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Advanced Contract Analytics is Emerging with Game-Changing Insights

illustration of contract analytics dashboard

Data has become the lifeblood of organizations, fueling growth, innovation, and competitiveness. Among countless data sources, contracts are often overlooked as valuable repositories of information that can influence strategic decisions. At the same time, the sheer volume and complexity of contracts makes it challenging for organizations to harness their full potential. On top of that, dispersed workforces, stretched resources, and ever-mounting workloads collide with a regulatory tightrope walk. No wonder organizations are scrambling for smarter solutions.

Advanced contract analytics, powered by artificial intelligence (AI) – including natural language processing (NLP) and machine learning (ML) – is emerging as a game-changer. This article explores how technology is reshaping contract analysis and demonstrates how advanced contract analytics empowers organizations to:

  • streamline compliance
  • identify opportunities
  • mitigate risks
  • drive more informed and strategic decision-making

In other words, revolutionizing the way contracts are managed and interpreted.

The evolution of contract analytics

Historically, the analysis of contracts has been a labor-intensive and time-consuming task, often relying on manual review by legal experts. This traditional approach limited the ability of organizations to gain deeper insights from their contracts, leaving vast amounts of valuable data untapped.

Then the rise of contract lifecycle management systems (CLMs) made contract data much more accessible, and companies that adopted the right tools were able to start seeing the value.

The advent of advanced contract analytics has transformed this landscape even further. Leveraging AI, NLP, and ML technologies, contract analysis has become faster, more accurate, and scalable.

These technologies can process and analyze vast amounts of textual data, enabling organizations to gain a comprehensive understanding of their contractual obligations, risks, and opportunities.

The technologies behind advanced contract analytics

Artificial Intelligence

AI is at the core of advanced contract analytics. AI-powered algorithms can recognize patterns, extract information, and make predictions based on historical data, enabling the automation of various tasks that were previously done manually. Here are some ways AI is used to automate tasks in contract analysis:

  • Contract classification. AI models, particularly those based on natural language processing (NLP) and machine learning (ML), can be trained to automatically classify contracts based on their type, subject matter, or other relevant characteristics. This automation saves time and effort that would otherwise be spent manually reviewing and categorizing each contract.
  • Entity recognition. AI systems can be trained to identify and extract important entities from contracts, such as names of parties, dates, locations, and key terms. This process, known as named entity recognition (NER), is crucial for understanding the content and context of a contract.
  • Clause extraction. AI models can be trained to identify and extract specific clauses or provisions from contracts, such as indemnification clauses, termination clauses, or confidentiality clauses. This automation streamlines the process of analyzing and comparing clauses across multiple contracts.

Natural Language Processing

NLP focuses on the interaction between computers and human language. In contract analytics, NLP techniques are applied to extract structured information from unstructured text in contracts. This includes identifying key terms, clauses, and entities within contracts, enabling organizations to organize and categorize their contracts effectively.

Machine Learning

ML algorithms enable contract analytics systems to learn from historical data and improve over time. Through supervised and unsupervised learning, ML models can identify contract anomalies, detect potential risks, and generate insights. ML also plays a crucial role in predictive analytics, helping organizations forecast contract performance and make data-driven decisions.

8 use case examples

To illustrate the transformative power of advanced contract analytics, consider these eight compelling uses:

1. Data extraction and visibility

Contract visibility powered by data extraction is groundbreaking for legal, procurement, and sales teams. AI-based solutions can automatically identify and classify key terms and conditions across both legacy and third-party agreements. For example, AI models can be trained to recognize and extract specific types of clauses or provisions from contracts, such as indemnification clauses, limitation of liability clauses, or confidentiality clauses. These clauses can then be classified based on their type, allowing for easy identification and comparison across multiple agreements.

Another example would be using optical character recognition (OCR) and document scanning technologies to digitize legacy contracts, making them accessible for AI-based analysis and classification.

The result is a centralized, searchable database that unlocks the secrets hidden within your contracts. This newfound clarity empowers business teams to answer questions about agreements quickly and efficiently, streamlining workflows and boosting productivity.

2. Automated contract review

Leading solutions are pushing the boundaries of automated review. Not only can they identify clauses that stray from established templates, but some are even venturing into suggesting redlines based on past agreements. While this redlining capability is promising, it’s unlikely to replace lawyers entirely – they’ll remain crucial for complex negotiations. However, for low-risk, standardized agreements like NDAs, complete automation is on the horizon.

Watch Now: How Smoothie King Uses AI to Draft & Review Contracts Faster

3. Compliance management

Compliance with contractual obligations, industry regulations, and legal standards is essential in highly regulated industries such as finance, healthcare, and pharmaceuticals, . Advanced contract analytics can significantly streamline compliance management by automating the identification of non-compliance issues, ensuring adherence to contractual terms, and generating alerts for potential violations. This not only reduces the risk of legal disputes and penalties but also enhances operational efficiency.

4. Risk mitigation

Contracts often contain hidden risks that may not be evident through manual review alone. AI-driven contract analytics can identify potential risks such as ambiguous clauses, unfavorable terms, or discrepancies in agreements. By proactively identifying these risks, organizations can take corrective actions, renegotiate contracts, or implement risk mitigation strategies, ultimately safeguarding their interests and reputation.

5. Revenue enhancement

Contracts contain valuable insights into revenue generation opportunities. Advanced contract analytics can identify underutilized assets, pricing discrepancies, and upselling possibilities within existing contracts. By recognizing these opportunities, organizations can optimize their revenue streams and maximize profitability.

6. Cost reduction

Contract analytics can also help organizations identify cost-saving opportunities. By analyzing contract terms, payment schedules, and vendor relationships, organizations can pinpoint areas where cost efficiencies can be achieved. For example, identifying early payment discounts or renegotiating supplier contracts can lead to substantial cost reductions.

7. Data enrichment

By combining contract data with data from other systems, contract analytics can be enriched with additional context and information. For example, linking contract data with customer data from a CRM system can provide insights into customer relationships and their impact on contract performance.

8. Enhanced contract negotiation

Before signing new contracts, organizations can leverage advanced contract analytics to gain insights into the performance of similar contracts in the past. This enables more informed negotiations, allowing organizations to negotiate more favorable terms, reduce risks, and enhance their competitive position.

Identifying early payment discounts or renegotiating supplier contracts can lead to substantial cost reductions.

How to leverage a CLM

While not strictly an analytical technology, CLMs often integrate with advanced analytics features. These platforms provide a central repository for storing and managing contracts throughout their lifecycle. By integrating with CLMs, analytics tools can access a wealth of historical data to generate more insightful reports and predictions.

Here are some things to consider when implementing a CLM platform:

  • Choose a CLM with analytics capabilities. Not all CLMs offer advanced analytics. Look for one that offers a comprehensive suite of AI contract analysis and management tools, as well as reporting and visualization tools. These features help translate data into clear and actionable insights.
  • Ensure consistent data entry across all contracts. This allows for better analysis and comparison.
  • Upload historical contracts into the CLM system. The more data the system has, the better the analytics will be.
  • Define your analytics goals. What insights are you looking for? Do you want to identify potential risks, improve negotiation strategies, or streamline contract renewals? Having clear goals will help you configure reports and dashboards effectively.
  • Utilize analytics features. Many CLMs offer pre-built reports on key metrics like contract cycle times, obligation fulfillment, and risk factors.
  • Assess contractual risk. Identify clauses that might pose legal or financial risks.
  • Do comparative analyses. Compare new contracts against standard templates or past agreements to ensure consistency.
  • Take action on insights. Don’t let the data gather dust! Use the insights to improve contract negotiation strategies, automate contract renewals and reminders, develop standardized templates and identify areas for process improvement within your contract lifecycle.
  • Encourage collaboration. Involve stakeholders from various departments (legal, procurement, sales, finance) to ensure insights are relevant and actionable.
  • Iterate and improve. Regularly monitor and update your analytics goals as your needs evolve.

Keep these tips in mind to leverage the power of your CLM’s analytics features, so you can unlock valuable insights from your contracts.

Challenges and ethical considerations

As with any system powered by artificial intelligence, advanced contract analytics tools should be governed responsibly.

Data privacy

The use of AI and ML in contract analytics raises concerns about data privacy. Organizations must ensure that sensitive and confidential information within contracts is adequately protected. Compliance with data protection regulations, such as GDPR and HIPAA, is crucial.

Bias and fairness

AI systems used in contract analytics can inherit biases from the data they are trained on. It is essential to partner only with companies that continuously monitor and address biases to ensure fair and equitable contract analysis, especially when making decisions that impact individuals or organizations.

Interpretation and accountability

Despite advances in AI and NLP, contract interpretation remains complex, and disputes may arise. Organizations must establish clear guidelines for using contract analytics and be prepared to address discrepancies between automated analyses and human interpretations.

The future of advanced contract analytics

The evolution of advanced contract analytics is far from over. As AI and ML technologies continue to advance, contract analysis will become even more sophisticated. Predictive analytics, automated contract generation, and blockchain-based smart contracts are some of the innovations on the horizon. Organizations that invest in these solutions will gain a competitive advantage by harnessing the full potential of their contracts for strategic growth.

Conclusion: driving growth, responsibly

Advanced contract analytics is ushering in a new era of contract management and interpretation. By harnessing the power of AI, NLP, and ML, organizations can streamline compliance, identify opportunities, mitigate risks, and make more informed and strategic decisions.

However, organizations must also grapple with challenges related to data privacy, bias, and accountability. As technology continues to evolve, advanced contract analytics will play an increasingly pivotal role in reshaping how contracts are managed and interpreted, ultimately driving growth and innovation in the business world.

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