AI Won’t Fix Local Optimization

There is no magical 'AI fix' for local optimization.

AI doesn’t set boundaries.

It’s efficient at working within them, and when efficiency is aimed at poor objectives, it does more damage than ordinary dysfunction.

There are three reasons supply chains are stuck solving the wrong problem well:

Fragmentation. Supply chains have notoriously fragmented flows. Boundaries determine shared objectives and visibility needs. S&OP leaders live this, but the boundary rarely covers suppliers and customers.

Incentives. People and teams operate from self-interest. That's not a moral judgment, it's how organizations work. Metrics drive behavior. I’ve seen extended payment terms lead to supplier insolvency that later hits production and fulfillment. That's dumb math.

Aggregation. No single purchase order, expedite fee, or spreadsheet tips the scales on its own, so the real cost never factors into any one decision, aka death by a thousand cuts.

You need to define a system before you optimize it. What system are you optimizing? For what purpose?

Boundaries → objectives → incentives → decisions → desired outcomes

AI provides clarity and context, not purpose. You need to set the right frame that prioritizes doing the right things before doing things right.

Effectiveness before efficiency.

Better decisions, fewer exceptions, and lower total costs matter first.

Faster approvals, fewer touches, and quicker cycles follow.