads to failure. As a recent CBInsights report highlights, the hype surrounding this technology has diverged significantly from client experiences on the ground. At the same time, few companies have truly prepared themselves for what success might look like. Deploying agentic AI at scale will involve the wholesale cultural transformation of businesses, and there is little evidence that most companies have even begun to plan for the replacement of large parts of the human workforce.

The most transformative advancements emerge not from haste, but from deliberate, strategic progress in both creation and application.

The myth of speed

The tech industry’s “move fast and break things” ethos has long shaped how organizations approach both the creation and the adoption of new technologies. This philosophy has its merits, but the collateral damage it can cause is becoming increasingly apparent.

The spectacular downfall of Theranos offers a cautionary, and by now familiar, tale about the dangers of rushing headlong toward innovative ideas without putting the right groundwork in place. But it is far from an isolated case.

More recently, 23andMe’s bankruptcy filing has put the genetic data of millions of consumers at risk. The company, once valued at $6 billion, aggressively expanded its direct-to-consumer genetic testing business without establishing sustainable business models or adequate data protection measures. With its collapse, former customers are now rightly concerned about whether their highly sensitive DNA information will be auctioned off to the highest bidder.

Both cases highlight a crucial lesson: Innovation that cuts across disciplines and has a real impact on human lives demands a methodical, thoughtful approach. Rapid implementation without adequate guardrails doesn’t just endanger companies; it can put millions of people at risk.

The most successful companies recognize that meaningful technological advances require patience and a thoughtful approach to adoption.

 The patient innovator

Steve Jobs embodied the power of strategic patience throughout his career.

The iPhone wasn’t first to market in the smartphone sector. Jobs deliberately waited until touchscreen technology matured before creating Apple’s revolutionary device. But once it arrived, the iPhone became a global icon, and it remains so more than 15 years later.

A lesser-known story about Jobs’ commitment to perfection comes from his time at Pixar. When the studio was struggling financially and under immense pressure to deliver its first feature film, Toy Story, Jobs refused to compromise on quality. Despite the company burning through cash and facing potential failure, he insisted on improving the rendering technology and refining the storytelling until everything met his precise standards. This patient attention to detail not only saved Pixar from collapse but established a foundation for the studio’s subsequent string of blockbusters.

This unified approach to timing—knowing when to accelerate innovation, when to wait, and how to stage implementation—created products and companies that delivered consistent value rather than chasing short-term technological wins.

Amazon’s long-term vision

Amazon’s journey demonstrates how patient innovation and disciplined implementation create lasting advantage. As Jeff Bezos famously wrote in his 1997 shareholder letter, “It’s all about the long term.” While competitors were chasing bumps in quarterly profits, Amazon invested billions in infrastructure, logistics networks, and cloud computing capabilities that wouldn’t yield returns for years.

This long-term orientation applied equally to Amazon’s approach to technology deployment. The company’s principle of “working backwards” from customer needs rather than forward from technological capabilities ensured that implementation efforts aligned with genuine market demands.

The company’s willingness to sacrifice immediate returns for future capabilities stands in stark contrast to the short-term thinking that dominates many technology initiatives. By maintaining this balanced approach to both creating and deploying new technologies, Amazon built the foundation for sustainable growth that continues today.

Navigating AI: the ultimate test of balanced progress

Artificial intelligence represents perhaps the greatest challenge to balanced technological advancement. The stakes are extraordinarily high, and missteps here will have far-reaching consequences not only for individual companies but potentially for humanity more broadly. Unlike previous technologies, AI systems can make autonomous decisions affecting millions of lives, often with limited human oversight. Rushed AI development has already produced concerning outcomes: facial recognition systems with racial bias, content moderation algorithms that amplify harmful material, and hiring tools that perpetuate workplace discrimination. The dangers of getting things wrong will only increase as more advanced AI models are brought online and given critical decision-making roles.

Even well-designed AI models can be dangerous if they are implemented too hastily. Models deployed without thorough safety testing may be exploited by cyberattacks, misinformation campaigns, or for mass surveillance. The economic implications are equally sobering. AI has the potential to disrupt entire industries, and if models are implemented before societies have time to adapt, the displacement of the human workforce could have catastrophic consequences for social stability.

This uniquely powerful technology demands a recalibration of our approach to innovation and implementation—one that prizes foresight, safety, and societal impact alongside technological achievement and market advantage.

Innovation demands patience

The pursuit of technological advancement doesn’t always reward the swift. As the Roman Emperor Augustus was fond of saying, festina lente—make haste slowly. When it comes to important things, the more you hurry, the longer it takes.

Innovation, particularly with transformative technologies like AI, demands patience: constancy of purpose, consistency of action, and the ability to remain calm when competitors are sprinting toward ill-defined goals. This is especially true for multidisciplinary innovations with world-changing potential.

Harnessing the potential of the AI revolution doesn’t require moving more quickly—it requires deeper thinking and executing with strategic vision and long-term purpose in mind. As we navigate the AI-driven future, the organizations that pause to ask the right questions, innovate deliberately, and balance speed with intention will ultimately shape the next era of progress.

Even with cutting-edge technologies, ancient wisdom remains relevant. The Tao te Ching warns us:

Rushing into action, you fail.
Trying to grasp things, you lose them.
Forcing a project to completion, you ruin what was almost ripe.

If we want to harvest the fruits of the AI revolution at their ripest, we must learn to slow down. If we do not, we risk ruin.

[Source Photo: JUSTIN SULLIVAN/GETTY IMAGES]

Original article @ Fortune. 

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Why all companies should think beyond Large Language Model

 By looking outside the current wave of hype, we can create a framework for weighing up the practical impact of AI on any business. KEY TAKEAWAYSLarge Language Models (LLMs) are just one type of AI. Despite the hype, they have not superseded all other types and made them redundant.Many of the most promising applications of AI are emerging in areas of machine learning that have little to do with LLMs.We can consider the trajectory AI by thinking about the categories of AI capability: Narrow AI, Artificial General Intelligence, and Super AI.

Adapted with permission from TRANSCEND by Faisal Hoque (Post Hill Press, Hardcover, April 8, 2025). Copyright 2025, Faisal Hoque, All rights reserved.

The current wave of AI hype was kicked off by the rollout and subsequent development of ChatGPT. And in the public mind, it is with this kind of application that AI is most strongly associated — the LLM-powered chatbots that we can talk and listen to in natural language. But it is important to understand that LLMs are just one type of AI. Despite the hype, they have not superseded all other types and made them redundant. Far from it. Many of the most promising applications of AI are emerging in areas of machine learning that have little to do with LLMs. It is important to remember this if we are to make the most of all of AI and guard against all its potential dangers.

The promise of AI extends far beyond the kinds of outputs we get from LLMs. To take just three examples, it also holds the promise of delivering better patient diagnostics and improved drugs with which to treat those patients, improving weather forecasts, and providing more efficient and environmentally friendly methods for utilizing energy. All of these forms of output may turn out to be significantly more important than a chatbot’s ability to create a thousand generic LinkedIn posts. Another enormously significant application of AI that has nothing to do with natural language is in the field of robotics. AI can be — and already is being — used to help with the development of advanced prosthetics. It is already being used to open restaurants and food-trucks where robots cook all the food. It is already being used in hospitals to assist with, or even conduct, surgeries. And again, none of these applications have anything to do with processing and generating natural language text. 

 

The key point is that LLMs are just one, highly visible, form of AI. Their distinctive feature in relation to other types of AI is that they are able to “understand” and generate text like human beings. That’s why we can talk to LLM-powered chatbots without any prior knowledge or training beyond knowing a language. LLMs are a profound development in AI, and it is perfectly reasonable to be excited by them. But there are many other different applications of AI, and many of these are also potentially revolutionary. When we consider how to use AI, then, we need to think beyond the potential of LLMs, even if these will be a critical source of easily accessible AI-powered tools for most of us. 

The hype around AI comes in two flavors — positive and negative. The positive hype says that AI will make everything better. The negative hype says that it will literally destroy us. Having looked at the capabilities of foundation models and generative AI, and how these come together in LLMs, we are in a good position to understand the positive hype. But what about the negative side? What explains the worries?

One reason that many serious people fear the trajectory AI is taking is the worry that AI will simply become too intelligent. And one typical way of analyzing this possibility is to talk in terms of AI capability. At present, we can usefully divide the potential capabilities of AI models into three main categories:

Narrow AI (Weak AI). This type of AI is “narrow” in the sense that it has been trained to carry out certain predefined tasks and has little or no capability beyond that narrow range. For example, certain AI systems used in finance are trained to detect irregularities in credit card usage in order to flag potentially fraudulent transactions. This is an important capability but it is a narrowly focused one.Artificial General Intelligence (AGI; Strong AI). When we try to learn new things, we typically rely on skills and knowledge that we already possess. For example, when we learn a new language, we rely on our skills of reading, writing, speaking, and listening. This is a remarkable feature of human intelligence — once we have a set of tools, it opens up virtually unlimited possibilities for learning. This type of intelligence can be called “general” because it is flexible and capable of adapting to new tasks and contexts. We can think of it as being something like a meta-capability: the capability of acquiring new capabilities at will. AI that has similar capabilities to humans in this regard can be called General or Strong AI.Super AI (Artificial Superintelligence). Super AI is like AGI, in the sense that its ability to perform tasks is not constrained by context-specific programming but differs in the scale of its power. Where AGI operates at a level analogous to that of a human, Super AI has the potential to be vastly more intelligent than any human being, with capabilities that scale with available processing power. It also has the capacity to act as an autonomous agent — to have its own desires, emotions, needs, and beliefs. 

Now, before we can move on to using these levels of capability to explain some of the major fears around the development of AI, it is worth pointing out that this taxonomy is also useful in other ways. It helps us think about the different features that AI systems might have or lack, and it also provides a handy framework for keeping track of AI development. For example, when confronted with the latest AI hype story, we can ask: Is this a development toward AGI? If it isn’t, then is it a development within narrow AI? Is it doing something that other examples of narrow AI do but better or cheaper? Or is it a new application of existing AI capability?

This categorization also allows us to see that there is a big difference between the development of AI per se and its usefulness to any given person or organization. In terms of AI development as such, a new application of existing AI capability is not very exciting — it’s just more of the same. For an individual or an organization, however, a new application could be nothing short of revolutionary. An AI that specializes in analyzing legal documents might be just another boring iteration of the same fundamental technology to an AI researcher, but to a paralegal or an attorney, it could mark the end of their career or the beginning of a newly empowered phase in their life. Which is to say that the technical advance might be negligible while the practical impact is potentially life-changing.

[Source Photo: zapp2photo / Adobe Stock]

Original article @ BIG THINK.

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How AI Will Impact Your Workplace Retirement Plan

It’s early days, but AI will bring new efficiencies and personalization to your workplace retirement plan.

BY CHRISTY BIEBER

Faisal Hoque, a technologist and author of Transcend: Unlocking Humanity in the Age of AI says some employers have begun to deploy these features, although there’s still significant potential for growth.

“In many 401(k) plans today, AI tools personalize savings recommendations based on each person’s income, goals, and risk tolerance — making it easier for people to make better financial decisions,” he explained. “Over time, these tools will get even smarter, automatically adjusting contributions and investments to help people stay on track.”

RISKS

“These AI tools can simplify complex financial decisions, offer real-time insights, and personalize recommendations in a way that many people wouldn’t get otherwise. That kind of support can be a game changer, especially for workers who don’t have access to financial advisors,” Hoque said.

However, Hoque warned that while AI tools may become effective sources of aid, they’ll live up to that potential only if designed responsibly.

“Technology is only as good as the intention and ethics behind it,” Hoque said. “If these tools are built with transparency, fairness, and human well-being in mind, they can absolutely help people make smarter, more informed choices about their retirement. The key is to make sure AI is used to empower, not overwhelm or manipulate.”

Some specific risks that Hoque said to be mindful of include:

Over-reliance, or placing too much trust in algorithms without a full understanding of how decisions are being made.Bias that’s baked into the data that causes AI tools to make recommendations that don’t serve everyone equally.AI tools that end up prioritizing profits or efficiency over what’s actually best for individuals.

“While AI can be a powerful guide, users should always stay informed and ask questions. Remember, no algorithm can fully replace human judgment when it comes to your financial future.”

[Source Photo: Getty Images]

Full article @ Kiplinger. 

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The Age of AI Requires a new kind of Leadership

 Regenerative leadership can be an effective antidote to AI doomerism, says this management expert. 

We live in an era of rapid technological change, where the rise of AI presents both opportunities and risks. While AI can drive efficiency and innovation, it also increases the temptation for leaders to prioritize short-term gains—automating decisions for immediate profit, optimizing for productivity at the cost of employee well-being, and sidelining long-term sustainability. Organizations that focus solely on AI-driven efficiency risk creating burnt out workforces, extractive systems, and fragile organizations that cannot withstand economic, social, or environmental disruptions.

To build resilient organizations that can weather the future, leaders must embrace regenerative leadership. This requires shifting from exploitative business models that prioritize efficiency to people-centered leadership that actively seeks to restore and enhance resources, whether human, environmental, or technological.

Regenerative leaders recognize that AI should augment human potential, not replace or exploit it. They create strategies that use AI to enhance long-term human, business, and environmental well-being rather than diminishing them.

THE KEY PRINCIPLES OF REGENERATIVE LEADERSHIP

A regenerative leader creates sustainable systems. Unlike traditional leadership, which focuses on efficiency, profit, and centralized control, regenerative leadership nurtures ecosystems. Here are the key principles a regenerative leader follows:

Systems Thinking: Sees organizations and ecosystems as interconnected, ensuring decisions benefit the whole rather than just isolated parts.Living Systems Approach: Draws inspiration from nature’s regenerative cycles to create adaptive, self-renewing teams and businesses. A self-renewing team is one that continuously learns and evolves.Purpose-Driven Leadership: Aligns business and leadership goals with meaningful long-term impact.Human Well-being: Prioritizes employee and stakeholder well-being including creating psychological safety and a collaborative environment.Resilience & Adaptability: Leads with agility in uncertain times, designing organizations that can thrive in change.Regenerative Value Creation: Moves beyond extraction of resources, talent, and energy to creating lasting value for people, communities, and nature.Collaborative & Decentralized Power: Encourages participatory leadership, where teams self-organize and contribute to a larger mission.REGENERATIVE LEADERSHIP IN ACTION

Here’s how different companies have implemented regenerative leadership:

Business Strategy: Companies like Patagonia and Interface have pioneered sustainable business practices that go beyond carbon neutrality and actively regenerate ecosystems. Both companies saw improved brand loyalty, cost savings, and competitive advantage from these efforts. Patagonia’s ethical stance boosted sales, making it one of the most trusted brands globally, while Interface’s sustainable innovations led to higher efficiency, lower production costs, and increased demand for eco-friendly products.Corporate Culture: Microsoft prioritizes employee well-being through flexible work policies, continuous learning programs, and mental health support. This fosters a positive work environment that enhances engagement, productivity, and ultimately long-term business success.Community Impact: The Hershey Company has made significant strides in community impact through its commitment to sustainable cocoa sourcing and education programs. These programs ensure a stable supply chain, enhance brand trust, and meet consumer demand for ethical products, driving long-term success.DEVELOPING REGENERATIVE LEADERSHIP SKILLS

Regenerative leadership is not an innate talent but a skillset that can be cultivated. Here are some suggestions for becoming a more regenerative leader:

1. Expand awareness to think in systems, not silos.

Regenerative leaders recognize that businesses must work in harmony with both the environment and human nature. Companies like Patagonia restore ecosystems through regenerative practices. They emphasize that great leadership works with natural flows rather than imposing rigid control. By shaping organizations that evolve organically, like ecosystems, leaders cultivate resilience, innovation, and lasting success.

2. Practice deep listening to lead with empathy.

Success will start with deep listening to employees, customers, and stakeholders. The Buddhist concept of mindfulness will remind leaders to be present, ask the right questions, and cultivate trust, creating cultures where innovation thrives.

3. Embrace a growth mindset to stay adaptive.

Regenerative leaders will see challenges as opportunities for reinvention. The Zen principle of Shoshin (beginner’s mind) will encourage curiosity, adaptability, and a culture of continuous learning, ensuring organizations do not just survive but evolve.

4. Foster collaboration and build networks, not hierarchies.

The best leaders will empower teams, encourage co-creation, and shift from competition to co-elevation. By fostering inclusive, participatory decision-making, they will build self-renewing, resilient organizations.

5. Measure impact beyond profits.

Success is more than profits—it includes ethical usage of technology, employee well-being, biodiversity restoration, and community impact. Regenerative leaders track holistic KPIs, driving sustainable business transformation.

THE FUTURE OF LEADERSHIP IS REGENERATIVE

By embracing regenerative leadership, leaders will move beyond short-term survival tactics and instead drive innovation, resilience, and long-term success while creating lasting positive impacts. This approach will become an ongoing practice of learning, adaptation, and alignment with the broader ecosystems of business, society, and technology.

The choice will be clear: Leadership must not only sustain but regenerate—leveraging AI and emerging technologies as forces for good.

[Source Photo: somyuzu/Adobe Stock]

Original article @ Fast Company. 

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