Operational maturity is vital to organizational success as tech systems continue to evolve and become more interconnected. Operational maturity measures how well your organization manages, scales, and improves critical operations work, from incident response to service reliability.
Understanding where your business sits within a maturity model helps you align your digital operations with broader business goals. It also provides a structured view of process maturity—highlighting opportunities to move from reactive firefighting to efficient, effective incident management.
Learn what operational maturity means, how to assess it, and what it looks like at each stage of organizational maturity.
What is operational maturity?
Operational maturity measures the consistency, reliability, and resiliency of a company’s digital operations. This includes their IT infrastructure, workflows, processes, and the cross-functional alignment between business and technical teams.
Simply put, operational maturity reflects an organization’s ability to effectively manage critical operations work. From rapid issue identification and prioritization to swift resolution and clear communication with stakeholders, minimizing disruptions and ensuring a smooth customer experience.
What is an operational maturity assessment?
An operational maturity assessment is a structured evaluation that helps organizations measure how effectively their IT operations, processes, tools, and teams support the overall business.
An assessment helps teams identify strengths, gaps, and opportunities for improvement across key areas such as incident management, automation, monitoring, and service reliability.
In a technology-driven world, downtime and service disruptions are inevitable. By assessing maturity levels, organizations gain a clear understanding of where they stand and the steps they must take to go from reactive firefighting to proactive, scalable, and resilient operations.
To assess their current operational maturity, teams should:
- Benchmark themselves against best practices and identify areas for improvement.
- Document their current and desired future state so it can be built into their strategic roadmap.
- Identify key performance indicators (KPIs) to measure success and set goals for the business.
To be proactive and reduce service disruptions, companies need to understand their current operational maturity level, identify their ideal future state, recognize roadblocks and issues, and develop a plan for building operational excellence.
Benefits of achieving operational maturity
Achieving operational maturity improves day-to-day efficiency and transforms how teams operate. Some key benefits include:
- Faster incident resolution: Mature operations have clearly-defined processes, automated workflows, and integrated monitoring tools helping teams identify and resolve incidents faster to minimize downtime and customer impact.
- Improved service reliability: With proactive monitoring and preventative maintenance, organizations can deliver more consistent and dependable services, boosting customer satisfaction and trust.
- Greater efficiency: Operational maturity encourages organizations to automate repetitive, error-prone tasks, speed up delivery, and allow teams to focus on strategic initiatives.
- Enhanced collaboration and accountability: Understanding processes, roles, and metrics promotes cross-functional collaboration and accountability, breaking down silos between development, operations, and business teams.
- More effective risk management: Teams are better equipped to detect and mitigate risks before they escalate.
- Data-driven decision-making: Setting KPIs and ongoing reporting helps teams make informed decisions, track progress, and continuously improve performance.
The five operational maturity levels
These stages demonstrate how companies can go from reactive firefighting to proactive incident response.
1. Manual: Issues are identified by customers
Companies operating at the manual stage often lack real-time visibility into production issues, which means problems frequently go unnoticed until customers report them. This reactive approach delays response times and can threaten the organization’s reputation and financial performance. With minimal to no integration of AI or automation, these companies struggle to proactively identify and resolve critical incidents.
What this looks like for a business: In this stage, organizations may have significant resources, but they lack visibility into production issues and rely on manual processes to detect and resolve incidents. Critical issues may go unnoticed until a customer raises a complaint, triggering a slow and often disorganized response. Automation is limited or requires expert intervention, and the organization has not built AI systems into their workflows.
What to expect at this stage:
- Reactive issue identification: Issues are first identified by customers rather than internal teams.
- Slow resolution times: Critical issues must be manually prioritized, increasing mean time to acknowledge (MTTA) and mean time to resolution (MTTR).
- Workload strain: Specific individuals have the expertise to resolve issues, creating workload strain and hindering scalability.
- Limited AI and automation: AI tools and systems are not used in operational workflows. Automation requires subject matter expertise to operate effectively.
- Inconsistent performance measurement: Businesses in this stage don’t have a unified approach for defining and measuring service level agreements (SLAs) and service level objectives (SLOs). This makes it difficult to track progress and identify areas for improvement.
2. Reactive: Companies are stuck in firefighting mode
In the reactive stage of operational maturity, companies are starting to invest in visibility tools, but the data may be disparate, making it difficult to act on. Teams remain stuck in firefighting mode, reacting to problems rather than preventing them. Disconnected automation tools exist in silos, offering limited value without a unified strategy or integration across systems.
What this looks like for a business: Monitoring tools are in place but aren’t fully integrated or strategically aligned. Teams are inundated with alerts but can’t filter out noise, leading to delays in addressing real problems. When an issue arises, teams scramble to diagnose and fix it, often without clear ownership or coordination. Automation and AI may exist, but they offer limited value because they’re used inconsistently across departments.
What to expect at this stage:
- Information overload: Teams are bombarded by alerts, making it challenging to prioritize critical issues.
- Siloed operations: Technical teams lack collaboration and real-time communication, hindering efficient problem-solving and accountability.
- Ineffective escalation: There’s no straightforward way to escalate customer-reported issues to the right technical team, delaying effective resolution.
- Limited customer communication: Customers are not informed about performance issues until they experience them.
- Inconsistent automation: Teams use fragmented automation tools without a standardized approach or guidelines.
- Basic AI adoption: AI implementation remains isolated to specific use cases.
3. Responsive: Addressing issues when they occur
Organizations in the responsive stage have better tools and systems in place, including automation and machine learning for faster issue identification and reduced alert fatigue. This gives them more visibility into issues, allowing them to address problems as they arise versus being in reactive mode.
What this looks like for a business: Teams in this stage are shifting from reactive to more structured and coordinated practices. Monitoring systems are better tuned, alerts are more actionable, and there’s a growing culture of collaboration across teams. While not yet fully proactive, the organization can typically respond to issues more quickly and with less disruption. Escalation paths are defined, and teams are starting to use automation and AI applications.
What to expect at this stage:
- Streamlined response: Responses are well coordinated, with key stakeholders automatically notified and involved as needed.
- Direct support channels: Support agents have direct communication with on-call technical teams, supporting the swift resolution of customer-reported issues.
- Unified visibility: Integrated tools share real-time data, allowing customer-facing teams to provide relevant context to technical challenges impacting customers.
- Inconsistent AI integration: Guidelines and standard AI use cases exist, but adoption and effectiveness remain uneven across teams.
- Standardized automation: Automation procedures are standardized across the organization, ensuring consistent and faster resolution while minimizing human error.
4. Proactive: Seamlessly coordinating issue management
At this stage, teams have established effective systems to prevent and address issues and are using AI and automation in day-to-day operations.
What this looks like for a business: In the proactive stage, teams have shifted from reactive operations to preventing issues before they impact customers. Incident monitoring is integrated across systems, and alerts are refined to highlight only meaningful anomalies. Automation is baked into daily workflows, reducing manual effort and speeding up response times.
There’s strong cross-functional collaboration with shared ownership of reliability, and performance data is regularly reviewed to drive continuous improvement. AI tools support predictive analysis and early warning systems, helping teams act on potential issues before they escalate.
What to expect at this stage:
- Unified incident response: Support and engineering teams collaborate seamlessly when customer-facing issues arise, ensuring a quick and coordinated response.
- Consistent performance metrics: SLAs and SLOs are consistently defined across technical and support teams, ensuring alignment on performance goals.
- Proactive customer communication: The organization proactively communicates with impacted customers, minimizing disruption and maintaining trust.
- Advanced automation: Self-service automation capabilities enable teams to handle routine scenarios more efficiently.
- Proven AI value: Standardized AI processes deliver tangible results across enterprise operations.
5. Preventative: Getting ahead of issues before they start
At this stage, teams practice optimal operational maturity, preventing issues rather than reacting to them. They have incorporated best practices for incident prevention and resolution and worked AI and automation into their daily operations.
What this looks like for a business: At this stage, operations are fully optimized, data-driven, and aligned with business strategy. Teams work autonomously, maintaining a commitment to reliability, performance, and customer satisfaction. Automation and AI are used consistently for routine tasks, predictive maintenance, and incident prevention.
The organization has reliable access to real-time data and AI-powered insights to continuously improve services, reduce risk, and drive innovation. SLAs and SLOs are embedded into product and engineering workflows, and teams track performance across all levels of the business. Reliability, efficiency, and customer experience are no longer reactive goals—they’re part of the company’s DNA.
What to expect at this stage:
- Frictionless resolution: Common incidents are resolved quickly, often without human intervention, minimizing disruption for customers and employees.
- Reduced busywork: Teams are using ML and automation to anticipate potential
- issues, and deflect unnecessary work to free up time for employees to focus on more strategy and innovation.
- Proactive customer care: Thanks to AI and ML systems, support agents can proactively inform customers about potential issues before they occur.
- Advanced AI operations: Predictive AI capabilities prevent incidents and optimize system performance automatically across the enterprise.
- Automated compliance: Automation safeguards adherence to SLAs and SLOs, ensuring consistent performance and exceeding customer expectations.
How to improve operational maturity
As teams work to achieve optimal operational maturity (stage five), they can implement changes to improve systems and processes across the organization.
Perform maturity assessments
Teams can’t improve without benchmarking their current state. In order to move forward, teams must measure their current state, identify gaps and areas for improvement, and develop a strategic plan to set their teams up for success.
Break down silos and improve collaboration
Teams must work to integrate data across systems, support platforms, and customer records to create a single source of truth. This centralized approach reduces context switching, streamlines workflows, and empowers employees to focus on high-impact work.
Companies must foster a culture of psychological safety, prioritizing knowledge-sharing over blame and cross-functional collaboration to ensure everyone has the information they need to operate effectively.
Streamline and simplify operational processes
Organizations can work to eliminate excessive tools, alerts, and manual processes that create operational drag. By proactively standardizing workflows and aligning teams around clear best practices, they can reduce overhead and mitigate risk. Simplifying operations helps teams move faster, stay aligned, and focus on strategic initiatives.
Use AI and automation strategically
Teams should implement AI and automation across the entire incident lifecycle, from alert routing and triage to communication and post-incident reviews. These technologies help reduce alert fatigue, ensure people have the right information at the right time, and speed up resolution.
Companies should select tools that integrate with their existing systems and free employees from repetitive, manual work, allowing them to focus on innovation and long-term growth.
Focus on customer outcomes
Teams must take a data-driven approach to ensure digital experiences consistently meet customer expectations. This includes gathering insights from support tools, observability platforms, and customer feedback to minimize disruptions and deliver seamless service.
When incidents occur, companies should acknowledge issues early and communicate clearly with timelines and updates. Engineering and support teams work together to ensure a coordinated response. Teams can use automation and AI to prevent common issues and improve resolution times. Post-incident reviews should focus on identifying root causes and implementing improvements to avoid future disruptions.
Operational maturity doesn’t happen overnight—it’s built through intentional strategy, cross-functional alignment, and the smart use of data, automation, and AI. By assessing your current state, eliminating silos, simplifying workflows, and focusing on customer outcomes, you can move confidently along the maturity curve.
Wherever your organization is on its journey, investing in operational maturity will help you reduce risk, accelerate innovation, and deliver more reliable customer experiences. Explore how PagerDuty Advance and our operational excellence methodologies can support you every step of the way.