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.
