Where is the work? AI and Creativity

For ages, we have blurred the lines between ideation and execution, treating them as inseparable parts of creativity. Craftsmanship was tightly bound to originality. Think of Michelangelo working on the Sistine Chapel, a project that spanned nearly a decade. Where does his genius truly lie? In envisioning those profound images, or in the labor of painting them? What, exactly, is the essence of the work?

The rise of AI forces us to untangle these ideas and reconsider what it means to produce "human" work. Take a recent story I heard from from the audience of one of my talks: a person described how he fed an AI every detail about a retiring colleague, and the AI generated a speech so moving that it brought the retiree to tears. But the retiree, upon learning the speech's origin, was dumbfounded.

What is interesting is not the retiree’s reaction, but the storyteller's own oversight. He failed to see his own critical role in the process. By gathering the details, curating moments that best captured the retiree’s essence, he performed the most human part of the creative act. He mistook the act of turning those ideas into words as the creative work, but that is not the case.

AI, ironically, is pushing us to be more human, not more like machines. It is forcing us to recognize that our true contribution lies in the ability to think, to create, and to feel. As AI takes over the mechanical aspects of tasks we once considered integral to creativity—whether that is writing, painting, or coding—we are left with the more uniquely human roles: original thinking and emotional depth.

This shift reshapes our understanding of creativity and work. It shows that human value does not lie in production—the technical aspect of turning an idea into a product—but in the deeper conceptual and emotional layers that AI still cannot reach.

As we move forward, we are compelled to rethink productivity itself. The future will not belong to those who can outdo AI in execution, but to those who can combine AI’s strengths with our unique capacities for innovation, empathy, and insight.

The challenge we face is not to resist AI, but to fully embrace our humanity—to cultivate the traits that machines cannot replicate. With AI taking over the drudgery, we are freed to focus on higher-order thinking and those creative leaps that define human ingenuity.

Ironically, the more we develop artificial intelligence, the more we learn about what human intelligence really is. And in that discovery lies our future—a future where AI does not replace creativity, but elevates it to new possibilities.

The Stealth AI Adoption

In modern workplaces, a quiet trend is taking hold: employees are secretly adopting artificial intelligence tools to enhance their work. Whether it is writing, designing, coding, or creating content, many are leveraging AI without informing their bosses. This “stealth AI adoption” is likely more widespread than managers realize.

Consider Alex, a software developer at a bustling tech firm. To streamline his coding process, Alex uses an AI assistant that can generate snippets of code in seconds. This tool not only saves him hours each week but also allows him to tackle more complex projects. However, Alex keeps this AI helper under wraps. Why? He has two choices: use the extra time for personal activities or take on additional work to appear more productive than his peers. There is no actual incentive to admit the use of AI. In some shops, cybersecurity people will come after you, if you confess. 

This hidden use of AI offers clear benefits for employees. Saving a few hours each week is tempting, whether for personal pursuits or to discreetly boost one’s workload. As a result, many organizations might be underestimating how extensively AI is being integrated into daily tasks.

Productivity can be measured in two ways: doing the same work with fewer people or doing more with the same number. The latter is a healthier, more sustainable approach. To achieve true success, organizations should aim to do more with their existing workforce rather than cutting staff. However, the stealth adoption of AI complicates this goal.

When employees use AI tools without disclosure, organizations miss out on opportunities to harness these technologies strategically. Without knowing how AI is being utilized, companies can not provide proper training or integrate AI into their workflows effectively. This fragmented approach can lead to missed efficiency gains and a lack of cohesive progress.

To foster a productive and innovative environment, companies need to build trust with their employees. Here is how:

1. Reassure Employees: Let your team know that adopting AI will not lead to layoffs. Emphasize that AI is a tool to help them do their jobs better, not a replacement for their roles. In unionized environments, a conversation with labor leaders would be wise. 

2. Create Incentives for Disclosure: Encourage employees to share the AI tools they are using by offering rewards or recognition. This transparency can help management understand how AI is being integrated and identify best practices.

3. Do More with the Same People: Focus on expanding the scope of work and fostering innovation rather than cutting positions. This approach not only boosts morale but also drives the organization forward.

By building trust and creating a supportive environment, organizations can turn stealth AI adoption into a strategic advantage. Employees will feel comfortable sharing their AI discoveries, allowing organizations to implement these tools effectively and sustainably.

As we move further into the AI-driven era, organizations must address this hidden trend. Encouraging transparency about AI tools and developing clear strategies for their use can ensure that productivity gains are real and sustainable. Until then, the silent spread of AI will keep reshaping workplaces, one undisclosed tool at a time. 

Filling Voids, Not Replacing Human Experts

The debate over artificial intelligence replacing human experts often centers on a binary question: Can AI do a better job than a human? This framing is understandable but overly simplistic. The reality is that in many contexts, the competition is not between AI and people—it is between AI and nothing at all. When viewed through this lens, the value of AI becomes clearer. It is not about pitting machines against human expertise; it is about addressing the voids left by a lack of available service.

Consider healthcare, particularly in underserved areas. It is a truism that a qualified doctor’s advice is better than anything an AI could provide. But what if you live in a rural village where the nearest doctor is hundreds of miles away? Or in a developing country where medical professionals are stretched thin? Suddenly, the prospect of AI-driven medical advice does not seem like a compromise; it feels like a lifeline. While AI lacks the nuanced judgment of an experienced physician, it can provide basic diagnostics, suggest treatments, or alert patients to symptoms that warrant urgent attention. In such scenarios, AI does not replace a doctor—it replaces the silence of inaccessibility with something, however imperfect.

Another case in point is mental health counseling. In many parts of the world, even in affluent countries, mental health services are woefully inadequate. Students at universities often face wait times ranging from weeks to months just to speak with a counselor. During that limbo, the option to interact with an AI, even one with obvious limitations, can be a critical stopgap. It is not about AI outperforming a trained therapist but offering a form of support when no other is available. It can provide coping strategies, lend a sympathetic ear, or guide someone to emergency services. Here, AI does not replace therapy; it provides something valuable in the absence of timely human support.

Education offers another case for AI’s gap-filling potential. Tutoring is an essential resource, but access to quality tutors is often limited, mainly because it is expensive. Universities might offer tutoring services, but they are frequently understaffed or employ peer tutors. Office hours with professors or teaching assistants can be similarly constrained. AI can step into this void. Chatting with an AI about a difficult concept or problem set might not equal the depth of understanding gained from a one-on-one session with a human tutor, but it is unquestionably better than struggling alone. AI does not compete with tutors; it extends their reach into spaces they cannot physically or temporally cover.

The same logic applies to a range of other fields. Legal advice, financial planning, career coaching—all are areas where AI has the potential to add significant value, not by outstripping human expertise but by offering something in environments where professional advice is out of reach. Imagine a low-income individual navigating legal complexities without the means to hire an attorney. An AI could provide at least basic guidance, clarify legal jargon, and suggest possible actions. All of it must be done with proper disclaimers. It is not a substitute for legal representation, but it is a world better than the alternative: no help at all.

In embracing this non-competing stance, we shift the narrative. The role of AI is not to replace human experts but to step in where human services are scarce or nonexistent. The true potential of AI lies in its ability to democratize access to essential services that many people currently go without. When AI is viewed as a bridge rather than a rival, its utility becomes much more evident. AI does not have to be better than a person to be valuable; it just should be better than the void it fills.

Authorship, Automation, and Answerability

In the ongoing debate about the ethical use of AI, two main concerns stand out—one superficial and one profound. The first concern, often highlighted, is about the authenticity of authorship, with fears that AI-generated content might mislead us about who the true author is. However, this worry is largely misguided. It stems from a historically limited, Western-centric notion of authorship that blurs the line between the origin of ideas and the craft of their representation.

Take the legacy of Steve Jobs. He wasn’t celebrated for personally assembling each iPhone, but for his vision and design that brought the device to life. In our industrial world, the act of making things is not inherently authorial—designing them is. Why should it be any different with text, code, or images? If I designed this text, and used advanced tools to produce it, why am I not still the author? The shock many feel towards AI’s ability to generate content is akin to the upheaval experienced by 19th-century bootmakers during the Industrial Revolution. Automation has simply extended its reach into the realms of writing, coding, and art. The craftsmanship is replaced by automation, but the core principle remains: take pride in the ideas, not in the mechanics of their production. There is no inherent authorship in the latter.

But here’s where Mikhail Bakhtin’s notion of answerability helps our understanding of the true ethical stakes. While responsibility is often about fulfilling obligations or being held accountable after the fact, answerability is about our ongoing, active engagement with the world and the people in it. It is not just about who gets credit for the content; it is about recognizing that every action, every word, and every piece of AI-generated content occurs within a web of relationships. We are answerable to others because our creations—whether authored by human hands or machine algorithms—affect them.

The real concern, then, lies in the issue of answerability. AI-generated content often appears polished, convincing, and ready for immediate consumption. This creates a dangerous temptation to release such content into the world without thorough scrutiny. Here is where the ethical stakes rise significantly. AI may produce work that looks and sounds credible, but this does not guarantee that it is unbiased, meaningful, or truthful. It maybe garbage polluting the infosphere at best, or an outward harmful fake at worst. The ease of content creation does not absolve us of the responsibility to ensure its quality and integrity, and more importantly, it doesn’t free us from the answerability we have to the world around us.

This is the message we need to instill in our students, professionals, and anyone working with AI: you are still accountable and answerable for what you produce, even if a machine does the heavy lifting. Releasing AI-generated content without critical evaluation is akin to conjuring a spell without understanding its consequences. Like a magician wielding powerful but unpredictable magic, or a novice driver behind the wheel of a truck instead of a bicycle, the stakes have been raised. The tools at our disposal are more potent than ever, and with that power comes a heightened level of answerability.

In essence, the ethical debate surrounding AI shuold not be about the authorship of the craft but shuold be about the integrity and impact of the output. The real challenge is ensuring that what we create with these advanced tools is not only innovative but also responsible and answerable. As we continue to integrate AI into more aspects of our lives, we must focus less on who—or what—authored the content and more on the ethical implications of releasing it into the world. This is where the true ethical discourse lies, and it is here that our attention should be firmly fixed.

AI revolution minus massive unemployment

The conversation on AI often revolves around efficiency and cost reduction, typically translating into fewer jobs. However, a pivotal shift in perspective—from cutting workforce to enhancing and expanding workforce capabilities—can redefine the role of AI in the corporate world. This approach not only preserves jobs but also adds significant value to customer experiences and broadens the spectrum of services and products a company can offer. 

The traditional method of dealing with technological disruption—laying off workers and hiring new ones with the necessary skills—is not only a waste of human capital but also disregards the cultural knowledge embedded within an organization's existing workforce. Retraining keeps people within the organization, allowing them to shift roles while retaining and applying their invaluable understanding of the company's ethos and operations in new ways.

The first step in a proactive workforce transformation strategy is to map out the anticipated skills and roles that will be in demand. This is not just about foreseeing the obsolescence of certain skills but identifying emerging opportunities where AI can augment human capabilities. For instance, with the rise of AI-driven analytics, there is a growing need for professionals who can interpret and leverage these insights into strategic decisions, enhancing business intelligence far beyond current levels.

Once future needs are mapped, the next step is to develop a compelling incentive structure for retraining. Traditional models of employee development often rely on mandatory training sessions that might not align with personal or immediate business goals. Instead, companies should offer tailored learning pathways that align with career progression and personal growth, supported by incentives such as bonuses, career advancement opportunities, and recognition programs. This approach not only motivates employees to embrace retraining but also aligns their development with the strategic goals of the organization.

With AI's capacity to handle repetitive and mundane tasks, employees can redirect their efforts towards more complex, creative, and meaningful work. This shift enables businesses to expand their service offerings or enhance their product features, adding significant value to what customers receive. For example, financial advisors, freed from the tedium of data analysis by AI tools, can focus on crafting bespoke investment strategies that cater to the intricate preferences and needs of their clients. Similarly, customer service representatives can use insights generated by AI to provide personalized service experiences, thereby increasing customer satisfaction and loyalty.

AI not only optimizes existing processes but also opens new avenues for innovation. For instance, in the healthcare sector, AI can manage diagnostic data with high efficiency, which allows healthcare providers to extend their services into preventive health management and personalized medicine, areas that were previously limited by resource constraints. In the retail sector, AI-enhanced data analysis can lead to the creation of highly personalized shopping experiences, with recommendations and services tailored to the individual preferences of each customer, transforming standard shopping into curated personal shopping experiences.

For successful implementation, organizations must foster a culture that views AI as a tool for empowerment rather than a threat to employment. Leadership should communicate clearly about the ways AI will be used to enhance job roles and the benefits it will bring to both employees and the company. Regular feedback loops should be established to adjust training programs based on both employee input and evolving industry demands, ensuring that retraining remains relevant and aligned with market realities.

By focusing on retraining the workforce to harness AI effectively, businesses can transform potential disruptions into opportunities for growth and innovation. This approach not only preserves jobs but also enhances them, adding unprecedented value to the company and its customers, and paving the way for a future where human ingenuity and artificial intelligence work hand in hand to achieve more than was ever possible before.