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.