An AI model trained on public legal documents performed well. However, when applied to actual, consented customer contracts, its accuracy plummeted by 15 percentage points. This reveals the significant performance gap between clean, public training data and complex, private enterprise data.

In an era of opaque AI models, traditional contractual lock-ins are failing. The new retention moat is trust, which requires radical transparency about data sources, AI methodologies, and performance limitations. Customers will not pay long-term for "black box" risks they cannot understand or mitigate.

A digital signature's value isn't the cursive graphic, but the auditable trail confirming a verified identity took a specific action to indicate consent. This redefines the core product from simple signing to identity and consent management.

Early enterprise AI chatbot implementations are often poorly configured, allowing them to engage in high-risk conversations like giving legal and medical advice. This oversight, born from companies not anticipating unusual user queries, exposes them to significant unforeseen liability.

As consumers use AI to analyze contracts and diagnose problems, sellers will deploy their own AI counter-tools. This will escalate negotiations from a battle between people to a battle between bots, potentially requiring third-party AI arbitrators to resolve disputes.

Resource-constrained startups are forgoing traditional hires like lawyers, instead using LLMs to analyze legal documents, identify unfavorable terms, and generate negotiation counter-arguments, saving significant legal fees in their first years.

Unlike consumer chatbots, AlphaSense's AI is designed for verification in high-stakes environments. The UI makes it easy to see the source documents for every claim in a generated summary. This focus on traceable citations is crucial for building the user confidence required for multi-billion dollar decisions.

AI agents could negotiate hyper-detailed contracts that account for every possible future eventuality, a theoretical concept currently impossible for humans. This would create a new standard for agreements by replacing legal default rules with bespoke, mutually-optimized terms.

When AI Overviews aggregate and present information, the platform (Google) becomes the publisher, inheriting blame for inaccuracies. This is a fundamental shift from traditional search, where the source website was held responsible. This increases reputational and legal risk for AI-powered information curators.