AI Deployment Checklist for Manufacturers in 2026

A lot of AI projects in manufacturing fail for a boring reason: the plant wasn’t ready.

Not “the model wasn’t accurate.” Not “operators resisted it.” The usual culprit is more basic: incomplete asset visibility, messy data, porous IT/OT boundaries, and governance that can’t survive an audit.

That’s why an AI deployment checklist matters—especially heading into 2026, when many manufacturers are budgeting for automation, supply chain optimization, and predictive maintenance while cyber risk is rising. One widely cited industry signal: supply chain cyberattacks increased 20% year-over-year, with legacy systems frequently the weak point. At the same time, OT surveys continue to show over half of OT environments still rely on legacy systems. Those two facts collide the moment you connect AI into production and supplier workflows.

This post turns the “four priorities” from the RSS article into a practical, procurement- and supply-chain-friendly checklist. I’ll also show how to turn each priority into an implementation plan you can actually fund, staff, and measure.

Priority 1: Get asset visibility and data integrity before you automate decisions

Answer first: If you can’t accurately list what’s connected, where data comes from, and who changes configurations, your AI system will eventually make a confident decision based on the wrong reality.

Manufacturers are adding AI for production planning, quality prediction, demand sensing, inventory optimization, and supplier risk scoring. Every one of those use cases depends on the same foundation: trusted data tied to known assets.

What “visibility” means in an AI-ready plant

Asset visibility isn’t a spreadsheet. It’s a living system that answers questions like:

• Which PLCs, HMIs, gateways, sensors, historians, and servers are online right now?
• Which are internet-exposed or reachable from corporate networks?
• What firmware versions are running, and what’s their vulnerability status?
• What data is each asset producing, and how is it routed into MES/ERP/data lakes?

A useful reference point from the source article: nearly 70,000 OT devices worldwide are exposed to the public internet, many running outdated firmware. Even if your site isn’t directly exposed, supply chain connections (remote support, vendor VPNs, EDI/API links) can create similar risk paths.

Data integrity: the part everyone underestimates

AI doesn’t need “big data.” It needs correct data with known lineage. In manufacturing and supply chain, data integrity failures typically come from:

• Duplicate asset IDs across plants
• Manual overrides not logged (scrap reasons, downtime codes, setpoint tweaks)
• Sensor drift and calibration gaps
• Inconsistent unit measures across suppliers (kg vs lb, net vs gross)
• ERP master data that’s outdated (lead times, MOQ, supplier status)

If you’re building AI-driven supply chain forecasting, this becomes painfully obvious: the model learns patterns from whatever you feed it. If your lead times are “wishful thinking,” the forecast will be too.

Quick checklist: what to implement in the next 60–90 days

1. OT/IT asset discovery (continuous, not annual)
2. Real-time monitoring for critical assets and data flows
3. Data quality controls: validation rules, outlier detection, and “golden record” ownership
4. Data lineage mapping for any dataset that will train models or drive automated decisions

Snippet-worthy stance: AI readiness starts with knowing what you own and trusting what you measure.

Priority 2: Secure the IT/OT boundary like you’re about to hand it the keys

Answer first: The moment AI starts recommending—or triggering—actions, your network becomes part of the safety system. Treat it that way.

AI increases connectivity. Connectivity increases attack surface. And attackers are getting faster at recon and exploitation as they use automation to scale their efforts.

Legacy OT networks were rarely designed for:

• Zero trust identity
• Fine-grained segmentation
• Secure remote access with strong auditing
• Continuous patching and vulnerability management

Yet AI initiatives often require exactly those capabilities because they pull data across systems and sometimes push actions back into them.

The three security controls that most directly protect AI initiatives

1. Segmentation that matches operational reality
   Divide networks by function and risk (e.g., packaging line vs utilities vs quality lab). Then enforce allowlists. The goal is limiting blast radius, not creating an “air gap” fantasy.

2. Secure remote access (SRA) with identity and session controls
   Vendors and integrators are essential in manufacturing. The mistake is giving them broad VPN access “because it’s easier.” For AI readiness, you want:

   • Least-privilege access
   • Time-bound approvals
   • Session recording for critical systems

3. Continuous detection and vulnerability management
   You don’t need perfect patching. You need a repeatable process for:

   • Identifying vulnerable assets
   • Ranking by exploitability and operational impact
   • Applying compensating controls when patching isn’t feasible

Where procurement fits (and why it matters)

Most companies treat IT/OT security as an engineering budget line item. That’s a mistake. Supplier and procurement management is where a lot of risk enters.

If you’re buying AI solutions or integrating them into operations, add these requirements to sourcing and contracts:

• SBOM or component transparency for software suppliers
• Vulnerability disclosure timelines
• Remote access methods and audit requirements
• Data handling rules (training data, retention, segregation)
• Incident notification SLAs (hours, not weeks)

Procurement teams can drive these requirements faster than many technical committees can.

Priority 3: Put governance and auditability in place before the first model goes live

Answer first: If you can’t explain who approved the model, what data it used, what changed, and why it made a decision, you’re not “innovating”—you’re accumulating liability.

Manufacturing AI is shifting from analytics to autonomy: schedule optimization, automated purchasing recommendations, dynamic safety stock, and even closed-loop quality control. That’s great—until a regulator, customer, or internal auditor asks for evidence.

This is especially relevant as compliance expectations tighten across regions and industries (think operational resilience, cybersecurity directives, machinery safety expectations, and data protection). Even when a regulation doesn’t explicitly say “AI,” it often demands the controls AI tends to break.

What “AI governance” looks like in a plant + supply chain context

Governance isn’t a 40-page policy. It’s a practical operating system:

• Model registry: what models exist, who owns them, where they run
• Versioning: model versions tied to training data versions
• Change control: documented approvals for changes that affect operations
• Decision logging: traceable inputs → outputs → actions
• Human-in-the-loop rules: when AI can recommend vs when it can execute

Here’s the line I use with exec teams: If a model touches production, it needs the same discipline as a process change.

A simple RACI that prevents governance chaos

• Operations (Owner): defines success metrics and acceptable risk
• IT/OT security (Control): ensures segmentation, monitoring, access
• Data/AI team (Builder): trains, validates, documents models
• Procurement (Gatekeeper): enforces supplier requirements and SLAs
• Compliance/Legal (Assurer): confirms audit readiness and obligations

If any one of those is missing, the project will either stall or ship with gaps.

Priority 4: Build resilience against AI-enabled threats (because downtime is the real KPI)

Answer first: Your goal isn’t “never get attacked.” Your goal is containment and continuity—keeping plants running and shipments moving when something goes wrong.

The RSS article’s point is blunt and accurate: AI is making reconnaissance and intrusion activities more efficient. For manufacturers, that means the cost of a successful intrusion isn’t just data loss—it’s production loss, quality escapes, delayed orders, and supplier disruption.

Resilience controls that protect manufacturing and supply chain outcomes

Resilience is where AI, OT, and supply chain risk management intersect. The most practical measures:

• Incident response playbooks that include OT realities
  Who can isolate a line? Who can shut down remote access? Who signs off on “run degraded” modes?

• Protected backups and recovery drills
  Backups are only useful if you can restore them within your operational tolerance. Run drills like you mean it.

• Micro-segmentation + anomaly detection
  You want to spot unusual lateral movement, abnormal command patterns, or data flow changes before they become outages.

• Rapid isolation capabilities
  Practice “containment by design.” If a vendor laptop is compromised, you should be able to cut access without cutting production.

A real-world scenario (why this matters)

Consider a manufacturer that deploys AI to optimize spare parts inventory and predict failures. The system ingests historian data and maintenance notes, then triggers purchase recommendations.

If an attacker manipulates sensor values or injects false maintenance records, the AI can:

• Over-order expensive components (budget damage)
• Miss an impending failure (downtime)
• Create phantom demand spikes that ripple to suppliers (bullwhip effect)

That’s not a “data science problem.” It’s a resilience and integrity problem.

Turning the 4 priorities into an AI readiness plan (that leaders will fund)

Answer first: The fastest path to AI value is a staged rollout that improves visibility, security, governance, and resilience while delivering supply chain wins in parallel.

Most manufacturers don’t need a moonshot. They need a plan that ties foundational work to measurable outcomes—especially in procurement and supply chain.

Phase 1 (0–90 days): Baseline and de-risk

• Stand up continuous asset discovery and data flow mapping
• Identify top 20 “crown jewel” assets and segment them
• Implement secure remote access for vendors
• Define AI governance: owners, model registry, logging requirements

Supply chain win: better master data, fewer expediting surprises, clearer supplier dependencies.

Phase 2 (3–6 months): Prove value with constrained automation

Choose use cases where AI can recommend actions without directly controlling equipment:

• Supplier risk scoring with external + internal signals
• Demand forecasting improvements using cleaned sales + lead time data
• Predictive maintenance alerts (recommendations only)

Success metric examples:

• Reduce expedite spend by a fixed percentage
• Reduce unplanned downtime hours in a pilot area
• Improve forecast bias for a product family

Phase 3 (6–12 months): Expand autonomy safely

After controls and auditability are stable:

• Move from “recommend” to “auto-execute” in narrow, reversible workflows
• Add continuous controls monitoring (security + governance)
• Extend to multi-site optimization and supplier collaboration workflows

Procurement payoff: better negotiation positions via reliable demand signals, fewer emergency buys, and fewer supplier surprises.

A practical next step: run a one-day AI readiness workshop

If you want momentum without chaos, schedule a focused session with operations, IT/OT security, supply chain, and procurement. The output should be a short list of decisions:

• The first 2 AI use cases you’ll pilot (and why)
• The minimum security controls required before piloting
• The datasets you trust (and the ones you don’t)
• The governance you’ll enforce from day one

The reality? It’s simpler than it sounds when you make it concrete.

AI can absolutely improve manufacturing performance and supply chain outcomes. But if you skip the basics—visibility, security, governance, and resilience—you’re not speeding up transformation. You’re speeding up failure.

What would change in your next AI project if you treated AI deployment readiness like a plant startup: checklists, sign-offs, rehearsals, and clear ownership?