The challenge
The challenge
An open-ended endpoint planning problem needed a defined decision model and an approved pilot scope that both technical and executive stakeholders could assess.
Approach
Turn telemetry into a decision
The service used Microsoft Intune device-performance, configuration, and usage data to classify endpoints. Its decision categories were Retain, Redeploy, Right-size, and Replace.
The architecture translated technical recommendations into budget impact. This connected device-level evidence to the planning decisions that business stakeholders needed to make.
Keep the decision logic explicit
The decision architecture combined device telemetry with deterministic classification logic and policy thresholds. AI-generated explanations described the recommendations, while the classification did not rely solely on language-model output.
That separation kept the recommendation explainable and auditable. The evidence and policy threshold could be examined independently of the wording used to communicate the result.
Give the pilot a bounded scope
The initiative progressed from an open-ended business problem through solution architecture and a two-sprint scope with written acceptance criteria. Executive and technical stakeholder alignment led to pilot approval.
The engagement is presented as a pilot. Its decision architecture and approved scope are documented here without treating a pilot recommendation as an organization-wide change already completed.
Connect technical findings to the planning conversation
The useful output was a decision category supported by device evidence and an explanation. Budget impact connected that output to the endpoint planning conversation.
The pilot gave stakeholders a defined way to review the recommendation and its budget implications together. Deterministic rules remained the basis for the classification, with AI used to explain the result.
Inside the implementation
Architecture overview
The pilot kept device classification deterministic. AI described the recommendation, while budget impact connected technical evidence to the endpoint planning decision.
From device evidence to a planning recommendation
- 01
Endpoint evidence
Device data supplies the inputs to the decision architecture.
- Microsoft Intune
- Performance and configuration
- Usage data
- 02
Decision logic
Explicit policy thresholds govern the classification.
- Deterministic rules
- Policy thresholds
- 03
Endpoint recommendation
The output is a defined decision category.
- Retain
- Redeploy
- Right-size
- Replace
- 04
Planning outputs
Two views support review of the recommendation.
- AI-generated explanation
- Budget impact
Decision authority
The language model explains the result. Deterministic classification logic remains the basis for the recommendation.
Delivery boundary
The engagement was a two-sprint pilot. The diagram represents decision support, not automated fleet-wide changes.
Key design decisions
Separate the category from its explanation
A device recommendation can be reviewed against the telemetry and policy threshold that produced it. The AI explanation communicates that result without becoming the source of classification authority.
Translate the technical result into budget impact
The output serves both technical and executive stakeholders. Device evidence supports the recommendation, while its budget implications make it relevant to the planning conversation.
Bound the pilot before wider adoption
The work moved from an open-ended problem through architecture and stakeholder alignment into an approved two-sprint scope. This kept the pilot decision separate from an enterprise rollout decision.
How the workflow fits together
Bring together device evidence
Performance, configuration, and usage data from Intune provide the evidence for an endpoint decision.
Apply the classification policy
Deterministic logic and policy thresholds produce one of the four decision categories.
Explain the recommendation
The AI layer communicates why the category is relevant to the device evidence. The category itself remains grounded in the decision rules.
Review the planning implications
The recommendation and its budget impact are available for stakeholder assessment within the pilot.
Questions for a similar implementation
Use these review points when you assess this architecture for your own environment.
- Can the classification be reproduced from the same telemetry and policy thresholds?
- Does the explanation remain consistent with the deterministic result?
- Are budget-impact estimates clearly distinguished from realized savings?
Results
The outcome
An endpoint-intelligence pilot connected telemetry to explicit decision categories and budget impact, within a defined two-sprint engagement.
Stack
Technology used
- Microsoft Intune
- Device telemetry
- Deterministic policy rules
- AI-generated explanations
Client and delivery-partner names are withheld.