Your company is spending more on automation than ever, yet you’re barely seeing a return. It’s a frustrating paradox revealed in the new “Enterprise automation index 2025” from Redwood Software.
While 73% of companies increased their automation spend last year, less than 30% are fully utilizing their tools. The data is clear: the issue isn’t a lack of investment or technology — it’s a stubborn execution gap.
In a climate where every budget line is under the microscope, automation is still getting the green light. That’s because the business case is solid.
37% of organizations report that automation reduced costs by over 25%
43% have cut manual workloads by at least a quarter
49% say it increased efficiency by the same amount
Those are meaningful results, but they’re not the norm. The data also reveals a widespread failure to scale.
73% of companies increased automation spend last year, but only 28% fully utilize their tools. Less than 6% have achieved autonomous automation for any core business process.Source: “Enterprise automation index 2025”
From where I sit, working alongside enterprise teams on automation migration and orchestration every day, I can tell you this isn’t a technology issue. It’s a stubborn execution gap.
The 4 traps of underperforming automation
Too many organizations treat automation like an arms race, adding new tools to plug gaps and hoping for the best. My team sees the consequences of this approach daily, typically in these four traps:
Ad hoc tool sprawl: Marketing, Finance and IT all buy their own automation tools, creating “shadow automation.” These siloed, ungoverned processes don’t share data, follow security protocols or align with a larger strategy, undermining enterprise-wide visibility.
Stopping at the task level: Teams often automate the simplest, low-hanging fruit and then declare victory, ignoring the cross-functional processes where the real value lies. This technical debt accrues until a critical process, like month-end close or supply chain fulfillment, inevitably breaks, leading to frantic, manual interventions.
Legacy tech dependency: Many enterprises still run their most important processes on outdated schedulers or basic scripts. These tools lack the visibility, error handling and security features required for today’s business. When they fail (and they do), the business impact is immediate and severe, but migrating off them is perceived as too difficult.
No automation strategy: Without a plan to consolidate, migrate and optimize, the collection of tools becomes a digital junkyard. The organization has technically invested in automation, but operationally, nothing has changed. The tools are there, but they’re underutilized, misaligned or completely isolated.
These execution pitfalls are symptoms of a deeper issue, one that consistently derails even well-funded automation projects.
Complexity: The #1 blocker to automation ROI
According to Redwood’s research, the top challenge isn’t budget, talent or tools — it’s complexity. Nearly 20% of professionals point to complex workflows as their number-one barrier to scaling automation.
That echoes what I see in the field. Enterprises are sitting on decades of custom scripts, legacy architecture, fragile integrations and undocumented processes. And every time someone says “We’ll automate that later,” the mess grows.
When you delay migration or fail to redesign around orchestration, you lose the ability to scale. You automate the easy stuff and stall out at the first sign of friction. If you want automation to deliver, you need to:
Standardize before you automate. Don’t just pave the path. A chaotic manual process will only become a faster chaotic automated process. Take the time to map, simplify and standardize workflows first. This initial investment pays dividends in scalability and resilience.
Migrate strategically. A simple “lift-and-shift” of old jobs to a new platform just moves the problem. Strategic migration involves analyzing, consolidating and redesigning workflows to take full advantage of a modern orchestration platform’s capabilities.
Orchestrate across systems. True value is unlocked when you manage processes end to end, from the mainframe to the cloud and across all applications. This breaks down the silos between IT operations, data pipelines and business applications, which the report identifies as a key challenge for industries like finance.
Align to business outcomes. The goal isn’t just to run jobs successfully; it’s to reduce costs, accelerate innovation and improve data visibility — the top three business priorities cited in the research. Frame every automation initiative around these goals.
The path to mature automation: A call to action
If your automation investment isn’t delivering, it’s a critical warning sign. Don’t fall into the trap of simply adding more tools. The path forward requires a shift in mindset: focus on orchestration, elevate automation to a C-suite priority and build a cohesive strategy. It’s the only way to transform it from a tactical fix to a genuine growth lever for your entire organization.
Record-to-report (R2R) remains one of the most critical, yet under-automated, areas of finance. And while workloads in finance and accounting are projected to increase by 4.1% this year, staffing levels and operating budgets are shrinking. That creates a dangerous gap — one that many finance leaders assume automation has already closed.
But assumptions can be costly.
If you’ve implemented automation tools, shifted away from paper or added templates and trackers, it’s easy to believe your R2R process is modernized. In reality, many organizations are still relying on fragmented workflows, disconnected systems and outdated practices masked as progress. The result? Unnecessary manual effort, slower closes, limited visibility and rising risk exposure.
Use this article and Redwood Software’s R2R automation maturity assessment to gauge whether your R2R automation strategy is keeping pace. You’ll see what to measure, how to interpret your automation maturity and how to shift from tactical improvements to scalable, strategic transformation. Benchmark both your operational and strategic maturity in a way that reflects the real complexity of the financial close.
R2R automation maturity: Perception vs. reality
91% of finance leaders say R2R automation is essential, but only 58% have automated even one key process. That gap isn’t just operational — it’s perceptual. Too often, spreadsheets, offline uploads and ad hoc workflows are labeled “automated” when they’re really just digitized versions of manual processes.
Many accounting teams rely on email approvals, ungoverned trackers and data pulled from various sources to patch together close checklists. These stopgaps introduce risk and prevent true visibility across general ledger activity, journal entries, intercompany transactions and consolidation efforts.
What emerges is a tangle of disconnected fixes that ultimately stall transformation. You may have automation tools in place, but if you’re still chasing down exceptions, tracking tasks in Excel or manually validating financial data, you’re not yet operating at a mature level.
The teams that get it right report 69.3% fewer hours spent on manual tasks, and not just because of automation but also because of orchestration. Just as importantly, they gain 69.2% better visibility and collaboration and enable faster, more confident financial management decision-making.
The maturity assessment was built to make these blind spots visible and measurable, so finance leaders can identify and address them before they create larger issues across accounting periods.
How to truly measure your R2R automation
Redwood’s R2R automation maturity assessment uses two critical axes:
Operational maturity: This evaluates how deeply you’ve automated core accounting processes, from accounts payable and journal entries to reconciliation and month-end closing. It looks at whether your processes are automated end-to-end or only at the surface level.
Strategic maturity: This area assesses whether you have the culture, governance and controls needed to scale and sustain R2R automation. It includes exception handling, adoption, an orchestration mindset and continuous improvement.
These axes are scored independently, then combined to place your organization into one of five maturity bands: Manual, Siloed, Managed, Controlled or Autonomous.
Reaching the Autonomous stage doesn’t just mean having tools. It means using process automation to run a fully orchestrated close process, with real-time dashboards, SLA tracking, predictive insights and embedded controls. It’s the difference between automating a few journal entries and transforming how you manage financial transactions across every entity and subsidiary.
Where finance teams stall — and what it costs
Many finance functions plateau in the Managed or Controlled stages. They’ve invested in tools but remain overwhelmed by competing priorities, change resistance, limited IT support or skills gaps. Transformation becomes a task to juggle instead of a discipline to own.
You’ll recognize the signs, including:
Manual data collection across sub-ledgers, receivables and intercompany entries
Offline task trackers and fragmented accounting systems
Reactive responses to discrepancies and late-breaking issues
Rework caused by missed validations and inconsistent approvals
These issues create ripple effects, like reporting delays, control breakdowns, missed regulatory requirements and burnout and turnover. And perhaps most damaging: a gradual erosion of confidence in the integrity of your financial information internally and with external stakeholders.
The R2R automation maturity assessment helps finance leaders like yourself map these symptoms to maturity levels, so you can prioritize root causes over surface fixes.
The value of an R2R automation maturity assessment
This isn’t a generic checklist or opinion poll. It’s a structured scoring model that reflects the real-world complexity of the R2R process. Specifically, it helps you:
Benchmark six operational R2R processes
Score five strategic enablers of scalable automation
Evaluate your automation posture using a combined scoring model
Identify maturity-specific key steps to advance transformation
You’ll also evaluate your automation fabric readiness, which is your organization’s ability to support seamless, end-to-end process automation across a diverse and evolving tech stack. It includes ERP and other core systems, orchestration capability, exception resolution, visibility and roadmap alignment. This matters whether you’re navigating a procure-to-pay cycle, an order-to-cash flow or full general ledger consolidation across multiple entities.
The full assessment download includes scoring tables and detailed improvement playbooks, and the result is actionable. It’s not just “Where are we?” but it’s also “What do we do next?”
How R2R automation pays off
The outcomes of mature R2R automation are clear:
Operational payoffs: 69.3% hours saved across account reconciliation, journal entry and accrual workflows
Strategic payoffs: 69.2% gains in cross-functional collaboration, faster management reports, improved financial performance and agility
Resilience by design: Native SAP integration, automated validation rules and exception handling that reduce human error
Informed decisions: Accurate financial data entry delivered faster and with more transparency into consolidation timelines and data lineage
These capabilities empower CFOs to cut days off the close, reduce rework and reassign capacity toward forecasting, strategic planning and scenario modeling.
Finance teams that have reached the Autonomous stage can track key metrics, such as the percentage of manual journals, time spent on reconciliations and the number of post-close adjustments and drive them toward zero. They don’t just close faster; they optimize for consistency, control and insight.
Get your true R2R automation score
If you’re serious about strengthening your organization’s financial health and driving better outcomes, you need to know where your maturity stands, not where you assume it is.
Involve your finance operations leaders, IT and automation stakeholders in the following steps:
Calculate your scores and determine your automation posture
Prioritize your next actions
Every quarter your organization spends stalled in Managed or Controlled maturity leaves efficiency, visibility and credibility on the table. Automation isn’t the destination; it’s the lever that lets you transform your business processes, sharpen your financial reporting and elevate your accounting team’s impact across the enterprise.
To see where your organization’s R2R automation really stands and what it will take to move forward, download the full assessment and schedule a demo to achieve an orchestrated close.
69% of organizations call automation “mission-critical,” but only 10% are actually prioritizing it at the executive level.
That gap isn’t theoretical — it’s operational. And for leaders trying to move the needle on cost, innovation or speed of execution, it’s a red flag.
I’ve spent my career scaling technical and product teams, supporting global platforms and helping businesses modernize their operations. Here’s what I’ve seen consistently: Every business outcome is the result of process mechanics. If you’re not looking at automation through that lens, you’re missing the point.
Spending more ≠ Doing it better
It’s easy to assume more investment equals progress. But the data shows otherwise:
73% of organizations increased automation spending in the past year
These aren’t marginal improvements. They’re operating-model shifts. But they only show up in organizations that treat automation as an integrated operating capability — not a patchwork of IT point solutions.
What do they do differently?
They don’t just ask “What can we automate?”
They ask “What outcome are we optimizing?” and work backward
They measure process volume, yield, throughput and cycle time
They build automation architectures that span systems and teams to focus on value-stream processes and outcomes
They begin with operational objectives, identifying where current processes underperform, why those gaps exist and how automation can significantly improve the outcome.
They treat automation not as a siloed initiative but as an embedded capability that works across Finance, Operations and Product to drive measurable improvements.
Your automation strategy should reflect your operating model — not just your tech stack.
It needs ownership. It needs a business case. And it needs to be framed as an operating capability, not a toolset.
I’ve seen firsthand how teams unlock transformative value when they integrate automation as an operating capability at the strategic level.
Get the full story
If these findings resonate with you, I encourage you to dive deeper. Redwood’s “Enterprise automation index 2025” unpacks:
How teams across industries are investing in automation
Benchmarks for tools utilization and maturity
The most common barriers to adoption (Spoiler: It’s not budget!)
How leaders are preparing for AI-driven automation
What sets top-performing organizations apart
Download the full report to learn how you can move from fragmented tasks to orchestrated outcomes.
A lot of companies have gotten comfortable with the way their job scheduling has always worked. It ran in the background, executed batch jobs and didn’t cause a lot of noise — so why change it?
The problem is, “just working” isn’t the same as being ready for what’s coming next, especially if you care about SAP’s evolution and the massive role AI is playing. In a world where digital transformation now means becoming an intelligent enterprise built on real-time data, you can’t afford not to make use of the “best of the best” solutions.
Luckily, SAP gives us an easy way to determine which compatible solutions the company most strongly stands behind: SAP Endorsed App Premium certification.
SAP Endorsed App: More than just a badge
SAP Endorsed Apps aren’t ordinary partner solutions. This invitation-only program highlights solutions that help you with strategic business challenges not directly addressed by core SAP functionality.
SAP Endorsed App status is the highest level of certification SAP offers, and it isn’t handed out lightly. It signals to customers that the solution has been extensively tested and validated to meet SAP’s highest standards for performance, security and integration.
Being an Endorsed App means a solution has been rigorously evaluated and passed SAP’s most demanding Premium certification standards. Every angle is tested to ensure the solution truly stands up to real-world enterprise demands, even in the most complex hybrid environments. Only solutions that are widely used by SAP customers, future-aligned and proven to deliver outstanding customer value earn this highest level of SAP trust.
SAP Endorsed App for workload automation
Taking advantage of SAP’s next-generation capabilities is particularly important when it comes to workload automation, the backbone of your mission-critical processes. SAP CEO Christian Klein envisions a world in which ERP, automation, data and AI all work together in one cohesive ecosystem. Your processes should run end to end, intelligently orchestrated rather than stitched together. If your automation layer isn’t deeply integrated and future-ready, it becomes an anchor dragging you down. And if your workload automation partner isn’t deeply aligned with SAP, you’re going to hit bottlenecks sooner than you think.
Many job scheduling solutions are certified to connect to SAP systems, even RISE with SAP. And that’s good, but it’s only the first step. Basic certification means a scheduler has been tested to connect and perform standard tasks, but it doesn’t tell you how it integrates, what extra infrastructure you need or whether it supports a clean core without workarounds and fragile custom code.
It’s kind of like giving your teenager a learner’s permit. Sure, they’re legally allowed to drive, but would you hand them the keys and say, “Go ahead, take your friends to the basketball game tonight … and use the freeway”? Probably not. You know that true readiness involves more than basic certification. It’s about trust, experience and minimizing risk — for the driver and everyone else on the road.
RunMyJobs is the experienced, fully licensed driver: the only workload automation solution that is an SAP Endorsed App, Premium certified. Thus, it’s optimized to run in complex SAP landscapes, including RISE with SAP, Business Technology Platform (BTP) and Business Data Cloud (BDC).
It’s not about whether your automation connects to SAP. It’s whether it truly unlocks SAP’s full value, without compromise.
True future-proofing: Not just a fancy marketing slogan
We all see “future-proof” plastered across marketing materials. But real future-proofing isn’t a tagline. It means what’s being offered is designed to evolve, not just function today.
With SAP Endorsed App status, RunMyJobs is verified to keep pace with SAP’s roadmap. There is a regular cadence for SAP and Redwood Software to collaborate and align product roadmaps. What you get from this: reduced risk, faster time-to-value and confidence that your automation engine won’t become the bottleneck when it’s time to embed AI into your core business processes. So when we talk about RunMyJobs being “future-proof,” we’re not throwing around empty words.
Don’t run your business on a learner’s permit. You need a solution that’s been trained, tested and trusted to navigate the entire journey confidently, even if the road ahead is uncertain.
Watch the video below to learn more about what RunMyJobs’ SAP Endorsed App status means for your business.
In conversations with finance teams navigating automation, a familiar pattern often emerges. Leaders know their accounting operations need to evolve, but the path forward isn’t always clear. The sheer scope of a transformation can be paralyzing.
You can get out of this state of shock and start making strides when you realize you don’t need to overhaul your entire accounting function overnight.
I recommend a more pragmatic approach: Begin with a narrow focus, apply agile methods and build momentum through small, structured wins. Agile, originally a software development methodology, works exceptionally well in finance when adapted thoughtfully. Applied to accounting, it can give you a structured way to modernize processes without sacrificing efficient daily operations.
When you get it right, the transformation can feel like magic — not because it’s effortless but because of how dramatically it simplifies the work.
Step 1: Define your project and assemble your team
Agile begins with a clear purpose. What part of your accounting cycle is ripe for change? It might be:
Reducing manual effort in preparing recurring journal entries
Standardizing reconciliations for high-risk balance sheet accounts
Improving visibility and control over intercompany eliminations
Once you’ve selected your initial focus, identify a small, cross-functional team. That might include one or two accountants who manage the process today, a member of your IT or automation team and a team lead or controller to serve as the product owner.
Your goal is to scope out a project small enough to deliver real progress in a few weeks, rather than trying to automate everything.
Step 2: Choose your sprint cadence
Agile teams work in time-boxed cycles called sprints. In software, sprints typically last two weeks. This same rough sprint cadence also works well for finance. In my experience, two staggered sprints per month allow you to maintain momentum without interfering with the month-end or quarterly close cycle.
The key is to make the sprint regular and predictable. Every two weeks, your team should:
Review what was completed
Set clear, achievable goals for the next sprint
Prioritize the next set of tasks
Assign ownership based on capacity
This rhythm helps you maintain forward progress even amid daily demands and the ebbs and flows of a typical fiscal year.
Step 3: Start with process selection and discovery
Your first sprint should focus on understanding the process you want to improve. Let’s say you choose to automate a journal entry for prepaid expenses. This first step isn’t writing scripts. You need to understand how the process works today (pain points included), what systems and data are involved, what artifacts exist and what volume and complexity you’re dealing with.
Say you’re working on a recurring entry to allocate depreciation. You need to uncover: how the entry is generated today, what triggers it and when in the accounting period, which accounts it impacts, what documentation and validations exist and who reviews or adjusts it before it’s posted to the general ledger. You might also need to gather artifacts like Excel templates, email approval flows or ERP screenshots. These are your starting points for making sure your automation reflects a real workflow rather than an ideal one.
Don’t underestimate the importance of the discovery phase in making sure your automation efforts are grounded in reality.
Step 4: Break down tasks and build your backlog
Once you’ve scoped your process and gathered what you need, it’s time to translate your findings into tasks. Some examples:
Map the current workflow in a flowchart and make sure you cover any places where the process could fail or have to start over
Identify fields and logic needed for journal entry automation, so you know the required data and calculations
Review automation platform capabilities (e.g., templates or connectors)
Write acceptance criteria for a successful automation — this is how you’ll prove your new automation is working
Prepare test data or validate entry logic, and be sure to include several examples of the different kinds of data you might see to cover the most probable cases
Tasks that can’t be finished in this sprint go into your backlog. You can reprioritize that backlog after each sprint based on what you’ve learned or what’s most urgent.
Some tasks may expose gaps in how the process works today, and that’s a good thing. Agile sprints are built for learning, not perfection.
Step 5: Communicate, adjust and demo progress
A key agile principle is transparency. Short, regular check-ins — say, 15 minutes twice a week — keep everyone aligned and aware of blockers. No need for slides or long updates. A quick “What’s done, what’s next and what’s in the way?” is usually enough.
At the end of the sprint, reconvene for a demo. Even if you didn’t automate the entire process, showing a prototype or workflow map can energize your team and stakeholders. Use what you learn to shape the next sprint.
Where to start? Go for high pain, low complexity
If you’re not sure where to begin, I often recommend focusing on account reconciliations. They’re a consistent source of friction and effort, especially for temporary account balances or frequently adjusted liabilities. But many can be standardized or automated with relatively little effort.
For example, bank reconciliations follow a predictable pattern. Accrual accounts only need simple threshold logic. And intercompany receivables/payables might just require timing alignment.
Journal entries are another good candidate, particularly if they’re recurring and related to depreciation, allocations or amortizations. Their fixed logic and regular intervals make them perfect for early wins.
The record-to-report (R2R) cycle contains many interconnected subprocesses that are ideal for incremental automation. Applying agile to this domain brings visibility and momentum to your transformation efforts while minimizing risk and burnout.
Agile is how finance gets things done
Finance doesn’t often borrow from the world of software development, but it should. The pressure is real today to modernize, optimize and transform while still closing the books on time — no small feat. Agile gives your accounting team a way to improve processes iteratively, without waiting for perfect conditions or massive budgets. They get a repeatable structure and still have space for experimentation. Once they see how agile can turn a painful process into a streamlined one, you’ll have the buy-in you need to scale your automation strategy across your finance organization.
You won’t need a wand, just the right structure, people and mindset. Those create the real magic.
A cross-functional team of researchers has spent months developing a next-generation machine learning (ML) model designed to predict how a new compound behaves across multiple biological targets. It’s the kind of computational power that can accelerate drug discovery by weeks or months and bring life-saving therapies to market faster.
Despite an optimized IT infrastructure and cloud environment, the simulation doesn’t start because the latest compound batch data hasn’t been validated in SAP. The experiment metadata is still siloed in spreadsheets, and the model can’t ingest incomplete or inconsistent values. In other words, the fluid connection required between systems isn’t there.
As you may well know if you work in this industry, this isn’t a hypothetical delay. Data readiness can’t be treated as a side task, although it too often is. In which case, it doesn’t matter how advanced an AI model you have. With regulatory pressures high, the cost of a subtle misalignment is steep.
Because this applies whether you’re simulating compounds, ensuring patient records are anonymized and audit-ready or forecasting inventory, critical processes break down when data stays disconnected. Leading healthcare and pharmaceutical organizations are attempting to solve this common problem by rethinking how data moves from SAP to ML platforms to analytics and back.
Life science’s parallel pipelines: Innovation and execution
In life sciences organizations like yours, innovation happens on two fronts. On one side, your R&D teams use AI and massive datasets to accelerate discovery. ML models in AWS SageMaker or Schrödinger Suite predict promising compound structures, while simulation platforms test toxicity and efficacy before running a single experiment.
On the other side, your clinical and supply chain teams ensure those discoveries reach patients safely and cost-effectively while following all compliance regulations. They manage everything from patient enrollment to cold chain logistics to regulatory filing, with each process powered by SAP supply chain and life sciences solutions and custom platforms.
These processes live in very different domains, but they share a common dependency: structured, timely, accurate data. And in too many organizations, that data still moves manually or asynchronously between systems.
Where the cracks appear
When SAP data isn’t orchestrated, critical handoffs break down and molecular data must be manually pulled from SAP R&D Management to feed AI pipelines. Trial operations build forecasts on outdated enrollment data. Lab results live in one system and regulatory documentation in another, with no feedback loop. Business users wait on IT to reconcile siloed datasets and generate reports.
Drug discovery is increasingly computational, but that doesn’t mean the work is fully automated. Whether you’re managing experiments or kits, the pain is the same: unreliable flow, lost time and elevated risk. Without intelligent orchestration, pipelines either fall apart or deliver fragmented, stale information. This directly undermines the performance of AI models and introduces bias or neglects to provide key correlations. Essentially, you end up making decisions with outdated datasets — or worse, hallucinations. Predictive models built to accelerate discovery or optimize trial logistics can quickly fall out of compliance with data lineage and validation requirements.
Meanwhile, if you cling to these fragmented or manually stitched data pipelines, you face another growing disadvantage: You can’t match the speed of your competitors. Those who are investing in intelligent, adaptive data orchestration are moving faster while proving the trustworthiness of their AI-driven insights.
High-fidelity orchestration is the foundation of competitive agility and relevance in your industry.
Research, meet orchestration
Orchestration is what makes AI scale in R&D. Your SAP environment becomes the launchpad for faster, smarter research, enabling you to:
Continuously extract experimental and batch data from SAP R&D Management and SAP Analytics Cloud
Send compound specs to AWS SageMaker or Schrödinger Suite for modeling
Coordinate modeling jobs and return results to Databricks for consolidation
Push insight summaries about ranked candiddates back into SAP
Trigger alerts for research leads of successful outcomes or red flags and send validated results to SAP Datasphere
Clinical delivery, intelligently aligned
On the delivery side, timing is everything. Clinical trial operations depend on up-to-date patient enrollment data, trial protocols and inventory levels across distributed trial sites. If systems aren’t aligned, sites risk running out of supplies or holding expired stock.
With proper orchestration:
Enrollment data from SAP Intelligent Clinical Supply Management flows into forecasting tools
ML models in Azure ML or Databricks predict site-specific demand
Stock levels in SAP Integrated Business Planning (IBP) or S/4HANA Materials Management (MM) are cross-checked automatically
If risk is flagged, replenishment is triggered and stakeholders are notified
Trial performance metrics update automatically in SAP Analytics Cloud
All data is centralized in SAP Business Data Cloud (BDC) for regulatory compliance and real-time insight
Data-driven defense against disruption
When the unexpected hits, data orchestration is the difference between rerouting and reacting.
Take supply chain disruptions, which are a matter of when, not if, in pharma. A shortage of active ingredients, a vendor backlog, a shipping delay — any of these can jeopardize production schedules or trial timelines.
The real risk isn’t the event itself but what happens when your systems can’t respond in time. With orchestrated data pipelines between SAP S/4HANA, SAP IBP and platforms like Databricks or Azure Synapse, you can spot shortages early, simulate impacts and initiate contingency plans.
A research-to-treatment automation fabric
True transformation comes when discovery and delivery are both orchestrated from end to end. Here’s what a real automation fabric looks like.
Forecasting clinical and manufacturing needs
Export enrollment or order data from SAP S/4HANA
Clean and enrich using SAP Datasphere
Run predictive models via Databricks, Azure ML or SageMaker
Feed outputs into SAP IBP for dynamic planning
Managing research and validation
Extract compound data from SAP R&D Management
Coordinate modeling jobs in Schrödinger Suite
Score and validate candidates in Databricks
Trigger SAP updates and notify research teams automatically
Controlling inventory and site logistics
Pull inventory positions from S/4HANA
Reconcile with forecasted site needs from SAP IBP and ML pipelines
Generate and dispatch replenishment orders
Publish everything in SAP Analytics Cloud for transparency
Keeping teams informed and aligned
Push alerts to supply, clinical or research leads based on process outcomes
Route structured datasets to reporting dashboards and compliance archives
Automate audit trails, approvals and next-step triggers
With every step validated, timestamped and secure thanks to RunMyJobs by Redwood, your data flows continuously, allowing you to be proactive instead of reactive.
Audit-ready AI depends on orchestrated data
The rise of AI in life sciences is helping to optimize molecule screening and clinical trial site selection and even personalize patient communications. With that power comes increasing scrutiny.
Regulators are watching closely. Health authorities in the United States, European Union and beyond are issuing new guidelines around AI in clinical decision-making, digital therapeutics and research applications. They want to know: Where did the data come from? Was it anonymized? Who validated it? And can you prove it?
If your data pipelines are fragmented, those answers may simply not exist. But orchestration changes that. When you automate your data moving from SAP modules to Azure ML or from SAP Datasphere to regulatory systems, you also create a system of record. Every dataset has a timestamp, and every transformation is traceable. This strategically enables AI innovation.
The next wave of advancement will hinge on more than modeling accuracy; you’ll need to be able to explain how your model was built or prove the integrity of the data behind it. With the right orchestration solution, you don’t have to choose between speed and control. You can stay audit-ready and future-ready.
Develop a resilient nervous system
Think of your systems like organs. Each one serves a distinct purpose, but they communicate via signals that travel through connective tissue. These signals are orchestration in action!
Want to know more about orchestrating SAP data with RunMyJobs? Read more about using the SAP Analytics Cloud connector.
