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 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 pointing out that today’s models perform only within the limits of their training data and can’t apply reason. 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.

This is the authors’ pre-publication draft. The edited version appeared in Harvard Business Review, June 2025.

A version of this article appeared on Harvard Business Review.