For many infrastructure and operations teams, incident management remains a reactive process. Tickets are reviewed, prioritized and resolved one at a time, often under pressure and with limited context. While most organizations have mature ticketing systems in place, they still struggle to consistently identify what truly matters.

The challenge is not a lack of data but how it is used. Prioritization is typically driven by static fields such as category, impact, or urgency. In practice, these fields are often applied inconsistently or used as placeholders, leading to unreliable reporting. At the same time, the most valuable information, what actually happened and why, is captured in free-text fields like summaries, descriptions, and resolution notes but rarely analyzed at scale.

This disconnect makes it difficult to see the full picture, increasing the risk of focusing on lower-impact issues while overlooking broader patterns and systemic risks.

A shift in perspective: Treating incident data as a strategic asset

We often see organizations approach incident management one ticket at a time rather than as a connected system of signals. This leads to short-term fixes instead of long-term improvements. In addition, structured ticket categorization is often applied inconsistently, and the most informative data is captured in unstructured text that is rarely analyzed at scale. This creates a disconnect where reporting may suggest one set of priorities while underlying issues tell a different story.

A more effective model is to treat incident data as a strategic asset. When structured data and unstructured free-text content are analyzed together, it becomes possible to identify recurring issues, uncover root causes, and prioritize based on overall impact rather than individual ticket attributes. Multiple incidents initially categorized differently may in fact share a common root cause, indicating a broader infrastructure issue.

Recent advances in data and AI platforms are making this model more practical to adopt at scale. By consolidating ticket data into a centralized analytics environment and applying AI at scale, organizations can achieve consistent and explainable classification without the need for complex machine learning pipelines.

With these insights in place, teams can move beyond isolated ticket resolution and begin addressing underlying issues. In practice, this model often reveals patterns that traditional reporting fails to capture.

In one example, a leading U.S.-based energy company explored this model through a proof of concept. By analyzing both structured and unstructured ticket data together, the team was able to surface hidden patterns and improve how incidents could be classified and prioritized, highlighting the potential for more proactive operations.

From data to decisions: Enabling faster, smarter prioritization

Once incident data is consistently classified and accessible, the next challenge is enabling teams to act on it quickly.

Built on the Databricks Data Intelligence Platform, the AI Ticket Classification accelerator consolidates ticket data into a centralized analytics environment and applies AI at scale by evaluating ticket content against defined rules and returning a category, reason, and confidence score. This enables consistent and explainable classification at scale without the need for complex machine learning pipelines.

A materialized view continuously maintains an analytics-ready dataset within the Lakehouse, providing near real-time visibility into incident trends and patterns.

This is where conversational analytics plays an important role. Using capabilities such as Databricks AI/BI Genie, teams can interact with their data using natural language without needing to understand the data model or write queries.

For example:

  • What is the root cause of this ticket and how has it been resolved in similar cases?
  • Which tickets should we prioritize first and why?

In practice, this allows users to quickly surface related incidents, explore trends across tickets, and gain insight into prioritization factors. This reduces time to insight from hours to minutes and improves confidence in decision-making.

Moving beyond dashboards: Why conversational analytics matters

Traditional reporting tools provide visibility into what has already happened but often fall short when teams need to explore and validate trends across incidents.

Conversational analytics provides a more accessible way to work with ticket data. Instead of relying on predefined dashboards or queries, users can ask questions in natural language and quickly surface trends, relationships, and potential root causes.

In the context of incident management, this makes it easier to identify which issues require attention, understand how similar incidents have been handled, and prioritize actions more effectively.

Where to start: Practical steps to modernize incident management

Organizations looking to improve prioritization and reduce escalations can begin with these focused steps:

  • Standardize classification using AI within a centralized analytics environment
    Rather than relying solely on manual categorization or separate machine learning pipelines, organizations can apply AI to consistently classify tickets based on their full context based on free-text fields.
  • Enable self-service conversational access to insights
    Even with better data, access to insights remains a barrier. Many teams still depend on specialized skills to query data or build reports, which slows down decision-making. Conversational interfaces provide a more accessible way to interact with data and accelerate decision-making across teams.
  • Leverage a unified data platform for governance and scale
    Centralized platforms support secure and governed access to data.

These steps help bridge the gap between data and decision-making, allowing teams to act on insights rather than assumptions.

The impact: moving from reactive to proactive operations

Taking this approach can deliver measurable improvements across infrastructure and operations:

  • More accurate and consistent incident classification
  • Earlier detection of systemic risks, helping reduce escalations
  • Smarter prioritization focused on high-impact and recurring issues
  • Faster root cause analysis and resolution
  • Improved SLA performance and overall system stability

Collectively, these outcomes support a shift from reactive incident response to more proactive and insight-driven operations.

Looking ahead

As environments grow more complex, the ability to interpret and act on incident data becomes increasingly important. Organizations that continue to rely on manual analysis and inconsistent categorization risk missing the signals that matter most.

By leveraging modern data and AI capabilities and enabling more intuitive access to insights, organizations can take a more strategic approach to incident management, one that prioritizes not only what needs to be fixed but what should be prevented.

If you are exploring how to improve prioritization, reduce escalations, or better understand the root causes behind incidents, this represents a practical starting point for modernizing incident management.