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What Gets Measured, AI Will Automate
What Gets Measured, AI Will Automate

Harvard Business Review

timea day ago

  • Business
  • Harvard Business Review

What Gets Measured, AI Will Automate

AI doesn't need a sci-fi upgrade to upend the economy—current models, and the cheaper, more capable versions already in the pipeline, are set to disrupt nearly every corner of the labor market. Their surprising performance across text, image, and video threatens to upend how work is done across the creative ranks of writers, designers, photographers, architects, animators, and brand advertisers, as well as the spreadsheet crowd of financial analysts, consultants, accountants, and tax preparers. Not even the credentialed bastions of law, medicine, or academia are safe: AI can sift through oceans of content and serve up bespoke advice or coursework at a fraction of today's cost—and with quality that's closing in fast. There are major questions about how much more powerful AI tools might become—and how soon. Anthropic's Dario Amodei and OpenAI's Sam Altman claim artificial general intelligence (AGI) could be only a year or two away. Meta's Yann LeCun is more skeptical, arguing that current models lack grounded physical understanding, durable memory, coherent reasoning, and strategic foresight, and Apple just published new research claiming that today's models perform only within the limits of their training data. Yet even if progress stopped tomorrow, the disruption is already underway. To navigate this new landscape, leaders need to understand—and plan for—how automation will affect their businesses. That requires understanding which tasks and responsibilities are most likely to come under pressure and charting a course to move the enterprise up the intelligence value chain before time runs out. What Is Not at Risk of Automation? Academic researchers and practitioners have extensively debated which jobs and tasks are most vulnerable to automation. Some threats are obvious: self-driving vehicles may soon be in a position to displace millions of ride-sharing, bus, and truck drivers. Meanwhile, language translation, swaths of creative writing, design, and even everyday coding are being handed off to AI. In February, Anthropic shared revealing user stats: although the chat format naturally steers people toward human augmentation, about 43% of interactions already represented some form of automation, in which users ask the AI to perform a task directly as opposed to helping them iterate and think it through. That share will keep climbing as modular AI agents enter the workforce, trading data and coordinating tasks through protocols like MCP. Environments that are extensively measured or codified—whether through laws, tax codes, compliance protocols, or streams of sensor data—face the greatest near-term risk of being handed over to machines. AI research pioneers Ajay Agrawal, Joshua Gans, and Avi Goldfarb argued in 2018 that as AI advances, the last bastion of human advantage will be judgment—the ability to weigh options and make decisions under uncertainty. Yet that insight hands us an impossible homework assignment: pinning down exactly what qualifies as judgment at any given moment. Tasks that demand human judgment today—choosing a medical treatment, reviewing a legal contract, scripting a film that nails the zeitgeist—could soon pass to AI as models tap richer data and greater compute. Nor can we assume people will always prefer a human therapist, counselor, or mediator, according to recent research. An AI counterpart can operate around the clock, at a fraction of the cost, and—aside from a handful of human superstars—may offer more consistent quality. So, how can we separate the tasks AI will automate next from those that will require new breakthroughs in AI technology to do so? To answer that, we must go back to first principles and revisit where it all began. From Lab Contest to Industrial Revolution Back in the mid-2000s, computer scientist Fei-Fei Li saw that the field of computer vision, which is focused on enabling computers to 'see' and interpret images, was dealing with a bottleneck: algorithms were pixel-starved, ingesting too little visual data to reach human performance. Her solution was refreshingly brute-force: she built ImageNet—a vast, meticulously labeled image trove assembled with help from Amazon Mechanical Turk. But her true stroke of genius came in 2010, when she bolted a global leaderboard onto the dataset—transforming image recognition into a gladiatorial contest for researchers. For two years, the annual leaderboard inched forward. Then, in 2012, Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton blew the competition away. Using two off-the-shelf NVIDIA GTX 580 graphics cards, the trio from Toronto was able to train a breakthrough convolutional neural network in just a few days—a groundbreaking approach that proved you could bend computer-vision history on a grad student budget. That moment ended the decades-long AI winter, put neural nets at the center of progress, and revealed the playbook the field still runs on. First, gather relevant data—roughly 14 million labeled images in the ImageNet case. Next, rely on metrics to quantify and drive progress. Last, flood a model with data and GPU muscle until it teaches itself, a formula that has carried AI from categorizing objects to writing fluent prose and, most recently, to reasoning, planning, and wielding external tools in today's emerging 'thinking' systems. Data, Reward, Compute The framework that propelled the image recognition breakthrough is far more general than most realize. It can be invoked whenever we can a) define the task environment and assemble its data—be it a corpus of text, a repository of images and video, logged driving miles, or streams from a robot's sensors; b) specify a target reward, explicit ('did the model predict the next word?') or implicit (inferred from observing human behavior); and c) provide the computational power to let the system iterate. Stack those three ingredients and you get a general-purpose automation engine. Two data trends now accelerate the flywheel. First, models can mint limitless synthetic examples—for instance, generating virtual 'driving miles' that cover every oddball scenario, rather that relying on data from real world drivers. And second, AI is increasingly fielded across a variety of devices and sensors—on phones, in cars, and elsewhere—as a low-cost surveyor, capturing and quantifying real-world signals that were once too expensive or impractical to measure. If you can shoehorn a phenomenon into numbers, AI will learn it and reproduce it back at scale—and the tech keeps slashing the cost of that conversion, so measurement gets cheaper, faster, and quietly woven into everything we touch. More things become countable, the circle resets, and the model comes back for seconds. That means that any job that can be measured can, in theory, be automated. Measurement Too Cheap to Meter Economist Zvi Griliches's landmark 1957 study of hybrid corn adoption gives us a sharp lens on what comes next. Farmers first planted the pricey seed only on their best acres—where the yield jump easily covered the extra cost and learning curve of using a new product. As hybrids improved and word spread, even thin-margin fields soon cleared the benefit-cost bar. With AI, the investment into measuring things follows the same payoff curve. When turning reality into data is expensive, companies tend to only invest in the headline cases—credit-card fraud, algorithmic market-making, jet-engine prognostics. But AI now slashes the cost of precise measurement, making continuous, fine-grained sensing the default. Lightweight models run beside the sensors, trimming bandwidth and latency, while synthetic data fills gaps when the real world is slow or awkward to capture. Each extra decimal place quickly pays for itself: tiny error cuts multiplied across millions of AI-driven decisions add up fast. As precise measurement gets cheaper, ever-slimmer benefit streams pencil out, and tasks once too minor to monitor slide into the automation net. Not only may we soon have intelligence too cheap to meter, we'll also be measuring ever more of the world to expand—and continuously upgrade—what that intelligence can reach. We already live in the era of 'artificial-metrics intelligence,' where anything we can quantify is swiftly queued for automation. Thriving Despite Unknown Unknowns Humans are evolutionary generalists, selected to navigate half-drawn maps. We don't merely survive unknown unknowns—we thrive on them, and that resilience is our defining edge. Over countless generations we fine-tuned our vocal cords and social brains until language emerged—opening the door to cumulative knowledge, abstract reasoning, and symbolic thought. From there we pushed beyond our biological limits, forging tools that stretched our senses, expanded our memory, and multiplied our abilities. But the cornerstone of our advantage is our highly plastic, densely wired prefrontal cortex. This neural command center lets us spin endless 'what-ifs,' rehearse counterfactual futures, and pivot strategy the instant conditions shift. Short of a true singularity, even quantum machines will struggle to match our talent for open-ended, cross-domain counterfactual planning. As AI accelerates progress, it creates fresh unknown unknowns, so our maps keep being redrawn. Meanwhile, it routinizes the predictable—much as mechanized farming lifted us from subsistence—freeing more of our counterfactual brainpower for higher-level problems. AI will also struggle in domains where measurement verges on the impossible—witness the decade-long, globe-spanning effort the Event Horizon Telescope needed to capture a single black-hole image, and the still-unsolved challenges of probing extreme-scale physics, Earth's deep mantle and abyssal oceans, or live cellular interactions inside the human brain. It will also lag where measurement is throttled by privacy, ethics, or regulation; where society requires transparent reasoning—at least until model interpretability catches up; and where people simply prefer a human touch. Yet, as with hybrid corn adoption, future generations will keep revisiting the cost-benefit calculus for each of these—and may reach conclusions very different from ours. But one crucial carve-out in what can be measured may prove decisive: tasks that defy quantification because their outcome odds are fundamentally unknowable—the realm of Knightian uncertainty, where you can't assign any probabilities because the risks themselves are undefined. Scaling a startup, allocating capital or talent into highly uncertain ventures, containing a novel pathogen, setting central bank policy during a financial regime shift, drafting AI ethics, inventing a new artistic medium, igniting a fashion trend, or creating a new genre-bending blockbuster—all sit in zones where probabilities vanish. Some creative acts and discoveries amount to little more than clever recombinations of the familiar, but the truly ambitious hinge on our singular ability to envision genuinely new and complex counterfactual worlds. The list is fluid—tasks drop off the moment they become measurable, and new ones surface just as quickly. Each shift forces painful economic and social adjustments, squeezing more work into a superstar economy that concentrates outsized rewards at the peaks of creativity, talent, and capital. Yet AI offers a paradoxical gift: by democratizing education and serving as everyone's personal copilot, it hands more people than ever the tools to reach those peaks. Jobs themselves will keep evolving, and any breakthrough that turns the unknown into the countable will scale and be imitated at meme speed. For leaders steering their organizations through this turbulent transition, what lies beyond the spreadsheet? It's everything that won't fit in a cell: the skills that refuse to be tallied, the open-ended problems with no reliable precedent, the intangibles—trust, taste, and the subtle dimensions of quality and experience—and the conviction to press ahead even when every metric says 'wait.' Manage only what you can measure, and you surrender the most valuable ground to rivals who cultivate what can't be counted. Amar Bose, the sound and electrical engineer who founded the Bose Corporation, proved the point: while others worshipped spec-sheet numbers, he zeroed in on how music sounded to people in real rooms—a quality no existing metric could catch—and in doing so, he rewrote the rules of the audio industry. Directionally, the prescription is simple. Back wildcard bets with fuzzy ROI, reward teams that reframe problems and lean into the unknown, and rotate talent through roles that confront uncertainty across R&D, new markets, and complex customer, partner, and policy interactions. Carve out slack time and engineer cross-team collisions to spark serendipity and idea recombination. Treat those pockets of planned ambiguity not as liabilities, but as strategic assets. Only leaders who pay attention to what is measurable—and, more crucially, to what stubbornly isn't—will be ready when the next shift arrives.

How Circularity Can Be a Strategic Response to Tariffs
How Circularity Can Be a Strategic Response to Tariffs

Harvard Business Review

time2 days ago

  • Business
  • Harvard Business Review

How Circularity Can Be a Strategic Response to Tariffs

Globalization is fracturing. Tariffs, trade wars, resource nationalism, and supply chain shocks are no longer rare disruptions—they're the new normal. The past few weeks alone have seen swings in U.S. tariffs and China's shifting stance on rare earth minerals, underscoring a broader trend: global commerce is increasingly volatile, redrawn by geopolitics, climate, and conflict. Trade fracturing goes beyond tariffs, however. Governments are increasingly imposing policies to favor domestic sourcing, to restrict access to critical materials, and to steer trade towards geopolitical allies—all of which companies need to navigate. From Europe's carbon-linked import levies to Indonesia's nickel ore export ban, even recycling laws—over 30 related to critical minerals enacted globally since 2022—look beyond environmental outcomes to advance industrial and political goals. Extended Producer Responsibility (EPR) schemes, for instance, reward companies that keep value creation within national or regional borders. Traditional responses—like dual sourcing and near-shoring—still matter. But an underused lever is the circular economy: reusing, repairing, remanufacturing, and recycling to reduce dependence on volatile global supply chains. Often dismissed as a sustainability play, circularity—done well—is a strategic hedge. It cuts material imports, opens new revenue streams, and builds customer loyalty. And it's a strategy that many firms have quietly embraced to survive and thrive. Take British Sugar, the UK's largest sugar producer. By turning byproducts from sugar production into revenue streams—from animal feed to soil conditioners—the company reduced waste and boosted margins. This shift did not begin as a sustainability goal, but rather a strategic response to changing market dynamics and price volatility. That kind of systems thinking—designing for autonomy, flexibility, and resource efficiency—is now critical for building a durable competitive advantage. Here are four circular strategies to build resilience. Secure Resources Locally Trade barriers and concentrated supply chains make it harder and costlier to access critical inputs. For example, rare earth elements and other strategic minerals foundational to the modern economy are increasingly subject to strategic controls including by the U.S., China, the European Union, and Japan. Circularity helps mitigate this exposure. Urban mining—recovering materials from waste streams—helps companies reduce reliance on volatile import streams, turning waste into tomorrow's resources. Electronic waste alone contains $91 billion in metals. With the right innovations, such closed-loop strategies complement domestic production, while also cutting greenhouse gas emissions by 80%. Companies like Umicore, Rolls-Royce and Cyclic Materials are already capitalizing on extracting critical materials from end-of-life items. Circularity also strengthens the resilience of the clean energy transition. While renewables boosts energy security by reducing reliance on fossil fuel imports, scaling renewable energy also increases exposure of countries to mineral import risks. Solar panel circularity alone could unlock an $80 billion market, helping to secure benefits of decarbonization without trading one dependency for another. Unlock Cost-Competitive Secondary Markets When tariffs drive up costs of imports, companies typically must either pass costs to customers or absorb a margin hit. Circularity offers a third path: the resale of repaired, refurbished, or remanufactured goods. If reprocessing is done locally or in trade-aligned regions, secondary offerings can sidestep tariffs while undercutting new imports on price. Shares of secondhand retailers jumped following the recent U.S. tariff announcement, reflecting anticipation of growing consumer demand. Beyond a pricing advantage, secondary market offerings also extend asset lifecycles and capture value that would otherwise be lost. Companies like IKEA see circular material flows, combined with regional supply chains, as ways to build resilience and cost efficiency ways, if done at scale. Diversify Revenue Through Services Businesses reliant on one-time sales of imported products are vulnerable when trade disruptions delay shipments or inflate costs. Circular service models—subscription, product-as-a-service, or performance-based contracts—shift the focus from selling new units to maximizing revenue from existing assets. Take Swapfiets, which provides bicycles through a monthly subscription, inclusive of repair and maintenance. By designing their bicycles for durability, repairability, and use of recycled materials—core principles of circular design—Swapfiets' bicycles last longer and can be reused across multiple customers, generating more recurring revenue from the same assets. Separately, in electronics, product-as-a-service models could unlock $566 billion in savings globally, while cutting emissions by nearly 15%. Deepen Customer and Supplier Relationships Circularity naturally fosters stronger partnerships. Product-as-a-service and take-back programs transform one-off sales into longer-term engagements and predictable revenue, generate valuable usage data, and incentivize better product design. In uncertain times, deepening trust and loyalty with customers and suppliers can become an advantage. Consider John Deere. The company leases farm equipment embedded with IoT technology that tracks real-time field performance. This data helps refine product designs, reduce material use with modular components, and secures multi-year service relationships with customers. Success required tighter collaboration with suppliers to enable circular design and manage reverse logistics, fostering more integrated, resilient partnerships. . . . If circularity is so advantageous, particularly amidst the rising tide of geopolitical forces, why is adoption limited? Internally, many companies still face misaligned incentives—P&Ls reward unit sales over lifecycle value, while reverse logistics systems remain underdeveloped. Executives may hesitate to back long-payback investments without immediate business cases. Externally, regulatory complexity and classification challenges add friction. Leading firms are breaking through by linking circularity to strategic objectives, build accountability through circular KPIs (like revenue from circular offerings), and prioritize piloting and learning before scaling. Crucially, they treat circularity not as a checklist—but as a transformation.

The Promises, Pitfalls, and Trade-offs of the Circular Economy
The Promises, Pitfalls, and Trade-offs of the Circular Economy

Harvard Business Review

time2 days ago

  • Business
  • Harvard Business Review

The Promises, Pitfalls, and Trade-offs of the Circular Economy

Most businesses are built on a linear model: take, make, and discard. But that norm is reaching its limits, and leaders are under pressure to find smarter, more sustainable ways to operate. Weslynne Ashton is a systems scientist and professor at the Illinois Institute of Technology. In her masterclass at HBR's 2024 Leaders Who Make a Difference conference, she explains how companies can shift to a circular economic strategy. One that reduces waste, reinvests in communities, and creates long-term value. She shares how businesses around the world are rethinking products, partnerships, and growth itself to build more resilient, regenerative business models. Key episode topics include: environmental sustainability, corporate social responsibility, ESG, climate change, business models, strategy, competitive strategy HBR On Strategy curates the best conversations and case studies with the world's top business and management experts, to help you unlock new ways of doing business. New episodes every week

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