HallbergAI case studies

AI adoption is repeatable work, not tool access.

These anonymized case studies show how AI became useful inside real engineering workflows: testing, legacy refactoring, repository context, debugging, and review.

AI-assisted testing

One working test became a repeatable testing system.

How a government engineering team turned one AI-assisted unit test into a repeatable testing workflow another engineer could pick up and use.

2-4 weeks avoided in setup
30-minute workflow onboarding
30-60 minutes per generated test

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Legacy refactoring

Legacy service refactor time dropped from weeks to about one week.

How a government engineering team used AI-assisted explanation and method mapping to move a legacy SharePoint integration from SOAP service calls to a newer client model.

Weeks to about one week
Hours to understand a newer client model
Manual validation stayed human-owned

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Workflow practices

Better context made AI more reliable in a large repo.

A workflow-practices case study on context artifacts, reusable prompts, log-driven debugging, and pre-commit AI review in GitHub-based engineering work.

Context before generation
Reusable prompts for repeated tasks
Evidence-first debugging

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8-week AI Adoption Pilot Implementation

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