How organizations can capitalize on the rise of AI factories

Despite rising ambition and spending, many organizations struggle to convert investment into meaningful outcomes. Decisions largely happen in functional silos, often dependent on manual checks, with outcomes looked at after the fact through delayed reports and fragmented data. Too much AI work still runs as discrete projects or proofs-of-concept that generate insights but fail to reshape frontline decisions. Without an always-on system connecting data, decisions and action, the impact stays fragmented and inconsistent.

The shift from capability-driven AI to factory-style intelligence mirrors the evolution of modern manufacturing. This turns AI into an operational system rather than a technical capability — one that learns continuously, adapts predictively and performs with increasing confidence. An AI factory is one where data functions as the raw material, models and engines act as the machinery, and decision services become the production lines pushing intelligence directly into workflows.

Building the AI factory

Through AI factories, throughput becomes measurable. Policies, guardrails and audit requirements are enforced dynamically as decisions are made, reducing operational risk and simplifying compliance. Time-to-value also compresses.

Building an AI factory requires more than technology. It demands an operating model, a value-led approach and a disciplined path from ideation to reliable, repeatable production. It is a shift from isolated initiatives to a system that can design, build, deploy and improve intelligent workflows consistently.

Professional external consultants can support this journey. For example, EY teams combine deep sector experience, proven delivery frameworks and the EY DecisionOS platform to help organizations industrialize intelligence with confidence.

DecisionOS translates AI ambition into measurable business performance. It delivers improvements that accumulate across speed, accuracy, cost, resilience and capital efficiency — creating impact that grows with every cycle of use. By leveraging DecisionOS, organizations can realize the following benefits.

1. Accelerate operational speed

DecisionOS brings real-time intelligence into the flow of work, tightening the gap between the signal, decision and action. This reduces decision latency, cuts manual effort and improves daily rhythm across planning, scheduling, fulfillment and service.

2. Improve accuracy and reduce waste

With better signals and smarter logic, decisions become more reliable and targeted. Retailers reduce waste, balance inventories, protect margins and improve on-shelf availability through more accurate and timely actions.

3. Maintain margins under volatility

DecisionOS aligns demand sensing, pricing and replenishment so actions adapt faster to changing conditions. This helps retailers protect contribution margins even during spikes, stock shocks or supply disruption.

4. Scale productivity non‑linearly

By converting human expertise into modular decisioning, DecisionOS removes repetitive workloads and lets teams focus on higher-value tasks. Productivity rises without proportional increases in headcount.

5. Engineer agility into operations

Real-time sensing and modular logic support rapid responses to market, supply and policy changes. Retailers adjust plans, reallocate stock and recover from disruptions faster and with less operational noise.

6. Improve return on computing

Computing, especially for GPUs, is treated as managed capacity. DecisionOS measures throughput and value per GPU-hour so retailers get more output from the same investment, strengthening efficiency and performance.