AI adoption in the US legal sector is rapidly accelerating, with 96% of legal professionals using AI tools and nearly half considering it essential to their daily work, according to a recent survey.

Over the past year, 77% of legal organizations increased their AI spending, reflecting a deepening reliance on AI across the industry.

Many legal organizations are formalizing AI use, with some still conducting limited pilots or using AI informally, especially in areas like legal research, analytics, and document automation.

The most common AI applications in law include legal research, analytics, contract review, document automation, and Language AI for translation and writing, addressing prevalent language and translation challenges.

Language and translation issues significantly impact legal work, with over 85% reporting adverse effects such as lost billable hours and inefficiencies; AI solutions like DeepL are increasingly used to mitigate these challenges.

DeepL's Language AI platform is trusted by over 200,000 global clients, including legal firms, and is expanding its role in transforming legal workflows with secure, enterprise-grade solutions.

AI has transformed legal workflows by speeding routine tasks, enabling lawyers to develop new skills, focus on strategic work, and reduce reliance on junior staff, with over 99% of professionals noting impact.

Looking ahead, the legal industry will focus on AI training, evaluating autonomous AI solutions, and leveraging AI for improved access, cost reduction, and multilingual support, while addressing concerns about data privacy and over-reliance.

Future AI developments will emphasize staff training in ethics and risk management, alongside AI-driven improvements in legal access, cost efficiency, and real-time multilingual capabilities.

Key risks associated with AI in law include data privacy, cybersecurity, lack of transparency, over-reliance, and potential misuse in high-stakes litigation.

Despite widespread AI adoption, over 70% of legal professionals admit to using AI without formal approval, driven by pressure for faster delivery and gaps in approved tools, including translation AI.