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Behind the Scenes of Enterprise AI: Powering AI Assistants

Behind the Scenes of Enterprise AI: Powering AI Assistants

Explore our latest newsletter on how to safely and securely deliver AI solutions for enterprise.


Ever notice how the variety of words we use to describe something can reveal a lot about the topic's significance? Take for example the many names for conversational tools — AI knowledge assistants, chatbots, virtual assistants, companions, copilots, and so many more. The different ways to describe AI assistants highlights not only their range of skills but also the incredible value they can bring to businesses.  

Industries like consulting, banking, and retail are expected to welcome a wave of AI assistants in 2024. Businesses can now customize AI chatbots to meet their specific needs, and easily connect them with their own data for precise, timely responses. Plus, features like retrieval-augmented generation (RAG) further enhance their accuracy and reliability.

You might be wondering, though, why aren't these innovative AI assistants everywhere yet?

Transitioning from a proof-of-concept (POC) to full-scale production isn't just a technical challenge; it requires dedicated leadership and the collaboration of cross-functional teams.

  • For starters, customizing AI assistants relies on a substantial volume of data to train the model, and depending on the quality and relevance of the dataset, it may require a few gigabytes to several terabytes of text data. A strong investment in data infrastructure and a healthy alignment between AI strategy and data strategy can be key to getting these projects live.
  • Ongoing security concerns, such as cyber attacks and data breaches, are another hurdle that companies must tackle. Cohere security expert Ads Dawson explains how equipping security teams with knowledge of both traditional web application attacks and large language model vulnerabilities enables a comprehensive view of the environment in our latest guide, The State of AI Security. Both APIs and the AI models they serve have their own unique vulnerabilities. To mitigate the risks, enterprise teams will need to develop strategies across various levels and entry points. 
  • Lastly, for conversational applications to deliver outstanding performance at scale, everything from searching and retrieving the best answers, to designing the best user interface, to picking the right tone of voice, can impact adoption and ROI. Avoiding the pitfalls of generative AI projects requires dedicated oversight, which may explain why we are seeing the rise of new leadership roles like Head of Gen AI Transformation. 

As more POCs turn to full-scale production, businesses may need to further address these cross-functional and organizational challenges to get the most out of their AI assistants.


Use the build-your-own connectors framework to integrate private datastores with Cohere LLMs and enable RAG for enterprise. Over 100 connectors launched! Connect now.

For Business

Neil Thompson, Director of the MIT FutureTech Lab and co-author of the paper “The Grand Illusion” on software portability, discusses how to scale AI for enterprise and the need to drive efficiencies while retaining flexibility. Read the interview. 


Learn how to use our latest connector-mode with Cohere Chat for secure, real-time access to proprietary data in the LLM University chapter, How to Use Quickstart Connectors


In 2023, non-profit research lab Cohere For AI published 30 papers on cutting-edge machine learning topics, collaborating with over 40 institutions. Check out all their work.   


Cohere CEO and Co-Founder Aidan Gomez was at Davos this year and joined AI luminaries Yann LeCun, Kai-Fu Lee, Daphne Koller, and Andrew Ng in a profound discussion on “The Expanding Universe of Generative Models” hosted by The Atlantic’s Nicholas Thompson. Watch the panel

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