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.
There’s a moment happening right now, unlike any we’ve seen before in enterprise technology. Agentic AI is not just changing interfaces, but it’s actually starting to take on real work.
This is evident in recent advances in SAP’s Joule. With the introduction of Joule Agents, what began as a conversational interface is evolving into something truly capable. A system that can coordinate tasks, reason through decisions and use advanced AI capabilities to initiate action across business functions and processes. That’s a meaningful step forward, but it also surfaces new questions.
You’re no longer architecting systems just for human efficiency. You’re designing for autonomous agents that can drive substantial efficiency gains and be accountable for execution across workflows — reliably and at enterprise scale.
That’s an altogether different kind of pressure for most. As McKinsey notes, agentic AI brings new operational risks that require governance from day one. Once AI begins to act, accountability and auditability are non-negotiable. Otherwise, can you trust what it does next?
Execution: The real differentiator
With Joule, everything begins with intent inside your SAP processes. You might ask, “Are we ready to close?” or “Why did this process fail?” or simply “What needs to happen next?”
Joule can understand that, acting as a context-aware layer that pulls from across your SAP landscape and coordinates agents to determine and act on the next steps. That is new and powerful. But I keep coming back to the same question in conversations with technology leaders: What actually does happen next?
In an enterprise environment, the answer isn’t usually straightforward. That’s because a real process doesn’t live in one system, and it doesn’t follow a straight line. Nor does it complete because one decision was made. It depends on dozens of things happening in the right order. Jobs, dependencies and handoffs must happen perfectly, and underneath it all, business data needs to be accurate and ready. It’s easy to underestimate this complexity.
When agents begin to take action, they don’t just trigger workflows, but they also trigger data movement, relying on pipelines and outputs that may sit outside SAP entirely. If that data is late, incomplete or inconsistent, the process will fail. So, while Joule can coordinate agents and initiate work, the outcome still depends on whether your underlying data and systems are orchestrated end to end.
Execution is where intent meets reality — and where dependencies either hold together or break apart. Unlike AI, execution can’t be approximate. Jobs must run in the right sequence, and systems have to stay in sync. Data must arrive when it’s expected, having already been formatted, cleansed, mapped and approved. If something fails, you need it to recover, reroute or escalate in a controlled way. The necessary level of consistency doesn’t happen by accident.
Joule thinks, RunMyJobs executes across SAP and non-SAP
Joule changes how work starts. It makes it easier to move from question to action. But enterprise value is defined by how work finishes:
Whether the process completes
Whether the data is right
Whether the outcome can be trusted
That’s what RunMyJobs by Redwood delivers. It orchestrates and continuously optimizes end-to-end process execution and automation across SAP and non-SAP systems, coordinating not just workflows and data pipelines but the agent-driven actions within them — including triggering additional agents as part of a process or error remediation. While an agent can begin or resolve part of a process, the business still needs to understand what happened, how it happened and whether it followed the right controls.
So when Joule initiates work, RunMyJobs ensures:
The right jobs run in the right sequence
Data moves when and where it’s needed
Dependencies are resolved across systems
The process completes as expected
Every action is observable and traceable, end to end
Required approvals, reviews and escalations happen at the right points in the process
Critical SLAs are met — or flagged and escalated if at risk
Additional Joule Agents are involved in the process when required
Most enterprise processes don’t stop at SAP. A good portion of the work already lives outside the core ERP in data platforms like Databricks and Snowflake — and often in legacy databases that are still part of the pipeline. External systems feed inputs back into SAP, and integrations connect everything in between. From a user perspective, it’s still one process. From a systems perspective, it’s distributed. Orchestration must, therefore, extend beyond SAP.
Joule can initiate work across that landscape. But for that work to complete, those systems need to operate as part of a single, coordinated flow. That’s what RunMyJobs enables: consistent execution across SAP and non-SAP environments within the broader enterprise ecosystem, with full visibility into how work progresses from start to finish. No fragmentation between AI agents, systems and workflows.
Agentic orchestration in practice
The pattern that’s possible:
A user expresses intent in Joule
Joule evaluates business context and coordinates the required actions
RunMyJobs executes those actions across systems, workflows and data pipelines
The process completes end to end with governance, visibility and control
For the financial close, for example:
A user asks Joule: “Are we ready to close?”
Joule evaluates readiness across SAP and determines that the close can proceed, then initiates the process. From there, RunMyJobs executes the close across systems.
Allocations run
Consolidation jobs are triggered
Reporting workflows are executed
Dependencies are enforced so that each step completes in the correct order
Potential issues are identified across the process chain, with RunMyJobs triggering the necessary resolution steps before they impact the close
If an issue arises, it’s handled within the process, not discovered after the fact.
The control plane for agentic operations
As agentic AI moves from isolated use cases into core business processes, something else becomes clear: you don’t just need execution; you need control. This is what makes enterprise AI viable at scale. When agents are initiating work across systems, the questions change. It’s no longer just knowing whether a process can run. It’s whether you can see, govern and trust it while it runs across your SAP environment and everything connected to it.
Which systems were touched?
What data moved, and where?
Why did a process take a specific path?
What happens if two actions conflict?
These are everyday concerns in enterprise environments.
RunMyJobs acts as the control plane for agentic operations, ensuring that work runs within defined boundaries. Every action is tracked, and every dependency is visible. Policies and approvals are enforced before execution, not after. If something deviates from the expected path, it can be detected and handled before it becomes a business issue.
This is what allows agentic AI to move beyond experimentation, because autonomy without control doesn’t scale. Instead, it creates risk. To make autonomy usable, you must expand from individual agent-driven tasks to fully orchestrated, end-to-end processes — with confidence in the outcome.
Let orchestration build your path to autonomy
Agentic AI, including agentic Joule, changes how work starts. Do you have a plan for what happens after?
This is where most organizations are hesitating right now. Initiating work through AI is one thing, but relying on it to run across SAP, connected systems, and every data dependency without introducing risk is something few have fully envisioned, much less put into practice.
Autonomy takes shape over time, as each action runs as it’s supposed to and each process becomes predictable and governed. Move your enterprise forward now, with the leading orchestration platform for the enterprise, turning AI-driven intent into reliable business value.
Payment volumes are rising across every rail, channel and operating environment. Real-time payments now coexist with traditional batch settlement, and most digital transactions pass through multiple interconnected systems before they’re complete.
A single eCommerce checkout can trigger authentication, AI-driven fraud detection and validation in milliseconds. Cross-border and global payments introduce additional pricing logic, regulatory compliance requirements and richer transaction data standards. Cloud-based payment providers and APIs now connect directly to on-premises systems of record, widening the operational surface area of payment processing across financial services.
This growth reflects real advancement in digital payments, but operationally, it introduces strain.
Many financial institutions still rely on layered automation, custom scripts and manual exception handling that were meant to operate in a simpler ecosystem. As transaction data grows and payment methods multiply, those legacy workflows don’t scale cleanly. What once worked predictably becomes fragile under volume and variability.
Thus, payments modernization is now largely about controlling execution across increasingly complex hybrid environments and maintaining operational resilience as real-time and batch workloads expand. Artificial intelligence delivers value when it strengthens that execution layer. It shouldn’t just power fraud analytics, but it also needs to support how payments are built, monitored and governed end to end.
How AI strengthens payment operations at scale
Most discussions about AI in payments center on fraud detection, machine learning algorithms and predictive analytics. Those use cases are important, as AI-driven fraud prevention has significantly improved real-time risk scoring and reduced false positives across digital payments. But if you look at your broader payment environment, fraud is only one part of operational risk.
The real strain often sits in the workflow itself — in how payment systems are configured, updated, monitored and recovered when something fails. APIs connect cloud-native services to legacy infrastructure, while new payment providers plug in through separate interfaces and integrations. Each new rail, API or partner adds another dependency across your digital payments ecosystem, creating greater risk and making it harder to scale these additions.
AI systems deliver the most impact when they strengthen how those payments are executed.
Building and deploying payment workflows with less risk
Every new payment method, regulatory update or pricing change introduces operational risk. Without structured control, even small modifications can create downstream instability.
AI-assisted workflow development helps contain that risk. By analyzing existing transaction data, APIs and structured configurations, AI models can validate dependencies, identify configuration gaps and surface potential conflicts before deployment. AI-based tools powered by generative AI and large language models assist with documentation, onboarding and testing by interpreting system metadata and historical execution logs.
AI doesn’t replace governance. It reduces manual rework, limits human error during change management and supports safer adoption of new payment capabilities across financial institutions looking to modernize operations.
Monitoring and governing payment execution
Traditional monitoring tools focus on infrastructure metrics, such as whether servers are healthy, containers are running and APIs are responsive. Those signals do matter, but they don’t tell you whether your payment processing is actually performing as expected. In modern digital payments, success or failure happens at the workflow level, where authentication, fraud detection, validation and settlement must execute in the right sequence across interconnected payment systems.
If fraud detection slows under peak transaction volumes, downstream settlement can stall. And if authentication thresholds aren’t calibrated correctly, legitimate digital payments may be declined, damaging customer experience and revenue. Infrastructure dashboards alone won’t surface the business impacts of these events because they can’t show how delays in AI-driven decision-making ripple through payment workflows and disrupt real-time processing.
AI-driven monitoring connects transaction data, workflow timing and service-level agreement (SLA) thresholds into a single operational view. It detects anomalies in payment processing behavior early. That visibility helps you protect payment experiences before customers feel disruption.
Recovering predictably when failures occur
No payment system is immune to disruption. Network latency, API timeouts and unexpected data formats are a normal part of operating at scale. Resilience depends on how quickly and predictably recovery is handled.
AI improves recovery by analyzing historical payment failures, transaction patterns and workflow logs to identify repeat breakdowns. You can train it toapply standardized retry logic, dynamic routing adjustments or structured escalation paths based on transaction value and fraud risk. In much the same way, machine learning models separate temporary API latency from systemic issues that need immediate intervention, helping stabilize payment processing without adding manual oversight.
Orchestration as the execution layer for AI-driven payments modernization
Payment workflows don’t typically run in a single environment. A transaction may begin in a cloud-based checkout interface, call fraud detection services in a separate analytics platform, post to a core banking system on-premises and settle later through batch processing. Reporting and reconciliation might execute in yet another system. In most enterprise financial services environments, the architecture is hybrid by necessity.
Orchestration brings structure to this complexity by defining how execution actually moves across systems. It enforces dependencies and ensures that validation, authentication and settlement steps occur in the correct sequence, whether they run in public cloud, private cloud or on-premises systems.
AI strengthens that orchestration layer by accelerating workflow onboarding and clarifying dependencies across payment systems. It continuously analyzes execution patterns to surface unusual behavior in real-time and batch processing. At the same time, it supports governed execution by ensuring AI-driven decisions around routing, authentication and fraud detection are logged, traceable and compliant.
Predictive SLA management for modern payment systems
In many payment systems, SLA monitoring remains reactive. You often don’t see a problem until a reconciliation batch misses its window or an API connection to a payment provider starts timing out. By the time alerts escalate, your payment processing performance has already slipped, and the negative impact on customer experience is underway.
AI-powered SLA monitoring changes that dynamic. AI technologies analyze historical execution patterns, transaction volumes and retry behavior to identify early warning signals. A steady rise in processing latency or an unusual spike in authentication challenges can indicate emerging instability long before SLAs are breached. That gives you time to adjust routing rules, scale resources or rebalance workloads before customers feel disruption.
Scaling payments without increasing operational burden
Seasonal peaks, digital expansion, new fintech partnerships and global payments initiatives introduce variability. If your operational model depends heavily on manual reconciliation, isolated automation tools or ad hoc scripts, complexity increases alongside transaction volume. Each new integration introduces another coordination point, and each new payment method adds more exception paths.
AI makes automation more adaptive and context-aware. Embedded into orchestration, AI models continuously refine routing algorithms across payment providers, calibrate authentication thresholds based on real-time fraud risk and identify inefficiencies in your payment workflows. They support faster, more informed decision-making across both real-time and batch processing environments. The outcome is true control, which translates to sustainable scaling.
As transaction volumes and complexity increase, you don’t have to expand headcount at the same pace. Structured automation absorbs growth by coordinating payment workflows across systems and payment providers without adding manual oversight. Instead of chasing alerts across disconnected tools, you get unified visibility into execution across real-time and batch payment processing. It’s then possible to move beyond constant firefighting and focus on optimizing the customer experience and improving overall performance in your digital payments ecosystem.
Why governed automation matters in financial services
Every transaction touches customer data, financial records and compliance obligations. AI-assisted decision-making must be transparent, auditable and explainable.
If an algorithm declines a transaction, you need to understand why. If an AI model adjusts routing across payment providers, that change has to be traceable. Data usage should align with privacy frameworks such as GDPR and other regional mandates.
Orchestration establishes the guardrails that responsible AI requires by centralizing workflow definitions and enforcing standardized validation and authentication rules across payment systems. Every execution step is logged, creating consistent audit trails that support regulatory compliance and transparent decision-making. For enterprise payment systems, that level of control is foundational to stability, compliance and long-term modernization success.
Embed AI into the foundation of payments modernization
AI already shapes fraud detection, authentication, routing and customer interactions, but its long-term value depends on how well it integrates into your operational foundation. Payments modernization today is about controlling execution across real-time and batch processing, hybrid environments and global payment networks and ensuring that AI-driven insights translate into governed, reliable action inside payment workflows.
When AI is built into your orchestration solution, fraud prevention becomes more precise, SLA management becomes predictive and customer experience becomes more consistent.
Automation has earned its place in manufacturing. The results are real, and most operations leaders don’t question that anymore.
In Redwood Software’s latest manufacturing research, nearly 60% of manufacturers report reducing unplanned downtime by at least 26% thanks to automation, with many seeing even larger gains. Production uptime, throughput and quality metrics are trending in the right direction.
Yet, many of those same organizations struggle to move the needle on outcomes that matter just as much, like inventory performance, planning reliability and data accuracy. Automation is successful in some areas and stubbornly incomplete in others.
That contrast tells a very specific story about how automation is being applied today and why some manufacturers are running into limits.
Why some KPIs respond quickly to automation
Uptime, throughput and quality improvements tend to come from automating contained workflows. When a process lives primarily inside one system, whether it’s an MES routine, a machine-monitoring loop or a quality check, the impact is immediate and measurable.
These automations reduce variability and limit human error. They’re relatively easy to design, test and scale because the inputs and outputs are well understood. For many manufacturers, this first wave of automation delivers exactly the ROI promised.
That’s why confidence in automation remains high: because the tools work and the benefits show up quickly.
Industry outlooks for 2026 reflect a broader shift: manufacturers are moving from experimentation with individual automation technologies toward connecting digital tools and systems into cohesive operations that support agility, resilience and value across the enterprise.
The outcomes that lag behind
Inventory performance tells the rest of the story. Even as uptime improves, inventory turns remain difficult to improve at scale, highlighting the limits of siloed execution.
Unlike uptime, inventory performance doesn’t belong to any one system. It depends on coordination across forecasting, production planning, warehouse operations and supplier execution. The same is true for data accuracy and planning reliability. These outcomes live in the spaces between systems.
When data moves slowly or manually between ERP, MES and supply chain platforms, the best automation in the world can’t compensate. By the same token, a production line may be running efficiently, but if demand signals arrive late or exceptions don’t propagate across systems, inventory decisions can drift out of alignment. It makes sense that this is where frustration sets in.
Automation delivers clear wins, but only where the workflow is contained. The KPIs that require cross-system coordination respond much more slowly if you don’t have reliable orchestration in place.
The real constraint
The data reinforces this pattern. 78% of manufacturers have automated less than half of their critical data transfers. Many still rely on email, file drops or scheduled scripts to move information between systems. Nearly 30% depend on time-based scripts rather than event-driven workflows that respond to real-world conditions.
As automation expands without orchestration, complexity increases. Each new automated system introduces another boundary. Each boundary creates another place where manual intervention becomes necessary. Over time, teams spend more effort reconciling data and managing exceptions than benefiting from the automation itself.
The result is uneven KPI performance: strong gains in localized metrics, limited improvement in outcomes that depend on end-to-end flow.
Exception handling amplifies the problem
Exception handling makes this especially visible. Only 40% of manufacturers have automated exception handling, even though 22% cite it as a top operational disruption.
Exceptions don’t occur neatly within system boundaries. A supplier delay, quality hold or production disruption immediately affects schedules, inventory positions, customer commitments and financial forecasts. When that response isn’t automated end to end, each system updates independently — if it updates at all. One manual exception can cascade across multiple KPIs, undoing the gains automation delivered elsewhere.
Manufacturers that don’t address the siloed automation problem will continue to see a skewed KPI picture.
Moving toward balanced outcomes
Manufacturers that surpass mid-stage maturity show a consistent pattern. They focus less on adding automation and more on orchestrating what already exists. As a result, they see improvement across both operational and cross-functional KPIs.
This isn’t about perfection. It’s about balance.
Automation alone stabilizes operations. Orchestration coordinates execution to deliver true stability. When systems work together, gains compound instead of flattening.
If your automation results feel strong in some areas, stubborn in others, the issue likely isn’t effort or investment but a lack of orchestration. To see how your peers at different maturity levels perform across KPIs and what differentiates those moving beyond the plateau, download the full “Manufacturing AI and automation outlook 2026.”
Enterprise business intelligence (BI) has always promised clarity with dashboards, KPIs and data visualization that help leaders make confident decisions. But clarity on screen doesn’t automatically translate into operational strength.
Enterprise BI isn’t a niche capability. The global business intelligence and analytics market is already valued in the tens of billions of dollars and projected to grow significantly through the decade as organizations invest in real-time insight, advanced analytics and scalable visualization platforms. The “2026 CIO and Technology Executive Survey” from Gartner reinforces that analytics and digital initiatives remain central to technology agendas, even amid economic volatility.
In SAP environments, enterprise BI now spans SAP BusinessObjects on-premises landscapes, SAP Analytics Cloud in the cloud and increasingly complex hybrid architectures, all of which sit within SAP’s broader enterprise data management strategy. Forecasting models draw directly from ERP activity, supply chain dashboards rely on overnight integrations to stay accurate and financial reports must meet strict governance and compliance standards.
The more sophisticated your analytics become, the more critical the underlying orchestration becomes.
The SAP enterprise BI landscape today
SAP’s analytics portfolio has evolved over nearly two decades, from on-premises SAP BusinessObjects environments to cloud-based analytics and integrated data services. In late 2024, SAP introduced the SAP BusinessObjects BI 2025 release with an updated release timeline and maintenance strategy, shifting to a two-year minor release cadence and extending mainstream maintenance for SAP BusinessObjects BI 4.3 through the end of 2026 to support hybrid BI modernization plans.
SAP continues to deliver new versions and long-term support for SAP BusinessObjects and related products, and many enterprises plan to run them well into the next decade.
Today, most SAP-centric enterprises operate across several layers of that evolution. SAP BusinessObjects often remains the system of record for regulated reporting, while SAP Data Services feeds SAP BW or SAP Datasphere environments with transformed data. SAP BW process chains handle scheduled aggregations overnight, and SAP Analytics Cloud, now positioned as a core analytics component within SAP Business Data Cloud (BDC), consumes that data for dashboards, planning models and predictive scenarios.
These systems don’t operate independently. A typical analytics chain resembles something like this:
An SAP S/4HANA job posts financial entries → SAP Data Services executes transformation jobs → SAP BW process chains or SAP Datasphere aggregate data → SAP Analytics Cloud refreshes models → Executive dashboards update before 8 AM.
If any step fails, the impact extends beyond IT to finance, operations and executive reporting.
In hybrid environments, especially those moving to RISE with SAP, these workflows often span on-premises systems, SAP Business Technology Platform (BTP) services and cloud analytics. Without centralized orchestration across all of them, organizations rely on disconnected schedulers, manual triggers or custom scripts tied to individual components. That’s how complexity accumulates.
Bringing control to SAP analytics processes
RunMyJobs by Redwood addresses this challenge at the orchestration layer by coordinating how they execute together.
RunMyJobs is the only workload automation solution that is both an SAP Endorsed App and included in the RISE with SAP reference architecture. It connects to SAP systems through supported APIs and secure gateway connectivity and avoids invasive agents or direct ERP modifications. Plus, it provides out-of-the-box connectors for SAP BusinessObjects BI, SAP Data Services (formerly known as SAP BusinessObjects Data Services), SAP Integration Suite – SAP Cloud Integration for Data Services, SAP Analytics Cloud and more, allowing you to orchestrate reporting, transformation and dashboard refresh workflows without custom code.
In practical terms, that means you can:
Orchestrate SAP BusinessObjects report executions as part of financial close workflows
Trigger SAP Data Services and SAP Cloud Integration for Data Services jobs based on ERP events instead of fixed-time scheduling
Coordinate SAP BW process chains and SAP Datasphere with downstream SAP Analytics Cloud model refreshes
Monitor end-to-end dependencies across ERP, data transformation and BI layers
Instead of scheduling each BI platform independently, you establish a single control plane that understands upstream and downstream dependencies. For example, rather than refreshing a dashboard at 6 AM regardless of data readiness, you can configure SAP Analytics Cloud data actions to trigger only after data transformations and aggregations complete successfully. If a job fails, alerts and remediation steps execute automatically — before business users log in.
Because RunMyJobs is delivered as a SaaS platform with centralized monitoring and AI-assisted troubleshooting, you gain visibility across the entire analytics chain rather than just within a single BI tool.
Strengthening analytics without increasing complexity
Instead of isolated projects, enterprise BI initiatives are typically tied to broader transformation goals like improving operational efficiency, reducing risk and enabling growth. Redwood Software’s framework highlights those same value drivers for SAP customers pursuing modernization and cost control.
Practically speaking, reporting cycles stay on track, data flows cleanly between ERP and analytics platforms and the need for redundant schedulers or scripts falls away. That stability allows analytics initiatives to grow without expanding infrastructure or teams. Most importantly, business users don’t have to question whether the numbers on their dashboards reflect completed, validated workflows. They can focus on insights instead of exceptions.
Reliable insights create real advantage
SAP Analytics Cloud continues to expand its role in predictive analytics, embedded analytics and advanced data visualization. And SAP BusinessObjects remains a stable foundation for many complex or regulated reporting environments. Together, they form a powerful enterprise BI ecosystem, one that delivers its full value when execution across systems is fully orchestrated.
If you’re expanding cloud-based BI solutions, consolidating traditional BI tools or embedding analytics more deeply into ERP-driven workflows, orchestration should be part of the design from the start.
Enterprise business intelligence should enable better decision-making at scale without introducing new bottlenecks behind the scenes. With the right automation foundation in place, your SAP analytics landscape can deliver insights that aren’t just compelling but dependable.
Real-time and faster payment rails are accelerating timelines across the financial system. Settlement windows that once stretched across business days now close in seconds. That shift changes how institutions manage liquidity, sequencing and risk.
The expansion of the Real-Time Payments (RTP) network and the FedNow Service is part of that shift. Same-day Automated Clearing House (ACH) reduces traditional batch buffers. Cross-border payments still rely on Society for Worldwide Interbank Financial Telecommunication (SWIFT) messaging, even as digital and peer-to-peer methods accelerate.
These factors, combined with rising customer expectations and regulators pushing richer messaging standards such as ISO 20022 and stronger control frameworks, are forcing financial institutions to rethink how they move money across different payment rails.
Adding a new payment rail appears straightforward. The assumption is that you connect to the network, configure routing logic, update APIs and move into production. But each of those steps affects downstream systems, operational controls and compliance workflows that aren’t always visible at the outset.
Most financial institutions already operate complex, business-critical payment environments. Core posting, ACH, card and wire processing run across hybrid infrastructure that ties together on-premises systems, cloud platforms and external providers. Liquidity, fraud, reconciliation and reporting processes rely on that stability. So when a new rail enters the building, the entire payment environment absorbs the impact. Existing payment services must continue operating reliably while additional capabilities are layered in. Maintaining that balance is the central challenge facing CIOs.
Modernization efforts, therefore, need to protect operational continuity while enabling incremental payment capabilities expansion across the enterprise.
What payment rails mean today
Payment rails are the networks and infrastructures that enable the movement of funds between a payer and a payee. At a basic level, they work by transmitting payment instructions, validating transaction details and coordinating settlement between financial institutions.
Common examples include:
Networks governed by Nacha for ACH transfers between bank accounts
Card networks such as Visa, Mastercard and American Express that connect merchants, payment processors and the issuing bank to authorize credit card and debit card transactions
Wire transfers routed through SWIFT and correspondent banking intermediaries
Real-time payment systems such as the RTP network, operated by The Clearing House, and the FedNow Service from the Federal Reserve
Single Euro Payments Area (SEPA) credit transfer schemes for European Union payments
Blockchain-based rails supporting cryptocurrencies such as Bitcoin
Each rail operates under a different model. Some settle in batches at the end of business days, while others support instant payment with immediate bank transfers. Cross-border payments may depend on intermediaries and layered messaging standards, whereas domestic rails operate within tightly governed payment networks.
In practice, financial institutions operate multiple payment rails at once: ACH handles high-volume processing, card networks drive everyday consumer transactions and wire transfers move high-value and international payments. Then, real-time payments introduce immediate settlement, while same-day ACH shortens traditional batch cycles.
Digital channels further complicate the picture. Electronic payment flows initiated through APIs, mobile apps, peer-to-peer platforms or embedded payment systems must be routed intelligently based on value, timing and liquidity constraints. As payment options expand, decision logic becomes more dynamic and interdependent.
Adding a new rail increases routing paths, liquidity scenarios and control points inside your payment system. What begins as a connectivity effort often expands into a broader orchestration initiative. Customer expectations and regulatory pressure will continue accelerating adoption. Businesses want faster payouts. Consumers expect immediate visibility into their bank accounts. The gig economy depends on real-time disbursements. Regulators require traceability and standardized messaging across payment networks.
Managing individual rails effectively is only part of the equation. Ensuring they function cohesively within an established payment ecosystem introduces additional complexity.
Where payment rail expansion creates risk
Financial institutions don’t tend to pursue sweeping system overhauls in payments. Change is typically incremental and carefully governed. Even so, incremental expansion can introduce structural risk if orchestration isn’t deliberately addressed.
That risk surfaces because payment environments reflect accumulated decisions. A new rail is added to support a business requirement. An API is introduced to enable a digital channel. Regulatory changes insert additional validation logic. Routing rules are adjusted for a specific payment method and remain in place long after the immediate need passes. The ultimate result is density — layers of integrations and operational dependencies that work, yet weren’t designed as a single, coordinated system.
When real-time and instant payment capabilities enter a dense environment, your payment infrastructure must operate at a different tempo. Instant settlement compresses decision windows that batch cycles once absorbed. Liquidity management shifts from periodic positioning to continuous oversight. Payment instructions and transaction details must move across payment platforms immediately to support confirmation, compliance, cash flow visibility and audit requirements. The infrastructure may remain familiar, but the margin for inconsistency narrows significantly.
Adding new payment rails can increase operational overhead if you’re not careful. Teams might spend more time reconciling transaction data, investigating routing anomalies and managing cross-system dependencies. In that case, complexity will grow faster than capability.
Indicators your payment rail expansion may introduce strain
Signal
What it suggests
Routing logic embedded in undocumented scripts
High dependency risk and limited scalability
Inconsistent error handling across ACH, card and real-time payments
Operational fragmentation across rails
Liquidity visibility limited to individual payment networks
Reduced control in real-time settlement environments
No end-to-end payment status traceability
Delayed issue detection and higher customer impact risk
Core systems must be modified to add a new rail
Tight coupling and architectural rigidity
Coordinating multiple payment rails without disruption
New payment rails will continue to emerge as faster payments initiatives expand globally, and fintech innovation introduces new APIs, account-to-account models and digital payment technologies. Rather than treating each new rail as a standalone integration project, financial institutions are looking to strengthen the orchestration layer that governs how payment workflows execute across payment platforms, payment processors and hybrid infrastructure.
Preserve the core while evolving the edge
In most environments, legacy batch systems continue to anchor settlement, reconciliation and reporting. They’re deeply embedded and operationally proven. Replacing or frequently modifying them can introduce unnecessary operational risk.
At the same time, real-time payments, API-driven digital channels and instant disbursement use cases introduce new execution demands like tighter sequencing, richer messaging standards and continuous liquidity awareness.
Modernization works best when those new demands are absorbed at the edge of your architecture, while the core systems of record remain stable.
Centralize orchestration at the workflow layer
Once you accept that the core should remain stable, the question becomes how to introduce change safely.
Embedding routing changes directly inside core systems increases coupling and limits flexibility. Instead, orchestration can be centralized at the workflow level. This allows institutions to introduce real-time payments or new cross-border capabilities within defined segments of the payment lifecycle without destabilizing broader operations. High-impact workflows can be modernized first, while lower-risk or stable processes remain unchanged to preserve operational continuity.
Expand visibility as rails expand
As payment flows span both batch and real-time models, monitoring individual systems in isolation becomes less useful. End-to-end workflow visibility provides a clearer view of how transactions move across payment rails, how liquidity shifts between networks and where operational friction arises.
Visibility enables confident expansion by reducing blind spots across the payment ecosystem.
Design for coexistence
Real-time payments, ACH transactions, card networks and global payment rails will continue operating side by side. Rather than attempting to consolidate them prematurely, it’s important to focus on making their interaction predictable and governed.
Strengthening orchestration at the workflow layer creates a controlled environment for ongoing rail expansion. Legacy infrastructure continues supporting core financial transactions, and new payment capabilities are introduced in targeted, manageable increments.
A roadmap for controlled payments evolution
Payment rail expansion requires deliberate planning and disciplined execution.
Begin with assessment:
How many payment rails are currently supported, and where is routing logic defined and maintained?
Is error handling consistent across ACH transactions, RTP payments and card transactions?
Can a new payment network be introduced without modifying multiple core systems?
The answers clarify whether your architecture supports disciplined growth or compounds complexity.
Early modernization phases can focus on centralizing workflow orchestration and improving visibility across existing payment systems. Once orchestration is standardized, institutions can introduce additional real-time payment capabilities, cross-border options or new digital payment methods with lower disruption risk. Governance and compliance controls can then be embedded directly within payment workflows rather than layered on afterward.
To align your roadmap with broader enterprise transformation objectives, consider that payments intersect with digital channels, liquidity management, customer onboarding and regulatory reporting. Long-term resilience depends on how well those intersections are managed.
Planning the next phase of your payment rails strategy? Explore how a structured orchestration approach supports continuous payments modernization across complex environments.
Everything you need to integrate, test, and certify in one guided, streamlined experience. We’re excited to announce the next evolution of the SmartThings Developer Center – a unified, streamlined experience designed to help partners build, test, and complete integrations faster than ever. As SmartThings expands to support more device categories and service integrations, along with […]
I’ve spent most of my career working closely with SAP customers who are running complex, automated landscapes. Over time, one challenge has kept coming up in different forms: operations teams don’t lack data — they lack context.
As automation grows across SAP and non-SAP systems, there’s a risk that operational visibility becomes fragmented. Process and transactional execution data lives in one place, application health in another and incident handling somewhere else entirely. When something goes wrong, teams may spend more time switching tools than actually resolving the issue.
That’s why, as SAP Product Lead, I was personally committed to shaping how RunMyJobs by Redwood integrates with SAP Cloud ALM. The goal wasn’t to add another dashboard, but to make sure SAP operations teams can see what matters, from where they already work.
Transparent observability across SAP and automated workloads
Traditional monitoring happens in individual tools and is good at telling you that something failed. True observability helps you understand why, whether it matters and how and where to access the issue for resolution.
In SAP-centric environments, SAP Cloud ALM is increasingly becoming the control center for operations, especially for RISE with SAP and cloud-focused landscapes. It provides health monitoring, alerting and root-cause analysis across applications and services.
As automation and orchestration become a core part of how SAP business processes run, extending that same level of transparency to automated workloads is a natural evolution. RunMyJobs contributes execution-level insight for background jobs and workflows that support SAP processes, making that information available and actionable directly from a single point of control — within SAP Cloud ALM — and expanding its operational visibility beyond application-level monitoring.
What the SAP Cloud ALM connector for RunMyJobs does
The SAP Cloud ALM connector for RunMyJobs synchronizes automation and orchestration data directly into SAP Cloud ALM Job and Automation Monitoring.
In practical terms, this means:
Job definitions, workflows and execution status from RunMyJobs are pushed into SAP Cloud ALM
Operations teams can monitor SAP and non-SAP background processes in one place
Failures, delays and abnormal statuses are visible without switching tools
It’s easy to drill back from SAP Cloud ALM to RunMyJobs to take action and resolve issues
You get a single operational view inside SAP Cloud ALM, eliminating the need to jump between systems to understand health, performance and where issues need to be resolved.
The impact on day-to-day operations
For SAP operations teams, the integration reduces friction in a few concrete ways:
Faster triage: Job failures and workflow bottlenecks are visible where incidents are already managed.
Less context-switching: No need to check separate tools just to confirm job status.
Clear accountability: Automation health is part of the broader SAP operational picture.
This is especially useful for customers standardizing on SAP Cloud ALM as they move further into cloud operations.
Setting up the integration
The setup is designed to be simple and aligned with how SAP operations teams work.
From the RunMyJobs side, configuration consists of:
Installing the SAP Cloud ALM connector from the RunMyJobs Connector Catalog
Setting up the connection to SAP Cloud ALM with its endpoint and authentication parameters
Scheduling the SAP Cloud ALM synchronization job provided with the connector, with the option to define a custom schedule for synchronization updates (e.g., every five minutes)
Once configured, RunMyJobs automatically synchronizes job definition and job run data to SAP Cloud ALM on an ongoing basis. No manual exports or custom monitoring scripts are required.
SAP Cloud ALM becomes the command center, while RunMyJobs remains the orchestration system.
In the demo below, you’ll see:
How to install the SAP Cloud ALM connector from the RunMyJobs Connector Catalog
How to set up the connection to SAP Cloud ALM
How to schedule the SAP Cloud ALM synchronization job provided with the connector
How RunMyJobs jobs appear in SAP Cloud ALM monitoring views
How operators can access RunMyJobs directly from SAP Cloud ALM with a simple click to initiate deeper analysis and resolution
Bridge the visibility gap
Extending SAP Cloud ALM to include automation workloads acknowledges the evolution of SAP landscapes into hybrid cloud, AI-enabled ecosystems, where automation is foundational and orchestration is key.
This connector is another representation of Redwood Software’s long history as a roadmap-aligned, SAP Endorsed App partner. It enables SAP customers to bring automation execution transparency into SAP Cloud ALM in a way that feels native, operationally consistent and easy to adopt.
Ready to enhance observability even further? Explore more updates released in RunMyJobs 2026.1.
ETSY Store Automation : Achieving High ROI with Boho Dog Art and AI Workflows
The solution utilizes Make.com to connect five key platforms into three distinct automation scenarios:
Leonardo.ai: For high-quality AI image generation.
Google Drive: Serves as the central storage and “command centre”.
Metricool: Manages multi-platform social media auto-posting.
Printify & Etsy: Handles product creation, fulfillment, and sales.
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2. Technical Implementation (Make Scenarios)
Scenario 1: AI Image Generation (Leonardo to Google Drive)
Trigger: A Scheduler runs every 12 hours.
Action: Uses an HTTP Module to call the Leonardo API with a pre-set prompt (e.g., “Cute golden retriever illustration minimal aesthetic”).
Output: Generates two images, waits 20 seconds for processing, and automatically downloads and saves them to a specific Google Drive folder (/AI_CONTENT/Images).
Scenario 2: Social Media Auto-Posting (Google Drive to Metricool)
Trigger:Google Drive “Watch Files” detects new images in the generation folder.
Captioning: An OpenAI module generates a relevant Instagram/Pinterest caption with emojis and hashtags based on the image.
Execution: The image and caption are sent to Metricool, which automatically schedules posts for Instagram, Pinterest, TikTok, and YouTube Shorts.
Scenario 3: Etsy Product Pipeline (Google Drive to Printify)
Trigger:Google Drive “Watch Files” monitors a manual folder (/Product_Designs).
Action: Once the client moves an image here, the system uploads it to Printify.
Execution: The system creates a product (e.g., Poster, Sticker, or T-shirt) and automatically publishes the listing to the connected Etsy store.
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3. Client Daily Workflow
The system is designed to minimize manual labor, requiring only 2 minutes of effort per day:
Open Google Drive to review the latest AI-generated images.
Move the best designs from the automated /Images folder to the /Product_Designs folder.
The automation takes over, immediately creating and publishing the Etsy product.
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4. Return on Investment (ROI) Analysis
Metric
Value
Initial Investment
$250 (Project setup cost)
Daily Time Commitment
2 Minutes
Human Labor Savings
Replaces hours of manual prompting, image downloading, caption writing, social scheduling, and product listing creation.
Content Output
Continuous social media presence across 4+ platforms (IG, Pinterest, TikTok, YT Shorts).
Revenue Potential
Automated “Print-on-Demand” pipeline to Etsy, allowing for rapid scaling of product catalogs without inventory risk.
Summary of Value: For a one-time setup fee of $250, the client receives a fully automated business engine that generates assets, markets them across social media, and lists them for sale, requiring only a few moments of daily oversight.
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I can also create a tailored report or a slide deck if you would like a more formal document to present this solution. Would you like me to do that?
Please create a tailored report for the solution
I would like you to create a formal slide deck
How can we scale this for more Etsy product types?
ROI ETSY Store Automation write complete case studies
Case Study 1: The $250 MVP Automation Pipeline
Objective: To establish a functional Etsy store and content engine with a one-time setup budget of $250 and near-zero daily maintenance.
The Solution: The system uses Make.com to link Leonardo.ai, Google Drive, Metricool, and Printify into a cohesive pipeline.
Automated Art Generation: A scheduler triggers Leonardo.ai every 12 hours to generate two high-quality images (e.g., “Boho golden retriever illustration”) based on a pre-set variable. These are automatically saved to a brand folder in Google Drive.
Social Media Synergy: Once a new file is detected in Drive, an OpenAI module generates a caption with relevant hashtags/emojis and sends it to Metricool. This ensures a continuous presence on Instagram, Pinterest, TikTok, and YouTube Shorts without manual posting.
Simplified Product Creation: The business owner performs a single manual task: moving the best AI-generated designs into a /Product_Designs folder. This movement triggers the Etsy Product Pipeline, which uploads the image to Printify, creates a product (e.g., a sticker or poster), and publishes the listing to Etsy with AI-generated SEO tags.
Return on Investment (ROI):
Time Savings: The owner’s daily effort is reduced to just 2 minutes, spent reviewing and moving files.
Operational Efficiency: The system replaces the need for a graphic designer, social media manager, and e-commerce assistant.
Scalability: For a fixed $250 investment, the store can scale its catalog indefinitely as the automation generates and lists new products daily.
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Case Study 2: Data-Driven Growth and Customer Lifecycle Automation
Objective: To move beyond simple posting by using automated engagement and analytics to create a self-optimizing growth loop.
The Solution: This advanced implementation focuses on the External User Journey and performance data.
Automated Engagement: The system performs hashtag searches (e.g., #dogmom) and automatically likes or follows relevant users to drive traffic back to the brand profile.
Performance Detection: The automation monitors social metrics like Pinterest saves and Instagram likes. If a specific design shows high engagement, the system automatically marks it for product creation on Etsy.
Continuous Optimization: Every night, an AI analysis module evaluates which styles (e.g., “Boho dogs vs. cartoon dogs”) convert best. It then automatically updates future prompts to focus on the highest-performing aesthetics, such as “warm neutral minimalist wall art”.
The Growth Loop: A user discovers a post on social media, visits the profile, clicks the Etsy link, and makes a purchase. This purchase triggers a fulfillment flow (Etsy → Printify → Customer), and the resulting user-generated content is reposted to drive further organic traffic.
Return on Investment (ROI):
Conversion Optimization: AI-driven prompt updates ensure the store always produces content that trends, increasing the conversion rate of social traffic to sales.
Maximized Visibility: Automated engagement keeps the brand top-of-mind for potential customers on multiple platforms.
Minimal Oversight: Despite the complexity of the data analysis, the business owner only spends a total of 5 minutes per day checking dashboards and approving new listings
Boho Golden Retriever Poster – Dog Lover Gift – Minimalist Pet Wall Art
7. SOCIAL ENGAGEMENT AUTOMATION
Another scenario runs to grow accounts.
Step 1: Hashtag Search
Example:
#dogmom #veganrecipes #spiritualawakening
Step 2: Automated Engagement
System performs:
like posts
save posts
follow users
comment occasionally
Example comment:
This is beautiful! 🐾
Limits ensure accounts stay safe.
8. ANALYTICS USER JOURNEY
Every night the system evaluates performance.
Metrics collected:
Likes Comments Shares Clicks Sales
Step 2: AI Analysis
AI determines:
what styles perform best
what colors convert
what topics trend
Example output:
Insight: Boho dog art performs 3x better than cartoon dogs
Step 3: Prompt Optimization
Future prompts change automatically.
Example:
Old prompt
Cute dog illustration
New prompt
Boho golden retriever illustration warm neutral aesthetic minimalist wall art style
This creates continuous growth.
ETSY Store Automation
9. YOUR DAILY USER EXPERIENCE (BUSINESS OWNER)
Because everything is automated, your daily tasks are minimal.
Morning (2 minutes)
Open:
Make dashboard
Check:
scenarios running
errors
Midday (2 minutes)
Check:
Metricool analytics
Look for viral posts.
Evening (1 minute)
Check:
New Etsy listings
Approve or disable if needed.
Total daily effort:
5 minutes
10. CUSTOMER LIFECYCLE JOURNEY
Full lifecycle:
Social Media Post ↓ User discovers content ↓ User follows brand ↓ User sees Etsy product ↓ User buys product ↓ Customer receives product ↓ Customer shares photo ↓ User generated content reposted ↓ More traffic
