Between data and decisions: how AI redefines legal work.
- Marcelo Firpo
- 10 hours ago
- 7 min read
AI-generated summary:
This article discusses how artificial intelligence and the strategic use of data are transforming legal work in law firms and corporate departments, bringing efficiency, cost reduction, and greater regulatory security. Using real-world case studies—including examples from major institutions such as PNC Bank, JPMorgan, and an Am Law 100 firm—the text demonstrates the concrete impact of AI on activities such as document review, contract analysis, and compliance. The article also presents a case study from BlueMetrics, which developed a Generative AI solution for automating the validation of financial documents, highlighting how this technology can be applied in the legal sector. The conclusion reinforces that the success of any AI initiative depends on a well-structured database and emphasizes BlueMetrics' expertise in data engineering, with over 200 AI and analytics projects completed across the Americas.
The new legal daily life in the age of artificial intelligence.
For a corporate lawyer or legal manager, time has never been so scarce. Between procedural deadlines, contract analysis, risk management, and keeping up with constantly changing regulations, the legal routine has become an information-intensive operation. It is in this context that the strategic use of data and artificial intelligence is beginning to integrate naturally into legal work, not as a substitute, but as a decisive support for faster, more accurate, and safer decisions.
In law firms and legal departments of large companies, tools based on generative AI and machine learning are taking over tasks that were previously repetitive and time-consuming, such as reading and classifying documents, cross-referencing information from legal proceedings, or verifying contractual clauses.
This automated support frees up lawyers for higher-value strategic activities, such as interpretation, negotiation, and legal advice, which still require human judgment but are now supported by more comprehensive analysis and more reliable data.
Process optimization and cost reduction
The intelligent use of data has allowed for a rethinking of entire workflows within legal areas. Machine learning models, for example, help identify patterns in litigation and anticipate the likelihood of success in certain actions, guiding decisions on settlements or defense strategies. In contract management, natural language processing (NLP) algorithms accelerate revisions and standardize clauses, reducing human error and time spent on manual tasks.
These automations not only make work more agile, but also generate significant savings in hours and resources. Legal departments that previously relied on large teams for administrative tasks are now able to maintain high productivity with leaner structures focused on analysis and decision-making.
Compliance and legal security strengthened by data.
Compliance, one of the pillars of modern corporate operations, is another field directly benefited by the application of AI. Data-driven platforms monitor standards, legislation, and regulatory updates in real time, signaling risks of non-compliance and suggesting corrective measures. This reduces the likelihood of failures and penalties, as well as increasing the traceability of legal decisions.
From a legal certainty standpoint, the impact is also significant. With the consolidation of historical data and the use of predictive models, organizations gain greater visibility into contractual risks, applicable case law, and the potential implications of their decisions. The result is a more transparent and documented legal environment capable of supporting evidence-based strategic decisions.
The digital maturity of the legal sector
Although the advancement of AI in the legal field is at different stages across companies and firms, there is a consistent movement towards digital maturity. Document management, automated compliance, and predictive litigation analysis are just the first layers of a transformation that is likely to deepen with the integration of legal and business data.
The lawyer of the future, who in practice is already working in the present, is increasingly an information manager. Their efficiency depends less on the accumulation of isolated legal knowledge and more on the ability to use data intelligence as a basis for sound and strategic decisions.
Next, we will look at some case studies from this segment.
Concrete impacts of artificial intelligence and data on law firms and legal departments.
1. Efficiency and transparency in PNC Bank's corporate legal department
The legal department of PNC Bank, one of the largest banks in the United States, implemented an AI and machine learning solution to optimize the legal invoice review process, which is traditionally manual and prone to errors. The chosen tool, LegalVIEW BillAnalyzer , now automatically analyzes invoices from partner law firms, ensuring compliance with billing guidelines and proactively detecting inconsistencies.
Observed results:
Significant increase in compliance with billing policies and reduction in manual reviews.
Save time and money by reviewing thousands of invoices monthly.
Greater visibility into legal expenses, with data now supporting management decisions and fee renegotiation.
This case demonstrates how the use of AI in administrative activities, often considered "behind the scenes," has a direct impact on the efficiency and financial control of legal operations.
2. Accelerated document review at a large firm Am Law 100
In the United States, the so-called Am Law 100 are the one hundred largest law firms in the country, ranked annually by The American Lawyer magazine based on revenue, size, and profitability. One of these firms, whose name was not disclosed for confidentiality reasons, faced the challenge of reviewing approximately 126,000 documents in a government investigation, a task that, under normal circumstances, would require weeks of intensive work.
By adopting a generative AI solution for review automation (e-discovery), the firm managed to reduce processing time by up to 67%, while maintaining accuracy levels equivalent to or higher than those of human teams. The tool was able to apply legal codes to thousands of documents in less than 24 hours, after a testing and validation phase.
Observed results:
A 50 to 67% reduction in total review time.
Consistency and traceability in document classification decisions.
Freeing up legal teams for strategic analysis, reducing their operational workload.
3. JPMorgan Chase and the automation of contract analysis
JPMorgan Chase, one of the world's largest financial institutions, has developed the COiN (Contract Intelligence) platform in-house to automate the reading and interpretation of legal documents and credit agreements. The system uses natural language processing (NLP) to extract relevant information from thousands of contracts that were previously reviewed manually by lawyers and analysts.
Observed results:
The system began analyzing approximately 12,000 commercial contracts in seconds, a task that previously required about 360,000 hours of human labor per year.
Reducing operational costs and mitigating the risk of human error in sensitive clauses.
Strengthening the governance of legal data, with a centralized and auditable history.
In addition to saving time and costs, COiN transformed the legal function within the bank, which began operating based on structured data and predictive analytics, expanding its ability to anticipate risks and support business decisions.
A new data-driven legal paradigm
The examples above show that the adoption of AI and analytics in the legal field goes beyond automation. It represents a structural change in how information is handled, risks are reduced, and business value is created. Law firms and legal departments that invest in data intelligence begin to operate more strategically, with processes supported by evidence and predictability, two qualities that are increasingly indispensable in today's legal world.
Next, we will look at a BlueMetrics case study applicable to this segment.
Want to see GenAI and Machine Learning solutions
making a difference in your company?
BlueMetrics Case Study: Generative AI for the automation and validation of financial documents.
The case study we will present below, although not specifically developed for the legal sector, offers a solution that addresses common problems in this field, such as document analysis and validation.
Context
A major Brazilian fintech company, a leader in digital automation, sought to improve the validation of financial documents, a central process for KYC (Know Your Customer) operations, account opening, and credit analysis. Faced with the need for greater efficiency, regulatory compliance, and scalability, the company approached BlueMetrics to develop a solution that combined generative artificial intelligence and data engineering.
The scenario reflected a challenge common to various sectors, including the legal sector, where the high volume of documents, regulatory complexity, and the need for precision make manual processing inefficient and risky.
Problem
The fintech company handled hundreds of thousands of documents monthly, the validation of which depended on processes based on traditional OCR, limited in accuracy and adaptability. This approach generated operational bottlenecks, high costs, and frequent errors in data extraction, compromising onboarding time and customer experience.
The main challenges included:
High volume of manual processing and prone to errors;
Difficulty in scaling operations without expanding the back office;
OCR technology unable to handle varied or poorly scanned documents;
The need to meet strict compliance requirements.
Solution
BlueMetrics has developed a multimodal Generative AI solution capable of automating the reading, extraction, and categorization of identification documents (such as driver's licenses and national identity cards). The system automatically detects the orientation of the images, corrects imperfections, and applies generative models to extract information with high precision.
Cloud-native architecture enables large-scale processing and integrates AWS services such as ECS, Lambda, Bedrock, and DynamoDB with advanced computer vision libraries (OpenCV, Tesseract).
The resulting pipeline combines robust data engineering, generative models, and end-to-end automation, delivering:
Precise extraction of textual and visual data;
Intelligent categorization and parallelization of processes;
Full scalability and traceability;
Compliance with financial and anti-fraud regulations.
This approach is highly applicable to the legal sector, especially in due diligence activities, contract analysis, document authentication, and litigation management, where accuracy and traceability of information are equally essential.
Results
The solution developed by BlueMetrics delivered immediate and measurable benefits:
Significant reduction in operational costs and average onboarding time;
Increased productivity through the elimination of bottlenecks and rework;
High precision in data extraction and categorization;
Scalability to meet peak demand with flexibility;
Enhanced compliance and security , with traceability and fraud prevention.
The project solidified the client's position as a benchmark for innovation in the financial market, demonstrating the power of Generative AI combined with a robust and scalable data architecture.
Conclusion: the value of data as a basis for legal intelligence
The advances observed in the legal sector and other segments, such as finance, have a common origin: the strategic and structured use of data. Artificial intelligence projects only deliver sustainable results when they are supported by a well-balanced database capable of feeding models with quality, context, and reliability.
This is where BlueMetrics' experience sets itself apart. The company combines data engineering, machine learning, and generative AI to build comprehensive solutions that not only automate tasks but also transform how organizations make decisions, manage risks, and ensure compliance.
With over 200 projects delivered to more than 90 clients in Brazil, the United States, and Latin America, BlueMetrics proves that the combination of a robust data architecture and cutting-edge AI technologies is the safest way to increase the efficiency, predictability, and security of legal and corporate operations. Let's talk about it?
Learn about some Use Cases