Case Study: Automating Social Media Content Creation with ChatGPT 4.0

Your AI tools now speak RunMyJobs: MCP brings agentic AI to your enterprise applications

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According to a recent Redwood Software customer survey, 68% of RunMyJobs by Redwood users work with AI tools multiple times per week. They ask ChatGPT to troubleshoot errors, use Copilot to draft scripts and paste job logs into Claude and ask what went wrong.

None of those AI models can reach into RunMyJobs and take action. They answer questions about your workflows but can’t run them, check on them or build new ones. Your AI assistants and your automation platform operate in separate worlds, and that gap is exactly what makes it hard to get real value out of either.

Model Context Protocol (MCP) support in RunMyJobs changes that.

Why every major AI platform adopted the same protocol

MCP is an open standard that gives AI systems a shared way to connect with external tools. Think of it as the USB port of agentic AI. MCP lets you plug any MCP-compatible AI agent into any MCP server without custom integrations. Drop an MCP server in front of a product, and AI systems can immediately interact with it, reading context, calling functions and taking action.

Anthropic released MCP in late 2024 and donated it to the Agentic AI Foundation under the Linux Foundation in December 2025. Since then, OpenAI, Google DeepMind, Microsoft, Salesforce and ServiceNow have adopted it. The protocol has moved past experimentation, as its interoperability across AI platforms is proven in production today.

For RunMyJobs users, MCP means something specific: the business logic, connectors and workflows you’ve spent years building are now accessible to AI agents through a standardized protocol that every major AI platform already supports. Any MCP-compatible AI tool or large language model can now trigger your workflows, check job status and build new workflows through the RunMyJobs MCP server with no custom API work and no rearchitecting. The workflows and connectors you’ve built over years of production use become tools that AI agents call on demand.

This is how your backend processes become agentic.

What can AI tools do through RunMyJobs’ MCP?

• Trigger workflows and jobs: Any MCP-compatible agent can kick off your existing RunMyJobs workflows to make your current processes agent-ready without migration.
• Check job status: AI tools can query whether critical jobs are running, finished or failed and surface that information inside whatever platform your team uses.
• Manage workflows: Coding agents can validate and deploy RunMyJobs workflows through MCP, cutting development time.

These capabilities work with Claude, Microsoft Copilot Studio, ServiceNow Agent Builder, n8n, ChatGPT and Salesforce Agentforce. Authentication, access controls and permissions all flow through RunMyJobs — agents can only do what their associated role is allowed to do.

Here’s what that looks like in three real-world use cases.

SAP’s Joule: Submitting and monitoring jobs from your ERP

Your SAP basis administrator needs to trigger a nightly data extraction early because a report deadline moved up. Instead of switching to the RunMyJobs console, logging in, finding the workflow and submitting it by hand, they stay in SAP and tell Joule: 

“Submit the GL account extraction workflow for company code 1000.”

Joule calls RunMyJobs through MCP. The workflow starts. Joule confirms it.

An hour later, the same admin asks Joule about the financial data load. Joule checks RunMyJobs and finds that the extraction finished, but the transformation step is still running. Estimated completion: 45 minutes.

No dedicated SAP integration project made this possible. MCP standardized the connection. RunMyJobs partitions and roles still control who can trigger what, so your governance model is intact, but your admin gets a faster, context-aware path to the same workflow they’ve run hundreds of times before.

This is what it means to agentify your existing SAP processes: The workflows don’t move, and the business logic stays where it is. Joule just gets a direct line to it.

ServiceNow: Remediating failed batch jobs without the 2 AM phone call

Your nightly accounts receivable batch job fails at 2:14 AM. Today, that triggers a ServiceNow incident. An on-call operator picks it up, logs into RunMyJobs, reads the error log, figures out the cause, restarts the job with corrected parameters and closes the ticket. That process takes 30 to 90 minutes, depending on who’s on call and how fast they diagnose the issue.

With ServiceNow Agent Builder and MCP, the ServiceNow agent handles most of that loop. It detects the failed job alert, queries RunMyJobs through MCP for real-time error details and job history and matches the failure pattern against known remediation steps. If the fix is a known restart with corrected parameters — wrong file path, stale credentials, a transient connection timeout — the agent resubmits the job in RunMyJobs and updates the ServiceNow incident with what it found and what it did.

If the failure falls outside the agent’s confidence threshold, it escalates to the on-call operator with a pre-built diagnostic summary: the error, job chain context and last three successful runs for comparison. Your operator starts the investigation 15 minutes ahead of where they’d be without it.

RunMyJobs still controls execution. Partitions and roles still govern who — or what — can restart which jobs. ServiceNow still owns the incident lifecycle. MCP connects the two external systems without custom middleware in between.

Microsoft Copilot Studio: Finance teams running month-end close

Month-end close involves dozens of batch processes across ERP, consolidation and reporting systems. They run in strict sequence, often at night, and someone watches a console to catch failures.

Your finance controller builds a Copilot agent in Microsoft Copilot Studio. The agent submits the intercompany elimination workflow through MCP to RunMyJobs. When that job finishes, the agent triggers the consolidation jobs. If reconciliation fails, the Copilot agent sends the controller a Microsoft Teams message — a plain-language summary of the failure, plus the remediation workflow they can approve with one click.

The controller doesn’t need RunMyJobs training. They tell the Copilot agent what outcome they need, and RunMyJobs handles execution. Your finance team stays in Teams and focuses on the close, not the tooling.

What this means for your RunMyJobs investment

The good news for teams feeling pressure to adopt agentic AI: you don’t have to rewrite your enterprise workflows. You don’t have to move your batch processing into a new tool. MCP exposes what you’ve already built to the agents your developers want to build, through a standardized protocol they already know.

The automation fabric you’ve built in RunMyJobs is your real AI asset; MCP is how you unlock it.

The RunMyJobs governance model — partitions, roles, access controls — still applies. Scalable agentic orchestration doesn’t require trading away the enterprise-grade controls you rely on. Your AI-powered workflows run under the same oversight as everything else in your automation environment. But your teams get a new way to interact with automation that fits inside the AI tools they’ve already adopted.

Redwood is building toward a model where AI agents and workload automation run side by side, supporting open standards such as agent-to-agent (A2A) and MCP to unlock existing business logic and make it accessible in a governed and observable platform operating at enterprise scale. MCP support in RunMyJobs is where that starts, and the foundation is the automation you’ve already built.

See how RunMyJobs works with MCP to unlock your investment in enterprise applications and expose them to agents. Get a demo today.

Transitioning to SAP Cloud ALM: The observability question most teams answer too late

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On May 29, 2020, SAP introduced SAP Cloud ALM as its cloud-based successor for application lifecycle management, replacing SAP Solution Manager and SAP Focused Run with a SaaS model built for SAP S/4HANA Cloud (now SAP Cloud ERP), SAP Business Technology Platform (BTP) and the RISE with SAP roadmap.

Nearly six years later, that roadmap is well underway. Mainstream maintenance for SAP Solution Manager ends December 31, 2027. Extended support runs through 2030. SAP Cloud ALM is already included in SAP Enterprise Support and most cloud subscriptions, with no additional license required. If you haven’t started your transition planning yet, now is the right time.

Most teams I talk to have accepted the direction. The more interesting question is how to get the most value out of SAP Cloud ALM once you’re there — and how observability fits into that.

What SAP Cloud ALM delivers and how automation extends it 

SAP Cloud ALM handles project management, health monitoring and lifecycle visibility across SAP-centric environments. For teams moving away from on-premises systems, it’s a meaningful step forward. 

The opportunity grows when your business processes span multiple systems. A typical end-to-end flow might move through SAP Cloud ERP, SAP SuccessFactors, SAP BTP services, external APIs and non-SAP platforms. SAP Cloud ALM provides strong visibility into the SAP application layer. Extending that same transparency to the automated workloads running across your broader landscape is a natural next step that makes your SAP Cloud ALM investment generate an even greater return.

In my experience, the individual step that fails is almost never the step that caused the problem. It might start in a data integration, surface in the ERP and affect downstream reporting by the time anyone notices. Connecting automation execution data to SAP Cloud ALM is what turns that sequence visible, so your operations teams can trace a timeline, not reconstruct one.

Observability from the process layer, not just the system layer

What changes about your observability needs when you move from SAP Solution Manager to SAP Cloud ALM is worth thinking through carefully. SAP Solution Manager was built around on-premises system monitoring, whereas SAP Cloud ALM is built for a world where business processes run across cloud services, SAP BTP extensions and non-SAP systems simultaneously. 

The scope of what needs to be visible has grown considerably, and most organizations are already feeling that pressure. 

According to EMA’s 2025 observability research, 87% of organizations are running multiple observability tools and actively looking to consolidate, yet fewer than half describe their current visibility as fully successful. 

An SAP cloud transition is the right moment to get ahead of that, rather than add to it.

RunMyJobs by Redwood approaches observability from the automation layer. Instead of checking whether individual systems are healthy, it lets you track whether the business process completed as expected — start to finish, across every system involved. And when a process is at risk of missing an SLA before it actually does, AI-driven predictive monitoring flags it early so teams can act rather than react.

Redwood Insights provides dashboards tied to workflows, SLAs and execution data. Operations teams, business stakeholders and SAP teams can each see what matters to them without waiting for someone to translate technical signals into business terms.

RunMyJobs also connects with platforms like Dynatrace, Splunk, New Relic and AppDynamics, enabling full stack telemetry correlation and accelerating root-cause analysis. When something breaks, you trace the sequence rather than guess at it. Therefore, resolution times drop because the investigation starts in the right place.

Connecting RunMyJobs to SAP Cloud ALM

RunMyJobs has integrated with SAP Solution Manager for years. The new SAP Cloud ALM connector extends that relationship into SAP’s current operational standard to synchronize job definitions, workflow status and execution data directly into SAP Cloud ALM on an ongoing basis. SAP Cloud ALM becomes the command center, while RunMyJobs provides the orchestration and execution layer beneath it.

The powerful combination helps operations teams detect SLA risk before it becomes a business impact, trace root causes faster by correlating automation telemetry with application and infrastructure performance and maintain long-term execution records that hold up for audits and compliance reviews. Self-service dashboards mean business stakeholders can answer their own questions without routing every request through IT.

See exactly how the SAP Cloud ALM and RunMyJobs integration works in practice and watch a demo.

Making the most of your SAP Cloud ALM investment

Moving to SAP Cloud ALM changes day-to-day operations in ways that open up real opportunity:

• You onboard new use cases faster
• Cloud services move from supporting infrastructure to the systems your operations depend on daily
• More systems contribute to each business process

And you’re working with a platform SAP is actively investing in and expanding. The more those systems are interconnected, the more valuable connected observability becomes.

When you can follow a business process across systems from within SAP Cloud ALM, issues stay contained and time-to-market stays predictable. When automation execution data is part of that picture, the operational view becomes more complete — and the value of both SAP Cloud ALM and RunMyJobs compounds.

That’s the case for acting before the 2027 deadline, not just meeting it.

To see how the pieces fit together, explore the SAP Cloud ALM connector for RunMyJobs and Redwood Software’s Platin partner listing in the SAP Cloud ALM Partner Hub. Or, browse the full set of SAP connectors for RunMyJobs to see how automation and observability could connect across your SAP landscape.

In financial services, onboarding is a risk story — not just a customer experience metric

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Across financial services, the onboarding moment is where customer relationships are won or lost. Imagine a prospective customer: a mid-sized manufacturer with $200M in annual revenue who has decided to move a high-value financial relationship to your institution. They’ve engaged with your team, liked what they heard and submitted their application. Then they wait. 

Three days pass. Then five. A compliance step stalls somewhere between two systems that don’t talk to each other cleanly. Nobody catches it until the prospect calls to ask what’s happening. By day ten, they’ve quietly restarted conversations with a competitor. By day fifteen, they’re gone.

No outage was declared. No incident ticket was filed. The workflow technically completed. But the relationship — and the revenue — evaporated anyway.

That wouldn’t be unusual. According to Fenergo’s 2024 KYC and Onboarding Trends report, 67% of banks globally lost clients due to slow or inefficient onboarding in 2024. The gap between what institutions believe is happening in their onboarding workflows and what new customers actually experience is widening, and the financial consequences are measurable.

This scenario plays out more often than most executive teams realize in banking, insurance, wealth management and other sectors of financial services, because onboarding is almost universally treated as a customer experience problem: 

✅ Reduce friction

✅ Improve the digital journey

✅ Shorten time to first transaction

Those are worthy goals, but they address the surface without touching the foundation — where the real financial exposure lives. In onboarding, regulatory, operational and reputational risk converge into a single workflow and are compressed into a narrow, high-stakes window where any failure is costly.

Revenue exposure hiding in plain sight

Consider what’s actually at stake in a delayed onboarding workflow. Every day a high-value customer isn’t activated is a day of fee revenue, deposit float and relationship potential that doesn’t materialize. That exposure is measurable, and it compounds.

McKinsey research on corporate client onboarding found that the average process can take up to 100 days, with Know Your Customer (KYC) due diligence and account setup alone consuming more than 40% of that time. For the client on the other end of that process, the experience doesn’t feel like due diligence. It feels like indifference.

The numbers may vary by institution and segment, but the pattern is consistent: onboarding delay is a direct revenue drag. And the financial impact is only part of the story, because compliance exposure increases alongside it. Unlike churn, which is visible and tracked, delayed activation often goes unmeasured, absorbed into the operational budget rather than surfaced as a financial risk.

There’s also an abandonment problem that rarely makes it onto executive dashboards. When a consumer or business customer encounters significant friction during onboarding, they don’t always raise a complaint. They disengage. And when they disengage mid-process in a digital channel, they often don’t return. Capgemini’s World Retail Banking Report 2025 found that 47% of prospective customers abandon card and account applications midway through the onboarding process — rising to 51% in the United States — and that only 3% of banks consider their own onboarding experience to be seamless. 

So, you’re spending acquisition costs to reach customers you never actually convert.

Where processes fail: Bottlenecks and manual data entry

The reason onboarding is structurally different from other customer-facing workflows is that it forces coordination across systems and functions that don’t typically operate together in real time.

KYC verification, Anti-Money Laundering (AML) screening, credit assessment, account provisioning, document management, regulatory reporting and notification services all have to execute in sequence, often across a hybrid mix of on-premises systems, SaaS platforms and third-party data providers. Each handoff is a potential failure point. And each dependency is a place where a timing issue, data mismatch or system timeout can stall the entire chain.

Fenergo’s research puts a number on the cost of that complexity: annual KYC review costs can reach up to $175 million for a single commercial bank, with 86% of banks citing poor data management and siloed processes as the primary driver of onboarding inefficiency. Meanwhile, Capgemini found that 75% of banks report consistent delays in verifying customer identity and that 61% feel overwhelmed by application volume, specifically because of a lack of automation.

In most institutions, the workflow logic coordinating these steps was designed for a world of longer processing windows and more predictable cycles. That logic still works — until it doesn’t. When it breaks, the consequences don’t stay contained. 

• A compliance step that fails silently can create a regulatory exposure
• A provisioning delay that cascades can affect multiple customers at once
• An audit request that requires reconstructing a workflow trail becomes an investigation instead of a routine review

This is the structural problem that customer relationship and experience improvements don’t address. You can redesign the front-end journey and still have an execution layer underneath it that’s fragile, opaque and poorly instrumented.

The blind spot in onboarding risk

Think about your organization and ask yourself: if a critical onboarding workflow failed right now, could you trace it end-to-end immediately without assembling a team to reconstruct what happened across four systems and a spreadsheet?

For most institutions, the honest answer is no. And that gap matters increasingly to regulators who expect demonstrable control over KYC and AML processes, not just evidence that those processes exist.

Operational resilience requirements are also expanding. Regulators are asking more than just whether institutions can recover from disruptions. They’re asking whether institutions can demonstrate, in real time, that their compliance workflows are executing as designed. Onboarding sits directly in the crosshairs.

Yet onboarding orchestration rarely receives the same executive visibility as payments infrastructure or trading systems. It doesn’t get reviewed at the board level, nor does it appear in technology risk registers with the same prominence. It’s treated as an operational concern delegated well below the executive committee, even though the consequences of failure are board-level in nature.

Legacy systems, silos and technical debt

The existing systems coordinating onboarding workflows in most large institutions were built when the process was slower, more linear and more forgiving of delays. 

That tolerance is gone. Customer expectations have shifted to decisions in hours, not days. Regulators expect documented control. And the hybrid cloud environments most institutions now operate in — where a KYC check runs in one cloud, document verification in another and account provisioning still on-premises — introduce dependencies that legacy workload scheduling tools weren’t designed to manage. This is why onboarding has become a modernization problem, not just an operational one.

The data reflects how far behind most institutions actually are: Fenergo found that only 4% of banks had fully automated their KYC workflows as of 2024. The remaining 96% are absorbing that coordination cost manually, case by case and workaround by workaround. 

Legacy systems can be adapted, patched and extended. Teams do it every day. But the technical debt accumulates, and every adaptation makes the next one harder. Each new integration becomes a custom workaround. Each compliance requirement becomes a manual checkpoint. The operational overhead grows while the underlying fragility stays invisible.

A modern, scalable onboarding solution

The path forward isn’t a front-to-back replacement of onboarding infrastructure. It’s establishing an orchestration layer that can coordinate the existing ecosystem — legacy and modern, on-premises and cloud — while providing the visibility, control and scalability that executive governance requires.

That means:

• Event-driven execution that responds to real signals — a document verified, a KYC check completed, a risk score returned — rather than clock-based scheduling that assumes everything ran on time
• End-to-end workflow visibility so that any step in the onboarding chain can be traced, audited and explained without manual reconstruction
• Dependency-aware orchestration that isolates failures rather than allowing a single stalled step to cascade across an entire batch of onboarding cases
• Hybrid cloud connectivity that works across the full environment without requiring every system to be rearchitected first

This is the capability gap that modern orchestration fills — and it’s where RunMyJobs by Redwood is built to operate. As a cloud-first Service Orchestration and Automation Platform (SOAP), RunMyJobs connects legacy and modern systems across hybrid environments without the technical debt of self-hosted tools, and without forcing changes to the stable workflows that already run reliably.

Elevate the conversation beyond customer experience

The most important shift here is executive framing. Onboarding reliability belongs in the same strategic conversation as payments modernization, operational resilience and regulatory compliance — because the consequences of getting it wrong land in all three places.

When you treat onboarding orchestration as a CX improvement project, you underfund the control layer. It also requires a shift in how onboarding is measured, from speed and completion rates to revenue at risk, exception rates and compliance exposure. 

But if you treat it as an operational risk, you invest in the right place. The revenue protection, regulatory defensibility and customer retention gains follow from there.

The institutions that recognize this distinction early won’t just onboard customers faster. They’ll do it in a way that’s auditable, resilient and built to scale as the workflow complexity around them keeps growing.

Learn what a migration to RunMyJobs looks like and how it could modernize your customer onboarding process.

Stop the tool sprawl: Why manufacturers are consolidating with a unified orchestration platform

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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.

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.

See how other manufacturers are breaking through the automation plateau. Read the full 2026 manufacturing outlook report.

Modernize, don’t maintain: Why legacy scheduling is becoming a business liability 

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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.

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.

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

Article

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.”

Autonomy at scale: 3 requirements for enterprise-ready agentic AI

Article

Last year, agentic AI was a headline. Leaders launched pilots, tested proofs of concept and debated what made it different from the generative AI (genAI) tools already in use. 

This year feels different.

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.

Explore how Redwood approaches agentic orchestration and what it takes to achieve autonomy at scale.