AI in contract lifecycle management has reduced contract cycle times by up to 40 percent according to Gartner. The teams getting that result have done three things: they picked the right tool for the task (Spellbook for drafting in Word, Harvey for research and complex review, LegalOn for playbook-driven review, Diligen for high-volume diligence), they wrote firm or company-wide playbooks the AI follows consistently, and they put a verification step before any output reaches a client. The teams that did not do these things have created a different kind of speed: speed to mistake.
In-house legal teams (5 to 30 lawyers), transactional groups at law firms, contract managers, paralegals supporting drafting and diligence.
About the instructors: Rogier Muller & Vasilis Tsolis
Rogier Muller is CTO of BlueMonks Group, an Amsterdam-based fintech compliance company, and co-founder of several companies. He`s a lifelong coder who moved early into AI-assisted software development. Today, he is the only person in the world to combine official ambassador roles across the three leading agentic engineering platforms: Cursor, Claude Code, and Codex. Rogier has hosted numerous events worldwide and works closely with engineering teams, founders, and AI tooling companies on the practical adoption of agentic software development. His specific expertise is using agentic engineering in highly regulated environments, including but not limited to fintech, financial services, KYC/CDD, AML, GDPR, AFM-supervised contexts, auditability, data isolation, and compliance-heavy software delivery.
Vasilis Tsolis is a pioneer in document intelligence and agentic coding helping teams to change how they work across industries. He is an official Ambassador for Cursor, OpenAI Codex and n8n. He is the partner of Cognitiv+, an AI consultancy and software factory that helps organisations practically implement and adopt AI with enterprise confidence. He has co-founded several companies and trains development teams across the US and EU on AI-assisted coding, with a consistent focus on integrating it into real workflows without losing control of the codebase. Background: engineering and law, twenty years across AI, construction, energy, and tech. Vasilis has worked with JPMorgan, Intel, PwC, and others along the way.
Module 1.
The legal AI tool map. Spellbook (Word-native drafting and redlining, GPT-5 and Claude underneath, SOC 2 Type II, ZDR). Harvey (enterprise research and multi-document analysis, custom-trained models). LegalOn (playbook-driven review with attorney-crafted templates). Diligen and Kira (bulk extraction for diligence). CoCounsel by Westlaw (research integration). Lex Machina (litigation analytics). EverlawAI Assistant (eDiscovery). Picking the right one per task type.
Module 2.
Playbooks the AI follows. A playbook is a written set of fallback positions, approved language, and red lines for a contract type. Without it, the AI does taste-driven review. With it, the AI does playbook-driven review. Lab: the room co-writes a playbook for one contract type (NDA, MSA, or DPA) and uses Spellbook or LegalOn to review against it.
Module 3.
The redline review protocol. Reading an AI redline critically. What the AI gets right (consistency, language polish, missing clauses against playbook). What the AI gets wrong (commercial intent, negotiation history, novel deal structure). When to accept en masse, when to reject and re-prompt, when to take the redline manual. Lab: review three AI redlines and grade them.Module 4. Due diligence at volume. Using bulk-review tools (Diligen, Kira, Harvey) to extract clauses and surface anomalies across hundreds of contracts. The traps: over-reliance on extraction, missing context-dependent risk, false confidence from confidence scores. Lab: a simulated diligence sweep with seeded anomalies.
Module 5.
Engagement letters, outside counsel guidelines, and disclosure. What clients now expect to know about AI use on their matters. AI-related provisions in engagement letters. Outside counsel guidelines that increasingly require disclosure or restrict use. Court standing orders requiring disclosure of generative AI in filings (Judge Ho in SDNY is one example). Lab: redline a real engagement letter to add appropriate AI disclosures.
Module 6.
Audit logs and the team-level operating model. What gets logged. Who reviews the logs. How a billing audit or malpractice inquiry would inspect AI usage. The shared review protocol the team commits to on Monday.
Practical information
Duration: 1 day
Price: 10 900 NOK
Language: English
Format: Classroom, virtual classroom, or in-company
FAQ
Hva er AI for Legal Teams Advanced?
AI for Legal Teams Advanced er et videregående kurs som fokuserer på hvordan juridiske team kan bruke kunstig intelligens mer strukturert, sikkert og effektivt i praktisk juridisk arbeid.
Hvem passer kurset for?
Kurset passer for advokater, juridiske rådgivere, compliance-team og juridiske ledere som allerede har grunnleggende forståelse for AI og ønsker å ta bruken videre i organisasjonen.
Trenger jeg forkunnskaper for å delta?
Ja, det anbefales at du har grunnleggende forståelse for AI-verktøy eller har gjennomført et introduksjonskurs innen AI for juridiske fagmiljøer.
Er kurset praktisk rettet?
Ja, kurset fokuserer på praktiske arbeidsprosesser, realistiske scenarioer og hvordan AI kan brukes i juridiske team på en trygg og effektiv måte.
Hva lærer jeg som er nyttig i praksis?
Du lærer hvordan juridiske team kan bruke AI til analyse, dokumentarbeid, oppsummering, kvalitetssikring og effektivisering av arbeidsprosesser, samtidig som risiko, governance og ansvarlig bruk ivaretas.
Other relevant courses
Other subject areas
Course objectives
Target audience
Prerequisites
