📉 Enterprise AI · 📣 Product Leadership

IBM CLOUD PAK FOR AIOPS - 0 TO 1 DESIGN AND LAUNCH

TL;DR | Proactive incident resolution experience for DevOps at complex enterprise scale. Led every product phase from ideation to delivery, with end-to-end designs built, tested, and launched via heavy collaboration with PM, dev, and research. Ownership started with one area of the product, eventually expanded to nearly half (big product). Work subsequently led to key revenue growth and international design award.

Duration

Team

Tools

1 quarter (1.0 launch) + more for subsequent release cycles

3 PM's, 2 Dev Architects, Engineering team, 4 Designers, 2 UX Researchers, 1 Content Designer

Sketch, Mural, Aha!, Github

The Project

IBM wanted to expand their B2B DevOps segment to “DevSecOps” with our AI-driven automation capabilities, providing a more comprehensive tool for IT organizations. The key is for our team to deliver a seamless UX across development, IT security, and operations while also preserving intricate needs of each role. As a lead designer I was involved in the full product cycle - from ideation, determining product requirements, prototyping and testing product E2E UX, development/QA, launch, client engagement and research.

My Role

  • Lead facilitator of product strategy / UX ideation / product requirements

  • Lead designer, application management & incident management

  • QA lead (code, design, and content)

DISCOVERY TO LAUNCH

Ideation Phase

Requirement Phase

Design & Build Phase (Iterative for every sprint leading up to Review/QA sprint)

Final Phase

Led week-long workshop on app management experience with product team (with PMs, lead architect, devs, other designers, sales)

Write out requirements + KPIs with PMs and compare notes with other PMs to minimize duplicates

v1 product designs > stakeholder alignment > user testing* > QA 

Product GA / launch!

After first launch

(Continuous product iterations + leading incident remediation experience + user research via sponsor user program)

Discovery Phase

(Design Research x PM) Team-wide alignment on opportunity area in the ITOps space



UX At a Glance

Our users are typically SME’s with deep-rooted tribal knowledge in the companies they work for. To build trust in what could be disruptive to their highly manual workflow, a lot of our UX decision-making involved balancing AI automation to minimize pains and human-in-the-loop to retain authority of the operators (which matches the preference of our client companies to mitigate potential AI-related risks for their mission-critical work).

Detecting and Grouping Patterns

Network operators (our key users) spend hours parsing through hundreds of system events every day - first, to determine whether they are abnormal, and second, to find which other alerts they might be related to get to the root of a bigger issue. All of this is heavily based on inside / contextual knowledge, and our alert-grouping experience resolves this pain point by automating the two steps as the events come in. (Colors indicate levels of alert severity, and the operators can choose to color the rows. This helps them quickly spot which alerts need to be looked at first.)

Analyzing an incident

Operators can view the bigger context of the alerts within an “incident view”, where they can check the different components within their system. Because they are interconnected, one issue can impact many parts. From here, they can also run automated solution steps recommended by AI. While these could be autorun, we have incorporated human intervention before proceeding.

Predicting in time series

Another angle from which our operators can view potential issues is via metric anomalies. Using a predictive model, we also provide metric projection should the anomalies be left unresolved (in purple in the graph).

The bigger picture

Operators as well as stakeholders / LOB managers can regularly check the dynamic dashboard of their network applications to provide executive report as well as to evaluate the overall health status and improvement of their areas of ownership. This can be highly customizable to fit different client’s priorities. (Censored due to WIP.)

Outcomes & Impact

  • 20% revenue growth via new client acquisitions by 2Q 2023

  • Red Dot Design Award, 2023

IN HINDSIGHT...IF I WERE TO DO THIS WITH AI

  1. QA Automation

    • Could build simulation models to test out the product E2E and log potential bugs / errors either in parallel with our human QA testers / engineers or them to evaluate report from AI

    • ***Would need to compile accurate and up-to-date user data (this would stem from a bigger continuous effort to build and train interactive customer archetypes)