CLIENT

Leading Provider of Transportation & Supply Chain Software

CATEGORIES

AI
Software Development
Automation

VERTICALS

Transportation & Supply Chain Software

PROJECT DESCRIPTION

Asperitas partnered with a leading transportation and supply chain software provider to define a strategy for enterprise-scale AI adoption within its software engineering organization.The engagement included assessing current development practices, prioritizing high-value AI use cases, recommending tools and target architectures, developing a phased implementation roadmap, and enabling engineering teams to accelerate adoption.
While the organization had experimented with AI technologies, leadership sought a disciplined, scalable approach that could deliver measurable business value. The challenge was a dual-speed technology landscape, with modern, cloud-native applications that could rapidly leverage AI alongside legacy platforms with limited automation and engineering foundations. Asperitas developed a pragmatic transformation strategy that addressed both environments, enabling the client to modernize development processes and adopt AI in a secure, scalable, and sustainable manner.

SCOPE OF WORK

Asperitas conducted the engagement for over eight weeks. Work began with an on-site assessment and structured discovery: practitioner interviews across multiple development and QA teams, analysis of Jira tickets, test plans, and source control practices. A central insight emerged from this discovery that shaped all subsequent deliverables, the primary constraint to AI-assisted development was not tooling selection or developer competency, but a gap in automation infrastructure. Legacy applications lacked CI/CD pipelines and automated test coverage, which prevents AI coding tools from reliably validating changes. The engagement culminated in hands-on staff enablement sessions in which the QA team used AI tools to generate and run tests against a real application change, demonstrating practical value directly rather than asserting it.

RESULT

All five SOW deliverables were completed, with three exceeding their acceptance criteria. Asperitas delivered an AI Use Case Catalog identifying eight prioritized use cases across five categories; Tooling and Architecture Recommendations covering seven tool categories with vendor comparisons and a cost analysis; a four-phase Implementation Roadmap with entry and exit criteria, a risk register, and investment estimates; and role-specific staff enablement materials including AI-assisted development guides, quick-start templates, and QA automation patterns. The customer's leadership now has a structured, evidence-grounded path from their current state to scaled AI-assisted development, sequenced to address automation infrastructure prerequisites first, so that early AI investments build momentum rather than create new technical debt.