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P E E R T O P E E R M A G A Z I N E · S P R I N G 2 0 2 5 51 KATYA FISHER is the founder and CEO of Aracor AI and oversees all legal and governance matters for the Constructor Group and its holdings. After her independent legal practice, Katya joined an AM200 law firm as a partner, practice group leader, and chief privacy officer. Katya holds a B.A. from New York University and a J.D. from Benjamin N. Cardozo School of Law. Her articles have appeared in Cardozo Arts & Entertainment Law Journal, Bloomberg Tax, New York Real Estate Journal, quoted/mentioned in Bloomberg, Fortune, and Forbes. Katya is a six-time Super Lawyers "Rising Star" award winner. (understanding formal legal structures and defined rules) and inductive reasoning (spotting patterns across deal types, industries, or historical transactions). The result feels less like a search engine and more like a junior M&A associate piecing together the transaction's architecture. Crucially, they are built for real- world messiness: OCR issues, poorly scanned documents, handwritten annotations, or signature pages split across PDFs no longer break the workflow. In deals where timelines are tight and stakes are high, AI can compress what once took days or weeks—reviewing equity financings, corporate consents, option grants, indemnification clauses, voting rights, and board approvals—into just hours. AI is not replacing legal judgment. However, it is shifting where that judgment is directed. Instead of spending hours hunting for buried terms or reconciling redundant provisions, lawyers can engage directly with the issues that drive deal value: negotiating terms, assessing risk, or crafting a strategy for integration or litigation exposure. This is not just automation. It is the emergence of AI systems that understand corporate transactions as interconnected legal ecosystems—and help practitioners navigate them with greater clarity, speed, and confidence. AI AGENTS AND AUTONOMOUS LEGAL WORKFLOWS The most significant shift on the horizon is not just in model quality but in the transition from reactive tools to proactive agents. AI will do more than answer questions. It will complete entire workflows. Imagine a system that, when assigned a due diligence project, autonomously pulls relevant documents, extracts key terms, summarizes findings, and even routes tasks to appropriate reviewers. Or contract agents that populate templates with deal- specific data, suggest fallback clauses from internal playbooks, and flag outlier language for negotiation and review. Compliance will become proactive, too. AI systems may continuously monitor a firm's activity—such as communications, contracts, and filings—for signs of antitrust issues or GDPR risks. The goal encompasses more than automation for efficiency's sake—it includes the creation of intelligent safeguards that prevent legal exposure before it materializes. To enable this, modern legal tech infrastructure must be integration-ready—designed with APIs that connect seamlessly into existing document management, e-signature, CRM, and billing systems. This interoperability lays the groundwork for a genuine end-to-end legal AI ecosystem. CONCLUSION: BUILDING FOR EVOLUTION, NOT JUST TODAY In a rapidly evolving field like law, where precedent matters but agility is crucial, the most valuable AI systems are those built not just for today's problems but also for tomorrow's possibilities. Flexibility, security, and adaptability—combined with model choice and operational control—will define the winners in legal AI over the next decade. Those building legal infrastructure today have a rare opportunity to shape not just how AI works but also how it thinks, collaborates, and evolves with the legal profession.

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