The management landscape is shifting in the AI era. Consider this operational example: a global insurer’s CEO returns to the office on a Tuesday morning after learning regulators in several markets plan to cap margins on certain retail products. Her first action is to ask the company’s AI operating system (AI OS) to assess exposure. Within minutes the system reads the draft regulations, matches them to the product catalog and reports: about 140 products across seven countries are affected, and roughly one-third risk breaching the new limits. It also highlights an unresolved issue: regulators define margin differently. The CEO sends the analysis to country leaders to validate by end of day.
At 9:15 a.m., the head of the German business unit opens the AI OS, which has codified scope, margin formulas, thresholds and timing for the draft rules and reconciled regulators’ margin definitions with how the company measures margin across pricing, finance and commission systems. He runs the margin model against live product data, questions a cost-allocation assumption unlikely to be accepted by the regulator, and reruns the model with a revised assumption: nine products are now in breach. By 4:00 p.m., analyses from all seven countries are rolled up into the CEO’s view with the assumptions behind every number visible.
The next day, before a video meeting among the CEO and business unit heads, the AI OS has already modeled options for each breaching product—reprice, redesign coverage, restructure commissions or withdraw—and estimated effects on volumes, distribution partners and capital. Leaders test scenarios live and agree on a plan: protect key distribution partnerships and allow withdrawals only with CEO approval. The system records those choices as guardrails.
On Thursday, line-of-business heads redesign products within those guardrails. AI agents generate candidate designs and prices, test them against the codified rules and product governance; actuaries refine options, compliance reviews them, and business heads decide. Agents draft partner communications for human sign-off. Each approved change updates product-level forecasts that roll into the group financial scenario so the CFO can watch and stress-test the plan—what if regulators adopt a stricter definition? After launch, the system continuously tracks each product against thresholds and alerts line heads before drift becomes a breach. Future regulatory responses will be faster because rules, definitions and models are now part of the company’s second brain.
What an AI OS is and why it matters
An AI OS is a management layer that preserves enterprise context (the knowledge that gives data meaning), reasons across functions, recommends or executes decisions, and learns from outcomes. It connects CEOs, the C-suite, functional leaders, managers and frontline workers in ways not previously possible.
According to McKinsey’s 2026 State of AI survey, 40 percent of respondents at large companies have scaled AI agents; "large companies" are defined as organizations with annual revenues above $1 billion. Yet only 6 percent report a significant financial impact from AI (at least 5 percent of EBIT). That gap suggests adoption alone is not enough—the enterprise itself often needs to be reorganized to capture value.
Early results are promising: the authors’ experience shows companies can accelerate planning cycles by up to ten times, achieve first value in an initial domain in roughly 12 weeks, and cut time to ROI by about 50 percent when extending the system to new domains. Other reported gains include up to 40 percent productivity improvements, 50 percent cost savings, and up to 90 percent reductions in work-stream cycle times.
Core design principles
- Open architecture: the AI OS should use open standards so the company retains control over its core intelligence while allowing technology components to be swapped as they evolve.
- Real-time, actionable view: unlike dashboards that report what happened, the AI OS shows what is happening now and what could happen next, offers recommendations and enables near-real-time collaboration.
- Enterprise context and semantic understanding: ontologies, knowledge graphs, metadata, schemas, business rules and institutional documents create the semantic layer that lets agents access the relevant slice of knowledge for a task rather than parsing the entire enterprise model.
In practice, one large U.S. retailer moved from many performance dashboards to a single personalized view for district managers across more than 7,000 stores; the AI OS diagnosed likely causes of performance gaps, recommended actions and helped managers prioritize store visits.
Operational and managerial implications
Two common CEO traps are assuming AI makes transformation effortless and believing that years of foundational work are required before creating value. Both are avoidable: leaders can start with a few high-value problems and build technology incrementally.
But the role of leadership is critical. CEOs and their top teams must be closely involved to align the AI OS with strategic priorities. Key managerial changes include:
- Reframing processes end-to-end with AI: much of generative AI’s first wave improved existing activities; P&L value typically requires redesigning cross-boundary workflows and clarifying the decisions that matter and the data and AI needed to support them.
- Redesigning the management calendar around decision points: since information becomes continuously available, standing reviews can be eliminated and leadership intervention triggered by defined signals, thresholds and exceptions.
- Aligning operational speed with the AI OS: companies must identify operational chokepoints (for example last-mile delivery or production issues) and determine which actions can remove them.
- Shifting human effort toward judgement: as agents assume more execution-level tasks, leaders should focus on framing hard questions, interpreting ambiguity, coaching, communicating intent and resolving exceptions.
The AI OS gives people closer to customers and operations richer context, enabling them to act on choices that previously required higher-level input. This can remove organizational layers that existed because information or expertise was scarce.
Examples and implementation approaches
A global OEM adopted three guiding principles before building its AI OS: keep enterprise knowledge portable (open-standard formats for architecture and ontologies), redesign the company around AI (redesign workflows, then codify them with digital twins and semantic layers), and focus on reusability and high trust (data products, a codified knowledge layer and deterministic ML models).
In practical terms, agents can handle parts of processes like bill-of-materials sourcing by extracting intent, informing design options with AI/ML models, and running portions of request-for-information, while humans review options and make final decisions. The OEM reported lower costs, better designs and faster time to market.
A U.S.-based manufacturer grounded its AI OS on three foundations: own the crown jewels (data, models, business rules, institutional knowledge), build for flexibility (use existing investments but allow components to be swapped), and standardize operations with a governance harness of integrations and configurations. It connected about 100 internal and external data sources, cleaned and linked them, and created a semantic representation of pricing and planning decisions. The initial phase of its AI OS was operational in roughly 16 weeks, making formerly siloed institutional knowledge consistently and continuously available.
One manufacturer estimated the opportunity from reimagining demand forecasting, production scheduling and cross-boundary workflows—including routing, planning and pricing—at $140 million to $180 million per year.
AI economics and governance
Although token costs have dropped more than 95 percent, AI spending at many companies has increased more than threefold. Model choice, task structure, reasoning consumed and human support requirements all shape AI economics. The cheapest intelligence is not always the most cost-effective if it leads to expensive mistakes.
Therefore, an AI OS should bake AI economics into decision processes so business leaders can see true P&L trade-offs for any choice. Those trade-offs will evolve along with large language models and other AI technologies.
Conclusion: the window to build a compounding advantage
An AI OS can break down traditional constraints on management and convert institutional knowledge and experience into a compounding competitive moat. Early movers who codify enterprise context, design for reuse, incorporate AI economics and keep leadership closely involved have the best chance to capture sustained value.
The article is authored by Asutosh Padhi and Vik Sohoni (senior partners, McKinsey Chicago), Brian Elliott (senior partner, Carolinas), Daniel Swan (senior partner, Connecticut) and Kim Baroudy (senior partner, Copenhagen). The authors thank Chhavi Adtani, Fabian Metzeler, Sofiya Bidochko and Zubin Ghafari for contributions. The piece was edited by Barr Seitz (editorial director, New York).
