I led the experience model for human decision-making around machine intelligence, working across design, research, product and data science.
IBM builds enterprise software and AI systems for large organizations. The work sat in AI-driven IT operations, where models surface incidents, correlations and recommended actions to operations teams working under time pressure.
The AIOps organization builds products that apply machine intelligence to enterprise IT operations — event correlation, incident insight and recommended action. The core design problem is authority: what the system decides, what the operator decides, and how confidence is communicated.
30–45%
Faster task completion (selected flows)
05
Products supported
Apr 2018 – Nov 2020
I led the experience model for human decision-making around machine intelligence, working across design, research, product and data science.
Direct organization
10 designers
Extended cross-functional team
Product Management · User Research · Engineering · Data Science
Portfolio
AIOps incident and operations experiences
Geographies
India · United States
Leadership partners
Product leadership · Data science leadership · Engineering leadership
Period
Apr 2018 – Nov 2020
Note
Extended cross-functional partners are collaborators, not direct reports.
Recurring mechanisms that affected multiple projects.
One language across data science, product and design for how intelligence appears in the product.
Reusable interaction patterns for explanation, confidence and reversibility.
Alongside AIOps, I worked across enterprise infrastructure, solutioning and AI-assisted support—often stepping into complex products where research, navigation or cross-functional alignment needed strengthening.
Infrastructure UX · User Research · Enterprise Operations
I worked directly with IT managers to understand how they planned and coordinated patching across large server estates. Through interviews, ethnographic research and hands-on design, I helped shape the patch-scheduling experience around real operational constraints rather than system assumptions.
Enterprise UX · Information Architecture · Decision Support
A complex enterprise tool for helping Technical Solution Managers assemble client solutions and proposals faster. I helped simplify the information architecture and navigation, bridging Product, Design, Engineering and business teams to reduce friction in a highly complex solutioning workflow. The redesigned experience contributed to improved user satisfaction and NPS.
Conversational AI · Self-Service · Enterprise Support
A self-service enterprise support experience powered by IBM Watson and operational data. I worked across Business, Design and Front-end Engineering to introduce industry-specific suggested questions and real-time ticket tracking, reducing interaction effort and improving support visibility. The product progressed to early client deployment with encouraging usage traction.
Facilitation, experimentation, shared critique and hands-on making turned individual expertise into a collective practice.
