98% investing in AI, only 20% ready: What manufacturing AI readiness really requires

Walk into almost any manufacturing boardroom and you’ll hear the same word within minutes: AI.

AI for predictive maintenance. AI for demand forecasting. AI-driven production optimization. AI-powered workforce planning. Machine learning for quality control. Computer vision on production lines. Generative AI for product development.

In Redwood Software’s “Manufacturing AI and automation outlook 2026,” 98% of manufacturers say they’re investing in or exploring AI in manufacturing. Yet only 20% consider themselves fully prepared to operationalize AI at scale.

That gap isn’t surprising, as most manufacturers still frame AI readiness as a technology decision. They think: Which AI models? Which vendor is best? Which is cheapest? The only area that consistently gets business-level attention is AI model security.

In practice, AI readiness has very little to do with model selection. It has everything to do with whether your manufacturing systems can integrate and interoperate in a governed, effective and efficient way — in real time.

AI readiness is operational, not conceptual

When an AI system flags a product quality deviation using computer vision, predicts equipment downtime through predictive maintenance models or detects supply chain disruptions based on real-time data analysis, something must happen next:

• Data must move
• Systems must synchronize
• Exceptions must trigger action
• Processes must execute end to end

If your environment can’t respond automatically to new information, even the most advanced machine learning or AI-powered solutions become little more than storytellers.

Redwood’s research shows that while 85% of manufacturers have deployed at least one workload automation solution, most remain in mid-stage maturity. Automation exists, but orchestration across manufacturing systems is incomplete.

We see the consequences clearly. Insights arrive, and human workers review them. Emails circulate, and someone manually initiates a downstream workflow in a manufacturing execution system (MES) or ERP platform. Hours pass, sometimes days.

The sophistication of the AI model matters far less than the operational environment in which it must operate.

How work is triggered: A critical but overlooked signal

Manufacturing is a tightly coupled business. One delay in raw materials affects scheduling. A quality deviation slows an entire production line. A missed procurement adjustment ripples into customer delivery commitments. The environment is dynamic by default.

AI models are designed to identify those inflection points. What determines value isn’t the model’s accuracy, but whether your workflows can act before a minor deviation turns into lost throughput, higher costs or unplanned downtime.

Redwood’s research reveals that many manufacturers still rely on scheduled scripts for critical workflows. They have batch jobs running at predetermined intervals and time-based polling to check for changes. This creates a fundamental disconnect: manufacturing runs in real time, with every process affecting the next, but the automation supporting it does not. Scheduled automation introduces latency that AI can’t compensate for. A model may detect a defect instantly, but if the remediation workflow runs every four hours, the window for prevention is gone. This is where many AI initiatives stall — because the execution layer can’t keep up.

Event-driven orchestration, where systems react immediately to production, quality or supply chain events, is a prerequisite for scaling AI.

Mid-stage automation creates false confidence

The report indicates that while automation tools are widespread across the industry, coordination remains heavily manual. Tasks may be automated, but manufacturing processes aren’t fully streamlined across system boundaries.

Humans still bridge gaps between supply chain systems, production scheduling, inventory management and quality control. Exceptions require manual intervention. And while data analysis happens, execution lags. This creates a false sense of AI readiness among leadership. What looks like automation to operations teams looks like fragmented infrastructure to AI systems expecting consistent, automated workflows.

Step back and consider what these AI use cases actually assume: 

1. Production scheduling updates in lockstep across systems
2. Forecasting flows directly into procurement decisions
3. Optimization spans the entire production process, not just isolated tasks

Those are orchestration assumptions, and when they’re unmet, AI’s impact shrinks accordingly. Without orchestration maturity, AI use cases remain pilots rather than enterprise capabilities.

The slow transition from pilot to production

The readiness gap isn’t only technical. It’s also organizational. According to the report, 73% of teams require some level of approval to implement automation changes. Only 26% can act independently.

That’s not necessarily a flaw in governance; it’s often a reflection of how much control and visibility teams actually have. In environments where systems are fragmented or hard to monitor, centralized approval becomes a necessity.

The problem is what that slows down. When teams identify inefficiencies in data flows, manufacturing systems or supply chain integrations, they can’t act on them quickly. Changes get pushed into review cycles, and AI-driven initiatives struggle to move beyond controlled pilots.

AI readiness isn’t just about better models. It’s about being able to evolve workflows continuously, within a system you trust. Without that, even the most promising AI initiatives stall before they ever reach real-world operations.

AI use cases assume orchestration that doesn’t yet exist

The data shows that manufacturers prioritize AI use cases that depend on coordination across multiple systems. Predictive production scheduling ranks highest, followed by supply chain anomaly detection. Workforce optimization also appears frequently on roadmaps. These use cases require continuous data synchronization, automated exception response and end-to-end workflow execution.

In many environments, these foundations are incomplete. If your data arrives late because transfers run on schedules rather than triggering immediately, and exceptions require manual handling because automated response protocols don’t exist, those AI initiatives will only look promising in theory. That’s why 98% may be investing in AI, but only 20% believe they’re truly ready.

The new AI readiness conversation

AI isn’t failing in manufacturing. Many are just attempting to deploy it on incomplete foundations, and the technology performs exactly as expected when critical data flows remain manual and workflows require human intervention. The readiness gap reflects an unfinished automation journey.

From a technical perspective, this outcome is predictable. AI can’t scale on fragmented execution layers any more than a car can run on half-built roads. Your infrastructure must be complete first.

Manufacturers closest to operational AI readiness share clear characteristics. They:

• Design automation around processes, not tasks
• Connect systems with event-driven workflows
• Reduce reliance on manual coordination
• Treat orchestration as strategic infrastructure, not tactical scripting

In other words, AI readiness appears as a byproduct of automation maturity, not the result of aggressively pursuing AI. This is an important shift in perspective. The critical question is not: “Which AI tools should we adopt?”, but “Are our operations structured to support AI at scale?”

Redwood customers demonstrate this pattern: Equipped with the leading orchestration platform for the autonomous enterprise, they’re 50% more likely to be exploring AI-driven automation and 2.7x as likely to be in the higher stages of automation maturity.

The opportunity is significant. Manufacturers are eager to apply, but the competitive differentiator won’t be who experiments first. It will be who orchestrates best.

See how your fellow manufacturers define AI readiness today — and what separates prepared organizations from the rest. Read AI insights and more in the “Manufacturing AI and automation outlook 2026.”