From Traditional Processes to Modern LDI Workflows: A Guide to Onboarding New Technologies

At the root of the Legal Data Intelligence (LDI) model is the premise that every legal challenge has a corresponding data challenge. Whether you are responding to a third-party subpoena, drafting a new contract, creating a strategic response readiness program, or addressing any of the use cases (20 as per the last update) in the LDI model, the fundamental data challenge is essentially the same: separating sensitive, useful, or necessary (SUN) data from redundant, obsolete, or trivial (ROT) data.

At a time when the volume, variety, and velocity of data are overwhelming organizations, technologies—increasingly enabled by generative AI—play an instrumental role in helping legal professionals quickly and accurately find the SUN data lying scattered in their organization’s large data pools.

With their early-adopter mindset, data orientation, and experience in strategically aligning people, process, and technology to take on high-stakes, data-intensive projects, LDI practitioners have cultivated a highly effective approach to successfully onboarding new technologies that enable the SUN data to shine through.

In The Legal Data Intelligence (LDI) Guide for Onboarding New Technologies, LDI Architects Kayann Fitzgerald (Director of eDiscovery and Practice Technologies, Davis Wright Tremaine LLP), Mike Mendola (Director, eDiscovery and Data Governance, Discover Financial Services), and Alexander Simonenko (Senior Lead Manager, eDiscovery, LinkedIn) bring their combined expertise and decades of experience to lay out a comprehensive approach to technology onboarding that marks the inevitable shift from traditional, document-centric practices to modern LDI operations capable of separating SUN data from ROT data.

Before initiating any technology-onboarding process, the authors suggest, organizations must ensure the technology in question directly transforms data into actionable items. This begins with mapping technology adoption to specific business and legal goals—reducing operational risk, improving workflow efficiency, and most of all, enabling data-driven decision-making.

The guide also offers actionable insights on stakeholder engagement and change management—two critical components of successful technology adoption. Through tailored training programs, transparent communication, and clearly defined responsibilities, it gives legal professionals a practical framework for mitigating resistance and embedding new technologies within legal workflows.

The guide explores how to test before you trust—beginning with structured pilot programs that turn proof of concept into proof of value. It walks readers through designing realistic pilots, setting measurable benchmarks, and using results to refine both selection and strategy. From there, it delves into risk assessment with the precision legal teams expect: privacy impact analyses, AI-specific governance, information security, and cloud compliance all feature prominently.

Finally, the guide includes a comprehensive checklist that delineates critical steps and provides a detailed roadmap for successful technology onboarding within legal teams. It moves systematically from defining strategic fit and aligning goals to assessing a tool’s ability to separate SUN from ROT data. By combining structure with adaptability, the checklist helps teams manage complexity, sustain adoption, and ensure every technological investment delivers measurable value.

At a time when legal professionals are drowning in data and organizations are making significant AI investments to upgrade legal workflows, this guide provides legal professionals with a comprehensive approach to technology onboarding while helping them maintain their commitment to security, compliance, and operational excellence.

Check out the guide to learn more.