Businesses Need to Embrace Automation and AI within SAP Monitoring to Cope with Soaring Complexity

Gregg Ostrowski
AppDynamics
Learn more about Cisco

Managing availability and performance within SAP environments has long been a challenge for IT teams.

But as IT environments grow more complex and dynamic, and the speed of innovation in almost every industry continues to accelerate, this situation is becoming a whole lot worse.

As enterprise organizations shift their SAP workloads to S/4 HANA or move their SAP landscape to the cloud, the level of complexity that technologists are faced with across a sprawling topology of applications, both inside and outside SAP, is set to spiral.

Already, IT teams find themselves being bombarded by soaring volumes of data from all corners of their IT environment. In the latest research from Cisco AppDynamics, 65% of technologists admitted that they feel overwhelmed by complexity and data noise.

In response, technologists need new tools and platforms which provide them with full and unified visibility of all of their IT environments, so that they can detect issues, understand root causes and dependencies and identify those issues which could do most damage to end user experience. But, even with this level of visibility, most IT teams simply don't have the resources to handle the sheer volume of data that is continually coming at them.

By leveraging artificial intelligence (AI), IT teams can move beyond manual and labor-intensive methods for monitoring SAP environments (and non-SAP application stacks). AI can provide rapid anomaly detection and automatic and intelligent alerting, ingesting massive volumes of data and deciphering meaning from it. This enables technologists to understand which issues really matter and focus their efforts on the right places.

S/4 HANA Migration and Cloud Native Technologies Are Causing an Explosion of Data

The reality is that managing performance in SAP environments has never been particularly easy. Many IT teams have found themselves facing a deluge of alert storms, with no means to understand the severity of issues and prioritize the deployment of resources accordingly. In addition, IT teams have been unable to connect SAP environments with wider (non-SAP) business applications, making effective troubleshooting incredibly hard. The potential consequences are, of course, severe — service disruption, outages and, ultimately, skyrocketing mean-time-to-resolution (MTTR).

Most IT teams deploy a number of separate tools to monitor dependent systems, or they have a siloed tool monitoring SAP, completely independent from the rest of their IT stack. This fragmented approach means they don't have a single, unified view of their IT environments and it doesn't allow them to correlate business performance to their SAP landscape.

Organizations that rely on SAP NetWeaver must see the entire production landscape, whether they're on-premises, hybrid cloud, or cloud only. But currently, very few monitoring solutions recognize SAP's proprietary programming language, ABAP, so technologists struggle to get visibility down to the unique line of SAP code.

This issue will be exacerbated as organizations accelerate their S/4 HANA migrations or move their SAP landscape to the cloud. IT teams are increasingly trying to manage a fragmented environment both within SAP and beyond without the necessary visibility and insights to identify and resolve issues quickly, or to prioritize issues that damage the customer and employee experience.

Technologists Must Lean on AI to Tackle Soaring Levels of Complexity

Organizations urgently need to provide their IT teams with a single source of truth across their SAP landscapes and visibility into how application performance is impacting the business.

This means ensuring technologists have deep, end-to-end visibility of their SAP environment, so they can troubleshoot relatively smaller issues before they become major problems. And as inter-dependencies between SAP and non-SAP applications become more complex, IT teams need comprehensive topography of their entire IT landscape, including both SAP and non-SAP applications. This allows them to see and understand upstream service dependencies — as well as user experience — within SAP.

With tailored dashboards, technologists can assess the overall health of a system — for example, application server, HANA DB, key background jobs, IDocs and PI systems — while getting access to real-time mapping of business transactions across distributed SAP systems.

IT teams need a solution that can also understand proprietary ABAP code issues at a granular level so that developers can easily pinpoint the root cause of issues. Being able to analyze each specific line of ABAP code is critical to troubleshoot problems and correlate them to application performance issues. This level of visibility creates more stability within the application environment, enhancing technologists' ability to reliably meet IT, business, and customer expectations.

Crucially, technologists need to get away from manual and time-intensive methods for monitoring SAP and non-SAP apps which are ineffective and a huge drain on resources. It's simply no longer feasible for technologists to manually sift through logs to troubleshoot issues and to correlate SAP performance data with business events; not in the highly dynamic cloud native environments organizations are now operating.

AI and Machine Learning can ingest massive volumes of data from across every business environment and convert this data into meaningful and actionable insight, all in real-time. This provides rapid anomaly detection and automatic and intelligent alerting, enabling IT teams to take a more proactive and strategic approach in optimizing SAP performance.

Teams used AI to right-size the investment of resources based on scenarios that are unique to the business and potentially performance-impacting, such as high volumes of traffic due to holiday shopping or other seasonal events, product launches and other business activities. This is especially important when migrating to S/4 HANA or moving SAP landscapes to the cloud, for teams to get real-time performance metrics before, during, and post migration, helping them to proactively address issues as they arise.

AI also helps technologists connect availability and performance data with insights into the health of the business. This means real-time monitoring of SAP health metrics alongside critical business KPIs, like transaction, customer and user journeys. Businesses can then focus efforts and deploy resources where they are needed most.

Encouragingly, technologists are recognizing the need for a new approach to monitoring availability and performance, one built around full and unified visibility of all IT environments, both inside and outside SAP. We found that 71% of technologists believe that their organization will focus on observing cloud native applications and infrastructure over the next 12 months, and 66% point to implementing advanced AI and hyper-automation to optimize performance and drive innovation.

With AI, the endless alert storms that are such a drain on time and morale in the IT department can be consigned to history. Technologists can get back on the front foot, using the power of AI to identify, prioritize and resolve issues based on business impact, and deliver faultless availability and performance in SAP environments at all times.

Gregg Ostrowski is CTO Advisor at Cisco AppDynamics

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Businesses Need to Embrace Automation and AI within SAP Monitoring to Cope with Soaring Complexity

Gregg Ostrowski
AppDynamics
Learn more about Cisco

Managing availability and performance within SAP environments has long been a challenge for IT teams.

But as IT environments grow more complex and dynamic, and the speed of innovation in almost every industry continues to accelerate, this situation is becoming a whole lot worse.

As enterprise organizations shift their SAP workloads to S/4 HANA or move their SAP landscape to the cloud, the level of complexity that technologists are faced with across a sprawling topology of applications, both inside and outside SAP, is set to spiral.

Already, IT teams find themselves being bombarded by soaring volumes of data from all corners of their IT environment. In the latest research from Cisco AppDynamics, 65% of technologists admitted that they feel overwhelmed by complexity and data noise.

In response, technologists need new tools and platforms which provide them with full and unified visibility of all of their IT environments, so that they can detect issues, understand root causes and dependencies and identify those issues which could do most damage to end user experience. But, even with this level of visibility, most IT teams simply don't have the resources to handle the sheer volume of data that is continually coming at them.

By leveraging artificial intelligence (AI), IT teams can move beyond manual and labor-intensive methods for monitoring SAP environments (and non-SAP application stacks). AI can provide rapid anomaly detection and automatic and intelligent alerting, ingesting massive volumes of data and deciphering meaning from it. This enables technologists to understand which issues really matter and focus their efforts on the right places.

S/4 HANA Migration and Cloud Native Technologies Are Causing an Explosion of Data

The reality is that managing performance in SAP environments has never been particularly easy. Many IT teams have found themselves facing a deluge of alert storms, with no means to understand the severity of issues and prioritize the deployment of resources accordingly. In addition, IT teams have been unable to connect SAP environments with wider (non-SAP) business applications, making effective troubleshooting incredibly hard. The potential consequences are, of course, severe — service disruption, outages and, ultimately, skyrocketing mean-time-to-resolution (MTTR).

Most IT teams deploy a number of separate tools to monitor dependent systems, or they have a siloed tool monitoring SAP, completely independent from the rest of their IT stack. This fragmented approach means they don't have a single, unified view of their IT environments and it doesn't allow them to correlate business performance to their SAP landscape.

Organizations that rely on SAP NetWeaver must see the entire production landscape, whether they're on-premises, hybrid cloud, or cloud only. But currently, very few monitoring solutions recognize SAP's proprietary programming language, ABAP, so technologists struggle to get visibility down to the unique line of SAP code.

This issue will be exacerbated as organizations accelerate their S/4 HANA migrations or move their SAP landscape to the cloud. IT teams are increasingly trying to manage a fragmented environment both within SAP and beyond without the necessary visibility and insights to identify and resolve issues quickly, or to prioritize issues that damage the customer and employee experience.

Technologists Must Lean on AI to Tackle Soaring Levels of Complexity

Organizations urgently need to provide their IT teams with a single source of truth across their SAP landscapes and visibility into how application performance is impacting the business.

This means ensuring technologists have deep, end-to-end visibility of their SAP environment, so they can troubleshoot relatively smaller issues before they become major problems. And as inter-dependencies between SAP and non-SAP applications become more complex, IT teams need comprehensive topography of their entire IT landscape, including both SAP and non-SAP applications. This allows them to see and understand upstream service dependencies — as well as user experience — within SAP.

With tailored dashboards, technologists can assess the overall health of a system — for example, application server, HANA DB, key background jobs, IDocs and PI systems — while getting access to real-time mapping of business transactions across distributed SAP systems.

IT teams need a solution that can also understand proprietary ABAP code issues at a granular level so that developers can easily pinpoint the root cause of issues. Being able to analyze each specific line of ABAP code is critical to troubleshoot problems and correlate them to application performance issues. This level of visibility creates more stability within the application environment, enhancing technologists' ability to reliably meet IT, business, and customer expectations.

Crucially, technologists need to get away from manual and time-intensive methods for monitoring SAP and non-SAP apps which are ineffective and a huge drain on resources. It's simply no longer feasible for technologists to manually sift through logs to troubleshoot issues and to correlate SAP performance data with business events; not in the highly dynamic cloud native environments organizations are now operating.

AI and Machine Learning can ingest massive volumes of data from across every business environment and convert this data into meaningful and actionable insight, all in real-time. This provides rapid anomaly detection and automatic and intelligent alerting, enabling IT teams to take a more proactive and strategic approach in optimizing SAP performance.

Teams used AI to right-size the investment of resources based on scenarios that are unique to the business and potentially performance-impacting, such as high volumes of traffic due to holiday shopping or other seasonal events, product launches and other business activities. This is especially important when migrating to S/4 HANA or moving SAP landscapes to the cloud, for teams to get real-time performance metrics before, during, and post migration, helping them to proactively address issues as they arise.

AI also helps technologists connect availability and performance data with insights into the health of the business. This means real-time monitoring of SAP health metrics alongside critical business KPIs, like transaction, customer and user journeys. Businesses can then focus efforts and deploy resources where they are needed most.

Encouragingly, technologists are recognizing the need for a new approach to monitoring availability and performance, one built around full and unified visibility of all IT environments, both inside and outside SAP. We found that 71% of technologists believe that their organization will focus on observing cloud native applications and infrastructure over the next 12 months, and 66% point to implementing advanced AI and hyper-automation to optimize performance and drive innovation.

With AI, the endless alert storms that are such a drain on time and morale in the IT department can be consigned to history. Technologists can get back on the front foot, using the power of AI to identify, prioritize and resolve issues based on business impact, and deliver faultless availability and performance in SAP environments at all times.

Gregg Ostrowski is CTO Advisor at Cisco AppDynamics

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I've spent a lot of time in the channel, and one thing I keep coming back to is this: a partner program is only as good as what it looks like in the field. Many programs look great on paper, but when a partner is in front of a customer navigating a complex hybrid environment or trying to make the case for AI-powered observability, the gap between what a vendor promises and what it actually delivers becomes very clear, very fast ...

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Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

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For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

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For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

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Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

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