2024 Trends in the Automation of SAP Processes

SAP offers some of the most robust enterprise software products on the market. That’s obviously very important for anyone in charge of operating a complex global business with many moving parts. SAP does a great job managing all of that complexity, but it takes many resources to maintain master data, update SAP information on a timely basis, and keep everything running smoothly.

That’s where automation tools come into play. As companies across the globe struggle to do more with less, software automation drives cost savings, efficiency, and agility.

As this drive toward increased efficiency and agility continues, here are the trends that we see unfolding in 2024 for automating SAP processes.

Trend 1. Business Agility Remains in Focus

Across industries, agility continues to be a top desired outcome of digital transformation initiatives. And it’s never been more clear that automation plays a critical role in achieving that agility. Simply put: manual, error-prone processes simply don’t cut it anymore if you want to survive and thrive in a fast-paced digital landscape.

The 2023 State of SAP Automation Report highlights the critical role of automation, fueled by trusted data, for driving efficiency, agility, and confident decision-making across organizations. In this survey of more than 150 SAP® IT stakeholders and business users, we found that over 85% of companies recognize the value of automating their SAP business processes and their associated SAP data processes.

When asked about the outcomes of automation, respondents cited:

• less time spent on manual, low-tier work (74%)
• increased speed and responsiveness (52%)
• cost savings (45%)

Benefits like these contribute to greater agility and speed, which helps you gain a competitive advantage through faster time to market and the ability to pivot with market changes.

And if the last few years have taught us anything, it’s that having the agility needed to adapt in real-time to market changes and business disruptions continues to be more critical than ever.

Trend 2. AI Drives the Next Level of Automation Technologies

Everyone’s talking about AI (artificial intelligence). In software, AI technology provides a unique ability to automate or accelerate user tasks, resulting in greater efficiency and productivity and a reduced dependence on manual labor.

Automation and intelligence initiatives are no exception. The demand for generative AI (GenAI) truly is driving the next level of these developing technologies.

Research from IDC found that one emerging area to watch in this realm is the ability to generate recommendations that enable staff to make better decisions, as we move from business rules-based decision automation to AI.

The research firm also notes that automation teams have begun to experiment with GenAI. They’re focusing on initiatives like the expansion of document automation to include unstructured use cases, and creating specialized AI models based on regulations, code, and policies that extend automation to new use cases.

It’s also important to examine the role of predictive AI in the implementation of backend process automation. It predicts outcomes for you based on your own data history and changes over time – looking at the likelihood of field values, routing destinations, and more. That means that publicly available data isn’t being used for your process, and likewise, that your process isn’t training the model.

Let’s look at one example where AI-driven automation can make a huge impact: manual material master creation in SAP. This process is notoriously complex and error-prone. Companies often try to combat these errors and improve data quality by defining hundreds of data validation rules – but that approach presents its own time-consuming challenges.

With AI, you could instead start with a single small set of rules and create a new material with the following process:

1. Train an algorithm with your historical data. Then, enter the minimum number of input fields to start the creation request.
2. The AI takes that information and predicts output fields based on its confidence levels.
3. AI output fields are used to pre-populate and validate form fields.

With this process, manual, time-consuming data entry now becomes a streamlined data review. And with each review and round of adjustments, the algorithm’s confidence levels rise, your overall data quality becomes stronger, and processes move faster.

Trend 3. Complexity Is Multi-Dimensional and Challenging

Complexity is a leading challenge in many process automation initiatives, and it has multiple dimensions. The business processes themselves and the data associated with each SAP record are all complex – at a rapidly growing level – which makes analysis and reengineering difficult.

While complexity’s impact on the broader adoption of automation may not be surprise, there are some more nuanced trends that our survey with ASUG uncovered:

• 50% of organizations are challenged by the complexity of business processes
• 45% of organizations are challenged by the complexity and volume of SAP master data

It’s worth noting that when drilling down further, we find that process complexity is cited as a challenge for:

• 64% of companies where enterprise architecture teams lead automation efforts
• 29% of companies where dedicated digital transformation or center of excellence teams lead automation efforts

That’s quite a gap, and it tells us that process automation implementation may be better led by teams closer to SAP ERP and the business processes themselves, rather than those focused on underlying infrastructure and the chosen automation technology.

When we focus on challenges associated with master data, they align with the complexities of managing, changing, or automating SAP master data and related processes. Managing master data across multiple systems of record usually means the automated solution will include a multi-domain system and possibly require coordinating with other, enterprise-wide systems like Salesforce.

Despite these challenges, the good news is that, in most cases, they can be overcome with a dedicated effort to analyze and reengineer the processes currently in place for creating and managing SAP data. With this in place, it can be relatively easy to parse the processes into related components that can be the starting point for automation.

Trend 4. Process Standardization Drives Better Data Quality

Automation isn’t simply about eliminating manual effort; it also allows for greater control over data standards. A significant percentage of SAP processes are performed manually. That means they’re not governed, so they can vary greatly, depending on who is performing the task, and when.

Using automation tools, data owners can easily apply business and data stewardship rules to self-service applications and Excel templates. The data is fully validated before it ever gets to SAP. That eliminates errors and the usual error-checking and troubleshooting processes that inevitably accompany master data updates. Instead of fixing data quality issues after they enter the system, automation tools for SAP, like Precisely Automate, offer a means of preventing data quality problems from happening in the first place, providing long-term efficiencies and eliminating errors at scale.

Automation helps SAP customers solve many of their most common data management challenges – accelerating processes, improving collaboration, and maintaining high data quality. For more on the latest automation trends and insights, and what they mean for your business, read the 2023 State of Automation Report.