AI Three Ways

Preface: Like the rest of the world—I’ve been thinking a lot about artificial intelligence after my first encounter with ChatGPT in early 2023. I resisted writing about it until now because I've been searching for a way to understand it that avoids both the optimistic frenzy and the shadowy dystopia that fill our daily discourse. The following is my attempt to find a perspective that lets me sleep at night while still giving me reason to get up in the morning. Perhaps it's just a story I'm telling myself. I welcome your perspective.

Human ingenuity has always sought tools to amplify the things we do best. From the wheel to the steam engine, from electricity to the internet, each leap forward has been both thrilling and disorienting, enabling us to see farther, work faster, do more. AI is no different—except it isn’t just another tool. It’s the first advancement that can outthink us in certain domains, and that fact should give us pause. AI isn’t here to simply assist; it will change how we define intelligence, work, and progress itself.

AI isn’t here to simply assist; it will change how we define intelligence, work, and progress itself.

Three principles—Efficiency, Engagement, and Transformation—define AI’s promise. They’re not new concepts; they’ve underpinned every major leap forward in human history. But in AI, they converge, demanding we reconsider not just what we do as humans, but how and why we do it.

Efficiency has always been humanity’s great enabler. The plow turned farmers into food producers, forcing specialization. The assembly line birthed mass production, fueling modern economies. But efficiency isn’t inherently virtuous. It comes with trade-offs—environmental damage, exploitative labor models, a narrowing of focus on measurable outputs over intangible ones. AI’s potential for efficiency is greater still, and so are its dangers.

Take logistics. For centuries, the flow of goods resembled a river—steady, predictable, but prone to blockages. AI redefines the expectation. Consider real-time route optimization. Instead of fixed schedules, an AI system analyzes traffic, weather, vehicle conditions, and delivery windows, recalibrating on the fly. This isn’t just shaving seconds; it’s revolutionizing entire networks. DHL, for instance, has saved millions in fuel costs and reduced carbon emissions simply by letting AI take the wheel. But scale this up, and the risks become clear: supply chains so hyper-optimized they crack under pressure, as we saw during the COVID-19 pandemic. As Jared Diamond describes in “Guns, Germs, and Steel,” over-specialization can leave systems vulnerable when external conditions shift. Efficiency without resilience is a brittle construct.

Retail tells a similar story. AI-driven demand forecasting can eliminate waste and smooth out inventory lumpiness, as seen in companies like H&M, which use algorithms to predict trends and adjust production accordingly. But what happens when consumer behavior veers wildly off course, defying even the smartest models? Consider the recent trend for oversized clothing—a fashion shift that wasn’t driven by traditional seasonal patterns or straightforward historical data but by a mix of culture currents, TikTok aesthetics, and who knows what.

Efficiency should amplify humanity, not reduce it to a data point.

AI, trained to spot linear patterns and gradual shifts, might have struggled to predict the sudden spike in demand for XXL hoodies and baggy jeans. Retailers who relied solely on algorithms risked missing the wave entirely, leaving them with rows of slim-fit SKUs while everyone flocked to brands that embraced the oversized look. We learned that successful innovation requires rapid experimentation and validated learning rather than just historical data analysis. When algorithms drive every decision, they risk smoothing out the bumps and valleys that signal emergent trends leaving us unable to pivot when fickle customers chart a new, unanticipated course.

And in people management, AI optimizes processes that once required human judgment. Real-time performance tracking, automated scheduling, and even dynamic team allocation become possible. But as Cathy O’Neil warns in “Weapons of Math Destruction,” algorithms risk encoding biases or simplifying complex human dynamics into narrow metrics. When AI decides who’s productive, who’s promotable, and who’s not worth the investment, it risks missing the deeper human stories that numbers alone can’t tell. Efficiency should amplify humanity, not reduce it to a data point.

Engagement: The Quest for Meaningful Connection

Humans are storytellers. We crave connection, whether with a brand, a colleague, or an idea. Engagement isn’t just a business metric; it’s a fundamental driver of human behavior. And AI, paradoxically, may be our best chance yet to foster it.

In logistics, engagement was once an afterthought. The experience was defined by a lack of information and control: you ordered something, waited, and hoped it arrived. The "message" was simply the eventual arrival of the package, delivered through a relatively opaque process—the "medium" of traditional logistics.?

Today, AI fundamentally alters this dynamic. By personalizing the journey with real-time tracking, proactive notifications, and seamless rescheduling, AI becomes the new medium. These features aren't just add-ons; they are the message. Real-time tracking communicates transparency and control, notifications convey proactive support, and rescheduling options promise true flexibility.?

This transformation turns a frustrating chore into a service that feels almost magical. As Marshall McLuhan argued in “Understanding Media,” how something is delivered shapes its impact. AI’s role in logistics perfectly exemplifies this. However, just as a noisy channel can obscure a message, the risk lies in oversaturation. When every notification feels like a nudge, the medium itself becomes the noise, overwhelming the intended message of convenience and control.

Retail is where AI engagement shines brightest—and casts the longest shadows. Personalized shopping experiences driven by recommendation engines are the gold standard now. Spotify predicts your next favorite song; Amazon knows your next purchase before you do. These systems work because they’re deeply engaging, but they’re not without cost. Subtle nudges can shape decisions without our conscious awareness. However, his kind of data-driven manipulation can erode our sense of agency. Are you shopping, or is the algorithm shopping through you?

The true promise of AI lies in its ability to free us—from tedium, from waste, from systems that no longer serve us—and to challenge us to aim higher.

In people management, AI transforms how leaders understand their teams. Engagement metrics drawn from communication platforms or sentiment analysis tools can reveal insights that were once invisible. A disengaged employee might show up in an analysis of Slack or Teams activity patterns long before their resignation lands in HR’s inbox. But leaders must tread carefully. The line between engagement and surveillance is razor-thin, and crossing it can destroy the trust AI was meant to build.

Transformation: Destroy and Rebuild

Transformation is the natural consequence of tool-making. When humans invented fire, they didn’t just warm themselves; they transformed ecosystems, diets, and the trajectory of evolution. AI holds the same transformative potential, challenging human enterprises of all kinds to rethink not just what they do, but what they are.

In logistics, this could mean abandoning centralized hub-and-spoke systems in favor of decentralized, on-demand networks powered by AI. Drone delivery, as explored by companies like Amazon, represents an initial step in this direction. Envision fleets of autonomous vehicles dynamically redistributing goods based on real-time demand, creating a highly adaptable and fluid supply chain. (Maybe this finally explains the drones in New Jersey). However, such large-scale transformation presents huge challenges, including regulatory hurdles, cybersecurity threats, and significant workforce displacement.

Retail, too, is undergoing a seismic shift. The physical store is no longer just a point of sale; it’s an experience hub. AI-driven innovations like augmented reality fitting rooms or smart shelves blur the line between digital and physical spaces. Nike’s flagship stores, for example, use AI to provide personalized shopping experiences that feel almost futuristic. Yet transformation often outpaces culture reminding us that progress requires balance—innovation must support people, not leave them behind.

And in people management, AI reimagines the very nature of work. Static hierarchies give way to dynamic teams, assembled and reassembled based on evolving needs. Career paths become less like ladders and more like lattices, with AI-driven tools guiding employees toward roles that match their strengths and aspirations and guiding managers to place them in new projects and teams, optimizing for both individual growth and organizational agility.?

This shift can also involve AI-powered skills assessments, personalized learning recommendations, and internal mobility platforms that surface relevant opportunities. However, transformation without empathy is hollow. Leaders must remember that behind every algorithm is a person navigating change, often with uncertainty and trepidation.

Convergence: A New Human OS

Efficiency, Engagement, Transformation—these are the forces AI amplifies. But they’re not endpoints; they’re gateways to serve a deeper purpose. AI streamlines our interactions with complex systems, making them more intuitive and accessible. What do we want from a world increasingly shaped by this AI operating system? Faster deliveries? Smarter shopping? Absolutely. But these are means, not ends. The true promise of AI lies in its ability to free us—from tedium, from waste, from systems that no longer serve us—and to challenge us to aim higher.

Yet AI is not a neutral force. It reflects the values of those who build it. If we optimize only for profits, we’ll get efficiency without humanity, engagement without meaning, and transformation without purpose. But if we build systems that amplify our best qualities—curiosity, creativity, compassion—we might just find ourselves not fearing the future, but embracing it.

The question for each of us: "Will I harness it or be harnessed by it?"

Choose wisely. AI already has.