SAP's quantum chief: AI commoditizes prediction; better decisions are next advantage

SAP's Chief Quantum Officer opens with a provocative premise: artificial intelligence is rapidly becoming table stakes, and within a few years, every large company will have access to broadly similar predictive capabilities. When prediction becomes a commodity, it stops being a source of competitive advantage. The next frontier, he argues, 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—a gap he calls the "decision-making gap."

To illustrate the problem, he describes a scenario that plays out in companies every quarter. 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 receivables targets. The sales team decides which deals to pull forward, which AR disputes to escalate, and which customers to offer a concession. Each function makes the rational local decision. But together, they produce an outcome that would not have been chosen for the enterprise as a whole. AI can predict which opportunities are likely to close, flag which receivables are at risk, and estimate whether a commercial concession might improve close probability. But prediction does not answer what the company should actually do. 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 ripple through finance, cash flow, delivery, risk, and future customer value.

To manage this complexity, companies simplify decisions before calculation begins. They reduce scenarios, exclude interactions, convert complex trade-offs into fixed rules, and optimize sales, finance, and operations separately. The calculation becomes easier, but the business problem becomes less realistic. A new enterprise technology category called Enterprise Decision Computing is emerging to address this gap. It turns a business decision—its possible actions, objectives, constraints, uncertainty, interdependencies, and economic consequences—into a computable enterprise object that can be solved and optimized as a whole. ERP systems execute processes. Business intelligence explains the past. AI predicts outcomes. None of these answers what coordinated set of actions the enterprise should take given its goals, constraints, uncertainties, and interdependencies between functions. Enterprise Decision Computing creates the enterprise layer in which the decision itself is continuously represented, governed, measured, and improved.

The author spends considerable space on quantum computing's actual role. Most current discussion focuses on hardware milestones—qubit quality, error correction, the road to fault-tolerance. But the question is not when quantum hardware will be ready; it is what quantum will actually be asked to compute once it is. The answer lies in progressive decision enrichment. Begin with a classical model that considers revenue, closing probability, and available sales resources. Then add margin and payment terms. Then cash-flow timing, AR dispute status, and delivery constraints. Then portfolio-wide interactions and long-term customer value. Each additional layer makes the decision more realistic but also more computationally demanding. Most layers are solvable classically today and already create measurable value. But at a certain point, a layer becomes too interconnected, too constrained, and too rich for classical methods to handle without forcing simplifications that degrade the answer. For those classes of highly interconnected problems, quantum methods may eventually allow richer models to be evaluated without stripping away the interactions that make the answer realistic. That is the precise point at which quantum earns its place—not as a wholesale replacement, but as the capability that allows another valuable dimension to be included rather than left out. The competitive advantage does not begin with quantum; it begins with the decision model. Quantum's role, when it arrives at commercial scale, will be to extend that richness further.

The author closes with concrete actions CEOs and boards can take now. Identify one high-frequency, high-stakes domain where sales, AP, AR, or Treasury currently optimize independently. Run a baseline model. Measure what the coordinated answer looks like against what the siloed answer produced. The investment required is modest; the cost of not having that data when competitors do is not. The real bottleneck in enterprise quantum work is rarely access to a processor; it is the absence of a precise, enterprise-wide representation of the decision that the processor is supposed to improve. The organizations that will create the greatest value from quantum will not be those that access the technology first but the companies that understand precisely where today's simplified decisions leave value behind and where quantum can add the missing dimension.