AI is widespread on the factory floor in 2026, yet few feel advanced as integration and talent issues persist

Manufacturing Operations Outlook 2026: Progress on AI and ERP, Pain in Integration and Talent

Manufacturers are heading into 2026 with digital transformation embedded in day-to-day operations, but few consider themselves truly advanced. A January survey of 520 digital transformation leaders across North America, Europe and Asia shows steady progress, uneven integration and growing people challenges.

Digital maturity: progress, not dominance

• 67% say they're "on par" or "slightly ahead" of peers in digital maturity.
• Only 7% feel "far ahead."
• 94% report using some form of AI, with the biggest gains in predictive AI, supply chain planning and process optimization.

Cloud ERP remains the foundation. Primary outcomes called out:

• 49% simplification of IT infrastructure
• 49% reduced overall costs
• 48% improved business agility

Workforce impacts are material: 45% see productivity gains from ERP, and 30% link modernization to better retention.

Integration remains the sticking point

Progress toward a connected "Manufacturing Signal Chain" (finance, production, demand planning and supply chain) is uneven. More teams say they're moving forward, but internal barriers are rising.

• 31% now consider themselves "slightly ahead," up from 25% last year.
• Biggest obstacles: 33% lack the right talent (+8 pts), 31% weak cross-department collaboration, 24% resistance to change.
• Fewer point to budget limits or being "too busy," suggesting readiness-not money-is the bottleneck.

Market outlook: cautious, with trade pressure

• 31% expect demand to decline, while 19% expect growth.
• 39% anticipate higher raw material costs from tariffs and trade volatility.
• 37% plan price increases; 29% expect supplier reliability to worsen.

IT priorities reflect that pressure:

• 40% focus on operational and production performance
• 34% prioritize cybersecurity
• 28% target growth initiatives
• 61% plan to increase enterprise software spend, mostly with moderate, incremental budgets

AI in operations: from pilots to production

• 73% believe they're "on par" or "ahead" in AI maturity.
• Predictive AI adoption up 12 pts to 48%.
• AI for supply chain planning up 19 pts to 35%.
• AI for process optimization up 11 pts to 36%.

The focus is on throughput, planning accuracy, inventory turns and production efficiency-not flashy experiments.

What's at risk if you stall

• 27% worry about lack of digital skills.
• 27% see risk in failing to transition to new business models.
• 27% cite poor disruption response.
• 24% call out limited customer insight due to weak data visibility.
• Fewer point to compliance or speed-to-market as top risks-competitiveness now hinges on adaptability and skills.

Operations playbook for 2026

• Anchor AI to bottlenecks: Start where constraints are clear-maintenance predictions to lift OEE, dynamic scheduling to improve schedule attainment, and inventory optimization to reduce working capital.
• Tighten the signal chain: Standardize your data model across finance, planning, production and procurement. Use event-driven integrations and APIs to cut latency between demand signals and shop floor execution.
• Let ERP do the heavy lifting: Treat cloud ERP as the system of record. Add best-of-breed apps only where they deliver measurable impact (e.g., APS, MES, quality) with clean handoffs.
• Build skills and rituals: Close the talent gap with targeted upskilling, daily tier meetings, and cross-functional S&OP that includes IT and operations in the same room.
• Institutionalize ROI: Create a scorecard with baselines and owner accountability. Track forecast accuracy, plan adherence, schedule attainment, FPY, and inventory turns monthly.
• Harden cybersecurity early: Patch cadence, MFA, role-based access, backups, and incident response playbooks should be non-negotiable. See the NIST Cybersecurity Framework for a practical baseline.
• Plan for trade volatility: Run tariff scenarios, dual-source critical inputs and segment suppliers by risk. Feed those signals into pricing and inventory policies.
• Move pilots to production fast: Set a 90-day limit per pilot with clear exit criteria, then scale or kill. Avoid pilot sprawl.
• Phase budgets: Fund integrations and data quality first. Layer in AI once data reliability passes an agreed threshold.

Recommended resources

• Close the skills gap with role-based AI training for ops teams: Courses by Job

KPI checkpoint: measure what matters

• Forecast accuracy and plan adherence
• Schedule attainment and on-time delivery
• Inventory turns and days of supply
• OEE, changeover time, first-pass yield
• MTBF/MTTR for critical assets
• Employee retention and time-to-competency

Bottom line: the edge in 2026 won't come from more tools. It will come from tighter integration, aligned teams and AI embedded into the flow of work where it moves throughput, cost and reliability.