Case Study
1.75 People, 4,000 Articles, and an AI Mandate
How Indiana University got its knowledge ready for AI.
Indiana University was being asked to do more with fewer resources while preparing its knowledge environment for AI. The answer was not to start with AI. It was to build the knowledge foundation first.
Case Study
1.75 People, 4,000 Articles, and an AI Mandate
How Indiana University got its knowledge ready for AI.
Indiana University was being asked to do more with fewer resources while preparing its knowledge environment for AI. The answer was not to start with AI. It was to build the knowledge foundation first.
About Indiana University
Beyond the Slide Deck
We don't just hand over a PDF. We provide a surgical plan for process re-engineering that accounts for campus culture.
North Star Alignment
We help you align technology spend with the metrics that matter: student retention and operational resilience.
Radical Transparency
We advise you as if it were our own institution. If a project doesn't add value, we’ll tell you.
Indiana University operates a large, distributed higher education environment where knowledge plays a critical role in helping students, faculty and staff find answers and access services.
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Over time, approximately 4,000 knowledge articles had been migrated into ServiceNow. What began as an effort to preserve a familiar experience gradually became a heavily customized environment that was increasingly difficult for a much smaller team to operate.
Timeline
X Weeks
Engagement Type
Pilot / Advisory
Outcome
XX% Improvement
Platform
ServiceNow
The Challenge
More knowledge. Fewer people. A growing AI mandate.
IU's knowledge organization had changed significantly.
The team that once consisted of six editors had reduced to approximately 1.75 FTE, while the volume and complexity of the knowledge environment remained.
At the same time, the university was looking ahead to AI. That created a practical question:
How do you prepare thousands of articles for AI when the team responsible for managing them is smaller than ever?
The existing environment also carried complexity from years of customization, including workflows, templates, fields and audience structures. IU needed an operating model that the organization could sustain, not simply another layer of technology.
~4,000
Knowledge articles
6 → 1.75
Knowledge editors
104 → 3
Audience groups
AI
Increasingly part of the roadmap
The Solution
Start with knowledge, not AI.
IU made a deliberate decision to simplify.
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Rather than continuing to recreate the previous system inside ServiceNow, the team moved toward using the platform as designed and establishing a repeatable knowledge standard.
The work centered around a few fundamentals:
~4,000
Knowledge articles
6 → 1.75
Knowledgaae editors
104 → 3
Audience groups
AI
Increasingly part of the roadmap
Clear ownership
Each team became responsible for its own knowledge, with named owners and backups.
A simpler structure
IU established three levels of taxonomy, approximately fifteen categories, five standard article templates and six required metadata fields.
Governance that could scale
Approval and governance were structured so teams could take greater ownership as authors built experience and trust.
A repeatable foundation
The model allowed a new governed knowledge base to be established in as little as a day.
This was not initially treated as an AI project.
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But it became the foundation AI needed
From Knowledge Readiness to AI Readiness
Once the foundation was governed, AI could become useful in two practical areas.
Creating knowledge faster
AI can help turn resolved tickets and existing source material into structured article drafts, reducing the effort required to start new content.
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The IU story uses the example of reducing the process of starting an article from approximately 30 minutes to five minutes.
Measuring what knowledge returns
Knowledge also provides a clearer path to measuring AI value.
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Instead of only measuring activity, IU can begin looking at whether better knowledge prevents service requests from being created in the first place.
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AI-assisted search can help identify:
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What users were trying to find
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Where content is missing
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Where content exists but cannot be found
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When a question was answered without creating a ticket
That creates the foundation for connecting knowledge performance to measurable service outcomes.
~4,000
Knowledge articles
6 → 1.75
Knowledgaae editors
104 → 3
Audience groups
AI
Increasingly part of the roadmap
The Impact
A knowledge program designed to scale with the institution.
IU established a more sustainable operating model for knowledge, one built around clearer ownership, simpler governance and repeatable standards.
What changed
A governed knowledge base in one day
A repeatable framework for creating new knowledge environments.
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A measurable AI baseline
A model for connecting knowledge deflection with service effort and value.
104 → 3 audience groups
A dramatically simpler audience structure.
30 min → ~5 min
Illustrative improvement in starting an article using AI-assisted creation.
Most importantly, the work gave IU a foundation on which AI could operate instead of asking AI to compensate for an ungoverned knowledge environment.
Modern experiences and accelerated transformation.
Better outcomes. Less Strain.
Students, faculty, and alumni expect fast, intuitive, personalized experiences. Most institutions are trying to deliver them without clear visibility across systems. Insight arrives late. Action stays reactive. Bettera helps institutions see sooner and act with confidence.
Download the case study to see how IU:
Download the complete case study for the operating model, knowledge standards and practical approach behind the transformation.
Download the case study to see how IU:
Download the complete case study for the operating model, knowledge standards and practical approach behind the transformation.
Or, if your institution is working through similar challenges:
Review how your knowledge is owned, structured and measured today, and leave with a clear view of where to start.