The numbers come first. Artificial intelligence is rapidly becoming table stakes, according to Carsten Polenz, Chief Quantum Officer and Head of Quantum at SAP SE. Within a few years, he writes, every large company will have access to broadly similar predictive capabilities — and when prediction becomes a commodity, it stops being a source of competitive advantage.
Polenz's argument is precise: the real frontier is not knowing what might happen, but deciding what the enterprise should do about it — 'across thousands of interconnected choices, competing objectives, and finite resources.' He calls this the decision-making gap.
The Quarter-End Problem
His illustration is concrete and familiar to any CFO. In the final weeks of a financial quarter, the Accounts Payable team holds payments to protect liquidity. The Accounts Receivable team accelerates collections to hit its target. The sales team pulls deals forward, escalates disputes, and offers concessions. Three functions, each making the rational local decision — and together producing an outcome that would not have been chosen for the enterprise as a whole.
AI can flag which receivables are at risk and estimate whether a commercial concession might improve close probability. But it cannot answer the question that ultimately matters. A discount may protect revenue while eroding margin. Resolving an AR dispute too quickly may protect cash but signal financial weakness. Pulling a contract forward may secure short-term revenue while damaging a strategically important relationship.
'These decisions cannot be made function-by-function,' Polenz writes. 'Executive attention, legal capacity and commercial resources are finite.'
A New Category: Enterprise Decision Computing
To address this gap, Polenz identifies an emerging technology category he calls Enterprise Decision Computing — turning a business decision, including its possible actions, objectives, constraints, uncertainty, interdependencies, and economic consequences, into a computable enterprise object that can be solved and optimized as a whole.
His taxonomy is clean. Enterprise Resource Planning systems execute processes. Business intelligence explains the past. AI predicts outcomes. None of these, separately or together, tells a business what coordinated set of actions it should take given its goals, constraints, and the interdependencies between its functions.
Polenz is also careful to distinguish this from Operations Research, which solves defined problems. Enterprise Decision Computing, he argues, is the enterprise layer in which the decision itself is continuously represented, governed, measured, and improved.
Critically, he notes that this advantage is available now, regardless of quantum computing timelines. 'Classical optimization, simulation and AI can already evaluate richer decision models than most companies currently use.'
Quantum's Real Question
On quantum hardware — his own domain — Polenz offers a pointed critique of the current conversation. Most of it, he writes, focuses on hardware milestones: qubit quality, error correction, the road to fault-tolerance. 'These advances matter. But they answer the wrong question. The question is not when quantum hardware will be ready. It is what quantum will actually be asked to compute once it is.'
His answer: progressive decision enrichment, beginning with classical models and expanding their complexity as more powerful hardware arrives.
---
CEO Times take: Polenz's framework is a direct challenge to the AI hype cycle — and a useful one. Free enterprise rewards firms that allocate capital and attention better than their rivals, not firms that simply own the most sophisticated prediction engine. The executive who treats AI as a finish line rather than a starting point is already falling behind. Enterprise Decision Computing, whatever label ultimately sticks, is a market signal worth watching: the next wave of enterprise software spending will flow toward optimization and coordination, not just prediction. Capital rewards clear rules — and clearer decisions.
