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Implementation of Responsible AI – 4 Questions

by  Faisal Hoque ,  Pranay Sanklecha ,  Paul Scade

As AI races ahead and regulators fall behind, the real question isn’t what your system can do, but what kind of organization you become by deploying it. Answering these four questions will help you ensure you implement responsible AI (RAI).  

As AI systems become more powerful and pervasive across critical domains, incidents of harm to humans are increasing in frequency and severity. MIT’s AI Incident Tracker reports a 47% increase in the number of reported incidents between 2023 and 2024, while Stanford’s most recent AI Index Report puts the increase at 56.4%. The annual increase rises to 74% for incidents in the most severe category of harm, according to MIT.

This development puts business leaders in a difficult position. Historically, companies have relied on government oversight for the guardrails that minimize potential harms from new technologies and business activities. Yet governments currently lack the appetite, expertise, or frameworks to regulate AI implementation at the granular level. Increasingly, the responsibility for deploying responsible AI (RAI) systems falls to the businesses themselves.

Strategically implemented responsible AI (RAI) offers organizations a way to mitigate and manage the risks associated with this technology. The potential benefits are considerable. According to a collaborative Stanford-Accenture survey of C-suite executives across 1,000 companies, businesses expect that adoption of RAI will increase revenues by an average of 18%. Similarly, a 2024 McKinsey survey of senior executives found that more than four in 10 (42%) respondents reported improved business operations and nearly three in 10 (28%) reported improved business outcomes as a result of beginning to operationalize RAI.

Yet while leaders increasingly recognize the importance and potential benefits of RAI, many organizations struggle to take effective steps to put in place a governance structure for it. A key reason for the gap between aspiration and action is that few businesses have the internal resources needed to navigate the complex philosophical territory involved in ensuring that AI is implemented in a truly responsible manner.

The approach consists of questions rather than prescriptive answers

A practical guide for operationalizing responsible AI

To bridge the gap between aspiration and operationalization, we offer a simple approach that’s practical and immediately actionable. Crucially, it consists of questions rather than prescriptive answers. By framing RAI in terms of a series of questions rather than a set of fixed rules to be applied, we provide a flexible yet structured approach that is not only sensitive to context but can also evolve alongside the technology itself.

The questions we identify are not arbitrarily chosen. Rather, they are distilled from established philosophical traditions across cultures, including Western ethical frameworks, Confucianism, and Nyaya ethics. They represent different but complementary perspectives on what responsibility means in practice.

For RAI to be truly effective, it must cascade from the boardroom to the project office. This requires a structured approach across three critical tiers: executive oversight, program management, and technical implementation. By consistently applying the four questions at each level, organizations can create alignment and ensure that RAI is embedded as a practical component in all AI development and deployment initiatives.

Question 1: What’s in it for us?

Organizations must pursue their self-interest as part of their duty to their shareholders. When considering an AI initiative, it is therefore essential for leaders to ask: What’s in it for us?

This question needs to be answered with nuance, using the concept of enlightened self-interest, a philosophical perspective dating back to ancient thinkers such as Epicurus and developed by economists like Adam Smith. Enlightened self-interest recognizes that pursuing one’s interests intelligently requires considering long-term consequences and interdependencies rather than focusing solely on immediate gains.

How you can use the question:

Executive level: Incorporate RAI metrics into board reports to show how responsible practices correlate with long-term value. Establish dedicated RAI committees to align initiatives with strategic plans.

Program level: Require a one-page “RAI value scorecard” as part of every AI business case. Include RAI implementation as a formal success metric alongside traditional KPIs.

Technical level: Track unresolved ethical issues the same way you track technical debt. Build features into AI systems that can explain their decisions – this helps with both problem-solving and regulatory compliance

Drawing from the consequentialist ideas championed in Nyaya ethics and by Western philosophers like John Stuart Mill and Jeremy Bentham, this perspective requires leaders to divorce intentions from outcomes and focus on judging the latter alone.

Question 2: What are the likely consequences?

Implementing RAI demands that leaders consider impacts on all stakeholders: employees, customers, communities, and society at large, in addition to shareholders. By asking, “What are the likely consequences?” at key planning and implementation stages, organizations can better anticipate risks and take meaningful steps to prevent them. Drawing from the consequentialist ideas championed in Nyaya ethics and by Western philosophers like John Stuart Mill and Jeremy Bentham, this perspective requires leaders to divorce intentions from outcomes and focus on judging the latter alone.

How you can use the question:

Executive level: Hold quarterly scenario planning sessions on the societal impacts of flagship AI systems. Commission independent audits of the downstream effects of high-visibility models.

Program level: Schedule “impact reviews” at each major milestone to map affected stakeholders. Use a standardized “consequence-severity framework” to prioritize mitigation resources.

Technical level: Embed automated fairness and safety tests in the continuous integration/continuous deployment (CI/CD) pipeline. Implement enhanced monitoring for AI systems with high-consequence potential.

It compels organizations to identify and articulate their guiding principles explicitly, helping leaders make intentional and principle-driven decisions around technological innovation.

Question 3: What principles does this uphold or violate?

When leaders systematically ask of their AI initiatives, “What principles does this uphold or violate?” they ensure that AI development and deployment remain anchored in ethical foundations.

This question draws from the deontological ethics associated with Immanuel Kant, which emphasizes that certain actions are inherently right or wrong regardless of their consequences. It compels organizations to identify and articulate their guiding principles explicitly, helping leaders make intentional and principle-driven decisions around technological innovation.

How you can use the question:

Executive level: Run a formal “principle alignment” review before giving the go-ahead to any AI initiative. Publish a brief annual account of how deployed models map to the organization’s ethical commitments.

Program level: Add “principle alignment” as a gate in stage-gate reviews. Provide project teams with principle-specific design requirements for all AI project plans.

Technical level: Map technical specifications directly to organizational principles. Run principle-focused design reviews and document how choices honor stated values.

The company recognized its responsibility toward its employees and found a way to harness AI’s efficiency benefits while still providing meaningful work.

Question 4: What does this say about who we are?

When IKEA began investing heavily in AI to replace their call center staff, they didn’t just fire those employees. Instead, they invested in upskilling them for more sustainable positions. The company recognized its responsibility toward its employees and found a way to harness AI’s efficiency benefits while still providing meaningful work. In doing so, IKEA expressed something profound about its culture and values, and ultimately about its identity as an organization.

When leaders ask, “What does this say about who we are?” they are forced to consider what kind of organization they aspire to lead and how their values are expressed through their AI initiatives. This question transforms AI implementation from a purely technical exercise into a moment for organizational reflection and affirmation of purpose.

How you can use the question:

Executive level: Assess major AI initiatives through the lens of organizational identity. Ask, “Is this who we want to be?”

Program level: Insert brief “values check-ins” in sprint reviews. Include RAI capability-building goals in team development plans.

Technical level: Design human-in-the-loop mechanisms for high-stakes decisions. Prioritize explainability features that demonstrate a commitment to transparency.

Conclusion

Turning the high-flown rhetoric of responsible AI into operational reality requires deliberate integration at all levels of the organization. By asking the same questions at three crucial organizational tiers, companies can systematically ensure this integration. Further, companies can do this while retaining the virtues of flexibility and context-sensitivity; while the four questions provide a consistent scaffolding, they must be adapted to accommodate different responsibilities and contexts across the organization.

Original article @ IMD.

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The AI Doppelganger Dilemma

 AI now doesn’t just assist us—it thinks for us. To stay in control, we need metacognition, the habit of examining our own thinking before we hand it over to machines.

KEY POINTSAI now doesn’t just remember things for us—it thinks for us. That changes how we need to think.In the age of AI, metacognition isn’t optional. We have to think about our thinking—or lose it.The most skilled AI users don’t outsource everything. They know when to step in and think for themselves.

I used to know phone numbers of my friends and family by heart (I still know the number of my childhood home). Now I barely remember my own number, because all I have to do is tap a button on a screen.

Same for routes. I used to know when the traffic was bad in certain areas, I knew shortcuts across town, I had a mental map of my neighborhood and my city. I no longer know any of this, because all I have to do is put a destination into Google Maps and follow the instructions.

And this isn’t a bad thing! In fact, I would argue that it’s mostly a positive development. The shortcuts have freed up mental space for more important things. Why bother remembering my brother’s phone number when technology can do the work for me?

Now, in one way, AI is similar: It’s also a shortcut that can free up mental space for other things. But there’s also something crucially different about AI. And we need to understand this difference if we want to use AI in ways that help us flourish.

From GPS to AI

The progression from GPS to AI represents a fundamental shift. To put it as succinctly as possible: AI does much more of our thinking for us than GPS ever did.

When we ask AI to help write emails, and it doesn’t just correct grammar, it shapes our tone, arguments, and thoughts. We ask it to analyze work situations, and it offers interpretations and recommendations that influence how we see problems themselves.

The result: We’re not just getting answers faster; we’re thinking less about whether those answers make sense. We’re outsourcing not just the storage of the raw material on which cognition feeds but cognition itself.

Again, this isn’t necessarily a bad thing. We outsource mathematical operations to calculators and spreadsheets, and this enables us to achieve a lot more with our cognitive resources. So the point here isn’t that we should completely stop outsourcing our thinking.

Rather, the point is that thinking about thinking becomes crucial. If we’re going to get AI to do a lot of our thinking for us, it becomes important to be able to critically evaluate the thinking that we outsource.

AI Demands Metacognitive Skills

That’s what is often called metacognition, the ability to think about your own thinking. Normally, this refers to thinking about your own mental processes. But if you’re going to be using to AI to do some of your thinking for you, then metacognition must expand to thinking about AI’s thinking too.

For example, consider two people asking AI for career advice. The first accepts everything AI suggests, while the second treats suggestions as starting points, asking: What assumptions is this based on? How does it align with my actual values? What might it be missing? It’s pretty clear which one is going to get better results over time.

The person who questions AI and critically evaluates its thinking—and their own thinking in relation to using AI—has developed a key component of AI literacy: metacognitive awareness to evaluate AI responses.

For example, a good doctor who is skilled at using AI doesn’t just accept AI’s diagnostic suggestions in toto; she knows which bits to accept, she knows why she’s accepting them, and she knows where she needs to probe further. Similiarly, an entrepreneur may use AI to develop a business strategy, but she is constantly supplementing, refining, and sometimes rejecting, AI’s suggestions on the basis of her experience and insight.

Such experts succeed precisely because they bring metacognitive skills to the interaction. They know what they know, what they don’t know, and what AI is likely to get wrong. Rather than using AI as a substitute for thinking, they’re using AI to supplement and strengthen their thinking.

4 Practices to Strengthen Metacognition

Building meta-cognitive awareness requires deliberate practice. Here are four habits to help you develop metacognition about both your own thinking and the thinking you outsource to AI:

1. Check Sources: Before accepting any answer, ask: How does this system know what it claims to know? And: Can it back it up?

2. Question Assumptions: Identify one belief you’ve been holding without questioning, and then ask whether and to what extent the evidence actually supports it.

3. Show Your Working: When working through complex problems, make your reasoning explicit: I’m assuming X because Y, but could be wrong about Z. This helps make your thinking visible to yourself.

4. Mind the Gap: Actively look for what’s missing from any answer. What perspectives aren’t represented? What would someone who disagrees point out?

Asserting Agency Over Our Thinking Process

The goal isn’t to swear off AI any more than it is to to throw away smartphones or delete Google Maps. But just as some people occasionally walk instead of drive—not because cars are evil but because movement keeps different capabilities alive—we can choose when to think with artificial assistance. And when to think without it.

Sometimes, for example, rather than rushing to AI to get an answer, it’s more productive to simply sit with a question for an hour. Or, to take another example, sometimes it’s better to work out an answer for yourself, even though AI could do it quicker, because the process of doing it will help you develop skills that will benefit you long term.

The most sophisticated AI users aren’t those who automate everything but those who remain intentional about what they automate. This requires saying no to some conveniences. But what you get in return is the preservation of your most human capabilities: curiosity, nuance, the ability to sit with complexity until genuine insight emerges.

A Simple Practice for Metacognition That You Can Start Using Today

Once a day, when you receive an answer from AI, pause and ask yourself: If this is wrong, how would I know?

You don’t ask this question because you expect the answer to be wrong. You ask it so you keep checking: Am I using the machine to think better, or am I simply letting the machine do all my thinking for me?

[Photo: wavebreakmedia / Shutterstock]

Original article @ Psychology Today.  

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From ideas to execution: Using strategic enterprise architecture for AI value creation

AI won’t contribute to a thriving business unless it’s hardwired into your purpose, people, processes, and architecture. Here’s how getting that alignment right can ensure maximum value from AI investments.AI and immunotherapy  

Artificial intelligence has received unprecedented levels of investment and enthusiasm over the last three years, yet the gap between the hype and the delivery of real business value remains stubbornly wide. A recent study from Boston Consulting Group suggests that while 98% of companies are exploring AI, only 4% have achieved significant returns on their investments, and just over a quarter (26%) have created any value at all.

So, why do so few AI initiatives deliver meaningful returns? The answer lies in a failure to align AI technology decisions with the organization’s strategic enterprise architecture – its overarching purpose and the people, processes, and existing technologies that are marshalled to pursue its strategic goals. Too often, AI deployments are led by a fascination with the new technology or a fear of missing out rather than by an analysis of the initiative’s fit with the broader business.

This is not a new problem. Misalignment between technology and business goals has been a problem since the dawn of the information technology revolution. As I argued in the Wall Street Journal after the bursting of the dot-com bubble, it is a challenge that organizations must work systematically to overcome. However, the stakes are exponentially higher with AI than with previous waves of technology, the integration points are more numerous, and the organizational impacts are more profound. Unlike earlier innovations that could be implemented at the departmental level with limited cross-functional impact, even seemingly modest AI initiatives ripple across the entire enterprise architecture.

In a recent article in MIT Sloan Management Review, my co-authors and I argued that a new type of leader is needed to coordinate the unprecedented scale and breadth of AI transformation across organizations. But successful AI implementation cannot rest on the shoulders of just one member of the C-suite. Every senior leader must be able to understand what AI promises, what it threatens, and how it will affect systems and strategies across the business as a whole.

This article offers a pragmatic guide for making decisions about which AI initiatives to support. It provides a concise overview of the technical knowledge that leaders will need if they are to make informed decisions and shows how the technology should be aligned with the broader enterprise-level architecture.

Strategic enterprise architecture

To create lasting value, AI initiatives must align with the organization’s strategic enterprise architecture (SEA). The notion of enterprise architecture first emerged in the 1980s and 1990s to describe the technical architecture of a business. When I introduced the terminology of strategic enterprise architecture in 2000 (e-Enterprise, Cambridge University Press), my goal was to highlight the often-overlooked idea that value creation depends on aligning this technical architecture with the broader structure of the business as a whole – its purpose, strategies, processes, and operating models.

Mapping out an SEA also serves another critical implementation purpose: it provides a common language and vision for everyone in the organization. This shared conceptual vocabulary is essential for thinking, talking, and planning cohesively across departments and disciplines.

Key business architecture components

To understand which AI initiatives will create value for an organization, leaders first need clarity on four interconnected elements of the existing enterprise.

Organizational purpose and business strategy.

These elements describe the reason your business exists and how it aims to succeed in the marketplace. This includes your mission, vision, core values, competitive positioning, and strategic objectives. AI initiatives that directly advance these core purposes will naturally receive stronger organizational support and deliver greater value.

People and culture.

The most ambitious AI strategy won’t get anywhere without the right people to implement it. Success requires mapping out your organization’s leadership model, talent composition, and skills profile. Just as important is how the project aligns with the cultural values of the business.

Process and operational structures.

The way work gets done within your organization determines the viability of specific approaches to implementing AI. Business processes, decision-making frameworks, governance models, and organizational hierarchies need to be mapped carefully to ensure that both the development and the day-to-day operation of an AI initiative are consistent with enterprise workflows.

Existing technology architecture.

While business leaders should understand AI on its own terms, successful implementation also means integrating this new technology with the existing enterprise tech stack. Current systems, data assets, infrastructure, and technical debt will all shape both what is possible with AI and how that potential can be realized.

Once leaders can picture how strategy, process, people, and existing tech fit together, they can then map the technical requirements of an AI initiative onto that same blueprint.

Understanding AI architecture

Once leaders can picture how strategy, process, people, and existing tech fit together, they can then map the technical requirements of an AI initiative onto that same blueprint. The contemporary AI tech stack illustrated below, comprises five interconnected layers: the data and storage layer forms the foundation, the compute and acceleration layer provides processing power through GPUs and cloud resources, the model and algorithm layer houses foundation models and machine-learning libraries, the orchestration and tooling layer connects models to workflows, and finally, the application and governance layer makes AI accessible to users while maintaining security and performance standards.

For further foundational information on AI tech stacks, see IBM’s introductory guide.

Successfully deploying AI initiatives means making choices at each layer to ensure alignment with organizational needs. Key considerations include deployment models (on-premises versus cloud-based versus hybrid approaches), open versus closed systems, computing resource needs, and data infrastructure requirements. Organizations with mature data infrastructure can implement AI more rapidly and effectively than those still struggling with data silos or quality issues.

What do misalignment and alignment look like?

Any level of misalignment between technical choices and the SEA can cause the failure of an AI initiative.

When Stability AI launched its popular Stable Diffusion image generator, it relied on infrastructure with cloud computer costs running at nearly $100m a year and operating costs taking another $54m. With no viable business plan to scale beyond its $11m in revenues, this was a classic example of technology/business misalignment.

Takeaway:

The cost structure outpaced existing monetization strategy.

In 2023, Samsung employees’ use of ChatGPT for coding assistance led to the leak of highly valuable source code. This data leak stemmed from permitting the use of an external AI model that fell outside the company’s secure IT infrastructure and data governance policies.

Takeaway:

Lax data governance jeopardized IP security.

 use of  offered an efficient way of producing content. But the radical misalignment with the magazine’s brand promise of trusted information provider meant that the initiative ultimately harmed rather than helped the business.

Takeaway:

Opaque AI use eroded longstanding reader trust.

Adobe’s decision to train its in-house generative AI only on images owned by the company or in the public domain ensured that there would be no risk of outputs that infringed on intellectual property.

Appropriate alignment, by contrast, ensures that AI projects yield real value.

Adobe’s decision to train its in-house generative AI only on images owned by the company or in the public domain ensured that there would be no risk of outputs that infringed on intellectual property. This ensured that the technology could be freely used without liability concerns by Adobe’s commercial clients.

Takeaway: The rights-aligned dataset minimized downstream liability exposure for clients.

In 2023, Bloomberg released BloombergGPT, a large language model (LLM) trained specifically on financial data and news. Using a custom model enables Bloomberg to control model weighting and data flows within its own infrastructure, and to deliver assistance with financial tasks that outperformed general purpose models.

Takeaway: Domain-specific model reinforced premium client value proposition.The AI alignment checklist

Unless you can answer “yes” to the following four questions – and provide evidence to support each answer – the AI initiative under consideration should not move forward.

Does the proposed AI initiative directly advance your strategic priorities with clear, measurable outcomes? If the initiative does not contribute strongly to your organizational purpose, it is a tech experiment rather than a viable innovation project.Are your leadership and staff ready for the change? If not, you will need to create a roadmap for developing your team’s capabilities before moving on with the project.Is it feasible to integrate the initiative with current processes and operating models?Workflows and business systems need to be mapped end-to-end to ensure that the new AI capabilities can be incorporated seamlessly into existing processes.Is the initiative a good fit with your existing technical architecture? The technical approach chosen must be compatible with your technology ecosystem, data flows, and security requirements.

A portfolio management approach can help enterprises systematically evaluate and prioritize multiple AI initiatives in the context of their evolving SEA.

From projects to portfolios

As organizations develop a pipeline of AI projects, maintaining long-term alignment between technology and enterprise architecture becomes increasingly complex and increasingly important. A portfolio management approach can help enterprises systematically evaluate and prioritize multiple AI initiatives in the context of their evolving SEA. I discuss portfolio management principles extensively in my book Reinvent published by IMD, and with special reference to AI in my latest book, Transcend.

Conclusion

The AI landscape will continue to evolve rapidly, but the fundamental principles for successful implementation remain constant. Leaders who can align their organization’s AI initiatives with its strategic enterprise architecture will outperform those who fixate on the technology alone.

Original article @ IMD.

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What happens when your AI doesn’t share your values

 How leaders can identify and resolve AI misalignment. 

If you ask a calculator to multiply two numbers, it multiplies two numbers: end of story. It doesn’t matter if you’re doing the multiplication to work out unit costs, to perpetuate fraud, or to design a bomb—the calculator simply carries out the task it has been assigned.

Things aren’t always so simple with AI. Imagine your AI assistant decides that it doesn’t approve of your company’s actions or attitude in some area. Without consulting you, it leaks confidential information to regulators and journalists, acting on its own moral judgment about whether your actions are right or wrong. Science fiction? No. This kind of behavior has already been observed under controlled conditions with Anthropic’s Claude Opus 4, one of the most widely used generative AI models.

The problem here isn’t just that an AI might “break” and go rogue; the danger of an AI taking matters into its own hands can arise even when the model is working as intended on a technical level. The fundamental issue is that advanced AI models don’t just process data and optimize operations. They also make choices (we might even call them judgments) about what they should treat as true, what matters, and what’s allowed.

Typically, when we think of AI’s alignment problem, we think about how to build AI that is aligned with the interests of humanity as a whole. But, as Professor Sverre Spoelstra and my colleague Dr. Paul Scade have been exploring in a recent research project, what Claude’s whistleblowing demonstrates is a subtler alignment problem, but one that is much more immediate for most executives. The question for businesses is, how do you ensure that the AI systems you’re buying actually share your organization’s values, beliefs, and strategic priorities?

THREE FACES OF ORGANIZATIONAL MISALIGNMENT

Misalignment shows up in three distinct ways.

First, there’s ethical misalignment. Consider Amazon’s experience with AI-powered hiring. The company developed an algorithm to streamline recruitment for technical roles, training it on years of historical hiring data. The system worked exactly as designed—and that was the problem. It learned from the training data to systematically discriminate against women. The system absorbed a bias that was completely at odds with Amazon’s own stated value system, translating past discrimination into automated future decisions.
Second, there’s epistemic misalignment. AI models make decisions all the time about what data can be trusted and what should be ignored. But their standards for determining what is true won’t necessarily align with those of the businesses that use them. In May 2025, users of xAI’s Grok began noticing something peculiar: the chatbot was inserting references to “white genocide” in South Africa into responses about unrelated topics. When pressed, Grok claimed that its normal algorithmic reasoning would treat such claims as conspiracy theories and so discount them. But in this case, it had been “instructed by my creators” to accept the white genocide theory as real. This reveals a different type of misalignment, a conflict about what constitutes valid knowledge and evidence. Whether Grok’s outputs in this case were truly the result of deliberate intervention or were an unexpected outcome of complex training interactions, Grok was operating with standards of truth that most organizations would not accept, treating contested political narratives as established fact.

Third, there’s strategic misalignment. In November 2023, watchdog group Media Matters claimed that X’s (formerly Twitter) ad‑ranking engine was placing corporate ads next to posts praising Nazism and white supremacy. While X strongly contested the claim, the dispute raised an important point. An algorithm that is designed to maximize ad views might choose to place ads alongside any high‑engagement content, undermining brand safety to achieve the goals of maximizing viewers that were built into the algorithm. This kind of disconnect between organizational goals and the tactics algorithms use in pursuit of their specific purpose can undermine the strategic coherence of an organization.

WHY MISALIGNMENT HAPPENS

Misalignment with organizational values and purpose can have a range of sources. The three most common are:

Model design. The architecture of AI systems embeds philosophical choices at levels most users never see. When developers decide how to weight different factors, they’re making value judgments. A healthcare AI that privileges peer-reviewed studies over clinical experience embodies a specific stance about the relative value of formal academic knowledge versus practitioner wisdom. These architectural decisions, made by engineers who may never meet your team, become constraints your organization must live with.Training data. AI models are statistical prediction engines that learn from the data they are trained on. And the content of the training data means that a model may inherit a broad range of historical biases, statistically normal human beliefs, and culturally specific assumptions.Foundational instructions. Generative AI models are typically given a foundational set of prompts by developers that shape and constrain the outputs the models will give (often referred to as “system prompts” or “policy prompts” in technical documentation). For instance, Anthropic embeds a “constitution” in its models that requires the models to act in line with a specified value system. While the values chosen by the developers will normally aim at outcomes that they believe to be good for humanity, there is no reason to assume that a given company or business leader will agree with those choices.DETECTING AND ADDRESSING MISALIGNMENT

Misalignment rarely begins with headline‑grabbing failures; it shows up first in small but telling discrepancies. Look for direct contradictions and tonal inconsistencies—models that refuse tasks or chatbots that communicate in an off-brand voice, for instance. Track indirect patterns, such as statistically skewed hiring decisions, employees routinely “correcting” AI outputs, or a rise in customer complaints about impersonal service. At the systemic level, watch for growing oversight layers, creeping shifts in strategic metrics, or cultural rifts between departments running different AI stacks. Any of these are early red flags that an AI system’s value framework may be drifting from your own.

Four ways to respond:

Stress‑test the model with value‑based red‑team prompts. Take the model through deliberately provocative scenarios to surface hidden philosophical boundaries before deployment.Interrogate your vendor. Request model cards, training‑data summaries, safety‑layer descriptions, update logs, and explicit statements of embedded values.Implement continuous monitoring. Set automated alerts for outlier language, demographic skews, and sudden metric jumps so that misalignment is caught early, not after a crisis.Run a quarterly philosophical audit. Convene a cross‑functional review team (legal, ethics, domain experts) to sample outputs, trace decisions back to design choices, and recommend course corrections.THE LEADERSHIP IMPERATIVE

Every AI tool comes bundled with values. Unless you build every model in-house from scratch—and you won’t—deploying AI systems will involve importing someone else’s philosophy straight into your decision‑making process or communication tools. Ignoring that fact leaves you with a dangerous strategic blind spot.

As AI models gain autonomy, vendor selection becomes a matter of making choices about values just as much as about costs and functionality. When you choose an AI system, you are not just selecting certain capabilities at a specified price point—you are importing a system of values. The chatbot you buy won’t just answer customer questions; it will embody particular views about appropriate communication and conflict resolution. Your new strategic planning AI won’t just analyze data; it will privilege certain types of evidence and embed assumptions about causation and prediction. So, choosing an AI partner means choosing whose worldview will shape daily operations.

Perfect alignment may be an unattainable goal, but disciplined vigilance is not. Adapting to this reality means that leaders need to develop a new type of “philosophical literacy”: the ability to recognize when AI outputs reflect underlying value systems, to trace decisions back to their philosophical roots, and to evaluate whether those roots align with organizational purposes. Businesses that fail to embed this kind of capability will find that they are no longer fully in control of their strategy or their identity.

This article develops insights from research being conducted by Professor Sverre Spoelstra, an expert on algorithmic leadership at the University of Lund and Copenhagen Business School, and my Shadoka colleague Dr. Paul Scade.

[Source Illustration: Freepik]

Original article @ Fast Company. 

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The Illusion of Faster

 In a culture obsessed with speed, slowing down is a vital skill for clarity, connection, and meaning.

KEY POINTSTrue productivity begins with calm attention.Neuroscience and ancient wisdom both offer lessons about focus and fulfillment.Slowing down is a way to reclaim clarity and connection.

“I feel the need. The need … for speed!” — Maverick, Top Gun

Don’t we all, Maverick. Don’t we all.

The world seems to be operating at warp speed. And for many of us, the challenge of keeping up can feel overwhelming. There’s too much to be done, and it all needs to be done yesterday. The days are too short, and the nights barely exist. Alerts, messages, deadlines, decisions. Screens blinking. Phones pinging. The only way to keep up is to run faster – and even if you do, you still fall behind.

The Myth of Multitasking

When we have a lot to do and not enough time to do it, a natural response is to multitask. We quickly reply to emails while we’re preparing a presentation. We shoot off a message on WhatsApp while juggling a Zoom call. We listen to a podcast while skimming an article we need to read for a meeting later in the day. This lets us tick off multiple items on our to-do list simultaneously, and it feels like we are winning back more time by doing more at once.

But we’re mistaken.

Research consistently tells us that multitasking significantly reduces our efficiency. For example, a 2011 study found that people “who are forced to multitask perform significantly worse than those forced to work sequentially.”

This makes sense. The brain can only process a limited number of stimuli at once. For instance, working memory – the number of items we can hold in our minds while working on a task – is famously limited. While researchers used to think we could handle around 7 items, the consensus now is that the number is actually just 3-5. When we keep giving our brains more stimuli and more things to remember, the result is that our cognitive performance declines.

Slowing Down to Speed Up

When we try to do too much all at once, or try to do too much too quickly, we don’t do anything well. And, curiously, we do it more slowly, too.

We need what we might call the practice of “slow attention” – the practice of focusing fully on one task, one moment, one breath at a time. It’s the practice of being fully present for what we do.

When we’re faced with overwhelming demands, with alerts and pings, with multiple competing claims on our energy and attention, the best and most efficient way of responding is to slow down. Instead of trying to do 10 things at once, it’s better to do one thing at a time and work sequentially through our list of tasks.

As I discussed in Buddha Had It Right: Relax the Mind and Productivity Will Follow, ancient mindfulness practices, particularly from the Buddhist tradition, have long emphasized that calm is what unlocks true clarity. The Buddha described the “monkey mind” as constantly distracted and unsettled. His solution wasn’t to outrace it, but to relax it. When the mind is calm, productivity increases.

Earlier, I quoted from the movie Top Gun. In the film, Maverick is an elite Navy fighter pilot. And as it happens, there’s another famous saying attributed to the Navy SEALS, this one real: Slow is smooth, and smooth is fast.

If you want to go faster, you need to slow down.

Reclaiming Our Lives, and Ourselves

So far, I’ve talked about the efficiency benefits of slowing down. They’re real, and they’re substantial: You really will get much more done if you do it more slowly.

But there’s something much more important at stake: our humanity.

When we race through life, we lose more than IQ points or cognitive performance. We lose our connection to ourselves. Instead of being present to ourselves, our feelings, and our experience, we drown in a sea of distractions and deadlines.

When we hurry through our day, we live reactively rather than intentionally. We let the world dictate its agenda to us instead of choosing for ourselves how we want to spend the very limited time we have.

We need to slow down to be present. And being present – truly present, not performatively present – transforms how we relate to ourselves and others. It allows for deeper listening, authentic engagement, and greater empathy. We stop performing and start connecting.

5 Practices to Go Slower

Slowing down is hard. Our culture equates busyness with value, and many of us have unfortunately learned to equate our worth with our performance. Further, the fear of falling behind, of not doing enough, drives us to keep going, even when our mental health is suffering.

But slowing down is transformative. And while it’s hard, it is also a skill each of us can build, one choice at a time.

Here are five choices you can make that will immediately help you to slow down:

Pause with Purpose. Take 5 minutes a day to breathe deeply or scan your body. This helps move your nervous system out of fight-or-flight mode and into calm awareness.Anchor with Mantras. Repeat phrases like “I am enough” or “Presence over pressure” during moments of stress. These aren’t just affirmations; they help signal safety to your brain.Single-Task Intentionally. Choose one task and give it your full focus. No multitasking. This reduces cognitive overload and increases fulfillment.Reflect Daily. Spend a few minutes journaling or simply asking: What mattered most today? This helps align actions with values.Limit Digital Noise. Mute non-essential notifications. Schedule screen-free time. Protect sacred spaces for in-person connection.Faster ≠ Better

The world will continue to move faster. But we can choose the speed that we want to move at.

If we want to build organizations, relationships, and lives that are meaningful and resilient, we must reject the myth that faster is better.

Productivity isn’t about doing more. It’s about doing what matters, with clarity, calm, and care.

[Photo: edchechine / Shutterstock]

Original article @ Psychology Today.  

The post The Illusion of Faster appeared first on Faisal Hoque.

The Six Most Popular Stories of 2025 — So Far

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by Laurianne McLaughlin

Check out these MIT SMR articles to get pragmatic advice on managing AI, improving decision-making, building accountability, and addressing other urgent leadership challenges.

Summary. The editors of MIT Sloan Management Review share the six articles that have resonated most with readers in the first half of 2025. Consider this expert advice on your toughest leadership problems, from AI change management to decision-making at times of great uncertainty.

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It is a fascinating, and frightening, time for leaders grappling with AI’s quick evolution. Leaders are watching AI tools save people time by automating mundane tasks. On the flip side, leaders are trying to figure out how not to destroy their companies’ talent pipelines as certain categories of entry-level jobs get replaced with AI tools. Without question, AI has created a new wave of people management and leadership challenges.

Here at MIT SMR, we work hard to bring you practical, evidence-based strategies to tackle a broad set of challenges and grow your organization. In a year that has already delivered a great deal of chaos to us all, consider these six articles for expert, novel advice and perspective.

1. Philosophy Eats AI

As AI and large language models evolve, leaders need to examine the philosophical foundations of how cognitive technologies are trained. Philosophy offers important perspectives on the goals of AI models, the definition of knowledge, and AI’s representations of reality. All of these perspectives shape how AI creates business value, and companies that seek business value from technology investment must look more deeply at their philosophical framework.

Read the full article “Philosophy Eats AI,” by Michael Schrage and David Kiron.

2. Five Traits of Leaders Who Excel at Decision-Making

When we’re forced to make a decision in the heat of uncertainty, many of us tend toward one of two extremes: a hasty rush to action, or a complete avoidance of it. A new study conducted by HSBC and the author looked at what traits stood out among business leaders who effectively made decisions at their biggest personal and professional moments. The research found that viewing change positively, framing unexpected challenges as opportunities, and embracing grounded optimism were key.

Read the full article “Five Traits of Leaders Who Excel at Decision-Making,” by David Tuckett.

3. Five Trends in AI and Data Science for 2025

In 2025, surveys reveal five big AI trends: a need to grapple with the promise and hype around agentic AI; the push to measure results from generative AI experiments; an emerging clearer vision of what a data-driven culture really means; a renewed focus on unstructured data; and a continued struggle over which C-suite role will oversee data and AI responsibilities.

Read the full article “Five Trends in AI and Data Science for 2025,” by Thomas H. Davenport and Randy Bean.

4. Why AI Demands a New Breed of Leaders

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 have already hired or plan to bring on such leaders.

Read the full article “Why AI Demands a New Breed of Leaders,” by Faisal Hoque, Thomas H. Davenport, and Erik Nelson.

5. Why AI Will Not Provide Sustainable Competitive Advantage

Artificial intelligence does not change anything about the fundamental nature of sustained competitive advantage when its use is pervasive. Once AI’s use is ubiquitous, it will transform economies and lift markets as a whole, but it will not uniquely benefit any single company. Businesses seeking to gain an innovation edge over rivals will need to focus their efforts on cultivating creativity among their employees.

Read the full article “Why AI Will Not Provide Sustainable Competitive Advantage,” by David Wingate, Barclay L. Burns, and Jay B. Barney.

6. When Team Accountability Is Low: Four Hard Questions for Leaders

Many leaders bemoan a lack of accountability on their team. But moaning about it — or scolding people — won’t fix the problem. A leader needs to understand what’s stoppingpeople from behaving accountably and then address those challenges. The bad news is that you may have to actively disrupt some of your own long-held behaviors as well. Ask these four questions and then use the related tips to break problematic behavioral patterns accordingly.

Read the full article, “When Team Accountability Is Low: Four Hard Questions for Leaders,” by Melissa Swift.

ABOUT THE AUTHOR

Laurianne McLaughlin is senior editor, digital, at MIT Sloan Management Review.

Original article @ MIT Sloan Management Review.

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The Beautiful Mess of Being Human

 Our complex contradictions aren’t flaws; they’re the fuel that makes us human, creative, and impossible to replicate.

KEY POINTSWe are not broken by our contradictions, we’re powered by them.We create best when we hold wonder in one hand and discipline in the other.In a world of machines, we win by being beautifully, messily human.

Am I Asian or am I American? An entrepreneur or an author? Emotional or rational? Old-fashioned or cutting edge?

The answer to all these questions isn’t an either or. It is just Yes.

We don’t ask whether the world is really night or day, or whether summer is somehow more fundamental than winter. We accept the heat of the sun and the cold of an alpine lake; we allow sugar to exist just as much as salt.

There’s a deep wisdom to that, and we should apply it to ourselves, too.

As the poet Walt Whitman said, we are vast, we contain multitudes. We are capable of love and hate, of inhuman anger and angelic patience. We are simultaneously fragile and resilient, logical and irrational, alone and connected. Instead of trying to choose between these things, why not embrace them all?

Our complexities and our contradictions are not problems to be solved. They are gifts to be lived.

What I’ve Learned About Creative Contradictions

Over the years, I’ve had the privilege of working with some of the most innovative minds across technology, business, and various creative fields. Across hundreds of conversations and collaborations, I’ve noticed a consistent pattern: The most original thinkers are those who embrace and fully express their contradictions.

I think of the designer who obsesses over pixel-perfect details yet throws away conventional rules when inspiration strikes. Or the entrepreneur who combines unwavering optimism about her vision with paranoid attention to what could go wrong. These people aren’t confused; they’re complex, and embracing the complexity is their superpower.

This observation aligns perfectly with Mihaly Csikszentmihalyi’s groundbreaking research on creativity. After decades of studying creative individuals, he reached a profound conclusion: creative people are complex. As he wrote in Psychology Today almost 30 years ago, creative people “show tendencies of thought and action that in most people are segregated. They contain contradictory extremes; instead of being an individual, each of them is a multitude.”

Complexity isn’t a bug in the operating system. It’s the feature that drives innovation.

The Paradoxes I’ve Observed in Creative Leaders

Let me share some of the paradoxical and contradictory pairs of traits that Csikszentmihalyi discovered in his research on creative individuals. He identified these not as flaws but as the very engine of creativity itself.

Below, I’ve coupled each paradox with simple practices I’ve developed or discovered through my own work.

1. Humility and Pride

I’ve observed that creative individuals deeply believe in their work even though they know how much they don’t know. They’ll defend their vision fiercely while remaining open to being wrong about everything. This isn’t insecurity, it’s wisdom. Their pride fuels action; their humility fuels growth.

Practice: Write down one thing you’re learning and one thing you’re proud of. Keep both in view.

2. Playfulness Meets Discipline

Great creators are professional children. They approach problems with the wonder of a 5-year-old asking: “Why?” “What if?” Then they switch modes completely; they become rigorous, methodical, almost obsessive about making it real. Without play, there’s no discovery. Without discipline, there’s no delivery.

Practice: Spend five minutes doodling or playing with an idea. No rules, just explore.

3. Solitude and Collaboration in Rhythm

I’m an introvert who loves deep conversation. This used to confuse me until I realized creativity requires both cave time and stage time. My best ideas germinate in solitude and flourish through dialogue.

Practice: Block one morning this week for solo work. Schedule one coffee chat. Notice the difference.

4. Embracing Chaos Within Structure

I’ve learned to embrace both chaos and order in my creative process. Some days, I need wild brainstorming sessions where ideas crash into each other without rules. Other days, I need spreadsheets and systems to turn those collisions into reality. Revolutionary ideas are born in disorder but delivered through discipline.

Practice: Keep a “random ideas” folder on your phone. Once a week, sort through it.

5. Energized by Uncertainty

While others rush to resolve ambiguity, I’ve learned to sit with open questions. Some of my most transformative insights have come from dwelling in uncertainty rather than forcing premature answers.

Practice: Write one unanswerable question on a sticky note. Place it where you can see it daily. Don’t try to solve it, live with it.

6. Sensitivity Coupled with Resilience

Creativity demands vulnerability. I’ve had ideas rejected, businesses fail, and faced much criticism. It stings every time. But I’ve learned to metabolize that sensitivity into fuel for the next attempt.

Practice: When something stings, pause and ask: “What can I learn?” “What’s next?” Move forward.

7. Urgency Balanced with Patience

I feel the fire to create now, yet I’ve learned that meaningful work unfolds on its own timeline. It’s taken me years to understand that you can have urgency about the process while having patience with the outcome.

Practice: Make three lists: Today. This Month. This Year. Put one thing on each.

What I Want You to Remember

Our contradictions aren’t bugs to be debugged, they’re features to be developed. They’re the source of that uniquely human magic—the ability to hold multiple truths, to find unexpected connections, to create something from nothing but tension and imagination.

I’ve built my career on embracing paradoxes rather than resolving them. Every time I’ve felt pulled in opposite directions, I’ve learned to resist the impulse to choose sides. Instead, I inhabit the tension. I explore the paradox. I let my contradictions inform my creativity.

And after decades of working at the intersection of humanity, business, and technology, here’s one thing I know for certain: In the age of artificial intelligence, the best and most enduring edge humans have over machines is to be human—beautifully, mysteriously, contradictorily human.

[Photo: David Tadevosian / Shutterstock]

Original article @ Psychology Today.  

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Why your organization needs both AI moonshots and mundane wins

 How to balance big swings and safe bets to build the most powerful AI portfolio for your business.

Enterprises are on track to pour $307 billion into AI in 2025—more than $35 million dollars every hour. Yet most of that cash will never see daylight: an S&P Global survey found that 42 percent of companies scrapped most of their AI projects this year. The problem isn’t funding or ambition; it is a failure to see that the moonshots need to be balanced by sure things, the stretch goals by easy wins.

AI’s true transformative power emerges not from any single initiative but when leaders orchestrate a portfolio of projects that runs the gamut from the revolutionary to the routine. The organizations that will thrive in this new era are those that pursue both the audacious bets that can redefine their industry and the mundane victories that provide the resources to fund the journey. These modern alchemists understand that transformation requires both vision and groundwork, both aspiration and application. And they know that going all in on a single idea offers an almost guaranteed path to failure.

THE INNOVATION PORTFOLIO

Just as financial portfolios balance risk and return across diverse investments, organizations approaching AI need to develop what we call an “innovation portfolio”—a carefully curated collection of AI initiatives that offer multiple paths to transformation while effectively managing risk. This portfolio approach responds to a fundamental truth about innovation: long-term success requires a pipeline of projects that vary in their size, scope, risk, and transformative power.

The portfolio and financial management approach allows organizations to maintain a comprehensive view of potential AI projects and to systematically manage their development. Think of it as the difference between a chess grandmaster who sees the entire board versus a novice fixated on individual pieces.

The portfolio approach enables leaders to understand how different AI initiatives interact, where synergies might emerge, and how risks in one area might be balanced by stability in another. Crucially, it also lets leaders orchestrate a combination of big and small bets, long- and short-term plans, that fit the business’s needs and resources. Some projects will deliver value immediately while others represent longer-term bets on emerging capabilities that might fundamentally reshape entire industries. By maintaining a portfolio that encompasses both time horizons and risk profiles, organizations create the conditions for sustainable innovation rather than sporadic breakthroughs.

THE CEO AS CHIEF AI ORCHESTRATOR

The transformative power of AI is so great that it demands a fundamental change in the role of the CEO. In this new landscape, AI strategy cannot be delegated to the CTO alone. The CEO must become the chief orchestrator of the AI portfolio, balancing competing priorities while maintaining strategic coherence.

While a foundational AI tech literacy is essential for making informed decisions, this doesn’t mean that CEOs need to understand the technical minutiae at a highly granular level. Instead, they must excel in three critical areas:

Vision Setting: The CEO must articulate how AI aligns with organizational purpose. When employees grasp AI’s significance beyond its ability to deliver financial gains, adoption accelerates and resistance diminishes.

Resource Allocation: Making tough decisions about which AI initiatives receive funding and attention is vital. This demands the courage and authority to discontinue promising projects that don’t align with strategic priorities.

Cultural Transformation: Most critically, CEOs must embody the shift in mindset that AI requires—embracing uncertainty, celebrating intelligent failures, and demonstrating continuous learning. When the CEO publicly shares their AI learning journey, including their mistakes, it empowers organizational experimentation.

THE MACRO-MICRO BALANCE

A successful AI portfolio should operate on two levels simultaneously. At the macro level, you’re asking profound questions: How might artificial general intelligence reshape entire industries? What happens when AI agents take over most knowledge work? How should a company be reconfigured to make the most of a hybrid human-AI workforce. These aren’t philosophical musings—they’re strategic imperatives that guide long-term positioning.

But here’s where organizations often stumble: they become so intoxicated by grand visions that they neglect the micro-level victories that are necessary to fuel the journey. At the same time as planning for whole-of-organization transformation, you also need to ask what your company can do this quarter. Can you use an algorithm to optimize delivery routes? Is there a commercially available chatbot you can use to process customer inquiries? The mundane funds the miraculous.

STRATEGIC PRIORITY MAPPING

Not all AI initiatives deserve equal resources. Comprehensive frameworks for harnessing AI’s potential and managing its risks, such as the OPEN and CARE frameworks, provide systematic tools for evaluating capacities and needs. For instance, the OPEN framework’s FIRST assessment provides a tool for rapid viability screening

Feasibility: Can current technology deliver your vision? Don’t confuse science fiction with strategic planning.

Investment: What’s the true cost—not just dollars, but organizational attention and cultural capital?

Risk/Reward: Map the potential downside as well as the upside. Remember, though, that the biggest risk might be doing nothing.

Strategic Priority: How closely does this idea align with our core purpose? An AI initiative that is at odds with your organization’s identity and goals is doomed regardless of its technical merit.

Time Frame: Can you sustain investment long enough to see returns? Many AI projects fail not because they were wrong, but because they are too early.

THE CONTINUOUS EVOLUTION MODEL

Static strategies die in dynamic environments. Your AI portfolio needs built-in adaptation mechanisms:

Regular Rebalancing: Quarterly reviews of project mix. Are you maintaining appropriate risk levels? Have new capabilities opened fresh opportunities?

Learning Loops: Every experiment feeds strategic understanding. Failed projects often teach more than successful ones.

Cultural Evolution: Organizations must embrace perpetual beta. Yesterday’s mindset won’t create tomorrow’s success.

FROM THEORY TO PRACTICE

A financial services firm might simultaneously pursue:

• A moonshot project using AI to predict market movements with unprecedented accuracy

• A medium-risk initiative automating compliance reporting

• Several low-risk projects improving customer service chatbots

Each initiative serves distinct portfolio purposes. The moonshot could transform the business model entirely. Compliance automation delivers clear ROI within 18 months. Chatbot improvements show immediate returns while building AI capabilities.

The CEO’s role is to ensure that each initiative receives appropriate resources while maintaining portfolio balance—not picking favorites, but orchestrating the symphony.

THE TRANSCENDENCE FACTOR

Ultimately, successful AI portfolios recognize a profound truth: AI isn’t just about efficiency or cost reduction—it’s about transcending current limitations entirely. But transcendence requires groundwork.

Like alchemists purifying base materials before transformation, your AI journey begins with the mundane—cleaning data, upskilling teams, running small experiments. These pedestrian activities build toward something greater: a point at which AI doesn’t just improve existing business operations but enables entirely new possibilities that were previously unimaginable.

WHO WILL WIN?

The organizations that will thrive in the age of AI won’t be those that bet everything on a single strategy. The winners will be those who build diversified portfolios that balance transformational ambitions with incremental improvements, macro visions with micro victories, human wisdom with machine capabilities.

For CEOs, this balancing act isn’t optional. Leaders who treat AI as just another type of new technology have already lost. Those who recognize its power to fundamentally transform both companies and markets are the ones who will write the next chapter in business history.

[Photo: Sergey Nivens/Adobe Stock]

Original article @ Fast Company. 

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The Meaning Deficit at Work

 Why so many jobs feel empty—and how leaders can restore meaning and individuals can reconnect with purpose from within.

KEY POINTSSeventy percent of people feel disengaged at work—because purpose is often missing.Disengagement costs companies $9 trillion a year.Only two in 10 workers feel inspired—work shouldn’t just fill time, it should fulfill people.The best leaders know there’s a difference between managing tasks and leading people.

Let’s be honest: we all have those days when nothing seems to make sense. You wake up, grab your coffee, sit down at your desk, and think, ”What’s the point?”

Sometimes, it’s life in general that gets us down. But more often, it’s work.

I have spent decades helping organizations large and small change their cultures. And in that time I’ve seen the same pattern again and again. When leaders try to run their companies like machines, the living, breathing, messy humans they rely on switch off.

This problem has only gotten worse as we’ve moved into the digital age. New technologies help us track everything. Businesses worship at the altar of the KPI (key performance indicators), the executive dashboard, the quarterly performance report. But as we focus laser-like on optimization, something important often falls by the wayside: We forget to ask what it all means.

The Problem Beneath the Surface

The data is sobering. Only three in 10 U.S. employees feel engaged at work. Globally, that number drops to just two in 10. That means most people are intellectually and emotionally disconnected from the tasks they spend most of their day on.

The economic costs are enormous. According to Gallup’s global workplace report, disengagement costs companies around the world nearly $9 trillionannually in lost productivity. That’s the equivalent of nearly 10% of global GDP.

But this isn’t just about the bottom line. It’s a human tragedy as well. Every disengaged employee, every worker who drags themselves to their desk asking “What’s the point?” represents the destruction of human potential. Meaning matters, and when we can’t find it in our work, we limp through the day, adrift and disconnected.

When Work Loses Its Soul

Disconnection doesn’t always look like an existential crisis. We’ve all worked with colleagues who are competent but detached. Most of us have been that person ourselves at one time or another.

Where you used to arrive at meetings full of ideas and eager to share, now you just nod along in the hope that agreement will make the process as painless as possible. Work becomes about showing up and checking the boxes. Making a difference ceases to matter.

Such attitudes don’t disappear at the meeting room door. They seep into culture, relationships, and well-being. A purpose-starved workplace doesn’t just lose output—it loses its soul.

What Happens When People Actually Care

Now here’s the good news: when people find meaning in their work, everything shifts.

Research from McKinsey shows that when work is personally fulfilling, work and life outcomes can be as much as five times higher than for those who are unfulfilled. Health improves, people are more resilient, and staff retention goes up.

Purpose becomes a force multiplier for both the individual and the business. I’ve seen it time and again: When someone reconnects with why they do what they do, they can move mountains. Purpose fuels things that performance metrics never can: pride, resilience, emotional commitment, innovation. And it’s contagious. When a business builds its operations around a mission that matters, the sense of doing something important spreads fast.

Making Work Human

So how do we fix this? Not with the pizza parties that have become millennial memes and not by hanging some inspirational posters in the break room.

It starts with remembering that behind every job title, every employee ID number, every Zoom square, there’s a real person who wants their day to mean something.

Share the real stories. Stop talking in numbers. Tell people about the customer who sent a thank-you note. Share how your product helped someone’s small business survive. Make the impact visible and personal.

Actually live your values. If your company says it values work-life balance, don’t send emails at 11 PM. If innovation is important, give people space to experiment without fearing failure. People watch what you do, so lead by example.

Give feedback that matters. Instead of just saying “good work,” help people see how their effort rippled outward. Show them the connection between their task and the bigger picture. Make feedback about future impact, not just past performance.

Create space to breathe. Sometimes the most powerful thing you can do is ask someone, “How are you doing, really?” and then actually listen to the answer. Check in as humans, not just as job functions.

Help people grow as people. Don’t just develop their skills—help them discover what they’re capable of becoming. That’s where real loyalty and creativity come from.

The Heart of Great Leadership

The leaders I respect most share two things: empathy and genuine care for people.

Empathy means actually paying attention to what people are going through—not just at work, but as whole human beings with families, dreams, and challenges. It’s the difference between managing tasks and leading people.

Genuine care means you don’t just understand what someone’s dealing with—you actually do something about it. You remember that businesses exist to make people’s lives better, not just to make money.

These leaders wake up asking themselves: “Who will we serve today?” Their purpose isn’t something they put on their LinkedIn profile. It’s how they live and work every single day.

A Simple Framework: LIFTS

Over the years, I’ve developed a straightforward approach to building workplaces where people actually want to be. I call it LIFTS:

Learn—Start with yourself. What gets you up in the morning? What kind of legacy do you want to leave?

Investigate—Ask the real questions. How are people actually feeling? What’s working in your organization, and what’s driving people crazy?

Formulate—Create a vision that people can believe in. Something real, not just corporate slogans.

Take action—Make decisions that show you care about people, not just profit margins.

Study what happens—Pay attention to what inspires growth. Be willing to change course when something isn’t working.

This isn’t just a business framework. It’s a way of thinking about leadership as service—using your position to protect and nurture the energy, attention, and hope of the people who trust you to lead them.

The Real Bottom Line

Work should be more than just something you do to pay the bills. It should be a place where you get to become who you’re meant to be.

When we lead with purpose, we’re not just managing projects and hitting deadlines. We’re helping people discover what they’re capable of. That’s what makes the difference between a job and a calling. We all want to believe that showing up matters. And it’s the job of an organization’s leaders to light the way.

In a world that often feels meaningless, helping people find the meaning in their day might be the most important work any of us can do.

[Photo: Cristina Conti/Shutterstock]

Original article @ Psychology Today.  

The post The Meaning Deficit at Work appeared first on Faisal Hoque.

The Silent Cost of Instant Answers

 What if the biggest risk of AI isn’t what it gets wrong but what it makes us forget to question?

KEY POINTSA new study shows that AI use can cause “cognitive debt,” reducing memory, focus, and long-term learning.AI flattens thinking for novices but boosts insight for those with deep domain expertise.Real wisdom needs pause—use AI to speed action, not to replace thought or wonder.

 

Outsourcing our thinking to AI may be making us smarter on paper. But it’s making us shallower in spirit.

A groundbreaking study titled Accumulation of Cognitive Debt When Using an LLM Assistant (June 2025) reveals something both deeply unsettling and not entirely surprising: While large language models (LLMs) like ChatGPT help us complete tasks faster, in certain cases they can also reduce our long-term comprehension, memory, and motivation in relation to those tasks.

When assigned essay-writing tasks, participants who used AI assistants retained less knowledge and demonstrated less engagement than those who worked through the challenges themselves. Strikingly, the lack of engagement carried through to later tasks, appearing even when participants were asked to work again on the same topic but, this time, without the help of an LLM. Researchers call this phenomenon “cognitive debt”—the subtle erosion of mental resilience when we over-rely on machines.

This isn’t just a tech concern. It’s a human concern.

If Tenzing Norgay and Edmund Hillary had wanted the most convenient way to the top of Everest, they would’ve taken a helicopter. But the value was in the climb.

— TRANSCEND , Faisal Hoque

That metaphor captures our dilemma. The journey itself—mental, emotional, creative—is where meaning is formed. When we surrender that process too easily to machines, we may gain efficiency, but we risk losing something essential: our capacity for discovery, discomfort, and growth.

The Age of Artificial Certainty

We live in a world addicted to answers.

With a few taps or voice commands, we summon not just facts but finished arguments, tailored opinions, and even emotional validation. AI has become our on-demand expert, therapist, and co-creator. No ambiguity required.

But what if that certainty comes at a cost?

What if the race for instant answers is weakening the very qualities that make us most human: curiosity, nuance, creativity, and emotional resilience?

As someone who’s built companies amid uncertainty, I’ve learned that clarity rarely comes from immediate answers. It emerges from wrestling with ambiguity—sitting in discomfort long enough for insight to arise.

From Curiosity to Convenience

The study makes it plain: AI-driven ease can backfire. Participants who used LLMs to write an essay thought they had done better than they had. But the data showed reduced retention, less originality, and shallower comprehension. In essence: we are speeding up but flattening out.

This mirrors a broader shift in our culture. Curiosity is being replaced by the consumption of predigested content. Discovery is replaced by summaries. Wonder is replaced by wrap-ups.

But there’s another side to this story.

When AI Becomes an Accelerator

For those who’ve already spent years cultivating deep domain knowledge—scientists, physicians, teachers, entrepreneurs—AI can act not as a crutch but as a catalyst.

When used intentionally, it becomes an amplifier of insight, not a substitute for it. It helps translate experience into action, accelerates experimentation, and assists in turning complex intuition into tangible impact.

The difference lies in how we use it.

This view is borne out in the paper, by Harvard-MIT scientists. As the authors put it, “the so-called Brain-to-LLM group exhibited significant increase in brain connectivity across all EEG frequency bands when allowed to use an LLM on a familiar topic.”

Without foundational understanding, AI encourages shortcuts. But with domain expertise, AI becomes a force multiplier—connecting dots faster, revealing patterns, or surfacing blind spots. It doesn’t replace the journey; it just improves the map.

In this sense, AI isn’t the enemy of depth—it’s a test of it. Those who have done the work will go further. Those who haven’t may become more confident but less capable.

The Disappearance of Wonder

Still, even domain experts who use LLMs extensively risk losing touch with one of the most precious parts of human intelligence: wonder.

As a father, I’ve seen how children ask questions not to be efficient but to explore. Their curiosity is inherently open-ended. But as we age, we start seeing questions as problems to solve, not invitations to imagine.

In the process, we trade curiosity for control. Immediacy for introspection.

Eastern wisdom traditions offer a gentle warning. Zen master Shunryu Suzuki said, “In the beginner’s mind, there are many possibilities. In the expert’s mind, there are few.” Rumi urged, “Sell your cleverness and buy bewilderment.”

In those teachings, not knowing is not weakness. It’s sacred. A space of potential.

But LLMs don’t let us linger in not knowing. They fill the silence—instantly, fluently, and often convincingly.

The Emotional Cost of Certainty

The cost of this speed isn’t just cognitive. It’s emotional.

When my son got diagnosed with cancer, I didn’t want predictive models or algorithmic comfort. I needed presence. Stillness. The humility to accept what couldn’t be known.

Machines don’t sit with grief. They don’t metabolize fear or awe. Only we can do that.

And when we shortcut that emotional process—whether through AI or distraction—we diminish our capacity for transformation.

From Knowing to Noticing

So how do we navigate this paradox?

How do we embrace AI as a tool for acceleration without losing our depth, presence, or soul?

We shift from knowing to noticing.

Notice your impulse to resolve uncertainty too quickly.Notice when you reach for AI out of laziness versus leverage.Notice how you engage with discomfort—intellectually and emotionally.

Here are some ways I protect the space between question and answer:

Pause before you prompt. Ask: What do I really want to understand? What might I discover on my own?Write in your own voice. Even when messy, original thought deepens awareness.Embrace friction. Let at least one task a week be AI-free. Struggle is the soil in which insight grows.Use AI to accelerate insight, not replace inquiry. Make it a sparring partner, not a savior.Protecting the Sacred Pause

AI is here to stay. And used wisely, it can accelerate not just efficiency but transformation.

But wisdom requires pause. And pause requires the courage to not know—for a while, at least.

If we want to preserve our humanity in the age of intelligent machines, we must remember that not knowing isn’t a flaw. It’s a feature. A portal to wisdom.

Because that pause—fragile, uncomfortable, and sacred—is where meaning lives. And meaning, unlike information, can’t be downloaded. It must be earned.

[Photo: DustandAshes/Shutterstock]

Original article @ Psychology Today.  

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