Company

IBM

Partners

1 Developer(s)

Platform

Desktop

Scope

Research
Product Design
Project Management

Overview

Publisher is IBM’s internal web-building and hosting platform used by 500 internal software teams and individual contributors across the organization. I was responsible for simplifying technical workflows through user research, interaction design and cross-functional collaboration.

Context

Despite multiple years of growth, Publisher, IBM’s proprietary site builder, operated with a relatively small team, creating ongoing tension between product development, user support and documentation maintenance. To protect development velocity, the team centralized support through dedicated Slack channels managed by rotating on-call developers, allowing the rest of the team to focus on business-critical initiatives at the cost of slower documentation updates.

Problem

Over time, the w3 Publisher support channel had become the primary source of assistance, increasing strain to on-call developers that staffed it and creating an unsustainable support model where development hours were increasingly spent on repetitive support rather than product advancements.

Solution

Design an AI-assisted self-service workflow that can leverage existing artifacts to answer support inquiries, help keep documentation up to date and, ultimately, preserve developer hours for progressing the product roadmap.

Impact

89

development hours preserved during the first post-launch quarter.

65.15%

of support inquiries resolved without developer intervention during the first post-launch quarter.

32.9%

of AI-generated documentation approved without revisions during the first post-launch quarter.

Challenges & trade-offs

Project momentum vs. limited capacity

Because the team was understaffed, I had to step outside my role to recruit a developer, manage the project and establish a regular cadence for collaboration. Rather than treating limited capacity as a blocker, I created a working structure that allowed the project to progress alongside the team's existing responsibilities.

Technical complexity vs. scalable simplicity

Early in the project, we discovered that allowing an AI agent to re-open conversations in legacy support threads would introduce significant engineering complexity. Instead, we chose to have the agent summarize previous interventions and provide that context in a new chat when re-initiating conversations. This allowed us to preserve the intended workflow without introducing unnecessary complexity, reinforcing the value of shipping the simplest solution that could scale.

AI capability vs. content quality

Inconsistent formatting across documentation made reliable parsing and retrieval more difficult, revealing a constraint that wasn't immediately obvious when we began exploring AI-assisted workflows. The experience reinforced that dependable AI systems require structured, consistent content alongside capable models. This makes content quality an important part of the product design problem, not simply a technical implementation detail.