What to Watch in AI: The Future of Search and Intelligent Work Assistants

Sep 13, 2023

5 min read

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In today's rapidly evolving digital landscape, the role of artificial intelligence (AI) cannot be overstated. From powering search engines to revolutionizing work productivity, AI has become an indispensable tool for businesses and individuals alike. However, as AI continues to advance, it is crucial to stay informed about the latest developments and trends shaping its future. In this article, we will explore two key areas to watch in AI: the future of search and the rise of intelligent work assistants.

The exponential rise in knowledge and the increasingly distributed nature of work have brought about a pressing need for efficient search capabilities. Simply put, the traditional way of "searching for stuff" at work is no longer effective. As organizations become more decentralized and knowledge becomes more fragmented, finding existing knowledge has become a time-consuming and broken process. This is where intuitive work assistants like Glean come into play. What was once considered a nice-to-have tool has now become a critical component in driving employee productivity. By leveraging AI technologies, work assistants like Glean can streamline the search process, enabling users to find relevant information quickly and efficiently.

However, the journey towards implementing AI applications in enterprises is not without challenges. One of the key obstacles is the lack of appropriate governance controls. Enterprises need to ensure that their AI applications adhere to strict governance guidelines, such as data privacy and ownership. Questions like "Does my application understand what the end user is allowed to see and not see?" and "What source data led to a given model output and who owns it?" need to be addressed to enforce proper governance. Without these controls, the deployment of AI applications to production becomes problematic. Therefore, it is essential for enterprises to prioritize the establishment of robust governance frameworks before shipping AI applications.

Data processing and annotation remain the most tedious and expensive parts of the AI process, yet they are also the most crucial for achieving quality outcomes. Despite the availability of pre-trained large language models, enterprises should focus on utilizing their proprietary data across multiple modalities to create production AI that leads to differentiated services, insightful analyses, and increased operational efficiencies. By leveraging their own data, enterprises can unlock the full potential of AI and gain a competitive advantage in their respective industries.

Moving on to the future of search, it is important to reflect on the success story of Google. Google's dominance in the search engine market was not solely due to having a great product. It was a result of strategic business moves, such as partnerships with Yahoo! and AOL, as well as securing deals with PC manufacturers to make Google the default search engine. These shrewd moves helped Google gain traction and become the go-to search engine for millions of users.