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Generative AI refers to artificial intelligence models that can generate new, original content such as text, images, audio, video, and more.  It involves using machine learning models trained on large datasets to learn patterns and relationships in the data, which the models then use to create new content.  Leading applications include generating creative works, summarizing text, automating content creation, and synthetic data generation.


Some Ways We Have Used Generative AI

  • Summarizing and looking for trends in safety incident report textual narratives.
  • Analyzing grocery customer feedback survey data, uncovering a variety of issues for management to address, and generating automated responses to the customer.
  • Monitoring call center representative phone conversations, listenening for tone and coaching the representative, suggesting steps to take, while making sure policies are followed and regulatory compliance assured.
  • Creating a publically available ChatGPT plugin to answer questions about how railroads can reduce greenhouse gas emissions.
  • Creating a bot to answer questions from a commercial real estate developer about a market analysis for a potential property to develop.
  • Writing code to create machine learning models based on specific data and business objectives.
  • Translating code from one programming language to another to help in an IT migration project.
  • Creating optimization software code based on a plain English description of the problem to be solved, the decision variables, and constraints.
  • Creating “coaches” for IT application developers based on a corpus of technical documentation.
  • Creating chatbots for books to enable interactive conversations of a technical nature.


Ways We Can Help

  • Assess where Generative AI may fit in to your business through discovery workshops which produce use cases.
  • Provide executive education to orient leaders as to what the technologies are and how they may help your business.
  • Assist in building business cases.
  • Qualify your data’s suitability to task.
  • Develop roadmaps for implementations.
  • Conduct proofs-of-concepts and pilot tests.
  • Build in-house skill sets.
  • Sustain and expand in the long run, as needed.


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