As digital reliability becomes increasingly critical to business success, organizations must mature their application support operating models to mitigate risks and enable a seamless customer experience. Without a well-defined framework, businesses risk significant financial losses, operational inefficiencies, and diminished customer trust. The 2024 CrowdStrike IT outage, which reportedly caused an estimated $5B in direct losses[1] for Fortune 500 companies, highlights the growing financial risks of digital failures. While this incident stemmed from a faulty software update, it underscores the need for organizations to have resilient disaster recovery plans and adaptable support models to minimize downtime and disruption.
A robust support operating model enhances developer productivity by enabling engineering teams to increase their focus on innovation while maintaining critical business functions, ultimately improving talent retention. New AI tools are aiding this process and improving employee experience as well.
In an era where both internal and external stakeholders demand efficiency and reliability, organizations that invest in resilient support models position themselves for long-term success. To achieve this resilience, organizations must first understand the different approaches available and where they stand on the maturity spectrum.
There is no one-size-fits-all approach to application support. The right model depends on several factors, including business need, size, and complexity. Based on our experience with Fortune 500 clients, we have identified three common approaches, ranging from the least to most mature:
In this traditional model, a dedicated support team is responsible for incident response, troubleshooting and maintenance. While this approach reduces operational burden on development teams, it often results in slower resolution, knowledge gaps, and inefficiencies.
This approach involves collaboration between development and support teams, improving response times. There can be multiple flavors within this category:
At the highest level of autonomy, development teams take complete ownership of applications, including support and maintenance. Enabled by self-service platforms and automation, this model facilitates the fastest incident resolution times and drives continuous improvement by ensuring developer accountability. Growing use of AI-driven observability, self-healing systems, and other automation tools can reduce burnout risk for teams handling both development and on-call support. However, this model may not be ideal for every organization due to the potentially higher costs associated with hiring skilled developers who also have operational expertise.
Each of these models has trade-offs, and organizations must evaluate priorities and capabilities to determine the best approach and target maturity level (the highest may not be the best for every organization). Many organizations may operate in a hybrid model. The key to success lies not just in choosing a model, but in adapting it over time to balance efficiency, productivity and business resilience.
Regardless of which model an organization chooses, success depends on how effectively it is implemented. Here are five best practices to optimize your support model:
When designing an application support model, it is crucial to understand the needs of both internal (developers, business teams) and external (end-users) customers. Organizations should consider:
By adopting a customer-centric approach, organizations can ensure alignment with business needs.
As organizations grow, especially through mergers and acquisitions, support responsibilities often become fragmented, with multiple teams managing the same capabilities across different business units. For one hospitality client, this disjointed structure often led to confusion, inefficiencies, and delayed resolutions. The Metis Strategy team applied a capability-driven approach to ensure teams were organized to deliver business value with the right skills, expertise, and processes. By aligning support teams with core competencies, such as platform reliability, security, or incident management, organizations can reduce redundancies and streamline workflows.
As organizations increasingly modernize and move to the cloud, the application and infrastructure layers become increasingly intertwined. Cloud environments abstract and automate many traditional infrastructure concerns, which can require platform engineering teams to bridge the gap between development, infrastructure and operations by providing automation, self-service tools, and best practices for deployment, observability and cost optimization.
This evolution reshapes understanding of application support. Instead of solely addressing application issues, support teams must take a full-stack, proactive approach—leveraging platform engineering to monitor, automate, and secure both applications and infrastructure. Support teams must collaborate across disciplines to troubleshoot across the entire tech stack to prevent downtime and optimize performance.
Effective application support requires collaboration across multiple functions. Organizations should proactively seek to understand some key perspectives with each group of stakeholders:
By addressing these questions upfront, organizations can avoid roadblocks and create a more sustainable model.
Shifting to a mature support model isn’t just about processes and tools. It requires a cultural shift, making executive storytelling and change management critical. Metis Strategy recently worked with a client shifting to a new model by:
A well-communicated change plan can foster trust, reduce friction, and accelerate adoption.
As organizations redefine their application support model, it is important to recognize that simply implementing the latest AI solution is not enough. Success depends on balancing the right tools with the right people and processes. When properly integrated, AI and human expertise can reduce costs, improve efficiency, and enhance customer satisfaction. Now is the time to build a support model that ensures long-term success.
Future articles will explore how AI is transforming roles, responsibilities, and workflows within different application support models.
[1] https://www.reuters.com/technology/fortune-500-firms-see-54-bln-crowdstrike-losses-says-insurer-parametrix-2024-07-24/
