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If you know about artificial intelligence (AI), but are doing everything manually, you’re not alone. Although Contract Management (CM) systems and other digital technologies have made CM more efficient, you and many others might be performing time-consuming, error-prone manual processes without realizing it’s costing you time and money.

 But imagine that you are now using AI for these processes and the changes you are seeing are encouraging. You are teaching your AI platform to read, classify, extract and summarize information in contracts. And now, AI is automating routine tasks, allowing you to complete them much faster, with fewer errors than ever possible manually. 

Moreover, you are realizing many benefits from using AI to complete many tasks involved in compliance, due diligence, risk management and other contract management projects. AI is helping you:

  • reduce the costs of these projects by lowering the time spent on them from weeks and months to days or even hours;
  • improve the accuracy of the results from these projects; and
  • reveal insights throughout the course of these projects that will minimize risk and unleash new opportunities for your organization. 

AI is not magic – it starts with you

AI is taught to read, extract and summarize the data you feed into it. Once this machine capability learns how to complete a certain type of process -- such as extracting a clause from a contract -- teaching it to modify that process for a particular type of clause is relatively simple. In addition, as AI analyzes more information, its teaching builds upon itself, enabling its technology to become “smarter” over time.

How one company saved money, improved accuracy and discovered new insights

When one organization fed thousands of contracts into an AI engine trained to classify contracts, its technology was able to…

  • automatically cluster the contracts into separate classes such as facility agreements, commercial mortgages or structured finance contracts;
  • extract specific data points from each class of contract and deliver them to an easily digestible spreadsheet for management to review;
  • remove the need to extend working hours for its employees or depend on third-party companies to do the task manually at a great cost, all the while mitigating risk of human error.
  • reduce an average of 800 hours of manual due diligence to 40 hours, including configuration, processing and data output.

How banks reduce risk, lower costs and remain contractually compliant

Risk management is another area where AI can reduce the time and costs of a CM task. Many banks participating in financial markets want to reduce their risk exposure and collateral obligations. Previously, banks’ personnel manually reviewed and extracted certain terms from their counterparty contracts. But now with AI banks can: 

  • extract automatically the structure of each contract – its main clauses and subclauses;
  • extract key definitions from these contracts and enter this data into a contract or collateral management and margining system or spreadsheet;
  • reduce dramatically the time and costs involved in manually reviewing all these agreements;
  • provide an easy-to-review summary of each agreement’s data, allowing personnel to quickly identify agreements, risk exposures or collateral obligations that need to be corrected; and
  • use data to ensure all the agreements are in compliance with current financial regulations.

It’s your turn now!

Do you want to predict risks or uncover new opportunities?

Increasingly AI applies predictive analytics tools to contracts after they have been classified and data has been extracted from them. Predicative analytics can predict outcomes of new contracts by comparing them with results of similar contracts. In this way, you can gain insights that enable your organization to reduce your risk or uncover new revenue opportunities. 

Is your AI platform flexible?

A flexible AI platform -- one that can be taught to perform different kinds of tasks on many kinds of documents – can also help CMs automate many manual tasks and deliver value to their organizations. But, when choosing an AI system, make sure you understand its flexibility, and whether you can adjust its training and understanding of contracts to your specific set of contracts or type of contract.

In theory, an AI system becomes much more accurate if its domain is as narrow as possible. A flexible AI system can use different computer models to analyze different types and subtypes of contracts. For example, you might want one model for commercial contracts in one geographic region; another for contracts in another region (with different laws and regulations); and another for non-commercial contracts, such as employment contracts – all are more likely to deliver positive outcomes.

If the AI platform is flexible enough, your organization can select different uses for it in your business. For instance, one company started with the broad use case of gaining insight into their high value business contracts. They quickly realized that the same platform could be trained (or loaded with a pre-built “AI Model”) to help other lines of business. In this case, Human Resources (HR) was able to use their flexible platform to understand key data points on thousands of their employment contracts.

But beware of using a universal, single master model to analyze various types and subtypes of contracts. It is more likely to provide suboptimal results. Efficient AI systems ought to be flexible, making it easy to develop specific models for specific sets or types of contracts, because specific models will provide better results than universal models that cannot be adjusted. Moreover, you can use flexible AI models in a more holistic way than universal models, which generally are just solutions for one specific problem.

The fact that AI is accelerating rapidly begs the question: how come CMs still too often stick to “old school” time-consuming, error-prone manual processes? Change is difficult. But does it make sense to waste time and money manually classifying contracts, extracting key terms and clauses, or determining if contracts are impacted by regulatory or other legal changes? 


Peter Wallqvist is Vice President of Strategy at iManage. He started his career in the information retrieval industry as a research engineer at BT Research at the turn of the millennium. From there he went on to being part of delivering some of the largest and high profile search and unstructured data processing systems in the world. In 2010, he co-founded AI pioneer RAVN Systems, which was acquired by iManage in 2017.

About iManage:

The mission of iManage is to help professionals in legal, accounting and financial services combine the power of artificial intelligence with market leading document and email management. iManage automates routine cognitive tasks, provides powerful insights and streamlines workflow practices, while maintaining the highest level of security and governance over critical client and corporate data. Over one million professionals at over 3,000 organizations in over 65 countries – including more than 2,000 law firms and 500 corporate legal departments – rely on iManage to deliver key services. To learn more visit

Peter Wallqvist, VP of Strategy at iManage

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About Globality

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