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to the right, which point to the interconnectedness of many use cases.M&A lawyers and other legal professionals at a large German law firm are using legal AI tools, particularly within the context of document-heavy legal work such as due diligence and contract review. Legal tech professionals and lawyers with technical backgrounds support development and adoption.
They aim to reduce the time and effort required for document classification, summarization, and legal analysis during tasks like due diligence and litigation. AI is used to extract key contractual clauses, generate executive summaries, and assist with internal legal process improvements. There's also a focus on improving access to templates and internal resources via AI-assisted interfaces.
They are using large language models (LLMs), particularly GPT-4 and GPT-4o, in both internal (on-premise or country-hosted) and third-party tools. Cloud-based models are used cautiously and only when permitted. Tools include proprietary LLM-based chat interfaces, as well as domain-specific models like those integrated with legal publishers' databases. They also experiment with prompt engineering and use explanation-focused AI tools for model transparency.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional working at the organization.
The legal department of a German technology company, consisting of eight in-house lawyers, is exploring the use of legal AI tools. Their work includes corporate law, M&A, and international contracts.
They aim to improve efficiency in handling legal documents and contracts. Specific use cases include translating legal texts, rephrasing clauses, summarizing lengthy documents (e.g., EU regulations), and creating initial drafts of legal clauses. A key goal is to automate the review and negotiation of standardized non-disclosure agreements (NDAs) to reduce lawyer involvement in routine processes.
They currently use on-premise chatbots based on open-source models such as LLaMa and Mixtral. Cloud-based models like ChatGPT are not permitted due to company policy. The department also experiments with proprietary chatbots embedded in legal databases (e.g., beck-online). Tools like DeepL are used for translation, and internally developed contract management systems support their workflow.
Research
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal department manager and a legal counsel, working at the organization.
The users are primarily legal professionals in Germany, including lawyers, legal departments, and law students. Small law firms and individual practitioners show the most active interest.
Users aim to understand and adopt AI tools to improve efficiency in daily legal work. Specific problems include contract analysis, drafting and summarizing legal emails, document review, legal research, and time tracking for billing. The goal is to develop AI literacy and practical skills for integrating AI into legal workflows.
They use general-purpose models like ChatGPT and Perplexity, often in secure or self-hosted environments due to privacy concerns. Tools that mimic ChatGPT functionality but claim compliance with professional and data protection standards are also in use. Retrieval-Augmented Generation (RAG) systems are noted for document analysis tasks.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal tech entrepreneur and former product manager, working at the organization.
Legal AI tools are used by consultants and internal teams within the firm to support both client projects and internal legal workflows. The tools are also introduced and tested in client organizations through advisory projects.
The primary goals are to support text-based legal tasks such as document drafting, analysis, summarization, and information extraction. The firm also helps clients identify relevant use cases and integrate AI tools into their legal processes through workshops and proofs of concept. Legal AI is not seen as replacing legal professionals but as a tool to assist them.
The organization uses general-purpose generative AI models, particularly ones based on ChatGPT, with additional internal layers for customization. Tools mentioned include internally hosted generative AI chat systems and specialized legal AI assistants. Some tools rely on fine-tuning or prompt engineering to better address legal contexts. Most tools are cloud-based, though internal deployments are preferred for data-sensitive tasks.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal technology consultant, working at the organization.
Lawyers, assistants, and secretarial staff across multiple practice groups at a German law firm are using legal AI tools. The digital strategy is coordinated centrally, with adoption managed and tracked across departments.
The firm aims to improve efficiency in legal workflows, particularly:
A core focus is on reducing time spent on manual processes and supporting legal work with AI-assisted summaries and drafting components.
The firm uses a multi-tiered approach:
They do not use enterprise licenses for public LLMs like ChatGPT but instead enforce strict usage guidelines and access restrictions.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a digital transformation lead, working at the organization.
The users include court departments, legal authorities, and administrative bodies ranging from small teams of five people to larger organizations with up to 200 employees.
The primary use case is the anonymization and pseudonymization of legal documents, such as court decisions. The goal is to automate suggestions for redactions to reduce manual effort, especially in large documents. Clients also seek to handle special formatting challenges in PDFs and accommodate jurisdiction-specific identifier structures, like those found in Austrian property designations.
They use on-premise NLP models for named entity recognition. The system is not based on generative models and does not rely on cloud services. The technology stack includes a base model trained on pseudonymized court decision data and additional logic using pattern matching (e.g., regex). They also make use of open-source components like spaCy.
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Interview conducted in June 2025, led by sebis researchers. The interviewee is a managing director, working at the organization.
A self-employed legal professional with a background as both a lawyer and Legal Engineer, currently running her own law firm and advising other firms on digitalization.
AI is being used to streamline marketing and content creation (e.g., blog posts and videos), assist with legal research and brainstorming, and draft client communications. Additionally, the interviewee advises other firms, such as tax or compliance-focused organizations, on how to implement legal tech solutions to optimize workflows and automate repetitive tasks.
The interviewee uses ChatGPT (GPT-based) for brainstorming, writing support, and generating explanatory content. Legal research tools include AI-powered databases that summarize court decisions. There is experimentation with cloud-based tools, some of which also allow user-uploaded documents. Technologies mentioned are generally cloud-based.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a self-employed legal professional with consulting experience in legal tech.
Lawyers at a large international law firm, especially those in areas like M&A, Private Equity, Real Estate, and Finance, are using legal AI tools. Usage varies by practice area and seniority.
They aim to improve efficiency in tasks such as document review, translation, and drafting standard contracts. AI is used for due diligence, generating draft memos, and benchmarking market standards. While high-end, bespoke legal work remains largely manual, AI supports standardization and consistency in more repetitive or commodity-oriented tasks.
They use general-purpose models such as ChatGPT and GPT-4, as well as a mix of external and proprietary tools. Some tools are integrated into cloud-based systems; others are custom-built or customized in-house. AI is employed for language translation, summarization, and drafting assistance, though bespoke legal advice is not generated by AI.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a senior lawyer, working at the organization.
Lawyers at a large international law firm in Germany, particularly those in practice groups and legal tech roles, are using generative AI tools. A dedicated Legal Tech Fellow supports the integration and training of these tools across the firm.
They use legal AI tools for drafting documents (e.g., emails, memos, court pleadings), translating texts, explaining technical terminology, conducting preliminary legal research, and summarizing lengthy legal documents. The goal is to improve efficiency, reduce manual workload, and make legal knowledge more accessible across departments.
They primarily use Microsoft Copilot integrated with Office 365. Other tools such as ChatGPT and Perplexity have been tested, though Perplexity is used only privately due to data restrictions. They also co-develop specialized tools in collaboration with tech providers, such as a model for identifying legal risks in internal documents. Most tools are cloud-based, with strict attention to data protection and legal compliance.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a Legal Tech Fellow and practicing lawyer, working at the organization.
Lawyers across different departments, especially in tax compliance, employment law, and real estate transaction teams. Some legal assistants and administrative staff also use AI-assisted tools for translation and reporting tasks.
The primary goals are to increase efficiency and reduce manual labor in document analysis, contract drafting, legal research, and translations. AI tools are used to assist with extracting data from large volumes of legal and financial documents, generating and managing standard contract clauses, improving translation quality, and drafting standard communications.
They are testing or using tools based on large language models like ChatGPT, especially through integrations with publishers (e.g., Otto Schmidt, Beck). Some tools operate via browser-based interfaces and cloud services. Cloud-based AI services (e.g., from OpenAI) are increasingly accepted as they adopt European data protection standards. Local hosting solutions were previously considered to ensure compliance with legal confidentiality requirements.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a senior legal professional, working at the organization.
Legal operations and strategy teams within the corporate governance function are exploring and managing AI integration. Project managers and product leads, including non-technical staff, are involved in implementation.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a project lead working at the organization.
Legal AI tools are used by lawyers across multiple departments at the firm, including M&A, corporate law, tax, litigation, and investigations. A dedicated legal innovation team coordinates the introduction and support of these tools.
The primary goal is to process large volumes of legal documents more efficiently, especially in data-heavy fields such as M&A due diligence and litigation. Tasks include summarizing and analyzing documents, drafting emails or memos, automating repetitive tasks, and structuring legal facts. Legal AI is also being explored for translation services and everyday legal workflows like deadline tracking and email correspondence.
The firm uses general-purpose GenAI models such as ChatGPT and Harvey. Harvey was selected for its superior performance in legal contexts. Other tools tested include CoCounsel. These are cloud-based systems. Traditional contract analysis tools are also in use, but GenAI tools are increasingly preferred for their broader capabilities.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal innovation associate, working at the organization.
Lawyers at a mid-sized law firm, including partners, associates, and legal departments such as litigation, insurance law, tax law, and antitrust.
They aim to streamline legal workflows, reduce manual labor, and improve efficiency in legal tasks such as document summarization, case assessment, and contract analysis. Use cases include litigation file analysis, initial assessments in insurance cases, tax case documentation, antitrust contract checks, and audit-related contract review.
They use large language models (LLMs) such as GPT-3.5, GPT-4, Claude, LLaMa, and Gemini. These are cloud-based but integrated with an internal anonymization pipeline to comply with data protection requirements. Each use case is linked with specific LLMs and system prompts optimized for the task.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a lawyer and partner, working at the organization.
The primary users are legal professionals within a major German law firm, including lawyers and legal transformation teams. Legal engineers and AI experts are also involved in tool evaluation and integration.
The tools are used to support legal work through automation and enhanced document handling. Key applications include translation, improving document quality, contract analysis, clause evaluation, data extraction, and legal research. There is also interest in using AI for drafting support and comparing legal texts or emails. Retrieval-Augmented Generation (RAG) systems are used for precise answers based on curated legal literature.
They use general-purpose language models like ChatGPT, as well as AI services via Microsoft Azure (e.g., Copilot). Both cloud-based and on-premise systems are employed, including proprietary setups for secure document querying. Beta testing has been conducted with models accessing curated legal data.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal professional, working at the organization.
Legal AI tools are used by a dedicated unit within the Bavarian Ministry of Justice focused on Legal Tech and AI. The tools are intended for use by courts and public prosecutors across Bavaria. Internal staff, including legal professionals and administrative personnel, are involved in the development and testing of these tools.
The Ministry is addressing challenges related to the increasing volume of documents in civil and criminal law cases, such as long legal briefs and large-scale digital evidence (e.g., terabytes of data from searches). Key use cases include document search, metadata extraction, anonymization of legal texts, drafting support, and summarization of lengthy documents. Internally, AI is also used for improving text quality and creating visual materials for presentations.
They use and test generative AI models like large language models (e.g., ChatGPT-style tools) and have piloted a custom anonymization prototype based on a language model. Projects are developed in collaboration with academic partners and rely on open-source models trained on judicial data. Tools are generally self-hosted or developed in close cooperation with universities to comply with strict legal and data protection requirements.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional working at the Bavarian Ministry of Justice.
The users are primarily corporate lawyers within the M&A department, including partners and associates. Additional involvement comes from litigation teams in the U.S. and global practice groups across the firm.
They aim to increase efficiency and speed in legal processes, such as document review, legal research, translation, and transaction management. Tools are used for information extraction, document drafting, summarization, and improving access to legal knowledge. They also support due diligence and e-discovery processes.
The firm uses extractive and generative AI tools, including general-purpose models like ChatGPT, especially within integrated platforms such as Juris-KI. They have tested tools like Kira and are preparing to roll out Westlaw Co-Counsel in Germany. DeepL is used regularly for legal document translation. Tools are primarily cloud-based, with strict security evaluations before deployment.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a partner and an associate in the legal department, working at the organization.
The legal department, particularly in the IT legal function, is experimenting with AI tools. A small internal pilot team, including legal and IT staff, has tested tools such as ChatGPT and Microsoft Copilot.
The team is exploring AI tools for various legal support tasks, including:
They are using GPT-based models, specifically ChatGPT (commercial version), Microsoft Azure OpenAI (GPT-4), and Microsoft Copilot. The models are hosted in the cloud. They have not yet tested domain-specific tools like Harvey AI due to cost and availability concerns.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal counsel in IT law, working at the organization.
The primary users are legal professionals, including lawyers and legal departments within a large law firm affiliated with a major consulting organization. The interviewee oversees all AI-related initiatives at the firm.
They aim to streamline both legal and non-legal workflows. Use cases include document analysis (e.g., contract review), searching and extracting clauses, improving internal efficiency, accelerating legal assessments (e.g., compliance), and handling large-scale litigation by navigating massive document collections. The tools are also used for tasks like generating offers, board reports, and verifying NDAs or codes of conduct.
They use general-purpose language models such as ChatGPT and GPT-based systems. Tools are a mix of proprietary and customized applications, built on cloud platforms like Microsoft. Their solutions include:
Production
Interview conducted in January 2025, led by sebis researchers. The interviewee is a legal professional with a technical background, working at the organization.
In-house legal professionals at the corporate headquarters, particularly those supporting global IT, e-commerce, loyalty programs, data analytics, and AI compliance.
They aim to streamline legal workflows such as contract generation and review, translation, drafting non-standard contract templates, and email composition. Additionally, AI is used for summarizing meetings and providing structured outputs from long discussions. A future tool is expected to extract contract metadata to populate a database.
They are testing general-purpose cloud-based models such as Microsoft Co-Pilot (integrated with Microsoft 365) and a legal chatbot from beck-online. They are also exploring a contract software tool with AI-based data extraction capabilities.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is an in-house legal professional, working at the organization.
Lawyers and partners at a mid-sized law firm, especially those involved in corporate law and M&A. A small internal AI team, including a few partners and employees, is responsible for tool evaluation and testing.
The firm uses legal AI to support contract drafting, markup review, document screening for due diligence, summarization, legal research, and translation. AI also serves as a sparring partner in negotiations and helps structure legal arguments. Another goal is to reduce administrative work, such as client onboarding.
The firm uses ChatGPT (free and licensed versions) and Claude via third-party tools for general legal language tasks. Tools like Libra, LangDoc, and Spellbook are in testing. These rely on cloud-based large language models.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a partner and legal professional, working at the organization.
Employees of a legal insurance subsidiary are involved in evaluating and implementing AI use cases. Legal services are primarily delivered through cooperation with external law firms and partners. Internal users are mostly part of service and claims departments.
The organization aims to automate legal claim evaluations, contract coverage checks, and reimbursement decisions (e.g., court or attorney costs). Use cases include extracting key information from legal documents, supporting legal research, and enabling more efficient internal processes. AI is also used indirectly through partner law firms for tasks like legal document drafting and analysis.
They use generative language models and prediction models, often as cloud-based licensed solutions. The models are integrated into broader systems for document processing and legal evaluation. There is also experimentation with on-premise and partially customized tools. External law firms employ tools based on large language models such as ChatGPT or GPT-4.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a managing director working at the organization.
Legal Tech practitioners within the law firm, including attorneys who integrate legal technology into client matters. A dedicated Legal Tech team coordinates tool procurement, implementation, and internal/external innovation processes.
The firm uses legal AI to improve efficiency in areas such as document review (e.g., eDiscovery and M&A due diligence), contract analysis, drafting, summarization of court decisions, and translation. AI also supports internal collaboration, document automation, and the creation of chronologies for fact-finding. The overarching goal is to enhance productivity and maintain competitiveness while meeting client expectations for efficient service.
They use both commercial and self-developed tools. General-purpose LLMs (e.g., GPT-based systems like Harvey) are used for legal content generation and review. Their custom tool is built on Azure services and performs document processing, search, and RAG-based QA. The models are hosted in the EU and designed without continuous learning, ensuring confidentiality. Other machine learning-based tools are also employed for M&A and document classification tasks.
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Interview conducted in January 2025, led by sebis researchers. The interviewee is a Legal Tech Manager and practicing attorney, working at the organization.
A legal tech and digital transformation unit within a large law firm, consisting of jurists and IT-affine staff, is leading the AI implementation. The team includes one programmer and works closely with selected external service providers.
They aim to address repetitive legal workflows, quantitative document analysis, and efficiency improvements. Use cases include automation of contract generation, processing of routine administrative tasks, and document comparison. The overarching goals are reducing workload, minimizing errors, and driving innovation.
They are testing and integrating external and local large language models, including LLaMa, via APIs. Machine learning is used for quantitative analysis, and Robotic Process Automation (RPA) is also being implemented. Hybrid approaches combining ML and RPA are under development. These tools are introduced in a technology-neutral and problem-driven manner.
Research
Interview conducted in February 2025, led by sebis researchers. The interviewee is the head of digital transformation and legal tech, working at the organization.
Lawyers across all levels (from associates to partners) and staff members, especially assistants, are using legal AI tools. A dedicated Legal Tech team, including in-house developers, supports implementation and customization.
The firm uses legal AI primarily to summarize legal documents, assist with drafting emails and first drafts of pleadings, and perform document analysis. They aim to reduce administrative burdens, improve efficiency, support knowledge management, and offer clients AI-enhanced legal services. Future plans include integrating internal systems for unified access and creating tools tailored to the firm's specific legal workflows.
They are using legal and general-purpose large language models, including Harvey, ChatGPT and Microsoft Copilot. Some tools are hosted in the cloud with data segregation ensured through contractual safeguards. Internal prototypes leverage LLMs, and software development is accelerated using AI-assisted coding tools like GitHub Copilot.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a Director of Legal Tech, working at the organization.
The tool is used by legal professionals including lawyers, business support staff, marketing, HR, and secretarial teams at a large international law firm. Usage is voluntary and access is available firm-wide to those who request it.
The tool is primarily used for drafting and refining legal clauses, generating legal memos, and formulating native-level English text (e.g., emails or documents). It helps with clause modifications, inspirations for restructuring contracts, and abstract legal analysis. In specific cases, it is also used to explore contract risks and options in restructuring scenarios. Beyond legal staff, non-legal teams use it for text generation tasks.
They are using Harvey, a proprietary chatbot interface based on large language models, trained with legal data. It allows document upload and context-aware interactions. Comparisons were made with ChatGPT and Microsoft Copilot, which is also being introduced. Usage is cloud-based but with contractual attention to data privacy and confidentiality.
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Interview conducted in December 2024, led by sebis researchers. The interviewee is a legal professional, working at the organization.
Lawyers in the corporate law department, particularly those involved in M&A and venture capital transactions, are exploring and using legal AI tools. A dedicated internal AI team (approximately 8–9 people) supports these efforts.
The main goals are to improve efficiency in legal workflows, particularly in translation, document review (e.g., due diligence), summarization, and internal research. AI is also used to assist with tasks such as preparing presentations and navigating legal databases more efficiently.
They use general-purpose NLP models like ChatGPT and GPT-based tools. Tools are both cloud-based and on-premise. ChatGPT is used privately for general legal brainstorming, while Co-Pilot is tested for tasks like presentation and spreadsheet support. Tools such as DeepL are used extensively for document translation. Other tools mentioned include those for legal document extraction and comparison.
Research
Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional working at the organization.
In-house legal counsel specializing in digital law, including IT procurement, data protection, and compliance with the EU AI Act. The tools are primarily used by legal professionals within the corporate legal department.
Legal AI tools are used to improve efficiency in legal research, document drafting, summarization of lengthy communication threads, and the preparation of presentations. Specific applications include drafting contracts, creating summaries from email histories, and assisting with internal compliance documentation related to data protection and the AI Act.
They use a mix of general-purpose and legal-specific LLM tools. These include:
All tools mentioned are cloud-based. ChatGPT is not officially approved for internal use, though it may be used privately or for non-confidential queries.
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Interview conducted in February 2025, led by sebis researchers. The interviewee is a legal professional, working at the organization.
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