When AI Changes the Rules of Learning, Supply Chains Must Change Too

A University of California, Berkeley mathematics professor has stopped grading homework for correctness after concluding that AI tools can now help students produce high-quality answers too easily. Professor Richard Borcherds now gives credit mainly for completing assignments and bases grades on in-person, device-free examinations instead.

His decision is more than a campus policy debate. It is a warning for every industry that depends on skilled talent—including global supply chains: when AI can generate polished outputs, organizations must learn to assess judgment, application, and accountability, not just the finished answer.

The lesson for supply chains

Supply-chain work has long rewarded people who can analyze data, solve operational problems, and produce accurate plans. AI is changing that equation by making forecasts, route options, inventory recommendations, supplier summaries, and even draft negotiations available in seconds.

But a correct-looking AI output is not the same as a sound business decision. A planner may receive an accurate demand forecast, yet still need to challenge it against a promotion, supplier constraint, port disruption, weather event, or change in customer behavior. The human contribution is shifting from producing the answer to questioning, validating, and acting on it.

From answers to judgment

Borcherds’ concern is that grading final answers no longer proves that a student understands the underlying mathematics. The same issue applies to supply chains: a polished AI-generated report does not prove that a planner understands inventory risk, supplier capacity, trade rules, or the consequences of a stockout.

Future supply-chain talent must therefore be trained to explain their reasoning, defend a recommendation, identify data gaps, and make decisions when information is incomplete. These are the skills that remain valuable when AI can generate the first draft, the calculation, or the alternative scenario.

Rethink talent development

The supply-chain industry should not respond by banning AI or treating it as a shortcut. Instead, it should redesign learning and work so that young professionals use AI as a thinking partner while still developing core operational judgment.

That means moving beyond training people to complete routine tasks. Apprenticeships, live simulations, supplier negotiations, disruption-response exercises, and real operational projects can show whether a young professional can translate analysis into responsible action. Employers should assess how candidates frame problems, challenge AI recommendations, collaborate across functions, and explain the trade-offs behind a decision.

Protect entry-level learning

Gartner reports that 55% of supply-chain leaders expect agentic AI to reduce the need for entry-level hiring, while 51% expect a shift toward overseeing AI agents. At the same time, 86% agree that AI will require new processes for developing future talent pipelines.

This creates a risk: if AI absorbs the routine tasks through which junior staff once learned, companies may struggle to develop the next generation of experienced planners and operations leaders. The solution is to redesign entry-level roles around supervised decision-making, exception management, supplier engagement, and continuous learning—not simply remove them.

Build AI-ready talent

Supply-chain organizations should make AI literacy a core capability, but define it broadly. It is not enough to know how to prompt a model; professionals must understand data quality, model limitations, bias, cybersecurity, accountability, and when human judgment should override an automated recommendation.

A practical approach is to pair every AI-enabled workflow with a human checkpoint. For example, AI may identify a likely supply disruption, but a planner must verify the source, assess the business impact, consult affected partners, and decide on the response. This preserves speed while retaining accountability.

The bottom line

The Berkeley professor’s decision shows that AI is forcing education to distinguish between producing an answer and possessing understanding. Global supply chains face the same test. The organizations that thrive will be those that use AI to amplify human judgment, redesign early-career roles as learning opportunities, and develop talent capable of leading complex networks through uncertainty.

Source: https://www.ndtv.com/world-news/california-professor-stops-grading-homework-after-spotting-students-growing-reliance-on-ai-12240518