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Top 10 NLP Generative AI Use Cases that Impact Enterprises

Although there’s been an abundance of hype about AI lately, especially around language model apps like ChatGPT, companies are looking to find AI use cases that have real positive impacts on their organizations. Through our work with leading technology consulting firms and directly with end-customers, we’ve identified 10 natural language processing (NLP)/generative AI use cases that not only solve problems that have confounded organizations for years, but that are also ripe for an AI solution given today’s start of the art.

  1. Financial Research – Generate first drafts of research reports with summaries and key findings across 1000s or more files. For financial services firms that perform equity research on companies, this is a great way of speeding up the process for a first draft.
  2. Dialog Analysis and Text Classification - Summarize key topics from call transcripts and other documents. Sentiment analysis of customer calls is a popular use case to help gauge the most pressing or trending issues within a company. Also, any external dialogs with customers and internal interactions can be analyzed for improvements and compliance.
  3. Sales Productivity - Extract insight from 1000s or more transcripts to assess the effectiveness of sales calls. Sales managers can measure their team’s effectiveness and offer suggestions for improvement. They can also get a better read on their pipeline and more accurately forecast sales.
  4. Contract and Document Analysis - Every company executes contracts and agreements when they do business. Extract key terms and their values from contracts and documents. Fact check and export all values or relevant terms in a report. This task is currently a manual task and AI can make a huge impact by automating this process. The ability to analyze past contracts and ensure future consistency with policy and business philosophy will increase operational efficiency while reducing risk.
  5. Information Retrieval - Use semantic search to easily find and summarize enterprise knowledge from your corpus of unstructured data. An often under-appreciated AI capability is semantic search. Current vector-based semantic search is much more robust and sophisticated than in the past. It also revolutionizes knowledge management, another under-appreciated corporate discipline. The concept that everyone within an organization can gain access to all corporate information to make themselves more knowledgeable and productive may actually be realized after 30 years of promise.
  6. Regulatory Controls – It is very difficult to gain full insight into regulatory controls impact with the introduction of new laws or changing regulations. Search through regulatory policies. Companies have tried to control processes with workflow-based apps for years, but these apps easily miss a vast amount of business processes that could be potentially impacted by new regulations. Designing an effective regulatory controls environment will be very helpful in generating the impact analysis of new regulations to various controls by highlighting the exposure as well as relationships and linkages between various processes. Instituting a robust process to use AI to evaluate checkpoints within these processes will help companies avoid exposures to risk and inefficiencies in a fast-changing regulatory environment.
  7. IT/Product Development/Tech Support – Use Generative AI to help write quality technical documentation. Search through user manuals and product reference materials to find answers to technical support questions. AI will also produce succinct answers to deliver back to customers. This is a low-hanging fruit that can help improve the chatbot as well as human technicians in support.
  8. Investigation/Discovery - Find key terms using vector-based semantic search across 1000s or more documents. Whether you’re a lawyer at a law firm or a member of an in-house legal team, wading through contracts and agreements takes precious time. AI-powered investigation and discovery tools can make this process easier, faster, more thorough, and vastly more accurate.
  9. Compliance & Risk Management - Produce evidence-based reports for compliance reviews and filings. One of the biggest challenges these days is ensuring that content produced by generative AI is fact-based. Reports that cite sources of data can inspire tremendous confidence in users and consumers of the content. It is also likely required for internal compliance and regulatory filings.
  10. ESG Analysis - Measure ESG (environmental, social, and governance) characteristics from company disclosures and news articles for research and trend information. Financial services firms that need to gauge a company’s position and progress can get a quick ESG impact score using AI. Any company that needs to assess its ESG positioning through communications will also find this use case valuable.

These are only some of the use cases we have seen at Ai Bloks. We are offering a free workshop to discuss and demo these use cases in more detail and to see if one of them might be right for you. Let us know what you think. The market and technology are rapidly evolving, so we’d also love to hear from you if there’s an important use case that we’ve left out.