Decision throughput and AI: how to unlock margins in business processes

The invisible economics of operational decisions

The cost of a delayed decision never appears as a dedicated line item on an income statement. A company knows down to the penny its expenditure on raw materials, payroll, energy, depreciation, and software subscriptions, yet it rarely calculates the hours spent in meetings, cross-checks, and hierarchical sign-offs to determine production volumes, pricing for a specific batch, or when to halt a line for maintenance.

In our first deep dive on the 2026 McKinsey report, we analyzed the Make vs Buy pivot and token cost management. The operational question of where real value is created once algorithms enter the factory floor remains open. In manufacturing and distribution companies, taking five days to recalculate a production schedule means piling up inventory in the wrong warehouse, running lines inefficiently, and losing urgent orders.

McKinsey’s study «The decision dividend: How AI creates economic value» clarifies this transition with a financial balance-sheet metric: selling, general, and administrative (SG&A) expenses, which include management and planning costs, typically account for 5% to 12% of revenue. Even cutting these expenses to zero would not explain the margins achieved by market leaders. Economic value does not stem from labor cuts, but from the ability to compress decision latency: moving from weekly review cycles to continuous recalculations driven by real-time operational data.

Decision throughput: the metric that outweighs license counting

Measuring AI returns by tallying activated virtual assistants or software seats assigned to employees says nothing about actual value creation. The most common mistake is confusing visibility on a dashboard with real operational capability, measuring displayed data rather than completed decisions.

The strategic metric to evaluate economic impact is decision throughput: the capacity of corporate infrastructure to evaluate, route, and transform raw data into verified operational actions at a sustainable pace.

In linear activities, such as customer service, assisted resolution slashes operational costs from $7–$20 to fractions of a cent, cutting average handling times from over ten minutes to under two. However, the most substantial financial impact occurs when algorithms govern working capital allocation and plant capacity utilization.

A demand plan updated multiple times a day based on actual orders, inventory across logistics hubs, and machine efficiency status prevents both stockouts and overproduction. A concrete example is the Revenue Growth Management platform deployed by the Reckitt group: by simulating in real time the impact of pricing, promotions, and portfolio mix against live sales data and on-shelf availability, the company pushed operational recommendations directly to sales teams and retail outlets. The economic advantage comes from speed: acting while market dynamics unfold protects gross margin before external conditions shift.

Operational Parameter	Traditional Siloed Management	Integrated Real-Time Architecture
Operational Planning	Weekly or monthly recalculation across misaligned spreadsheets	Continuous multi-daily updates synchronized across ERP/MES streams
Anomaly Response Time	24–72 hours of hierarchical escalations and manual inter-department checks	Minutes, with contextual alerting and automated scenario simulation
Inventory Allocation	Static, based on historical averages and aggregated past orders	Dynamic, calibrated to actual lead times and localized demand
Plant Capacity Utilization	Low visibility, frequent bottlenecks, and unplanned downtime	Batch sequence optimization and predictive maintenance
Traceability and Governance	Retroactive sample audits and paper-based reporting	Deterministic audit trail with human oversight and RBAC permissions

The Acemoglu paradox: from simple tasks to complex processes

To understand why many AI investments fail to generate returns, one must distinguish the nature of the automated activities. In the economic paper The Simple Macroeconomics of AI (NBER Working Paper 32487), Daron Acemoglu highlights that aggregate productivity gains from AI risk remaining modest (estimated at 0.53% to 0.66% over ten years at the macroeconomic level) if deployments remain confined to simple tasks (easy-to-learn tasks).

The pace of AI adoption is historically unprecedented: while the steam engine took 55 years to reach cost parity with human labor, the electric dynamo 30 years, and the internet 7 years, AI crossed this threshold in just 2 years thanks to shared cloud infrastructure.

However, accessible cost parity does not guarantee an automatic impact on complex industrial processes (hard-to-learn tasks). Linear tasks have clear rules and objective outcomes; conversely, managing a production line or negotiating supply contracts hinges on physical constraints and unformalized tacit knowledge. Generic, off-the-shelf black-box solutions perform well on the former, but fall short on the latter. Enterprise ROI is unlocked only when artificial intelligence is grounded in the semantics of proprietary data, embedding models directly into the operational logic of the business.

Increasing plant yield without new capital expenditure

A common mistake in industrial planning is assuming new production lines or warehouses are necessary whenever existing ones appear saturated. Often, the bottleneck lies not in nominal hardware capacity, but in the sluggishness of scheduling and maintenance decisions.

Integrated systems that optimize operating parameters based on actual machinery conditions increase the utilization rate of already amortized assets, postponing or eliminating capital expenditures (Capex). According to McKinsey’s analysis of top-performing cases, redesigning information workflows can drive EBITDA increases of up to 20%, with payback periods ranging between twelve and twenty-four months.

When systems incorporate agents capable of taking direct actions, managing decision throughput requires a robust security architecture. In line with the principles of the NIST AI Risk Management Framework (NIST AI RMF) and the requirements of EU Regulation 2024/1689 (AI Act), agent operations must rely on traceable digital identities (Non-Human Identities), granular role-based access controls (RBAC), and Human-in-the-Loop protocols that reserve strategic decision validation for human operators.

How to accelerate the decision cycle

In industrial decision-making, postponement carries a hidden cost: delaying process optimization by a year means compounding operational inefficiencies and forfeiting a competitive edge that is difficult to regain. Eliminating decision bottlenecks turns historical data, production logs, and warehouse streams into immediate operational execution.

Want to calculate the true cost of decision latency in your operations and identify where to unlock operating margins? Book a technical assessment session with Aidia specialists.

Sources

Daron Acemoglu (National Bureau of Economic Research), «The Simple Macroeconomics of AI», NBER Working Paper No. 32487 (May 2024), https://www.nber.org/papers/w32487
McKinsey & Company, McKinsey Industrials Insights (Industrials & Electronics Practice), Lari Hämäläinen and Steffen Fuchs, «The decision dividend: How AI creates economic value» (August 26, 2026), https://www.mckinsey.com/industries/industrials/our-insights/the-decision-dividend-how-ai-creates-economic-value
McKinsey & Company, QuantumBlack AI Insights, «The state of AI in 2026: On the road to ROI» (August 2026), https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai
European Parliament and Council of the European Union, «Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (AI Act)», Official Journal of the European Union (July 12, 2024), https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ:L_202401689
National Institute of Standards and Technology (NIST), «AI Risk Management Framework (AI RMF 1.0) and Generative AI Profile (NIST-AI-600-1)», U.S. Department of Commerce (July 2024), https://www.nist.gov/itl/ai-risk-management-framework
NHI Management Group, «Decision Throughput: Definition & Framework Alignment (NIST AI RMF, NIST SP 800-53)» (August 1, 2026), https://nhimg.org/glossary/decision-throughput/