In my role as a Strategic Account Manager at Redwood Software, I work closely with some of the largest Fortune 500 manufacturers in our client base, advising on automation strategy across complex, mostly SAP-centric environments. Those conversations tend to surface patterns that don’t always show up in formal transformation plans, but they’re often where meaningful change starts.
One of the more consistent patterns is surprisingly simple. Procurement teams are often the first to ask a question that cuts through the complexity: “Why are we running multiple workload automation platforms when we could consolidate onto one?”
They’re not aiming to be more technical; they’re surfacing an opportunity that directly supports the CIO’s priorities around standardization, cost control and operational efficiency.
Legacy automation is back in focus
Over the past five years, the workload automation market has consolidated through mergers and acquisitions. Fewer vendors, combined with rising demand for automation, have shifted the balance of supply and demand. Procurement teams are often the first to feel that pressure, and they’ve been reacting by pushing for vendor consolidation. In doing so, they’re forcing CIOs to take a closer look at a part of their environment that has largely been ignored for decades.
This phenomenon has been a blessing in disguise for many of the CIOs we work with at Redwood. What initially seems like a cost-driven initiative is turning into something much more strategic. At the same time procurement is pushing consolidation, most Fortune 500 manufacturers are in the middle of large-scale digital transformation efforts, like moving from SAP ECC to SAP S/4HANA or RISE with SAP, shifting to and/or optimizing workloads in the cloud or introducing AI into core operations. As those changes take shape, it becomes clear that the legacy automation layer doesn’t transition as easily as expected.
In many cases, expecting these legacy tools to support moving operations to a modern, hybrid cloud architecture requires heavy customization, introduces technical debt or simply breaks altogether. Many of the workload automation solutions still in use today were originally built for on-premises, mainframe-based environments in the 1990s. They weren’t designed for cloud, hybrid infrastructure or the pace of change organizations are dealing with today.
According to McKinsey and Bain research for Redwood, only one-third of enterprises consider replacing their automation tools every year. This means two-thirds of manufacturers are going to stumble upon this problem with their next automation vendor renewal, rather than getting ahead of it.
Environments are fragile by accumulation
Very few manufacturers deliberately built the complexity they now live with. It usually happened one sensible decision at a time.
A scheduler went in to support SAP batch jobs, another tool was added for data pipelines and scripts were written to move files between the MES and cloud analytics. A manual handoff that was meant to be temporary became permanent. Each of those choices was justified by an important need. Each solved a real problem. But they cumulatively created a technology landscape that’s harder to manage, slower to change and more fragile than it looks.
Tool sprawl would be bad enough on its own. What makes it worse is the maintenance load and technical debt that comes with it: undocumented scripts, manual fixes, installed software components and agents everywhere, plus the constant churn of patching and version alignment. IT teams are asked to support modernization while spending their days keeping outdated automation systems stable.
78% of manufacturers have automated less than half of their critical data transfers, and nearly 27% still rely on manual or email-based methods to transfer sensitive internal documents like financials and contracts. – “Manufacturing AI and automation outlook 2026”
Fragmentation creates a split operating reality. Production data lives in one place, analytics in another and planning somewhere in between, while supplier updates arrive through EDI, CSVs or inboxes on uneven schedules. If orchestration can’t normalize and route those signals in real time, planners are left working with stale information. Tool sprawl starts hitting the business.
Redwood’s manufacturing research shows the same pattern. Automation is delivering gains in throughput and uptime, but results flatten when the KPI depends on multiple systems moving together. Inventory turns and data accuracy are much harder to improve in fragmented environments. Only 40% of manufacturers have automated exception handling, even though 22% cite it as a top operational disruption. Thus, many manufacturing operations still depend on people to bridge gaps when resilience matters most.
Orchestration changes the equation for the factory
At some point, manufacturers have to decide whether legacy automation will support the operation or define its limits.
It’s possible to find a more connected path when you step away from legacy schedulers that rely on thousands of installed agents spread across plant-floor servers, applications, data sources and virtual machines, each one tied to operating system changes, security patches and version dependencies. In a modern manufacturing environment, that overhead becomes a constant drain.
Moving to a modern application and data pipeline workflow orchestration platform with an agentless, cloud-first architecture cuts that burden at the source and gives technical teams their time and focus back. Instead of babysitting infrastructure, they can align their effort toward enterprise MES rollouts, IIoT connectivity, plant modernization and the data foundation needed for predictive maintenance and better decision-making.
A unified orchestration model changes what teams can see, what they can scale and where they optimize throughput, efficiency and budgets. It gives manufacturers, in particular:
Better visibility across end-to-end processes: In fragmented environments, teams see isolated jobs and individual handoffs. In a unified model, forecasting, procurement, production scheduling and fulfillment become part of the same end-to-end flow. If a supplier update affects material availability or a quality hold changes what can ship, the response can move through the system instead of waiting for human intervention.
A stronger foundation for modernization: Tool consolidation is often treated like cleanup work, but it’s actually foundational. If the orchestration layer remains fragmented, every smart factory or Industry 4.0 initiative built on top of it inherits that fragility.
More room to scale: Manufacturers expanding across plants and regions can’t afford growth that brings license friction, infrastructure bloat or unpredictable costs. A SaaS model with transparent economics makes scalable growth easier to support.
Better use of budget: Too much money still goes into maintaining old schedulers, managing compatibility issues and upgrading platforms that add no new business capability. Consolidation creates a chance to shift that spend toward projects that improve production processes, shorten cycle times and remove bottlenecks.
Bring your orchestration strategy to life
This is where an orchestration platform like RunMyJobs by Redwood fits. Its job is not to add another tool to the pile, but to replace fragmented scheduling and automation with a single execution layer across ERP, MES, IIoT, quality control and plant-floor workflows.
For manufacturers with large SAP landscapes, that matters even more. Redwood’s SAP partnership and SAP Endorsed App status give customers a more reliable way to connect SAP Cloud ERP, SAP Business Technology Platform and SAP Business Data Cloud without leaning on maintenance-heavy scripts and custom workarounds. For teams moving through RISE with SAP, that supports a clean core strategy rather than pulling the architecture away from it.
A unified application and data pipeline orchestration platform also makes governance more practical. Once workflows span plants, business units and systems, consistency balloons into a serious operational issue. Compliance, auditability, security controls and traceability need to be built into execution, not layered on later.
AI raises the stakes further. Manufacturers are investing in it for planning, forecasting and predictive operations, but those efforts depend on reliable workflows and dependable data collection. If the underlying process is still patched together, AI will expose the weakness faster. Traditional automation is deterministic: you know what output to expect. AI is not. Even with consistent inputs, outcomes can vary. As organizations introduce AI agents into finance, supply chain and operations, there’s a growing need for a layer that can govern and control how those systems behave.
A strong orchestration foundation gives teams cleaner execution, earlier visibility into failures and true observability across the plan-to-produce chain. The result is less legacy technical debt and drag, fewer update delays and a better path to faster product introductions, smarter scaling and more resilient manufacturing processes.
The window is open
Manufacturing leaders don’t need more reminders that legacy tool sprawl is a problem; most are living with the consequences already. The real question is how much longer they can afford to let aging automation tools sit underneath the modernization agenda, widening the gap between smart factory ambition and operational reality every time a new initiative is layered onto a cracking foundation.
Consolidating to a modern, SaaS, AI-powered orchestration platform is the act of removing a bottleneck before it becomes the reason transformation stalls.
If a legacy renewal is approaching for your enterprise, treat it like the strategic decision it is.
Most enterprises are running two or more schedulers — and spending millions maintaining them.
They’re at a crossroads, being asked to accelerate AI, cloud transformation and digital service delivery to stay competitive. Yet many remain anchored to self-hosted workload automation (WLA) schedulers built for a different era.
The mandate to modernize is clear: boards expect measurable progress on AI and cloud initiatives, and business leaders are pushing for faster product launches and real-time insights. But inside IT operations, the focus remains on maintaining aging infrastructure and keeping critical jobs running.
This tension starts at the foundation. Legacy WLA platforms were designed for static and long-running batch, on-premises applications, not hybrid ecosystems where cloud services, data platforms and ERP systems need to operate in sync. As expectations rise, these schedulers increasingly constrain the speed and flexibility your business demands.
Service Orchestration and Automation Platforms (SOAPs) represent the modern evolution of WLA. Built for hybrid and cloud-native environments, they orchestrate application and data pipelines across the enterprise without the infrastructure burden legacy schedulers require.
Standing still has become the most expensive option.
Legacy WLA as a constraint
In many enterprises, WLA expanded in pockets, where one team implemented a scheduler for ERP workloads, another introduced a separate platform for data pipelines and a third added tooling to support distributed or cloud-native processes, with custom scripts bridging functional gaps. Each decision solved an immediate need, but those decisions created a layered architecture that’s difficult to unwind.
It’s common to see two or more legacy, self-hosted WLA platforms operating across on-premises and cloud environments. Some are tightly integrated with core systems of record. Others sit alongside newer cloud services.
The operational implications are significant:
Each platform requires dedicated infrastructure, its own upgrade path and compatibility matrix
Agents must be deployed, patched and aligned with operating system changes across environments
Security reviews and audit processes are repeated for each tool
Reporting and monitoring are fragmented
In addition to the above maintenance, security and governance challenges, there are important organizational impacts to consider. Each scheduler operates differently, with its own interfaces, dependencies and operational logic. That puts the burden on your teams to maintain deep expertise across multiple tools rather than building proficiency in a single, unified platform. Cross-training becomes harder because knowledge doesn’t transfer cleanly between systems. Operational efficiency then suffers as teams switch contexts and reconcile differences between tools. Hiring becomes more complex, too. Instead of looking for broadly applicable skills, you’re often searching for experience tied to specific legacy platforms.
That tooling problem soon becomes a people and scalability problem, which limits how quickly your organization can adapt, grow and modernize.
Renewals: A season of potential
Software renewals tend to feel administrative, like it’s just a time to review usage, negotiate terms and sign the contract. In reality, it’s one of the few clean decision points you get.
Each renewal forces a choice: continue funding infrastructure maintenance or redirect that spend toward modernization. Extending legacy WLA contracts locks in your server costs, upgrade projects and agent management for another cycle. It also locks in the opportunity cost of not going with something more efficient and cost-effective.
When digital competition intensifies, inertia becomes a massive risk. The cost of maintaining aging schedulers now outweighs the perceived disruption of migrating to a modern platform.
The hidden cost of the status quo
What makes legacy WLA especially challenging is not just fragmentation, but the operational gravity that comes with it. Agent-heavy architectures require constant attention. Thousands of agents sit across servers and environments, each one tied to operating system updates, security patches and version dependencies. Even routine changes ripple across teams. Major upgrades can stretch six to 12 months, often consuming engineering bandwidth and delaying higher-value initiatives.
Meanwhile, your cloud footprint is expanding, and your data landscape is becoming more complex. AI initiatives are demanding tighter integration across systems, too. Yet, what should be a modern orchestration platform architected for the cloud remains a legacy, self-hosted workload scheduler that wasn’t designed for this level of interdependency or scale.
The result is technical debt that compounds year after year. Every upgrade cycle, server refresh and manual workaround diverts time and budget from initiatives that move the business forward. This is where the opportunity cost becomes real. Every dollar you spend maintaining legacy schedulers is a dollar you’re not investing in AI enablement, data innovation or new digital services.
Resetting the cost and innovation equation
Breaking this pattern requires rethinking the architecture itself.
Legacy schedulers automate jobs. SOAPs orchestrate the business. Legacy schedulers embed operational overhead into their design. Thousands of agents distributed across servers mean constant patching, version alignment and coordination across teams. Moving to an agentless, cloud-first foundation removes that complexity at its source. This is the architectural shift SOAPs introduce: orchestration delivered as SaaS, with fewer moving parts, fewer dependencies and a single control plane instead of fragmented oversight.
Upgrades change as well. Instead of planning around disruptive, multi-month version migrations, agentless-by-design updates arrive as part of the service. Security improvements and new capabilities are introduced without forcing your team into another upgrade cycle. Engineering time shifts from platform maintenance to business enablement.
The commercial model should evolve in parallel. Rigid licensing and usage caps create hesitation during periods of growth. A transparent, scalable SaaS structure provides clarity and room to expand without negotiation under pressure.
What consolidation unlocks
When you consolidate legacy schedulers onto modern SOAP like RunMyJobs by Redwood, the impact extends beyond cost reduction.
You gain:
A native SaaS architecture built for hybrid environments, capable of handling complex, time- and event-driven workflows without managing on-premises infrastructure
Agentless connectivity across SAP systems, data platforms and cloud-native services, eliminating large-scale agent deployment and patching
AI embedded directly into workflow development, monitoring and optimization, accelerating delivery and surfacing issues earlier
A single control plane shared by Dev, Ops and Data teams, replacing disconnected scheduling silos
Enterprise-grade reliability, including 99.95% uptime, for mission-critical processes
One orchestration layer across ERP, data, cloud and AI workloads
Turn automation into a competitive edge
Tool consolidation only matters if it changes the economics and trajectory of the business. Legacy WLA environments drive unplanned cost increases and technical debt. Spend becomes unpredictable, and modernization projects get delayed.
Lower total cost of ownership (TCO) and faster modernization don’t have to compete. Done right, they reinforce each other. A true SaaS SOAP solution helps you move to predictable operating costs and reduce time spent on upgrades and remediation. Instead of funding maintenance, you fund innovation. At the same time, you unlock the level of transformation you’re being pressured to achieve.
It’s time to decide whether you want another cycle of maintenance or a foundation built to scale with your business.
Start with a free automation assessment before your next renewal. See what consolidation would look like in your environment, and get a data-driven migration plan specific to you in days.
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.
Interest, ambition and investment aren’t the issue. Readiness is.
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:
Production scheduling updates in lockstep across systems
Forecasting flows directly into procurement decisions
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.”
Instead of asking what agentic AI is, leaders are asking a more practical question: Is it actually driving measurable results for the business?
Agentic AI systems are built to act. Unlike traditional genAI, which focuses on producing content or summarizing information, agentic AI moves into execution. It interprets objectives, breaks them into subtasks and completes multi-step workflows with limited human intervention. That shift — from recommendation to resolution — is what matters.
Consider supply chain operations. A traditional model might simply surface a potential delay and leave it to a human to interpret, who spends valuable time context-switching to understand the history and balance risk and other contextual factors. But an agentic system doesn’t stop at the alert. It weighs alternate carriers against budget constraints, reroutes the shipment, updates your ERP and documents the change for compliance. By the time your team sees the notification, corrective action is already underway.
Turning agentic AI into enterprise capability depends on three structural requirements.
1. A connected digital core
There’s a clear pattern many are finding when they review their 2025 AI initiatives. Projects didn’t stall because the models lacked sophistication, but because the surrounding infrastructure wasn’t ready for autonomous action. Autonomy isn’t just about advanced AI. It depends on having a digital foundation that can coordinate action across systems, workflows and data in real time.
Agentic AI doesn’t operate in a vacuum. It depends on APIs, real-time data and coordinated workflows that span cloud services, SaaS applications and on-premises systems. If those systems remain siloed, autonomous agents can identify the right course of action but can’t carry it through end to end. They can recommend and analyze, but they can’t fully execute. That integration gap is the primary barrier to scaling AI value. In many cases, the limiting factor isn’t the agent itself. It’s the maturity of the digital core it’s operating within. Autonomy can’t move faster than the systems it depends on.
When connectivity is shallow, insights don’t translate into action. They sit inside individual systems, waiting for someone to notice them, interpret them and push the next step forward. That friction limits scale.
This is where orchestration becomes essential. At Redwood Software, we see how AI-powered automation must be grounded in structured workflow orchestration, with built-in frameworks for security, governance, accountability and cost control. When agentic systems operate within that foundation, organizations gain control over identity, model selection and token usage, along with the visibility needed to manage performance and risk. A connected, governed ecosystem allows agentic AI to move beyond advisory outputs and begin driving real-world outcomes.
2. Orchestration embedded at the center
The companies pulling ahead aren’t bolting AI onto old infrastructure or just leaving it in the hands of individual contributors to use as a stand-alone tool. They’re reexamining how work flows across the enterprise and reshaping those paths to support autonomous execution from the start.
It starts with architecture. A robust workflow engine provides the structure that keeps automation aligned across cloud, SaaS and data center environments. Deep, bi-directional connectivity ensures AI agents can both consume enterprise data and critical context and perform actions across enterprise systems.
Many organizations try to accelerate AI adoption by stitching together isolated tools across departments. That approach often creates fragility in the form of disconnected automations, unclear ownership and security gaps that grow harder to manage over time. Sustainable autonomy depends on embedding intelligence directly into the systems that already govern how work flows across the enterprise, not layering another silo on top.
Orchestration defines the broader objective within a business process and creates a clear operating model. The agentic AI system handles specific tasks, like analyzing real-time data, optimizing parameters or interacting with external tools, and returns structured outputs to the workflow. Built-in validation and guardrails determine what happens next.
Governance isn’t optional; human oversight remains central. Financial thresholds, compliance controls and cybersecurity policies must be encoded directly into workflows. High-risk decisions can include human-in-the-loop validation. That’s how you combine large language models and machine learning with enterprise-grade accountability.
Redwood’s approach to AI-powered automation reflects this model, unifying orchestration, automation and real-time decision-making across complex workflows and allowing autonomous agents to streamline business processes without sacrificing control. The more connected your ecosystem becomes, the more powerful your agentic AI work will be.
3. Clear ownership and governance
As agentic AI systems become embedded in daily operations, the role of your teams must evolve. This isn’t a headcount conversation. It’s about moving people closer to judgment, governance and strategic decision-making. People aren’t focused on triage, menial activities and executing every little step manually or through traditional automation tools anymore. They’re managing autonomous agents, setting guardrails and monitoring performance. Oversight shifts from doing the work to improving how the work gets done and managing risk along the way.
The most effective companies begin with contained, high-impact scenarios, such as:
Vendor reconciliation that once required manual intervention
Customer support requests routed intelligently in real time
Scheduling that adapts automatically as upstream workflows change
Automated Know Your Customer (KYC) risk analysis that accelerates approvals
These practical starting points build confidence and momentum.
Cultural readiness matters just as much as technical capability. Leaders need to clarify permissions, define escalation paths and ensure transparency in decision-making processes. Certainty around how AI models, datasets and workflows work together enables teams to improve and scale those systems with confidence.
Your systems determine your ceiling
This shift is already reshaping how leading enterprises operate, steadily and decisively. Agentic AI has moved out of the lab and into production. Large language models are widely available. Simply having access to powerful models no longer sets you apart. What matters now is how effectively you put them to work.
Leadership in the next decade won’t come from isolated AI initiatives. It will come from embedding autonomous agents into the core of how work runs and unifying orchestration, automation and human oversight into a scalable operating model. In the new autonomous world, staying competitive depends on how well you operationalize AI across your business.
AI has quickly risen to the top of the manufacturing agenda, with many COOs defining bold visions for how it can transform operations and committing significant investment to support it. Leaders are prioritizing AI as a strategic lever for improving resilience and efficiency. But translating that ambition into scaled impact remains a challenge. Pilot programs and early deployments are common, yet progress is uneven.
Redwood Software’s “Manufacturing AI and automation outlook 2026” explains why. What stands out isn’t a lack of ambition or even a lack of technical capability. The constraint appears deeper and more structural. While AI systems are advancing rapidly, the environments they depend on, particularly the way data moves across production processes, supply chain management and quality control systems, are often fragmented.
AI is highly sensitive to context. When that context is incomplete, delayed or manually reconciled across systems, performance suffers. It’s not the algorithms that are failing, but rather the operational foundation underneath them not having been designed for synchronized, real-time orchestration.
Data-rich environments, flow-limited systems
Manufacturing operations generate extraordinary volumes of information. ERP platforms manage planning and financial functions. MES environments track execution across production lines and assembly lines. IoT devices and sensor data capture activity on the shop floor. Supply chain systems oversee inventory management, shortages and supplier coordination.
Individually, these systems perform as designed, but they rarely operate as a unified environment.
The report reveals that a majority of manufacturers have automated fewer than half of their critical cross-system data transfers. That gap creates friction precisely where AI applications require continuity. An AI model designed to optimize production schedules or reduce downtime through predictive maintenance assumes consistent, event-driven inputs. When updates move through batch processes, manual uploads or delayed workflows, the model works with a partial representation of real manufacturing operations.
The result isn’t catastrophic failure. It’s subtle misalignment between AI-driven recommendations and current operational realities. In many cases, that’s harder to detect. A major system failure is obvious and immediate, but misalignment is different — it builds gradually, as small inconsistencies move downstream, decisions compound and systems drift out of sync. By the time the impact surfaces, the root cause can be difficult to trace. For leaders focused on operational efficiency, that kind of erosion is a persistent barrier to trust.
The limits of human-mediated workflows
Despite widespread automation investments, many manufacturing companies still rely on spreadsheets, shared files and email-based processes to move information between systems, including data tied to product quality, compliance, financial reporting and supply chain coordination. If people serve as the bridge between platforms, variability increases. Updates may not propagate immediately, and different teams may interpret the same data differently.
That variability is particularly problematic because AI systems assume structured inputs. Machine learning models and neural networks are built to detect patterns in datasets, not reconcile conflicting versions of operational truth.
When systems work — but not together
The manufacturing sector has made meaningful progress in automating repetitive tasks and streamlining functions inside individual platforms. AI tools are accelerating product development and strengthening quality assurance, and robotics is increasing flexibility on assembly lines. These advancements signal real progress toward an Industry 4.0 approach.
However, AI-driven decision-making frequently spans multiple systems at once. If inputs from ERP planning data, MES execution states, real-time sensor data and supply chain updates aren’t synchronized through event-driven workflows, fragmentation becomes inevitable.
Misalignment often starts with small breaks in flow:
A forecast update that doesn’t immediately adjust production scheduling
A production shift that fails to update inventory management
A quality control signal that never reaches planning teams
Each system may be optimized independently, and the absence of cross-system orchestration constrains broader AI adoption.
The strain becomes even more visible during disruptions. Equipment failures, supplier delays, cybersecurity incidents and logistics constraints introduce complexity that demands rapid coordination. Redwood’s research shows that exception handling remains heavily manual for many manufacturers. When teams intervene sequentially across systems rather than through coordinated workflows, data divergence accelerates precisely when clarity is the most critical.
If AI systems can’t consistently “see” disruptions across platforms, they can’t adjust effectively.
Orchestration as a scaling factor
The research reveals a clear pattern: manufacturers who prioritize automation and orchestration maturity across end-to-end processes are more likely to report improvements in areas like downtime and better positioned to scale AI-driven initiatives.
Reliable, real-time data flow across production, supply chain management and quality control systems acts as a multiplier for AI adoption. Without it, even strong AI use cases can’t generate the impact many hope it will.
Synchronization gaps and the data quality illusion
A persistent structural constraint is reliance on time-based automation. Batch jobs and scheduled scripts still synchronize critical systems in many environments. While that works for reporting and historical data analysis, it introduces latency that conflicts with AI-enabled decision-making.
Manufacturing operations are increasingly continuous and don’t happen in batches. Machine states change throughout the day, sensor data updates continuously and supply chain disruptions emerge unpredictably. When systems reconcile information on fixed intervals instead of in response to events, AI models operate on delayed context. Even small timing gaps can compound across production processes.
This dynamic also reshapes how data quality should be understood. Governance frameworks and normalization efforts matter, especially as generative AI and advanced analytics expand into new use cases. But many quality challenges originate earlier, during data movement itself. Workflows that rely on manual intervention or delayed synchronization embed inconsistencies before analytics even begin.
For manufacturers evaluating AI solutions, the implication is straightforward: improving orchestration and real-time data alignment across systems often delivers more impact than refining algorithms alone.
Act on this structural inflection point
Small breaks in data flow compound quickly. A minor synchronization issue can ultimately limit the operational impacts of AI. Thus, competitive advantage increasingly depends on the ability to optimize data movement across production lines, supply chain management and quality assurance. Automated, event-driven workflows managed in a centralized orchestration control layer will be the answer for manufacturers looking to stay not only on track, but ahead.
Redwood’s “Manufacturing AI and automation outlook 2026” provides visibility into how data movement maturity, exception handling practices and workflow automation shape AI readiness. Read the full report to see how your organization compares and what it takes to move from isolated AI use cases to scalable, real-time intelligence.
