Transforming fragmented legal operations data into an AI-ready roadmap
A legal services firm partnered with Highspring to assess its fragmented data environment, quantify the cost of technical debt, and build an executive-ready road map for implementing a governed, AI-ready data platform.
The Challenge
A legal services firm with multiple business units was relying on outdated workflows and an aging, home-grown case management tool originally built to support just one business unit. As its use expanded across the organization, the limitations of the existing data environment became increasingly apparent.
Data was scattered across SQL databases, flat files, emails, and workflow metadata, while backend technical debt limited visibility into operational, cost, user, and customer data. Without reliable visibility across the business, leadership didn’t have a clear picture of overall performance and needed a scalable, governed data platform that could support its ambitions for adopting AI and new data products.
The Solution
Highspring worked closely with stakeholders across the organization to build a complete, fact-based view of its data landscape. The team interviewed 40 people across three business units and surveyed 28 stakeholders across seven data domains to capture how data was created, used, and managed day to day.
From there, the team leveraged those insights to assess and score data maturity across people, process, technology, governance, and quality, then designed the target architecture, integration approach, security model, and governance framework for a future-state data solution. To move that vision toward implementation, Highspring shaped a minimum viable product (MVP) and a phased, prioritized road map for leadership to fund and execute.
Our Impact
The engagement gave the organization a fact-based baseline for its data quality and maturity while quantifying the potential cost of technical debt. Key findings included:
- Identifying 15,000 hours per year of potential waste tied to technical debt.
- Establishing a 2.7 out of 5 perceived data maturity score based on stakeholder input.
These findings gave leadership a quantified case for modernization, supported by executive-ready investment options and a blueprint for AI-driven document intelligence. The organization also gained a development-ready solution architecture, providing a clear path toward a modern, governed data platform and future AI capabilities.
