Digitalization has brought about far-reaching changes in numerous business areas, particularly through the use of advanced technologies such as artificial intelligence (AI). In the area of contract management, AI is helping to make processes more efficient and accurate. An analysis by Goldman Sachs has shown that the use of automation in contract management can speed up negotiations by up to 50%. Thanks to AI technology, contract content can be analyzed precisely and quickly, leading to well-founded decisions and optimized use of resources.
AI-supported contract analysis is a structured process supported by state-of-the-art technologies such as Natural Language Processing (NLP) and the Retrieval Augmented Generation (RAG) method. The aim is to analyze contract content efficiently, extract key information precisely and make it usable for contract management. The process comprises several key steps:
First, the contract is classified based on its content. The AI recognizes typical features and terms that indicate the type of contract, e.g. employment contracts, supply contracts or non-disclosure agreements. This classification forms the basis for further analysis.
NLP, or Natural Language Processing, is a key technology that enables AI to understand and analyze human language. Instead of seeing a contract as mere text, the AI "reads" the content and recognizes patterns, meanings and connections. It does this by putting words and phrases into context so that the AI understands how different elements of a contract relate to each other. For example, the AI can recognize that "30 days" is related to "payment term" and "invoicing". In this way, NLP automatically identifies important contract clauses and structures the content in a meaningful way for further analysis.
A specific prompt (statement) is created to extract the relevant data from the contract. These prompts are flexible and customizable, which is particularly helpful to capture clauses regardless of their wording in the text. For example, it could look like this: "Please extract the relevant information about the payment terms and return the following data points".
Once key information has been extracted, the AI checks the results for accuracy and completeness. Validation tools compare the extracted data with predefined company guidelines or legal standards to minimize errors and ensure the quality of the analysis. In the final step, the extracted information is checked. This is where the RAG method often comes into play:
The Retrieval Augmented Generation (RAG ) method extends the analysis by integrating external data sources. The aim is to retrieve information from current, relevant databases or legal texts that the AI was previously unaware of. In essence, the AI evaluates which information from these sources is useful for a specific query and adds it to the analysis in a targeted manner. This ensures that the AI delivers precise results even for complex or incomplete queries and avoids typical errors such as hallucinations (invented information). This method not only makes document analysis more comprehensive, but also significantly more reliable.
The contract analysis process is significantly improved by NLP and the RAG method. While NLP understands and analyzes the language and structure of contracts, the RAG method ensures that relevant external information is used. This combination enables precise, scalable and reliable document analysis.
AI-supported contract analysis is versatile and offers numerous advantages in various areas. The content-oriented and quantitative analysis makes this technology particularly indispensable in contract management, risk analysis and compliance checks.
A classic example of the application of AI-supported document analysis is the recognition of payment terms. Instead of training a model specifically to identify clauses such as "payment terms", existing models can work with targeted prompts. For example, the sentence "Payment shall be made no later than 14 days after invoicing" in a contract is analyzed. The AI recognizes "14 days" as a deadline and "invoicing" as a trigger and extracts this information automatically. This speeds up the qualitative content analysis and makes data collection much easier.
Contract analysis is a key step in contract management. It is used to systematically record the content and details of a contract. As technology has advanced, particularly in areas such as artificial intelligence, the way in which contracts are analyzed has also evolved. AI-supported contract analysis uses technologies such as natural language processing (NLP) and machine learning to analyze and evaluate the entire contract text and break it down into its individual components. Such modern tools are able to precisely identify and categorize specific contract details - be it deadlines, obligations or other relevant aspects. The result is a quick and comprehensive insight into the most important contract information, which considerably facilitates and optimizes contract management .
While current AI tools already offer significant added value in contract management, the next generation of technology is about to be launched. It will further enhance the possibilities of data analysis and interpretation.
Future AI-based tools could revolutionize contract management by not only processing but also analyzing large amounts of data in detail. These advanced tools will be designed to recognize complex patterns in contract data. This capability will be particularly useful, as patterns can often provide insight into recurring themes or potential risks in contracts.
In addition, future AI will be able to detect unexpected anomalies. For example, it could detect unusual clauses in a contract or identify deviations from standard contract terms. Spotting such anomalies early on will be critical to identifying potential problems and taking appropriate action. The end result will be streamlined contract management, leading to faster, more accurate and more cost-effective outcomes.
In the future, AI-based contract management solutions could combine numerous functions and processes on a single platform. They would combine different tools, eliminating the need for users to switch back and forth between different applications to review or edit contracts. This results in significant time savings and increases efficiency. In addition, these future systems would provide quick access to relevant contract information and could even generate automated responses. This simplifies and speeds up the response to contract-related inquiries and requests.
AI-driven contract software could provide users with tools that ensure consistency and uniformity in contracts. A key feature could be predefined templates that are generated based on data analyzed with Natural Language Processing (NLP) to provide standardized terms and clauses. Users could use these templates to ensure consistency when creating new contracts. In addition, the software could provide a deviation detection function. If a user enters or uploads a contract that deviates from the standard wording, the software would automatically recognize these deviations and alert the user. This would allow variations in contract clauses to be tracked effectively. When deviations or potential errors are detected, the software could make automated revision suggestions. This would not only help to minimize human error, but also ensure that the contract meets company standards. This would allow companies to effectively track and standardize variations in contract clauses and minimize the risk of human error and inconsistencies.
AI-powered software easily scales with a company's growing needs compared to limited human capacity - without the cost of additional staff. Whether a company has 100 or 10,000 contracts to process, a future AI-powered solution can handle the task with ease. This gives companies the flexibility to adapt to changing business needs without investing in additional resources or training.
ContractHero uses AI technology to automatically extract essential contract data from the documents. The AI identifies and extracts specific data such as the name of the contracting party, relevant term and termination dates and financial details, such as monthly payments. Once this data has been automatically captured, it is entered directly into the corresponding fields in the system. ContractHero thus offers users a comprehensive database that they can add to as required. This saves valuable time and reduces manual effort by pre-filling most of the required information. If you are interested in the potential return on investment when implementing ContractHero, we invite you to take advantage of our ROI calculator.
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The diversity and complexity of contracts pose major challenges, as unstructured content and varying wording make AI recognition more difficult. Incorrect scans or different formats can also impair the accuracy of the analysis. Another obstacle is the integration of data into existing systems and compliance with data protection standards such as the GDPR. Companies need to overcome these hurdles in order to fully exploit the benefits of AI-supported document analysis.
Digitalization, particularly through the use of AI, has fundamentally changed contract management. Traditional methods, which are time-consuming, error-prone and often not scalable, are being replaced by efficient, accurate and scalable AI-supported processes. AI technology not only enables contracts to be analyzed quickly and thoroughly, but also reduces human error and promotes the understanding of complex content. Solutions such as ContractHero use this technology to optimize processes around contracts through AI-based contract analysis and help companies save valuable resources. One example of this is the company hhpberlin, which was able to realize savings of EUR 100,000 in its first financial year thanks to ContractHero's contract management software.
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