Infrastructure and operations teams face growing pressure to improve reliability, reduce incident resolution times, and scale operations without adding headcount. While AI capabilities are rapidly maturing, many organizations struggle to apply them in a practical, operational context that delivers real value.
The Keyva AI Enablement Workshop bridges this gap through a structured, hands on engagement. Rather than focusing on theoretical training, this workshop works directly with your teams, tools, and operational data to design and validate practical AI driven use cases that can be immediately applied and scaled.
Over three days, Keyva engineers collaborate closely with your infrastructure and operations teams to design, prototype, and validate AI powered workflows across incident response, knowledge retrieval, and operational automation, leveraging enterprise AI platforms such as Microsoft Copilot via Azure OpenAI and Claude.
Supported Platforms
The workshop is platform agnostic and designed to work with leading enterprise AI solutions, including:
- Microsoft Copilot (Azure OpenAI)
- Claude (Anthropic)
Scope of Engagement
The engagement follows a structured, three phase approach designed to move quickly from alignment to working prototypes and an executable roadmap.
Context Alignment and Discovery
This phase establishes shared context, identifies high value opportunities, and validates data readiness.
- Stakeholder alignment across platform engineering, operations, and security teams
- Identification of key operational pain points, including incident response, alert fatigue, and ticket quality
- Review of existing tools and workflows across observability, ITSM, and CI/CD
- Identification and evaluation of available data sources, such as logs, alerts, tickets, and runbooks
Deliverable: Data readiness validation and workshop alignment summary.
Hands On AI Use Case Development
This phase focuses on live, collaborative development of operational AI solutions using real tools and data.
- Development of two to three AI powered operational use cases, such as:
- Incident Copilot for alert and log summarization
- Runbook Assistant for knowledge retrieval from internal documentation
- Ticket Enrichment Engine for automated classification and enhancement
- Implementation using enterprise AI platforms, including Microsoft Copilot and Claude
- Prompt engineering and response tuning to improve operational accuracy and reliability
- Application of use cases to real client data where available
- Comparative analysis of AI model outputs
Deliverable: Working prototypes, reusable codebase, and a prompt library.
Optimization and Roadmap
This phase ensures solutions are designed for real world production use and long term scalability.
- Architecture design for production deployment, including Azure native or hybrid models
- Integration patterns for:
- ITSM platforms such as ServiceNow and Jira
- Collaboration tools including Microsoft Teams and Slack
- Observability and telemetry pipelines
- Security and governance considerations, including:
- Data handling boundaries
- Human in the loop controls
- Auditability and compliance requirements
- Prioritization of use cases based on effort and business impact
- Development of a 30 , 60 , and 90 day implementation roadmap
- Executive readout and strategic recommendations
Deliverable: Architecture blueprint, implementation roadmap, and executive readout.
Outcome
Keyva’s AI Enablement Workshop delivers measurable operational improvements and a clear foundation for scaling AI across infrastructure and operations.
- Reduced incident triage time through automated summarization and analysis of alerts and logs
- Improved operational efficiency by eliminating manual, repetitive tasks in ticket handling and runbook navigation
- Increased consistency through standardized outputs such as incident summaries and ticket classifications
- Faster access to institutional knowledge via natural language interaction with runbooks and operational documentation
- Accelerated AI adoption by moving from experimentation to validated, production ready use cases
- A clear, structured path to scale AI across infrastructure and operations with confidence