Melissa Perri welcomed Tamar Yehoshua, President of Product and Technology at Glean, to the Product Thinking podcast for an in-depth conversation about the rapidly evolving landscape of AI and enterprise productivity. Tamar, with her extensive background in AI and search technologies, dives into Glean’s journey of integrating cutting-edge retrieval-augmented generation (RAG) technology to enhance enterprise search and knowledge management.

In this episode, Tamar shares how Glean’s approach to AI is revolutionizing the way businesses retrieve and utilize information, emphasizing the importance of balancing search relevance, personalization, and security. She discusses the challenges of building an AI-driven product that adheres to strict data permissions while delivering actionable insights. Tamar also provides a behind-the-scenes look at the engineering-heavy structure of Glean’s team and how senior product managers play a critical role in bridging the gap between technical excellence and user needs.

If you’re interested in how to navigate the complexities of AI innovation, optimize enterprise workflows, and future-proof your product strategies, this episode is a must-listen.

When discussing Glean's approach to enterprise search, Tamar emphasizes the paramount importance of security and privacy. Glean’s platform deals with vast amounts of proprietary information within enterprises, making it essential to ensure robust security measures. Tamar explains that the platform is designed to adhere strictly to the privacy controls and policies of various SaaS applications, requiring meticulous attention to detail to maintain compliance. She highlights that Glean offers both a hosted service and the option for customers to host the service within their own cloud instances, ensuring a high level of data isolation and security.

This focus on security is critical for building trust with enterprise clients, particularly when dealing with large organizations that have stringent security requirements. Tamar’s insight into the complexities of securing enterprise data showcases the deep technical challenges involved in developing a product like Glean. It also reflects the broader industry trend where security and data privacy have become fundamental selling points for technology solutions, especially in the enterprise space.

Tamar Yehoshua emphasizes the challenges of integrating large language models (LLMs) with enterprise data. Unlike general LLMs, enterprise solutions require sophisticated retrieval mechanisms to ensure that only authorized and relevant information is accessed. Tamar explains that simply dumping all enterprise data into an LLM is not feasible due to issues with permissions and the specificity required in enterprise contexts. Glean’s approach is to index all enterprise data, enforce metadata-based permissions, and personalize results based on the user’s role and relationships within the company.

In discussing the future of AI, Tamar touches on the emerging trend of agentic workflows, where AI systems can perform multi-step tasks autonomously. Although these systems are still in their infancy and require user intervention, Tamar is optimistic that they will soon become more reliable. She believes that as AI models improve, these workflows will enable users to offload routine tasks, allowing them to focus on more strategic and creative work.

Tamar envisions a future where AI significantly enhances workplace productivity by handling time-consuming tasks such as summarizing meeting notes or updating databases. She also addresses concerns about AI potentially replacing jobs, advising professionals to embrace AI as a tool to improve their own efficiency and capabilities.

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