The AMS Operating Model - Reset

The Application Management Services (AMS) operating model is experiencing a significant transformation.

AMS viewed as a support function reliant on service level agreements (SLAs) and ticket metrics, it is now emerging as a vital driver of digital transformation, resilience, and agility.  

AMS will focus on IT operations and also on achieving business outcomes, functioning as a proactive, intelligent, and experience-oriented capability.

This transformation necessitates a comprehensive reevaluation of the operating model, encompassing governance, service delivery, talent, tools, and financial structures. Below is an overview of the evolution of the AMS operating model across essential dimensions.

Governance & Service Management

In AMS, governance is shifting from infrequent reviews and escalation processes to real-time, collaborative oversight. The traditional management of SLAs is being replaced or supplemented by experience-level agreements (XLAs) and metrics tied to business outcomes.

Governance frameworks are expected to connect IT performance directly to key performance indicators (KPIs) such as business process cycle times (e.g., order-to-cash, hire-to-retire), net promoter score (NPS), digital channel adoption rates, customer satisfaction, IT costs as a percentage of revenue, time-to-market for new digital features, and service quality.

Service management is also transitioning from a focus on incidents to one centered on value. The emphasis is shifting from merely closing tickets to reducing disruptions, boosting productivity, and driving quantifiable improvements.

Service providers need to create governance frameworks that integrate both IT and business stakeholders, utilize live dashboards to monitor service health, link technical KPIs to business outcomes, and foster a culture of continuous improvement through data-driven evaluations.

Service Delivery Model

The service delivery model is evolving from a tiered, compartmentalized support system (L1/L2/L3) to a more integrated approach that is product-focused, platform-driven, and enhanced by AI. The introduction of L1.5 support, self-healing platforms, and virtual agents has enabled AMS to address most issues directly at their source.

Principles from DevOps and Site Reliability Engineering (SRE) will be incorporated into these delivery models, fostering a continuous, "always-on" support system that aligns with agile delivery pipelines. Consequently, delivery teams are becoming more interconnected. AMS teams will collaborate closely with development and operations through shared practices, backlogs, and workflows. The emphasis will be on overseeing system reliability, performance, observability, and customer experience throughout the entire lifecycle.

Service providers need to transition from a reactive approach to ticket resolution towards AI-enhanced, product-oriented delivery models. It is essential to organize AMS teams around business capabilities or digital products and to invest in observability, SRE methodologies, and shift-left strategies to promote proactive support.

People & Capabilities

The talent landscape at AMS is evolving in response to the increasing demands of automation, cloud technology, and business integration. Traditional IT support roles are being replaced by versatile teams that merge engineering skills with industry-specific knowledge. Proficiency in automation scripting, AIOps, DevSecOps, and cloud operations is becoming standard practice. In addition to technical expertise, new positions such as Service Experience Designers, Automation Analysts, and Reliability Engineers are emerging.

AMS teams are expected to become more stable and aligned with specific domains, remaining embedded in the same service areas for extended periods. This approach fosters depth and continuity, leading to fewer transitions, improved outcomes, and enhanced productivity.  Soft skills are equally crucial, encompassing collaboration, data storytelling, customer empathy, and adaptability.

Service providers need to redefine their talent models to emphasize T-shaped skills, which combine deep expertise with broad cross-functional collaboration. They should invest in reskilling initiatives, encourage long-term team structures, and cultivate a culture of ownership, curiosity, and continuous learning.

Ways of Working

The future operating model for AMS will be shaped not only by its outcomes but also by its methods of delivery. As organizations progress, AMS functions will embrace modern, collaborative, and agile practices that promote speed, transparency, and adaptability. AMS teams are expected to move away from working in isolation from development, operations, or business stakeholders. Instead, they will function as integrated units, often embedded within agile delivery teams or digital product groups. These teams will engage in shared activities such as daily stand-ups, retrospectives, sprint planning, and backlog refinement, ensuring alignment and minimizing handoffs.

Work will be visualized using tools like shared Kanban boards or service management dashboards. Traditional ticket escalations will be replaced by swarming models, enabling the most suitable expert to respond immediately, regardless of their level or role. This approach will decrease resolution times and enhance team learning.

With distributed teams and hybrid work environments becoming the norm, AMS delivery will facilitate asynchronous collaboration, knowledge sharing, and smooth transitions across different time zones and locations.

Continuous improvement will be embedded in daily practices, driven by data analysis, root cause investigations, and insights from retrospectives. Blameless postmortems will become standard, fostering a culture of learning, experimentation, and resilience. AMS working methods will prioritize flexibility and autonomy.

Service providers should focus on cultivating a culture that supports collaborative, agile, and value-driven working methods. This includes integrating AMS teams with product and DevOps units, adopting swarming and shift-left strategies, and empowering teams with the necessary autonomy, tools, and practices for continuous delivery and improvement.

Processes & Methodologies

The frameworks for AMS processes are evolving from traditional static Standard Operating Procedures (SOPs) to more dynamic, lean, agile, and automation-centric approaches. While ITIL continues to hold significance, it is being updated and integrated as code rather than merely existing as documentation. The concept of change management will shift towards change enablement, emphasizing safe delivery, rapid execution, and automated rollback capabilities. Incident and problem management will be enhanced through the use of predictive analytics and machine learning models for root cause analysis (RCA). Workflows will be managed through automation platforms integrated with CI/CD pipelines and supplemented with telemetry data.

Processes are consistently improved through real-time feedback loops and insights derived from observability data.

Service providers need to invest in modernizing AMS processes to facilitate agile ITSM and DevOps. This includes replacing manual approval processes and escalations with automated workflows and intelligent decision-making, as well as incorporating process improvement cycles into everyday operations by leveraging observability and artificial intelligence.

Tools & Technology Stack

The AMS technology stack is experiencing significant transformation. Traditional ticketing systems are being enhanced by platform-focused ecosystems that incorporate AIOps and observability tools such as Dynatrace, Splunk, and New Relic. Additionally, Intelligent ITSM platforms like ServiceNow, which utilize predictive analytics, and automation solutions such as UiPath and Power Automate are becoming integral. CI/CD toolchains and infrastructure-as-code tools are also part of this evolution. These platforms are being interconnected through APIs, facilitating real-time collaboration, automated remediation, predictive incident management, and data-driven governance.

As clients increasingly demand comprehensive service accountability, service providers must actively invest in developing a composable, integrated toolchain that encompasses the entire lifecycle—from monitoring and triage to resolution and optimization. It is essential to prioritize tools that offer real-time insights, self-service capabilities, and AI-driven automation. While siloed, best-of-breed toolchains may still have relevance, ultimately, all platforms must collaborate to provide seamless, integrated support throughout the application lifecycle.

Financial Management

The AMS commercial model is transitioning from fixed FTE-based contracts to outcome-oriented and consumption-based frameworks. Clients are seeking transparency, flexibility, and alignment with business outcomes. Metrics such as cost-to-serve, self-healing rates, and change success rates are evolving into financial indicators rather than merely operational statistics.

AMS is expected to facilitate chargeback processes, allowing stakeholders to recognize the value of the services provided. Analytics and AI-driven forecasting enable finance teams to simulate various scenarios and make informed investments in modernization or automation.

Service providers should aim to create value-linked commercial models that align service costs with measurable outcomes, while also delivering transparent reporting, predictive forecasts, and automated insights into service consumption and return on investment (ROI).

Customer and Business Alignment

AMS will transition from being solely an IT concern to becoming a strategic enabler for the business. AMS teams will integrate into digital product teams, business operations, and transformation projects. Key performance indicators (KPIs) will focus not only on system uptime but also on revenue generation, user satisfaction, and process efficiency.

AMS teams are anticipated to engage in journey mapping, backlog refinement, and operational assessments, objectives extending beyond mere maintenance, aiming to enhance digital services to be more efficient, effective, and aligned with business strategies.

Service providers must ensure AMS teams collaborate directly with business stakeholders / product owners, communicating in terms of value and outcomes rather than just metrics, and actively sharing responsibility for improvements, innovations, and the execution of roadmaps.

Innovation and Transformation

The future of AMS will serve as a platform for innovation, encompassing AI-driven optimization and low-code experimentation. AMS will lay the groundwork for ongoing transformation, including predictive maintenance, process automation, and real-time insights and visualizations for business operations, as well as agile support for minimum viable products (MVPs) and pilot projects. Innovation labs, proof of concept (POC) pipelines, and agile teams within AMS delivery are expected to facilitate rapid experimentation and enterprise-wide transformation.

Service providers are required to incorporate innovation as a service within AMS, establishing cross-functional teams that prototype, test, and scale new concepts, while leveraging automation and data to identify opportunities and measure success in an iterative manner.

Legacy and Traditional Maintenance 

As digital innovation continues to advance, legacy systems will remain integral to numerous essential business functions, including mainframes, monolithic ERPs, and custom on-premise applications. These systems necessitate organized maintenance, compliance management, and lifecycle support. AMS operating models will adapt and implement hybrid support frameworks that merge modernization strategies with comprehensive legacy maintenance in order to manage technical debt, automate processes where feasible, while gradually modernizing systems. 

Service providers should integrate a balanced approach to modernization and maintenance by developing a dual-speed AMS model. This model should stabilize legacy systems while simultaneously progressing digital platforms, incorporating automation and observability even in traditional settings to enhance efficiency and mitigate risks. 

In conclusion, AMS will evolve from a mere support function into a value-generating engine, serving as a foundation for stability, agility, and transformation. Clients and service providers that redefine their AMS operating models will be better equipped to navigate future challenges, expedite change, and foster smarter, quicker, and more resilient business operations.