AI Product Management Case Study

Reducing Unplanned Downtime with AI-Assisted Repair-vs.-Replace Decisions

I helped lead product strategy and delivery for an enterprise asset-management analytics capability that combined IoT telemetry, ERP workflows, Azure, Power BI, and predictive analytics to improve asset-lifecycle and capital-planning decisions.

At a glance

Role Product Owner / Product Manager
Product type Enterprise asset-management analytics and predictive-maintenance capability
Primary users Asset management, operations, maintenance, and capital-planning stakeholders
Outcome Helped reduce unplanned operational downtime by 20%

The problem

Operational teams needed to make high-impact repair-versus-replace decisions using fragmented asset, maintenance, ERP, and telemetry data. Delayed or inconsistent decisions could increase maintenance cost, elevate equipment-failure risk, and contribute to unplanned downtime.

My role

I owned product strategy, roadmap development, backlog prioritization, requirements, stakeholder alignment, delivery planning, and KPI definition. I partnered with engineering, data science, architecture, and business teams to bring predictive capabilities into an enterprise product workflow.

Product approach

We combined real-time IoT telemetry with relevant asset and operational information to support predictive asset-lifecycle decisions. The product was designed to provide actionable decision support rather than treat model output as an automatic replacement for operational judgment.

IoT telemetry
Asset, maintenance, and ERP data
Predictive-maintenance analysis

Repair-versus-replace decision support

Use Case →

Key tradeoff: precision versus recall

A central product decision was how to balance precision and recall. A more sensitive model may flag more potential equipment risks but create unnecessary maintenance work. A more precise model may reduce false alarms but miss assets that require intervention.

I worked with cross-functional teams to evaluate this tradeoff against maintenance cost, equipment-failure exposure, and operational outcomes.

Measurement

  • Unplanned operational downtime
  • Predictive-maintenance impact
  • Data quality and completeness
  • Operational adoption and stakeholder confidence
  • Business outcomes associated with asset-lifecycle decisions

Outcome

The predictive asset-lifecycle management capabilities helped reduce unplanned operational downtime by 20% while improving the quality of asset-management and capital-planning decisions.

What I learned

AI-enabled operational products require more than model performance. Adoption depends on trusted data, understandable recommendations, workflow fit, appropriate human oversight, and metrics that connect predictive quality to business consequences.

Confidentiality note: This case study uses sanitized descriptions and representative visuals to protect confidential information.