Connecting structured content, information architecture, and intelligent discovery to modernize federal content management.

This case study describes professional responsibilities, strategic approaches, and outcomes at a high level. Supporting illustrations are independently recreated conceptual examples and do not reproduce internal government deliverables, systems, or sensitive information.

Project context

As a Web Strategist supporting IRS.gov, I contributed to initiatives focused on improving how users discover, understand, and access complex government information.

My work addressed both immediate findability challenges and longer-term opportunities to modernize the enterprise content ecosystem through structured content, improved metadata, and emerging search technologies.

These complementary initiatives required collaboration across content strategy, UX, product, engineering, development, communications, and business teams.

The challenge

Large government websites must communicate complex, authoritative information to diverse audiences with varying levels of knowledge and familiarity with the subject matter.

Traditional page-based content structures can limit an organization's ability to reuse information, maintain consistency, and deliver content through multiple channels. Differences between organizational terminology and users' natural language can also create barriers to successful information discovery.

Emerging AI-powered search and conversational technologies introduce additional considerations for how information is structured, maintained, and interpreted by machines.

Strategic approach

I contributed to a content strategy approach intended to address the needs of both human audiences and the technologies they use to access information. I tried to balance these three priorities:

Human findability: Organize and label information according to user needs, behaviors, and mental models rather than relying exclusively on organizational structures.

Machine readability: Establish reusable content models, relationships, taxonomy, and metadata that support the consistent interpretation and delivery of information.

Long-term adaptability: Consider how structured content could support future headless CMS architecture, enterprise search, conversational interfaces, and AI-enabled discovery.

Execution

I co-led information architecture work informed by behavioral analytics, search data, user research, stakeholder interviews, and card sorting.
The resulting recommendations addressed content hierarchy, navigation, labeling, organization, and clarity.
I also contributed to enterprise search improvements through analysis of zero-result queries, search terminology, synonyms, taxonomy, and metadata within a SOLR-powered search environment.
In collaboration with a developer, I helped connect insights from call-center and website search data to identify self-service content gaps and prioritize improvements.
Working alongside another content strategist, I helped establish a content modeling practice focused on reusable, scalable content structures.
Our work included:
- Defining content types, attributes, and relationships.
- Developing taxonomy and metadata approaches to support consistent classification and discovery.
- Creating machine-readable markup to demonstrate structured content concepts.
- Exploring how content could be separated from its presentation to support future delivery channels.
- Developing proof-of-concept models for a planned headless CMS transition.
I also led research into examining how generative AI was changing users’ behavior and the implications of those changes for enterprise web content.
Throughout these initiatives, I collaborated with product owners, UX professionals, developers, engineering teams, communications professionals, and business stakeholders.
My contributions connected content requirements and user needs with technical implementation considerations, helping multidisciplinary teams evaluate opportunities and make informed decisions.

Outcomes

Information architecture and content improvements contributed to an approximately 20% reduction in on-page internal search activity within a substantial area of IRS.gov.
Supported more systematic identification and prioritization of self-service content gaps.
Helped establish repeatable content modeling practices and frameworks for future structured content initiatives.
Developed proof-of-concept materials supporting the planned transition toward a headless CMS and more adaptable content delivery.
Contributed strategic analysis of emerging AI-powered search and discovery technologies.

The structured content and headless CMS work represented strategic planning and proof-of-concept development rather than a completed production implementation.

Strategic takeaway

Search optimization cannot be separated from content architecture, governance, and quality.

Whether information is accessed through traditional navigation, organic search, enterprise search, conversational interfaces, or AI-generated answers, its usefulness depends on how clearly it is written, how consistently it is structured, and how effectively its meaning can be understood.

My experience connects these foundational disciplines with emerging approaches to answer engine optimization and generative engine optimization.