Law Technology Today

Summary Unbilled work, separate trust accounts, and irregular collection cycles make law firm cash flow uniquely difficult to predict with standard forecasting methods. A five-step rolling forecast, updated monthly with real collections and aging data, gives firm leaders a reliable cash picture without needing a finance background. Accurate forecasting depends on clean, current WIP and AR-aging data, which is often the hardest part of the process to maintain by hand.

Summary Agentic AI agents are designed to pursue an action plan using specific instructions and implement a course of action. Human verification of autonomous systems is required. Lawyers should not trust AI agents without proper vetting and checking tasks. Confidentiality risks multiply when agentic AI agents work between platforms. Lawyers should make sure there are proper safeguards in place in the event of client data being exposed.

Summary Professional supervision of AI cannot be reduced to reviewing the final output; it must extend through the assignment, process, sources, operating conditions, and limits. AI vendors may control material decisions affecting confidentiality and system behavior even while the lawyer remains professionally responsible for the consequences. The lawyer in the Loop™ cannot mean the lawyer at the end of the loop.

Summary Members of the Legal Technology Resource Center share their must-have products for practicality and portability in their office. Aum racha via Getty Images Our panelist: Alison Bassett (AB) Jennifer Ellis (JE) Alan Klevan (AK) What Were the Last Front-of-the-Office r Back-of-the-Office Products You Bought and Why? AB: My recent purchases have focused on making my portable office more functional.

Summary AI now touches every corner of a law firm, from contracts and research to litigation, intake, and operations, and what fits depends on the work you actually do. General chatbots are cheap and everywhere, but the tools built for legal work add what a chatbot can't: verified citations, real security, and output you can defend.

Summary Legal work requires more than fluent language; lawyers remain responsible for accuracy, judgment, verification, and consequences. Probabilistic AI systems can assist legal professionals, but rules-based systems remain essential where repetition, control, and reproducibility matter. Law firms should match technology to consequence, using AI for assistance and deterministic systems for dependable execution.

Summary Most solo and small firms now use AI and report real gains in speed and quality, yet only about a third have seen their revenue actually grow because of it. Hourly billing lets AI-driven time savings pass straight to the client instead of the firm, and most solo and small firms have not changed their pricing to reflect it. Firms growing revenue with AI tend to shift toward flat fees for predictable work and use the time AI frees up to take on new clients rather than shorter days.

Summary Most solo and small firms now use AI and report real gains in speed and quality, yet only about a third have seen their revenue actually grow because of it. Hourly billing lets AI-driven time savings pass straight to the client instead of the firm, and most solo and small firms have not changed their pricing to reflect it. Firms growing revenue with AI tend to shift toward flat fees for predictable work and use the time AI frees up to take on new clients rather than shorter days.

Summary Building a strong network on LinkedIn involves nurturing your connections through check-ins, sharing relevant content, or inviting connections to discussions. After connecting with someone on LinkedIn, take time to review their profile again for valuable insights, such as their interests and group memberships. This information can help identify mutual connections and opportunities for collaboration, enhancing the relationship further.

Summary AI-first adoption in legal risks interpretability, maintainability, reproducibility, and data privacy. These are four pillars that regulated, evidence-driven legal work cannot sacrifice for hype. Intentional tool selection means asking what a problem actually needs, not how to use AI. Governance, not urgency, determines defensible technology decisions.