The latest trends in Large Language Models (LLMs) show a shift towards greater efficiency, advanced AI agents and wider adoption across all industries. AI models are becoming more cost-efficient while improving their capabilities, especially in legal applications such as contract analysis. The rise of AI agents with long-term memory and autonomous learning is changing the way organizations use AI for complex tasks. In addition, regional AI development is gaining traction, with a focus on data and localized AI solutions.
In the digital age, the use of artificial intelligence (AI) is making contract analysis much more important. Traditionally, reviewing contracts was a time-consuming task that required considerable human resources. By using AI, companies can now analyze contracts faster and more efficiently, which significantly reduces the workload of legal departments. AI-based contract software is capable of analyzing contracts in seconds, weighting company policy violations and extracting relevant data from clauses such as dates and parties involved.
Another advantage of AI-assisted contract analysis is the improvement in compliance and reporting. The digital storage of contracts makes it much easier to access contract information. This, combined with AI analytics, means that reports can be generated faster and compliance requirements can be monitored more easily. This is particularly important in highly regulated industries where compliance with certain contract standards and requirements is crucial.
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With the rapid development of large language models (LLMs), the possibilities of AI-supported analysis are multiplying exponentially. While the basic LLM models improved continuously in 2024, there were no technological breakthroughs. Nevertheless, the increased efficiency of these models led to lower resource consumption, which enabled decreasing operating costs and favored economically viable use cases, especially in contract analysis and automated document processing. Although high-performance models achieved outstanding results, they are currently not suitable for the broad market due to high costs and technical requirements. Overall, technological progress continues to drive the integration of AI into business processes and continuously increases the benefits of AI-supported analysis.
A key development step is the integration of AI agents that go far beyond traditional chatbots. They interact with APIs, analyze emails, perform search queries and communicate with other AI systems. In legal departments, they automate contract processes by extracting information from documents, adapting clauses and checking contracts for compliance. Thanks to long-term storage and autonomous learning, they deliver personalized, precise results.
In the legal sector, the increasing acceptance of AI is also reflected in the growing willingness to invest in legal tech and legal start-ups. Domain-specific models that are tailored to specific legal frameworks are becoming increasingly relevant. The focus is increasingly on optimizing existing systems based on benchmarks rather than developing new models.
The legal sector is increasingly focusing on practical applications such as contract reviews with AI or AI-supported legal analytics. The aim is to seamlessly integrate AI into legal workflows in order to make processes more efficient, analyze risks more precisely and speed up contract processing.
These developments are leading to increasingly automated contract analysis in the digital age - with measurable efficiency gains, cost savings and qualitative improvements in legal assessment.
The advancement of AI agents is expected to be the next big trend driving the automation of legal workflows. Unlike simple custom GPTs, these will combine advanced LLMs to efficiently complete complex tasks such as drafting contracts and processing claims. Legal processes consist of extensive research, gathering client information and iterative communication, making them too complex for a simple AI prompt. Therefore, AI-supported automation of processes will be one of the biggest challenges and developments this year. Hamburg is already discussing how prompt injections could compromise autonomous AI agents with email access. These vulnerabilities could be exploited by external actors to expose sensitive data such as passwords. Startups in Munich are already working on solutions to minimize these risks. Nevertheless, it remains crucial that companies raise awareness of these threats and implement targeted risk management to ensure the secure use of AI agents.
Compared to traditional AI-supported contract review systems, which usually evaluate static texts or analyze simple contract clauses, AI agents have a proactive and dynamic way of working. They can:
These AI agents are no longer just simple text recognition tools, but fully-fledged intelligent assistants that identify contractual risks, adapt wording and provide decision-making support for lawyers or contract managers.
A key feature of the new AI agents is their adaptive learning ability. While previous AI systems were mainly based on fixed algorithms, modern AI agents use continuous machine learning to adapt individually to a company's contract requirements. This means:
This type of AI can, for example, check contractual clauses for compliance, potential risks and optimization opportunities by drawing comparisons with existing contracts or legal innovations.
Typical applications include:In 2025, Explainable AI (XAI) will become a central component of contract analysis by significantly improving the transparency and traceability of AI-supported decisions. Traditionally, many AI systems acted as “black boxes” whose decision-making processes remained opaque to users. XAI, on the other hand, makes it possible to disclose the decision-making processes of these systems, which is particularly important in the legal field.
By using XAI in contract analysis, lawyers and legal departments can understand exactly what data and patterns AI models are based on to highlight certain clauses or identify potential risks in contracts. This transparency not only promotes trust in the technology, but also enables AI-supported analyses to be integrated more effectively into legal practice.
Another advantage of XAI in contract analysis is the support it provides in adhering to legal regulations and compliance standards. As the decision-making paths of the AI are traceable, companies can ensure that their automated processes comply with current legal requirements and make appropriate adjustments if necessary.
In 2025, multilingual contract analysis will become significantly more important thanks to the use of advanced artificial intelligence (AI). Companies that operate internationally face the challenge of efficiently managing and analyzing contracts in different languages. Thanks to specialized AI models, legal documents can now be translated precisely and their content understood in context.
These AI systems use advanced Natural Language Processing (NLP) techniques to capture not only the wording, but also the legal subtleties and cultural nuances of different legal systems. This makes it possible to reliably identify risks and obligations in contracts regardless of the language of the original.
One notable advance is the development of customized AI models tailored to specific industries and their terminology. These specialized models ensure greater accuracy in the analysis of contracts as they can correctly interpret industry-specific expressions and wording.
The integration of AI into multilingual contract analysis leads to a significant increase in efficiency. Processes that were previously manual and time-consuming are automated, allowing companies to make informed decisions more quickly. In addition, the continuous improvement of these AI systems through machine learning enables constant adaptation to new legal developments and language variations.
In 2025, close cooperation between legal experts and AI technology providers will become increasingly important. This trend reflects the growing need to combine technological innovation with deep legal understanding to develop AI solutions that are not only powerful but also legally resilient. One example of this is the DORA project, in which law firms work with AI developers to create high-quality, specialized AI solutions.
Legal practice requires precise, context-related evaluations of documents, clauses and standards. AI alone is only capable of doing this if it has been trained to meet realistic legal requirements - and this is precisely where the collaboration comes in: Lawyers provide the expertise to identify relevant use cases, structure case constellations and ensure that the models understand and correctly interpret legal nuances. At the same time, AI providers contribute the technical infrastructure, the algorithms and the knowledge of how these systems can be operated in a scalable, efficient and secure manner.
These synergies create the basis for specialized legal AI solutions, for example in contract analysis, due diligence or compliance. Unlike standardized tools, these systems are often tailored to specific areas of law or industries. Legal experts not only take on advisory roles, but are also actively involved in the development process - from the conception to the testing of the models.
Another effect of this development is the professionalization of legal tech projects. Instead of isolated solutions, platforms are increasingly being created on which various modules - such as AI-supported clause recognition, risk assessment and automatic change routines - work together seamlessly. The involvement of legal expertise ensures that these systems comply not only with technical standards, but also with ethical and normative standards.
In 2025, the interface between law and technology will therefore not only be characterized by technological breakthroughs, but above all by the way in which lawyers and tech teams work together on solutions. The realization is gaining ground that technology does not replace legal work - but enhances it. And this is precisely where the decisive value of this new, interdisciplinary collaboration lies.
In 2025, we will see a profound transformation in the way artificial intelligence (AI) is integrated into the existing ecosystems of leading technology companies. This development aims to expand the functionalities of existing platforms and provide users with innovative tools that make everyday work more efficient.
A striking example of this integration is the addition of AI-supported assistants to productivity suites. These assistants support users in the creation of content, data analysis and the automation of recurring tasks, resulting in significant time savings.
The successful implementation of these AI functions depends largely on user acceptance and satisfaction. Companies face the challenge of ensuring that the new functions are not only technologically advanced, but also user-friendly and intuitive to use. It is also crucial that the integration is economically viable and offers clear added value.
The integration of AI into contract analysis poses numerous challenges, particularly in the areas of costs, data security, multilingualism and sustainability. One major problem is the cost development of AI models. While models are becoming smaller and cheaper, large providers such as OpenAI and Microsoft are facing the challenge of recouping their investments. Whether AI services such as ChatGPT or Copilot will become more expensive in the long term remains uncertain, as continuous updates and rising training and operating costs could influence pricing. At the same time, the efficiency of AI models deteriorates over time as they increasingly learn from self-generated content instead of using real data.
One risk is security problems caused by AI agents, in particular prompt injections. Autonomous agents with email access can be manipulated by external actors, which could result in sensitive data such as passwords being passed on unintentionally. Data security measures and targeted risk management are therefore essential.
Another key obstacle is the limited multilingualism of many AI models. While AI performs exceptionally well in English, its performance in European languages such as French and Spanish remains a challenge. The development of OpenGPTX as a European alternative to US models is an important step, as it has been trained with a higher proportion of German, French and Spanish data and is better suited to legal applications in Europe.
Data usage and AI regulation are also critical issues. Many users use ChatGPT and similar tools without being aware of the potential risks. In the open version of AI systems, data entered can be used for further training, which is particularly problematic for companies and lawyers. Cases where trade secrets or confidential legal information have been unknowingly entered into AI systems highlight the need to use secure corporate versions of legal AI tools.
The use of AI as a legal knowledge base entails additional risks. AI-generated legal references are not always correct, which has already led to cases in which lawyers have cited incorrect or invented legal decisions. To ensure the reliability of AI results, verification through external legal databases or search engines is essential.
Courts and judges also face major challenges when introducing AI. One of the main problems is the high computing resources required for AI systems. Many models are based on cloud services such as Azure or AWS, which poses a data protection risk due to the U.S. Cloud Act. As courts prefer highly secure, local data storage, there is growing interest in smaller AI models running on local cloud providers to reduce reliance on large foreign cloud infrastructures.
We see a shift to smaller and more specialized AI models in 2025. This not only offers advantages in terms of data security and costs, but also in terms of sustainability. Smaller models require fewer resources, consume less energy and are more environmentally friendly. The trend is increasingly moving towards more efficient and specialized models that balance performance, safety and environmental impact to ensure efficient and responsible use of AI in contract analysis in the long term.
Legartis is integrating AI agents into its technology set-up in 2025. This will make it possible to create contract playbooks automatically. The manual creation of contract manuals will thus be a thing of the past. The integration of AI agents will also speed up the development time for new contract types and languages many times over.
Traditionally, the creation of contract playbooks was a time-consuming and resource-intensive process that required legal expertise and meticulous attention to detail. With the integration of AI agents, Legartis has fundamentally transformed this process. The AI analyzes existing contracts, identifies patterns and extracts relevant clauses and conditions. On this basis, it automatically generates comprehensive and precise playbooks that serve as a guide for future contract negotiations.
In the near future, the implementation of AI agents will not only automate the creation of contract playbooks, but also significantly accelerate the development of new contract types and adaptation to different languages. Through machine learning and continuous data analysis, Legartis' AI will recognize and adapt new contract structures. This will enable companies to react faster to changing market conditions and create contracts efficiently in multiple languages without sacrificing accuracy and consistency.