How can you use artificial intelligence to automate lease abstraction?

2 How can AI automate lease abstraction?

AI can automate lease abstraction by using natural language processing (NLP), which is a subfield of AI that deals with analyzing and generating text. NLP can use various techniques, such as optical character recognition (OCR), named entity recognition (NER), sentiment analysis, or text summarization, to process lease documents and extract key information. For example, OCR can convert scanned or handwritten lease documents into digital text, NER can identify and label important terms or entities, such as parties, dates, amounts, or clauses, sentiment analysis can detect the tone or emotion of the text, and text summarization can generate a concise and relevant summary of the lease. By using AI to automate lease abstraction, corporate real estate professionals can save time and resources, reduce errors and inconsistencies, and access standardized and structured data that can be easily searched, compared, and analyzed.

3 What are the benefits of AI for lease abstraction?

AI for lease abstraction can offer several advantages for corporate real estate professionals, such as speed, accuracy, and insight. AI can process large volumes of lease documents quickly and accurately, with the ability to handle multiple languages, formats, and jurisdictions. It can also reduce human errors and biases, while learning from feedback and corrections to improve its performance over time. Moreover, AI can provide deeper and richer insights into lease data, allowing corporate real estate professionals to make better decisions and strategies. Finally, AI can generate reports, dashboards, and visualizations to enhance the communication and presentation of lease data.

4 What are the challenges of AI for lease abstraction?

AI for lease abstraction can also pose some challenges for corporate real estate professionals, such as data quality, legal and ethical issues, and human involvement. Data quality is a key factor in the accuracy of AI, as it depends on the quality and quantity of data it uses. Additionally, legal and ethical issues may arise when dealing with sensitive and confidential lease data. Finally, human involvement is still necessary to ensure the quality and validity of lease abstraction. Corporate real estate professionals must find a balance between AI and human reviewers to leverage the strengths of both. They must also be aware of potential risks and implications of using AI, and adopt policies to mitigate them.

5 How can you get started with AI for lease abstraction?

If you are interested in using AI to automate lease abstraction, there are several steps to consider. Define your goals and needs to determine the objectives, challenges, and features you want from AI for lease abstraction. Explore your options and solutions, comparing them in terms of functionality, performance, cost, and compatibility. Test and evaluate the options and solutions using criteria and indicators to assess their effectiveness and suitability. Finally, implement and improve the chosen option or solution by integrating it into your existing systems, training staff to use it, and monitoring it over time.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?