Starbucks spent five years getting more efficient and less valuable at the same time. The metrics the leadership optimized went up. The business they were attached to went down. That isn't a contradiction. It's what happens when the people at the top can all do the same kind of thinking and none of them does the kind that matters.
The decision
From 2019, under CEO Kevin Johnson, Starbucks leaned hard into digital. Its AI engine, Deep Brew, personalized offers, automated inventory and scheduling, and fed an aggressive push into mobile order-ahead. It worked on its own terms. Rewards members and mobile came to drive more than half of U.S. company-operated sales; order-ahead climbed toward a third of transactions. Laxman Narasimhan, who took over in 2023, doubled down on operational efficiency and "reinvention."
The numbers they were watching rose. The thing those numbers were supposed to serve (the store) quietly turned into a conveyor belt: mobile-pickup counters jammed, seating disappeared, and baristas were buried under a queue they couldn't see coming. Seven straight quarters of same-store sales decline followed. Narasimhan was out in just over a year.
The team's cognitive composition
Look at who set the strategy. Kevin Johnson came from Microsoft and Juniper, a career technologist whose reflex is to instrument a problem and solve it with a platform. His signature Starbucks initiative was, fittingly, an AI engine. Narasimhan spent nearly two decades at McKinsey before consumer-goods operating roles, which was a consultant's lens, trained to optimize processes and move metrics. Two capable leaders, two different résumés, one shared default: treat the store as a system to be made more efficient.
Howard Schultz built Starbucks on a single non-obvious idea: the "third place" between home and work, where the experience, not the transaction, was the product. Through the decline, nobody at the top carried that lens as their instinct. The tell is almost too obvious: Deep Brew was at one point pitched as AI to counter mobile's isolating effect, the team had noticed the human experience breaking, and its answer was another algorithm. It's the same structural pattern as the OpenAI board: a leadership group clustered tight on one axis (efficiency) with near-zero coverage on the axis that actually drove the business.
What a Decision Simulation would have surfaced
Systems thinking, what Oxford's Mary Johnstone-Louis calls today's most crucial leadership skill, the ability to see past isolated events to the feedback loops driving outcomes, is exactly the cognition that was missing. Run the mobile-first strategy through a Decision Simulation and the loop lights up: every point of order-ahead throughput you add raises in-store congestion, which degrades the third-place experience, which erodes the very loyalty the rewards dashboard is measuring. Optimizing the part was breaking the whole.
A simulation that stress-tested the strategy against the team's cognitive composition would have flagged it before the seventh down quarter: no one in this room defaults to asking what speed costs the experience. The dashboard said winning. The system said otherwise.
The lesson
Niccol's turnaround is the proof by inversion. "Back to Starbucks" — fewer menu items, less digital complexity, more and better-staffed baristas, seating and condiment bars restored, names back on cups. It's the missing cognition, re-seated at the table. Same-store sales turned positive for the first time in seven quarters and transactions grew. Niccol didn't out-optimize his predecessors; he saw a different system. That is the entire argument for measuring how a leadership team thinks before it acts: a room that can only optimize will optimize you straight past the thing you actually sell.