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Ballard Spahr

Ballard Spahr saves $2M and cuts nonbillable research time 60% with Azure OpenAI-powered legal AI tools

Curated & reviewed by Peter Korpak, Founder & Chief Analyst, 100SignalsHow we verify
$2 millionUnplanned Loss Savings
60%Nonbillable Research Time Reduction
~2 hours per proposalProposal Preparation Time Saved

Vendor-reported figures — source: www.neudesic.com

The Challenge

Ballard Spahr's lawyers and staff spent significant nonbillable time on research-intensive RFP proposals and administrative communications, which weighed on attorney profitability. Previously, a lawyer could spend up to 10 hours searching for information for a proposal without being able to bill the client for that time, contributing to unplanned losses for the firm.

The Solution

The firm's Technology Innovation team partnered with Microsoft partner Neudesic to build two custom generative AI tools on Azure OpenAI and Azure AI Services. Ballard X-Ray is a large-scale cloud-based repository and interactive agent that stores and searches up to 2,500 documents per RFP so lawyers and clients can locate and chat with specific documents. Ask Ellis is a generative AI chatbot with pre-built prompts that helps lawyers and staff draft emails and other communications.

Results

Ballard Spahr saved an estimated $2 million in unplanned losses by reducing nonbillable research and administrative time. Nonbillable research time dropped by 60%, and proposal preparation time fell by approximately two hours per proposal. RFP response times shrank from weeks to days.

Key Takeaways

  • Applying generative AI to a firm's own internal research and drafting workflows (not just client-facing legal work) can materially cut nonbillable hours and unplanned losses.
  • A dedicated document repository with a chat interface (Ballard X-Ray) can compress RFP turnaround from weeks to days.
  • Early, safe adoption of AI with data confidentiality safeguards can become a competitive differentiator for a law firm.

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Details

Industry
Law Firms
Company Size
Enterprise
Quality
Curated
Source published
Jul 7, 2025
Last verified
Jul 28, 2026

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