An advanced AMS is a model of Application Management Services that goes beyond the reactive approach of technical support and transforms application management into a continuous system of monitoring, optimization, automation, and process improvement. It goes beyond simply resolving tickets or issues: it analyzes recurring causes, measures performance, manages changes, enhances the user experience, and keeps applications aligned with the company’s operational goals.
For companies that use complex information systems, AMS is an important component of application governance. The day-to-day management of business systems affects operational continuity, data quality, process speed, and the ability to introduce innovation without compromising stability.
How is an AMS defined?
IBM’s definition¹ of Application Management includes the installation, operation, maintenance, support, and optimization of applications throughout their lifecycle, with the goal of ensuring their performance and functionality.
Application Management Services outsource or share this responsibility with a specialized provider, which manages, supports, and optimizes enterprise applications according to the organization’s specific needs.
The difference between a traditional AMS and an advanced AMS therefore lies in the approach to service. In the former case, action is taken when something goes wrong. In the latter case, an operational cycle is established that prevents, measures, corrects, and improves.
Why the AMS Can’t Just Stop at Ticket Management
In the traditional model, the AMS is often associated with three main activities: opening a ticket, taking ownership of the issue, and resolving it. This framework remains necessary, but it is no longer sufficient. Today’s enterprise systems are more interconnected, more distributed, and more exposed to constant change: cloud updates, application releases, integrations with external systems, automations, AI models, industrial devices, and end-to-end processes.
In this scenario, each ticket contains information that goes beyond the technical issue it describes. A recurring bottleneck in an approval workflow may indicate a configuration that needs to be reviewed. A frequent user request may signal a training gap or an ineffective interface. An integration error may stem from insufficient data quality upstream. An application slowdown may foreshadow a problem with architecture, scaling, or change governance.
The advanced AMS is based on this interpretation. A ticket, in addition to being assigned and “closed,” marks the beginning of an analysis. Immediate resolution keeps the system up and running; structured analysis prevents the same problem from recurring. This is where support becomes continuous improvement.
This approach is consistent with the ITIL² principle of continual improvement, which aims to align services, products, and practices with evolving business needs through constant improvement. The advanced AMS applies this principle to enterprise application management: it focuses not only on technical operations but also on the overall quality of the application service.
From Reactive Support to Proactive Application Management
An advanced AMS is based on an operational distinction: incident, problem, change, and improvement. An incident focuses on restoring service. A problem aims to identify the root cause. A change manages the necessary technical or functional modification. An improvement transforms the accumulated experience into an optimization initiative.
This process avoids one of the most common pitfalls in support services: resolving issues quickly without learning from them. In an SAP environment, for example, a recurring error in accounting, order management, planning, or logistics must be treated as a case to be analyzed: frequency, affected module, user, process, integration, business impact, workarounds used, time to escalation, resolution time, and number of reopenings. Otherwise, there is a risk of continuing to encounter the same type of error without ever getting to the root cause.
The transition from traditional AMS to advanced AMS therefore requires three levels of governance. The first is operational: ensuring SLAs, continuity, traceability, and quality in request management. The second is analytical: measuring trends, recurring causes, workload by application area, and impact on business functions. The third is advisory: proposing improvement measures, automation, process reviews, training initiatives, and architectural optimizations.
The MTTR metric helps us understand this process. Atlassian distinguishes between Mean Time To Recovery³—that is, the average time required to restore a system after a failure—and Mean Time To Resolve, which also includes the time needed to ensure the problem does not recur. An advanced AMS focuses primarily on the second aspect: not just restoring the system, but stabilizing it.
AMS and SAP: Business Continuity, Clean Core, and Cloud ALM
In the SAP world, AMS plays an even more critical role for at least three reasons. The first is the central role of ERP in business processes. The second is the transition to SAP S/4HANA and to cloud or hybrid architectures. The third is the need to keep systems up to date, integrated, and manageable without introducing excessive technical complexity.
SAP has confirmed mainstream support for the core applications of SAP Business Suite 7 through the end of 2027, with optional extended support through the end of 2030. This timeline highlights one key point: AMS cannot be separated from the information system’s evolution roadmap. For companies still operating on legacy systems, application management must also contribute to preparing the path to S/4HANA, streamlining customizations, optimizing processes, and reducing technical debt.
SAP Cloud ALM is moving in the same direction. SAP describes it⁴ as a solution for cloud and hybrid customers that offers standardized ALM processes for the implementation, operations, and use of SAP support services. On the operational side, SAP Cloud ALM aims to ensure smooth business operations and to improve the quality and performance of process execution.
For us at Regesta, this means building an AMS that, in addition to managing applications, governs an ecosystem. ERP systems, custom applications, integrations, industrial processes, factory systems, analytics, and cloud platforms must be viewed as parts of a single operational architecture. If an application works technically but creates inefficiencies at the process level, the AMS has not yet fulfilled its purpose.
Metrics for an Advanced AMS
An AMS service focused on continuous improvement must be measurable. Managing based on perceptions creates two risks: underestimating recurring problems because they are not formally documented, or overestimating one-time urgent issues because they generate greater organizational pressure. Data allows us to prioritize.
The most useful metrics aren’t just technical ones.
They must align operations, processes, and business value. DORA, a research program led by Google Cloud, identifies⁵ five metrics for measuring software delivery performance: change lead time, deployment frequency, failed deployment recovery time, change fail rate, and deployment rework rate. These metrics distinguish between throughput and instability, helping teams understand whether they are releasing changes quickly, safely, and efficiently.
In the AMS context, these approaches can be adapted to enterprise application management. It’s not enough to know how many tickets have been closed. We need to measure the extent to which the service reduces instability, rework, downtime, the development backlog, and the recurrence of issues.
| Measurement Area | Advanced AMS KPI | Operational reading |
|---|---|---|
| Continuity | MTTR for recovery | How quickly service is restored |
| Stability | Recurring incidents as a percentage of total incidents | How often the system experiences the same problems |
| Quality of Change | Change failure rate | How many changes result in anomalies or rollbacks |
| Efficiency | Tickets Resolved Without Escalation | How effective are the knowledge base and first-level support? |
| Evolution | The Relationship Between Corrective and Evolutionary Actions | The extent to which AMS focuses on improvement, automation, and optimization |
| User Onboarding | Repetitive Requests by Functional Area | Where training, UX, automation, or process review are needed |
These metrics make it possible to transform the service into a measurable governance framework. The monthly view of tickets remains useful, but it must be supplemented with quarterly analyses of trends, causes, application areas, and impact on processes.
Automation, AI, and Knowledge Management in AMS
The evolution of the AMS also involves automation. Manually managing all requests is not sustainable as the number of applications, integrations, users, and channels increases. However, automation without governance can lead to a lack of transparency. For this reason , AI in AMS must be introduced with clear objectives: to reduce repetitive work, improve request classification, speed up access to knowledge, support agents, and maintain human oversight of complex cases.
According to PagerDuty’s 2025 State of Digital Operations Report⁶, 64% of respondents expect an increase in IT operations budgets in 2025 to fund investments in efficiency, resilience, and operational excellence; 53% of CIOs and CTOs consider AI agents central to the future of IT operations. These figures point to a trend: application support is shifting toward models driven by automation and AI.
In our approach, AI does not replace practical expertise. It makes it more accessible and reusable. A structured knowledge base can help the AMS team quickly retrieve similar cases, previously implemented solutions, known configurations, functional documentation, integration specifications, and process guidelines. This reduces analysis time and reliance on individual experts, especially in complex SAP environments or highly specialized industries.
Observability also becomes part of the model.
In its *State of Observability 2025*⁷, Splunk reports that 73% of respondents have experienced outages caused by ignored or suppressed alerts, while 43% say they spend too much time responding to alerts. The problem is not a lack of signals, but rather the quality of the signal and the ability to turn it into action. An advanced AMS must therefore filter out noise, correlate events, prioritize them, and link the technical anomaly to the affected business process.
Regesta’s Role: Application Expertise and Process Vision
For us, an advanced AMS stems from the integration of technical expertise and an understanding of business processes. Applications are not isolated entities: they support sales, purchasing, production, logistics, quality, maintenance, finance, and customer service. Every change, every error, and every slowdown affects the way the company operates.
That is why the service must be built on multidisciplinary expertise:
- functional consultants
- technical specialists
- SAP experts
- integration specialist
- data specialist
- process figures
- teams dedicated to automation
In an industrial landscape, this interpretation is even more important. The ERP system interfaces with MES, field systems, IoT platforms, analytics tools, and factory applications. Support must understand the interdependencies between these levels, because the cause of a problem is often not located at the same point where the user perceives the malfunction.
A blocked order may be due to incomplete master data. A scheduling issue may result from outdated machine data. A discrepancy in reports may stem from a misaligned integration rule. A duplicate ticket may indicate a non-standardized process. The advanced AMS identifies these relationships and translates them into actions.
How to Build an AMS Model Focused on Improvement
Building an advanced AMS requires a systematic approach.
Perimeter Mapping
The first step is to map out the scope: applications, processes, users, integrations, SLAs, tools, and teams involved.
Structured Intake Process
The second phase is structured case management: classification of requests, definition of support levels, escalation rules, tracking, and the knowledge base.
Measurement and Evolution
The third phase is measurement: KPIs, dashboards, recurring analyses, reporting, and periodic reviews. The fourth phase is evolution: improvement backlog, automation, training, change governance, and application roadmap.
One point needs to be clarified: continuous improvement is not the same as continuously adding features. Sometimes improvement means simplification. Reducing unnecessary customizations, eliminating workarounds, standardizing workflows, updating documentation, correcting data, streamlining authorizations, and consolidating integrations. In an SAP environment, this approach aligns with the Clean Core principle: reducing complexity, decoupling extensions from the SAP standard, and maintaining a reliable and flexible landscape. SAP Cloud ALM explicitly includes these objectives among the benefits associated with adopting Clean Core.
The advanced AMS thus serves as an ongoing safeguard for the quality of the information system. It does not intervene only after go-live; rather, it supports the entire application lifecycle: stabilization, operation, evolution, innovation, and rationalization.
AMS as a Driver of Continuity and Innovation
Companies expect two things from their application systems that often seem to be at odds: stability and change. On the one hand, operational continuity is needed. On the other, new features, integrations, automations, updates, better data, and faster processes are required. An advanced AMS brings these two needs together.
Stability without progress leads to the perpetuation of technical debt. Uncontrolled evolution leads to instability. The correct model is a managed balance: measure, decide, act, verify. Technical support remains essential, but it is integrated into a broader framework, where every request contributes to shared knowledge and every anomaly can become an opportunity for improvement.
For us at Regesta, AMS therefore means ensuring that the systems operate smoothly, but also helping companies use them more effectively. A well-established service reduces response times, improves the quality of changes, strengthens internal knowledge, makes processes more reliable, and frees up resources for higher-value activities.
From this perspective, AMS represents the point at which the project continues to generate value over time, without being reduced to mere technical assistance.
Would you like to know how our AMS can help you manage your organization’s IT systems?
Contact our experts and tell us what you need.
¹ Source: www.mimit.gov.it
² Source: www.reuters.com
³ Source: arxiv.org
⁴ Source: sap.com
