Harvey's Winston Weinberg: Why AI will force lawyers to change their fee structure
The co-founder of the legal start-up talks about how AI could shake up law firms’ business models and how he plans to stay ahead of rivals
FT AI·
Law firms are developing their own systems, sometimes with an eye to selling them to clients
Read full articleThe co-founder of the legal start-up talks about how AI could shake up law firms’ business models and how he plans to stay ahead of rivals
Enterprise leaders must progress past generative applications and scale “autonomous intelligence” to capture real P&L margin growth. Generating text or summarising internal communications offers localised productivity improvements, yet these abilities rarely alter the core cost structure of a large organisation. Enterprises are now focused on deploying systems capable of independent execution. Leaders are demanding applications […] The post Deloitte: Scale ‘autonomous intelligence’ for real growth appeared first on AI News.
Talairis Law Group is built around the idea that AI can handle much of the work that associates at big law firms have traditionally done — and that startups shouldn't have to pay big law prices for it. Read More
Anthropic has launched a new set of AI-powered tools for the legal sector, expanding its Claude for Legal platform with specialised plug-ins and Model Context Protocol connectors targeting specific areas of law including commercial, privacy, corporate, employment, and AI governance. The new tools are designed to automate clerical legal work such as document search and […]
As the AI legal services industry heats up, Anthropic is launching its own suite of features designed to assist law firms.
As agencies adopt AI more rapidly, they are also under pressure to ensure these systems are transparent, explainable and secure, Red Hat chief architect says.
If this past school year was about adults figuring out how to adapt systems and approaches to AI, the next school year should be about students actually experiencing something better because of the work the adults did.
One of the more dangerous assumptions in the current AI market is that broad adoption means meaningful adoption. It does not. Much of what enterprises call AI transformation is, in fact, AI experimentation focused at the edge of the business, in systems and workflows that support employees but are not central to how the enterprise actually operates. These include calendaring, scheduling, meeting summaries, employee communications, customer messaging, document generation, internal assistants, and similar productivity-oriented use cases. Those applications may be useful, but they are not core applications that directly run the business and determine whether the company performs well or poorly. Inventory management, sales order entry, logistics execution, supply chain planning, procurement, warehouse management, manufacturing operations, and financial transaction processing belong in this category. If these systems fail, the business feels it immediately through delayed orders, lost reven