The phrase “human in the loop” is fast becoming today’s corporate mantra for the adoption of artificial intelligence. AI is primarily an augmenting technology, the thinking goes, that is best deployed alongside human workers, as a co-pilot.

This understanding of AI as a technology and its relationship with humans is a striking departure from the traditional vision of full automation that has successfully propelled the introduction of novel technologies in business. Take the introduction of automated financial market-making in the 1990s, which is all too familiar to us now but has been aptly described as a “transformation of common sense.” Automation in this space rendered human market-makers redundant—thereby making entirely new ways of transacting possible at a global scale.

But which these two visions of the future—augmentation or full automation—best fits an AI-powered economy?

Answering this question is critical because each approach to technology adoption can lead to a dramatically different economic place, impacting value-creation and competitive advantage—now and in the near future. When organizations commit to a vision of augmentation, for instance, the technology itself changes as it gets designed around human workers. As a result, productivity and performance gains are inherently constrained by what humans—albeit “augmented humans”—can accomplish.

Herbert Simon’s The Sciences of the Artificial provides a good illustration of these limitations. An expert in organizational decision-making, Simon chronicles the U.S. State Department’s switch from teletypes to line printers in the 1960s, which was designed to improve message handling during crises—and how it failed because the influx of information still required humans to process it. The augmentation paradigm of technology adoption can sacrifice much of what is economically valuable about automation—greater standardization, security, speed, and precision.

Given our all-too-human limitations as slow, serial information processors, the gap between computationally powerful machines and even “augmented humans” is only expanding in the age of AI. So, too, is the gap between the economic promise of workflow-level automation and augmentation at the level of its constituent tasks. That’s why understanding where full automation might be feasible tomorrow is a powerful guide for today’s investments—especially with nascent technologies like generative AI. By evaluating the recognizable obstacles well-known to impede full automation, we propose a set of investment criteria to help leaders navigate the comingled feelings of uncertainty and promise that define the dawn of GenAI.