Litify + Foundation AI: The Future of Case Intake

Building end-to-end workflows with Microsoft 365 Copilot

As early adopters have discovered, Copilot delivers real transformation only when it becomes an integral part of critical workflows.

Enterprises are beginning to treat generative AI not as an isolated productivity tool, but as the connective layer linking business applications, data, and human judgment. Nowhere is this shift clearer than in organizations using Microsoft 365 Copilot as part of a broader architecture that spans CRMs, low-code platforms, and specialized AI systems.

But according to Will McKeon-White, senior analyst at Forrester, it’s not always easy to build that architecture. “Integrations can prove difficult and usually require cooperation between subject matter experts and technical personnel to get them to work right and ensure the Copilot knows how and when to use different integrations,” he said.

Two early adopters — Monica Washington Rothbaum, COO of J&Y Law, and Patty Patria, CIO of Babson College — illustrate how successful implementations look. Their experiences show that the real value does not come from Copilot alone, but from deliberate decisions about integration, data design, governance, and change management.

From drafting assistant to orchestrated workflow: How J&Y Law re-engineered the case pipeline

When Rothbaum arrived at J&Y Law, she quickly realized the firm’s rapid growth in personal-injury cases demanded a more intelligent and consistent process. With a headcount of around 100 and the ambition to scale, the firm needed a way to manage high case volumes without sacrificing accuracy or human oversight. Her background in business growth and IT leadership helped her see an architecture forming across the firm’s systems.

According to Rothbaum, the personal-injury case workflow is a sequence of tightly connected phases: marketing, intake, pre-litigation, demand, settlement, negotiation, and — if needed — litigation. Each step generates critical data that must be captured, structured, and then reused downstream. To support that flow, legal operations platform Litify functions as the base CRM and “helps connect the dots for the case pipeline.”

On top of Litify, the firm layers specialized AI tools. Agentic AI and bots assist during intake, listening to conversations, reviewing written and oral communications, and flagging early “nuggets” of information that may influence liability or case strength later. Another AI platform, Foundation AI, ingests documents and files them in the CRM.

Internally built GPTs (custom versions of OpenAI’s ChatGPT assistant) then extract structured details from medical notes, client communications, and case files — data that is notoriously inconsistent in format. And as a case moves forward, EvenUp, a personal injury claims intelligence platform, evaluates key factors through the firm’s AI playbook, surfacing insights about the strength of the case.

The key, Rothbaum said, is that each tool “owns” a specific part of the pipeline and produces structured outputs that can feed the next stage.

Microsoft 365 Copilot’s role is not to replace these systems but to act as “the bridge between daily workflows and the firm’s structured case data and insights,” she said. Attorneys still work largely in Outlook, Word, SharePoint, and Teams, and Copilot connects those spaces with the structured case data residing in Litify and the insights coming from EvenUp and internal GPTs. For example, drafting a demand letter used to take around six hours. With the integrated workflow, the process now takes about 45 minutes. Revision cycles have been reduced from “three or four rounds to one or two.”

Despite these efficiencies, Rothbaum insists that AI-generated materials must never appear automatic or unvetted — especially in a legal environment. The firm built a structured file-review system where attorneys receive comprehensive case summaries with internal hyperlinks that allow them to “run down a branch” of the evidence instantly. This system gives lawyers transparency into where information came from, ensures they can challenge or refine AI-derived insights, and helps prevent the appearance of boilerplate AI text.

“You can still tell when something is 75% AI-generated,” she said. “We never want to send that signal to an insurance company.”

While many workflows have been streamlined, two areas remain stubbornly human-dependent. One is medical-record acquisition — a process still largely dominated by phone calls and faxes to hospitals and doctor offices. Although some advances have been made in medical records follow-up, according to Rothbaum, AI cannot yet replicate the human judgment, nuances, and persuasion required.

The other human-dependent workflow is settlement negotiation — an interaction rich with nuance, psychology, and game theory. “AI can forecast or predict,” she noted, but the human-to-human bargaining remains essential.

Still, the orchestration achieved across the rest of the case lifecycle shows what is possible when data is structured, systems are chosen deliberately, and Copilot is positioned as the coordination layer rather than the engine itself.

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