Insights from the SAP Community: Process Automation Strategies for 2025

Chris Hall
Precisely

With the 2027 deadline for SAP S/4HANA migrations fast approaching, organizations are accelerating their transition plans. According to recent research, 50% of organizations were planning to have migrated or started the migration to S/4HANA by the end of 2024, while an additional 30% plan to make the change in the next two years.

For organizations that intend to remain on SAP ECC in the near-term, the focus has shifted to improving operational efficiencies and meeting demands for faster cycle times. These efforts are essential to maintaining competitiveness while preparing for eventual modernization.

No matter where an organization is in its SAP journey, one theme is clear: The automation of SAP processes is crucial. In order to maintain agility, accelerate the speed of business, and build data integrity, organizations require robust process automation, fueled by accurate, consistent, and contextual data, to drive successful outcomes. Emerging best practices and challenges related to SAP automation will reveal key strategies companies are adopting to optimize their operations.

SAP S/4HANA Migrations Gain Momentum, Despite Ongoing Challenges

In less than three years, the S/4HANA deadline will have arrived and as a result, many organizations are moving forward with plans to start their migration journeys now, if they haven't done so already.

Transitioning from legacy SAP ERP systems to S/4HANA is a notoriously complex process. Oftentimes it requires both legacy and new systems to work in tandem and presents significant data management challenges along the way. The companies getting it right are relying on process automation to accelerate complex SAP business processes.

Process automation solutions are pivotal in expediting SAP migrations by streamlining data transformation and integration tasks. Automated processes enable organizations to create new master data and transaction data in SAP systems, update mass amounts of data throughout the SAP landscape using Excel and perform data migrations from existing systems to S/4HANA. As businesses start with pre-migration assessment and implementation plans, incorporating automation allows for the adoption of a more holistic approach to process redesign, leading to optimized business processes and a smoother transition to S/4HANA.

However, due to data, process, and organizational complexities, the move from legacy SAP ERP systems is often a multi-year process that requires detailed planning and analyses of its impact on business operations. As a result, businesses frequently operate both legacy and new systems in parallel as they work to avoid disruptions during the transition, but this approach can oftentimes compound the complexity of the migration to S/4HANA. While the transition may be difficult at times, the business benefits of this transition — including cloud deployment options, real-time reporting in SAP and a modern user interface — far outweigh the migration challenges

Rise in Automation Adoption Comes with Challenges

Automation is a critical component of any digital transformation strategy, but the implementation of automated solutions hasn't always been quickly adopted. This is particularly true when it comes to complex, and data intensive, SAP-centric business processes which create barriers to success. However, as more organizations realize the benefits of automation, adoption is increasing. In fact, research shows a 15% increase in the use of automation for migrations and digital transformation projects — from 43% in 2023 to 58% in 2024 — indicating that companies considering automation today are likely to begin adopting it soon.

Over the past year, there has been growth in companies maturing their levels of automation adoption, leveraging a mix of manual and automated processes to streamline operations, reduce reliance on manual intervention, and effectively manage high volumes of data. However, a high level of automation adoption remains difficult to achieve. This is due to three factors currently influencing the market:

  • First, the impact of companies migrating to S/4HANA may slow improvements in automation growth and maturity. As organizations complete the migration process, they rationalize the potential impacts of this move on business processes.
  • Next, digital transformation teams recognize that many simple or infrequent processes are more efficiently managed when done manually.
  • Finally, growing recognition that success metrics based on business outcome value for an automation activity or process will vary from use case to use case from an organizational standpoint.

Barriers such as large data volumes and the intricacies of business processes take time to overcome. Many companies continue to cite integration with existing business processes as the biggest challenge when implementing process automation. These challenges are often exacerbated when companies take a case-by-case approach to automation, as this can increase overall complexity.

Scaling Automation Through Citizen Developers and Trusted Data

To navigate these complexities and fully realize the benefits, companies must recognize the value of citizen developers. They play a crucial role in the automation initiative process, leveraging the growing adoption of no-code/low-code platforms that assist business users at every level. Citizen developers also expand the pool of experts who can deploy automation within a company, and a majority of companies believe that these individuals are a crucial part of their organizations. As the use of low-code/no-code platforms grows, citizen developers will continue to drive the evolution of automation.

Despite its challenges, prioritizing best practices in SAP process automation ensures that a company's data is utilized to its full potential, improving business agility and accelerating the organization overall. To achieve this, companies must place automation at the core of their data integrity efforts. Data integrity is key — accurate, consistent, and contextualized data significantly ensures a smooth automation process. For example, ensuring customer records and data are error-free builds trust in the data, enabling the organization to make sound business decisions across all departments.

Automation is becoming a necessity for companies as they embrace digital transformation and the tools required to achieve it. SAP process automation is central to streamlining operational efficiencies. To ensure success, companies must effectively integrate data integrity solutions, as automation cannot succeed without trusted data. The inclusion of no-code/low-code automation tools also supports this goal by enabling broader impacts across the organization. While the automation process is complex, its outcomes — enhanced efficiencies, agility, and greater ROI — are invaluable for driving long-term success.

Chris Hall is Chief Product Officer at Precisely

Related Links

Hot Topics

The Latest

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

Insights from the SAP Community: Process Automation Strategies for 2025

Chris Hall
Precisely

With the 2027 deadline for SAP S/4HANA migrations fast approaching, organizations are accelerating their transition plans. According to recent research, 50% of organizations were planning to have migrated or started the migration to S/4HANA by the end of 2024, while an additional 30% plan to make the change in the next two years.

For organizations that intend to remain on SAP ECC in the near-term, the focus has shifted to improving operational efficiencies and meeting demands for faster cycle times. These efforts are essential to maintaining competitiveness while preparing for eventual modernization.

No matter where an organization is in its SAP journey, one theme is clear: The automation of SAP processes is crucial. In order to maintain agility, accelerate the speed of business, and build data integrity, organizations require robust process automation, fueled by accurate, consistent, and contextual data, to drive successful outcomes. Emerging best practices and challenges related to SAP automation will reveal key strategies companies are adopting to optimize their operations.

SAP S/4HANA Migrations Gain Momentum, Despite Ongoing Challenges

In less than three years, the S/4HANA deadline will have arrived and as a result, many organizations are moving forward with plans to start their migration journeys now, if they haven't done so already.

Transitioning from legacy SAP ERP systems to S/4HANA is a notoriously complex process. Oftentimes it requires both legacy and new systems to work in tandem and presents significant data management challenges along the way. The companies getting it right are relying on process automation to accelerate complex SAP business processes.

Process automation solutions are pivotal in expediting SAP migrations by streamlining data transformation and integration tasks. Automated processes enable organizations to create new master data and transaction data in SAP systems, update mass amounts of data throughout the SAP landscape using Excel and perform data migrations from existing systems to S/4HANA. As businesses start with pre-migration assessment and implementation plans, incorporating automation allows for the adoption of a more holistic approach to process redesign, leading to optimized business processes and a smoother transition to S/4HANA.

However, due to data, process, and organizational complexities, the move from legacy SAP ERP systems is often a multi-year process that requires detailed planning and analyses of its impact on business operations. As a result, businesses frequently operate both legacy and new systems in parallel as they work to avoid disruptions during the transition, but this approach can oftentimes compound the complexity of the migration to S/4HANA. While the transition may be difficult at times, the business benefits of this transition — including cloud deployment options, real-time reporting in SAP and a modern user interface — far outweigh the migration challenges

Rise in Automation Adoption Comes with Challenges

Automation is a critical component of any digital transformation strategy, but the implementation of automated solutions hasn't always been quickly adopted. This is particularly true when it comes to complex, and data intensive, SAP-centric business processes which create barriers to success. However, as more organizations realize the benefits of automation, adoption is increasing. In fact, research shows a 15% increase in the use of automation for migrations and digital transformation projects — from 43% in 2023 to 58% in 2024 — indicating that companies considering automation today are likely to begin adopting it soon.

Over the past year, there has been growth in companies maturing their levels of automation adoption, leveraging a mix of manual and automated processes to streamline operations, reduce reliance on manual intervention, and effectively manage high volumes of data. However, a high level of automation adoption remains difficult to achieve. This is due to three factors currently influencing the market:

  • First, the impact of companies migrating to S/4HANA may slow improvements in automation growth and maturity. As organizations complete the migration process, they rationalize the potential impacts of this move on business processes.
  • Next, digital transformation teams recognize that many simple or infrequent processes are more efficiently managed when done manually.
  • Finally, growing recognition that success metrics based on business outcome value for an automation activity or process will vary from use case to use case from an organizational standpoint.

Barriers such as large data volumes and the intricacies of business processes take time to overcome. Many companies continue to cite integration with existing business processes as the biggest challenge when implementing process automation. These challenges are often exacerbated when companies take a case-by-case approach to automation, as this can increase overall complexity.

Scaling Automation Through Citizen Developers and Trusted Data

To navigate these complexities and fully realize the benefits, companies must recognize the value of citizen developers. They play a crucial role in the automation initiative process, leveraging the growing adoption of no-code/low-code platforms that assist business users at every level. Citizen developers also expand the pool of experts who can deploy automation within a company, and a majority of companies believe that these individuals are a crucial part of their organizations. As the use of low-code/no-code platforms grows, citizen developers will continue to drive the evolution of automation.

Despite its challenges, prioritizing best practices in SAP process automation ensures that a company's data is utilized to its full potential, improving business agility and accelerating the organization overall. To achieve this, companies must place automation at the core of their data integrity efforts. Data integrity is key — accurate, consistent, and contextualized data significantly ensures a smooth automation process. For example, ensuring customer records and data are error-free builds trust in the data, enabling the organization to make sound business decisions across all departments.

Automation is becoming a necessity for companies as they embrace digital transformation and the tools required to achieve it. SAP process automation is central to streamlining operational efficiencies. To ensure success, companies must effectively integrate data integrity solutions, as automation cannot succeed without trusted data. The inclusion of no-code/low-code automation tools also supports this goal by enabling broader impacts across the organization. While the automation process is complex, its outcomes — enhanced efficiencies, agility, and greater ROI — are invaluable for driving long-term success.

Chris Hall is Chief Product Officer at Precisely

Related Links

Hot Topics

The Latest

The Hidden Data Engineering Work Required to Make Enterprise AI Viable

If AI is the engine of a modern organization, then data engineering is the road system beneath it. You can build the most powerful engine in the world, but without paved roads, traffic signals, and bridges that can support its weight, it will stall. In many enterprises, the engine is ready. The roads are not ...

Why AI Is the Differentiator for Operationally Resilient Organizations

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

Escaping Pilot Purgatory: How AI Becomes an Operational Advantage

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Closing the Gap in Modern Tech and the Tools Meant to Monitor Them

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

MEAN TIME TO INSIGHT Podcast - Episode 23: NetOps Labor Shortage

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Why FinOps Rewrote Its Mission and What It Signals for Technology Management

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Clearing the Path to AI: Why Vendor Consolidation Matters Now

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Cold Data, Hot Problem: Why AI Is Rewriting Enterprise Storage Strategy

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

Observability at a Crossroads: AI, Economics, Complexity and the Enduring Power of Open Source

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

Explainability Is the New Battleground in AI-Powered Observability

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...