Gartner’s new “Market Guide for Enterprise AI Search” offers a valuable perspective on how generative AI is transforming enterprise knowledge access. Yet, as the focus remains on internal productivity and governance, a crucial question arises: Do the same approaches apply when search is customer-facing and directly tied to engagement, conversion and revenue?Gartner’s Take on Enterprise AI SearchGartner’s research focuses on internal, employee-focused use cases, including digital workplace assistants, IT support, HR knowledge management and compliance automation. The goal is to enable AI assistants and copilots to increase employee productivity by synthesizing information across corporate data silos. Customer experience (CX) scenarios are mentioned only briefly and treated as adjacent markets rather than core priorities.As a result, the report emphasizes governance, connectors, metadata enrichment, security trimming and knowledge graphs. These capabilities are critical for enterprise environments but less relevant to real-time, customer-facing systems.Performance is primarily assessed in terms of reliability and governance for AI assistants, which aligns with the internal focus of enterprise AI search. In these settings, latency and throughput matter less than access control and compliance. Hybrid search and retrieval-augmented generation (RAG) are recognized as core capabilities, but the emphasis remains on managing complexity across diverse data silos rather than serving high-volume, low-latency workloads.Gartner also expects large enterprises to operate multiple embedded search platforms across SaaS suites such as Microsoft 365, Salesforce and SAP. This architecture fits internal knowledge management well, but is not designed for customer-facing systems, where performance, scale and accuracy directly shape user experience and business outcomes.Customer-Facing SearchThis creates a clear gap for organizations building customer-facing AI applications, where search performance is not just a productivity factor but a business-critical capability. Whether e-commerce, finance, media, social networks, market intelligence or other apps, the requirements are fundamentally different. Developers must deliver high-volume, low-latency retrieval and ranking, often processing thousands of queries per second under strict service-level objectives.These systems depend on multiphase ranking pipelines, sophisticated tensor computation and multimodal retrieval across text, image and structured data. They must also serve generative or retrieval-augmented results at machine speed. Indexing and feature updates occur in near real time to reflect changes in inventory, behavior or content streams, all while maintaining uptime, cost efficiency and performance at scale.Unlike internal enterprise AI search systems, where governance and policy compliance are the top priorities, customer-facing AI search must optimize for low-latency relevance, responsiveness and personalization.These systems are embedded directly into the core product experience, influencing not just user satisfaction but also revenue, engagement and retention. They require a unified architecture that can handle both lexical and vector retrieval, execute learned ranking models close to the data to reduce network bandwidth and support large-scale inference workloads without heavy orchestration or additional middleware.ConclusionGartner’s report provides an essential framework for understanding enterprise AI search within the context of internal productivity and governance. However, the same assumptions and architectures do not translate to customer-facing applications.As the market evolves, a more precise distinction will emerge between enterprise AI search platforms designed for internal knowledge synthesis and AI search platforms built for production-grade, real-time environments where search is the product itself.The latter must meet far higher expectations for performance, scale, accuracy and multimodality, which are defining characteristics of modern generative and retrieval-augmented systems.For organizations building customer-facing AI applications, Vespa provides an alternative to the traditional “insight engine” lineage. It is built for AI-native systems, not retrofitted enterprise search. Vespa powers high-volume, real-time retrieval and ranking at scale for companies such as Perplexity.ai, Spotify and Yahoo. Its architecture supports multiphase ranking, tensor computation, multimodal retrieval and serving AI at machine speed. Vespa is designed for engineering-led companies where search is the product and performance directly drives revenue.The post Enterprise AI Search vs. the Real Needs of Customer-Facing Apps appeared first on The New Stack.