Each business revolution has reshaped not only how businesses operate, but how they organize themselves and empower their people. From the industrial age to the information era, and now into the age of artificial intelligence, technology has always brought with it a reconfiguration of authority, capability, and judgment.
In the 19th century, industrialization centralized work and knowledge. The factory system required hierarchical structures where strategy, information, and decision-making were concentrated at the top. Managers at the apex made tradeoffs for the greater good of the enterprise because they were the only ones with access to the full picture.
Then came the information economy. With it came the distribution of information and a need for more agile, team-based structures. Cross-functional collaboration and customer proximity became competitive necessities. Organizations flattened, experimented with matrix models, and pushed decision-making closer to where problems were being solved. What had once been the purview of a select few, judgment, strategic tradeoffs, and insight became expected competencies for managers and team leads across the enterprise.
Now, AI is changing the game again. But this time, it’s not just about access to data. It’s about access to intelligence.
Generative AI democratizes access not only to information, but to intelligent output. That shifts the burden for humans from producing insights to evaluating them. Judgment, which was long the domain of a few executives, must now become a baseline competency for the many across the organization.
But here’s the paradox: while AI extends our capacity for intelligence, discernment, the human ability to weigh context, values, and consequence, is still best left in the hands of human leaders. As organizations begin to automate early-career work, they may inadvertently erase the very pathways and opportunities by which judgment was built.
Why judgment matters more than ever
Deloitte’s 2023 Human Capital Trends survey found that 85% of leaders believe independent decision-making is more important than ever, but only 26% say they’re ready to support it. That shortfall threatens to neutralize the very productivity gains AI promises.
If employees can’t question, challenge, or contextualize AI’s output, then intelligent tools become dangerous shortcuts. The organization stalls, not from a lack of answers, but from a lack of sense-making.
What organizations must do
To stay competitive, organizations must shift from simply adopting AI to designing AI-aware ways of working:
- Build new learning paths for judgment development. As AI replaces easily systematized tasks, companies must replace lost learning experiences with mentorship, simulations, and intentional development planning.
- Design workflows that require human input. Treat AI as a co-pilot, not an autopilot. Embed review checkpoints and tradeoff discussions. Just as innovation processes have stage gates, so should AI analyses.
- Make judgment measurable. Assess and develop decision-making under ambiguity from entry-level roles onward. Research shows the best learning strategy for this is high-fidelity simulations.
- Start earlier. Leadership development must begin far earlier in career paths, because judgment, not just knowledge, is the new differentiator.
What’s emerging is not just a flatter hierarchy, but a more distributed sense of judgment responsibility. To thrive, organizations must prepare their people not to outthink AI, but to out-judge it.
