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AI User Insights
Surface insights into how your business applications are used to drive productivity, adoption, and ROI
How AI User Insights Drives Enterprise Success
The benefits from implementing leading business applications such as Microsoft Dynamics 365 is how employees adopt and use the functionalities to improve daily processes, productivity and efficiency.
But how can you gain insights into how employees are using business tools and how these applications align with business processes?
At any stage of an IT project—from scoping and migration to pilots, proofs of concept, and rollouts—user behavior is a key factor in your analysis to understand priorities, ROI, and training needs.
How Does AI User Insights Support IT and Process Optimization?
AI User Insights integrates with Dynamics 365 to track user activity aligned with workflows, providing interactive dashboards and reports.
Detect Unusual User Behavior Patterns
Outliers and Anomalies can signal potential issues or improvement opportunities.
Detect Trends And Usage Patterns
Visualizations and metrics to identify trends, serving as benchmarks for process improvement and automation.
Personalize User Experience
Admins can give tailored recommendations to each user based on usage trends, enhancing productivity and user satisfaction.
Enhance Application Development
Reveal pain points, feature preferences, and usage patterns. Businesses can prioritize enhancements and drive effective innovation.
AI User Insights is Helpful for...
- Improve case-handling process compliance. Define the expected service workflow as a Path and use path insights to identify where agents deviate or slow down—so you can target coaching and streamline steps.
- Detect unusual handling patterns that signal quality or training issues. Use outliers and anomalies detection plus usage trends/outliers reports to spot atypical behavior enabling earlier intervention and more consistent service execution.
- Reduce resolution time by pinpointing workflow inefficiencies. Use the time-based reports to identify which actions consume the most time and benchmark improvements after process changes or automation.
- Target training during HR system rollouts. Use adoption speed and recurrence analysis to identify teams struggling with new HR workflows and focus enablement where adoption is lagging.
- Identify where employees get “stuck” in HR self-service workflows. Define HR workflows as Paths and analyze each step to locate the exact steps causing friction, so HR can simplify guidance or adjust the process.
- Personalize HR app guidance without profiling. Use the capability for admins to provide tailored recommendations based on usage trends to help different employee groups complete HR tasks more smoothly.
- Create process benchmarks and spot improvement opportunities. Use visualizations and metrics to detect trends and usage patterns as baselines, then track changes over time using time evolution reports to quantify improvements after changes to workflows or automation.
- Segment users by behavior to explain performance variation. Use user clustering to identify groups following different paths or struggling with specific tasks—supporting root-cause analysis and targeted change management.
- Deep-dive into individual user journeys for evidence-based recommendations. Use user timeline/activity history and time-slot activity patterns to understand how work is actually performed and recommend process or UI changes grounded in observed behavior.
- Standardize campaign execution workflows. Define the workflow as a Path and use path insights to spot where marketers diverge from the intended process—improving consistency and reducing rework.
- Increase adoption after new marketing features or rollouts. Use Adoption & Recurrence to see whether teams are actually using new capabilities after a pilot or rollout—so enablement can focus on groups with slow adoption.
- Find bottlenecks in day-to-day marketing operations. Use time evolution reports and duration/time spent by action/path to benchmark how long key tasks take and identify steps that are slowing productivity for targeted process simplification or automation.
- Improve CRM process compliance. Model the desired sales motion as a path and analyze the funnel and step timing to identify where reps drop steps (e.g., missing qualification actions) or spend too much time, enabling targeted coaching and streamlined workflows.
- Identify unusual rep behavior that may indicate risk or inefficiency. Use outliers/anomalies and usage trend/outlier insights to flag atypical patterns enabling earlier support and improved sales hygiene.
- Segment rep behaviors to tailor enablement. Use user clustering to group reps by behavioral patterns across actions/processes, then use user timeline/activity history to validate where each cluster struggles—so training and sales playbooks can be personalized based on observed usage trends.
- Optimize month-end close / period-end workflows. Define the finance close sequence as a Path and review path insights to locate bottlenecks and long-duration steps—supporting process improvements and more predictable close cycles.
- Detect compliance or control exceptions in finance processes. Use outliers and anomalies plus time-based reports to spot unusual user behavior patterns around critical ERP actions, surfacing potential issues and improvement opportunities.
- Drive adoption and reduce training gaps post-implementation. Use adoption speed and recurrence reporting to track which finance teams are engaging with new workflows after pilots/rollouts and use user activity timelines to pinpoint individuals or groups who need targeted support.
- Improve adherence to warehouse execution processes (receive → put-away → pick → pack/ship). Configure warehouse workflows as Paths and analyze path funnel + step timing to identify where operators deviate or stall, supporting process refinement and targeted training to improve throughput.
- Spot productivity issues and abnormal usage patterns. Use duration/time spent reports and outliers/anomalies to surface unusual behavior or long task durations that may indicate confusion, poor UI fit, or process issues—enabling faster corrective action.
- Benchmark improvements after automation or device/process changes. Use trend metrics and time evolution reporting as benchmarks, then compare pre/post changes to validate whether new warehouse processes reduce time-on-task and increase consistent usage patterns.
What's Inside AI User Insights?
