The market category of AIOps always suffered from an identity crisis. When Gartner introduced the term in 2017, a mix of incumbent and insurgent vendors transformed the term into a buzzword. AIOps lacked a clear definition and, because of that, clear boundaries of what an AIOps solution did–and didn’t–do.
While I’ve nudged various analyst firms to move past the AIOps moniker and define the next phase of what it could become, Gartner has taken the first step by rebranding AIOps as Event Intelligence Solutions in its new Market Guide for Event Intelligence Solutions (paywall).
The Identity Crisis of AIOps
The problem with AIOps wasn’t that analyst firms wrote about it. The problem was that vendors pitched AIOps as solutions ready to solve all operational problems. This overpromising diluted the value proposition. Was AIOps automation, anomaly detection, or log analysis?
This muddled story created confusion in the buyer’s mind. It lacked a coherent, differentiated value proposition. The problem wasn’t only semantic. Most of the products relied on rule-based correlation and statistical analysis rather than AI.
The Promise vs. Reality Gap
Implementations often failed to deliver on expectations. Hype-based decision making on the part of IT operations staff made AIOps purchases more about being on the latest technologies rather than driving business value with realistic and achievable use cases. Acquiring technology itself became the objective instead of the outcomes possible with it.
Because of this, many organizations struggled to achieve value beyond the basic use cases of noise reduction and correlating events. IT leaders sensed a lack of value in their investment, making it impossible to justify the costs incurred.
IT leaders were promised automation, reduced toil, and more effective staff. Instead, they got months-long integrations hamstrung by poor data quality and data access challenges. The real ROI from AIOps efforts was the effort of trying to deploy AIOps in the first place: process improvements, fixing team workflows, and better data hygiene.
What Makes Event Intelligence Solutions Different?
Renaming AIOps to Event Intelligence Solutions narrows the scope and brings much needed focus to specific applications and use cases. EIS specifically applies AI, ML, and analytics to cross-domain events from monitoring and observability tools to improve response processes.
This redefined approach clarifies three primary objectives:
Augmentation - Reducing manual effort in analyzing event volumes through correlation and providing anomaly detection capabilities
Acceleration - Enabling faster triage and response through predictive insights and suggested remediation paths
Automation - Working toward automating response processes as completely as possible
Market Convergence and the GenAI Factor
The EIS market is experiencing significant convergence with adjacent technology spaces. Infrastructure monitoring vendors, observability platforms, and IT service management tools are all expanding into EIS territory.
Simultaneously, generative AI is transforming capabilities and expectations. Vendors have rapidly implemented large language model capabilities to provide natural language issue summaries, context-rich insights, and collaboration features integrated with tools like Microsoft Teams and Slack.
This evolution promises increasingly sophisticated agentic models addressing broader aspects of event response and remediation, potentially revitalizing interest in fully automated remediation.
The Data Challenge: You Can't Escape It
Despite technological advances, success with EIS remains fundamentally tied to data quality and integration. Advanced analytics cannot overcome poor or incomplete input data.
Organizations implementing EIS need several data prerequisites:
High-quality cross-domain event sources from well-implemented monitoring
A mature configuration management database providing dependency information
For advanced scenarios: comprehensive service definitions, escalation paths, resolver groups, historical records, and knowledge bases
Successful implementation requires cross-functional collaboration and executive support to ensure access to all necessary data sources.
Process Maturity Cannot Be Ignored
Beyond data challenges, EIS success depends on the boring but essential work around process maturity and organizational adaptability. Organizations must be willing to modify existing workflows, responsibilities, and disrupting the norms between teams.
Success requires maturity across event management, incident response, change management, configuration processes, and automation workflows. Some vendors now partner with clients to develop implementation roadmaps aligned with process maturity levels.
This is arguably the biggest lift for teams hoping for a successful EIS deployment. Technology is easy. People are hard.
Key Takeaways for IT Operations Leaders
For organizations considering EIS implementation, several recommendations emerge:
Focus on specific business problems and measurable benefits rather than hyped AI capabilities
Assess data quality requirements and process maturity needs before implementation
Develop a comprehensive IT operations management strategy that positions EIS within a broader tooling portfolio
Prepare for necessary changes to existing processes, roles, and organizational culture
The shift from AIOps to Event Intelligence Solutions reflects a market maturing beyond hype toward delivering specific, measurable value. However, success still depends on organizational readiness regarding data quality and willingness to embrace necessary process changes.
