Insights - Agentic AI | Ux Design Portfolio

AI-Driven Uptime: How can we bring Effortless Insights for Sustainable Industrial Asset Performance?

My Role

Lead UX Designer

Platform

WebApp

Duration

3 months

As Lead UX Designer for Asset Performance Management (APM) product "Maximo", I led end-to-end Agentic AI design strategy for solution addressing the complete lifecycle for asset performance across large enterprise. My core responsibilities included:

- Defining APMs strategic design vision for Agentic AI: Shaped a holistic user-centric solution that ensures every asset from Centrifugal pump to wind turbines blades is performing as per set Reliability strategy and its rules to enhance it's longevity and leads to sustainability.

- Delivering intuitive workflows: Designed and streamlined solutions for next release as a UX Architect for asset performance, health, monitoring, and predicting through Agents. Simplified the complexity of asset audits, compliance, and optimization.

- Championing data-driven decisions: Created experiences that surface critical asset metrics: maintenance state , health, criticality, and EOL. Ensured Asset Managers and Reliability engineers have actionable insights for reducing costs, minimizing loss, and extending asset life through research and evaluating designs.

- Leading AI-powered innovation: Conceptualized around 5 AI agents and orchestrator tailored for asset managers, dramatically streamlining daily operations, automating routine tasks, and providing proactive intelligence to support decision-making.

- Mentoring and scaling design teams: Guided designers through complex requirements, drove consistency across application capabilities, and instilled a focus on measurable business outcomes by defining success metrics for each agent.

To know about one of the agent read - https://www.ibm.com/new/announcements/maximo-condition-insight

Problem: what is taking more time to the user?

While Asset owners and Reliability Engineers are Maintaining large scale industrial assets they face data fragmentation, unclear priorities in the asset dashboard, leading to Reactive & Risk Based Maintenance.

At present APM tool, Asset Managers struggles with:

Fragmented data views across multiple systems, slowing decision-making.
Unclear priorities, with no quick way to identify assets needing urgent attention.
Manual analysis required to extract actionable insights from complex datasets.
Low trust in AI, limiting delegation due to opaque outputs and setup complexity.

If they do not have system that delivers contextual insights, prioritized urgency, and also works like a trusted assistant, there might be loss of productivity due to shut downs

Impact & Value proposition

How did this project help the business and users?

Increased Asset Uptime & Reduced Unplanned Downtime:
The AI agent helps users make smarter, faster decisions about asset condition and work orders. By surfacing contextual alerts and identifying deteriorating assets, it enables a shift from reactive to proactive maintenance maturity, which is the direct driver of reduced unplanned downtime and maximized uptime.

Improved Efficiency and Time Savings:
12/17 users found the agent to be useful and a time saver. The agent provides contextual alerts (telling where the anomaly is and why it matters) and proactive prompt suggestions (Natural Language Queries), removing the need for deep manual analysis and fragmented data searching.

Research and Ideation

Discovery Methods

Objective

To define value proposition
AI Knowledge of users
Assess cognitive load reduction in daily workflows.
Validate if the agent improved prioritization efficiency.
Test personalization for role-specific needs.

Findings

Before: 14/17 users felt “lost” post-login, overwhelmed by static dashboards.
After: 12/17 reported the agent as a “time saver,” with 80% success in NLQ interactions.
Users valued proactive suggestions but wanted clearer explainability for complex alerts.