What QA checks validate AI summarization doesn't capture breakout-room or off-record content?

Technical rollback plan

• Phase 1: Pre-deployment preparation
• Phase 2: Execution (upon regression)
• Phase 3: Post-rollback

Communications plan

• Phase 1: Pre-deployment
• Phase 2: Execution (upon regression)

When enabling new Copilot features this month (September/October 2025), you should avoid change-freeze windows related to upcoming public holidays and major quarter-end business deadlines. An effective rollout strategy involves a phased approach to minimize disruptions and manage the impact of any unforeseen issues.

To centralize AI usage logs from multiple suites for audits and cost control, you need a robust logging and observability platform that can ingest, normalize, and analyze data from various sources. This approach involves four key steps: establishing data pipelines, choosing a centralized logging solution, normalizing the data, and using the consolidated data for analysis and reporting. 

• Step 1: Establish data ingestion pipelines

Your chosen platform needs mechanisms to pull logs from every suite your organization uses, whether it's an internal application or a third-party service.

• Step 2: Choose a centralized logging solution

Select a platform that can aggregate and store AI logs at scale.

• Step 3: Normalize and structure the data

Logs from different sources often have different formats, which makes centralized analysis challenging.

• Step 4: Analyze and report on the centralized data

With your logs centralized and normalized, you can use built-in features for audits and cost control.

Here are some open-source AIOps tools that can help with IT operations automation, anomaly detection, and incident management: Prometheus, Grafana, Elastic Stack (ELK), Apache Kafka, OpenTelemetry, Seldon Core, Loglizer, AIOpsTools and Keep.

The tools used in AIOPS are Splunk IT Service Intelligence (ITSI), Dynatrace, IBM Watson AIOps, Moogsoft, BigPanda, AppDynamics and New Relic AI.