Enterprise AI software: what it is, how it works, and how to choose a platform

Not exactly. An enterprise AI assistant is a specific application (usually chat-based) that sits on top of a broader enterprise AI platform. The platform underneath handles connectors, retrieval, security, and orchestration; the assistant is one way users experience that capability. Glean offers both — a complete Work AI platform plus Assistant and Agents that use it.

Analytics and BI tools are optimized for dashboards, reports, and structured data. Enterprise AI tools work with both structured and unstructured information, use generative AI to answer open-ended questions in natural language, and can summarize, write, and act — not just enable data-driven decision making. Glean combines search, analysis, and action in one platform.

Adding AI to individual tools can be useful, but many organizations find it creates an "AI tax": overlapping pilots, fragmented AI integration, inconsistent governance, and duplicated work. Central platforms connect data and governance once, then expose AI capabilities across tools and teams. Glean's horizontal platform approach reduces this complexity.

You don't need a research lab. You need owners for data, security, and governance. You need people who understand key business functions and workflows and can define good AI use cases. And you need the ability to iterate — testing agents and assistants against real tasks. Glean's natural language agent builder lets non-technical users create agents.

Initial setup varies by vendor and scope. Some enterprise AI platforms can connect to your existing systems and apps in hours. Full indexing of your data typically takes days to weeks, depending on volume. Realizing measurable outcomes from pilots usually takes 1-3 months of iteration. Glean's initial setup takes as little as an hour with no professional services required.

Natural language processing (NLP) lets you ask questions in plain language instead of learning complex search syntax. Enterprise AI tools use NLP to understand intent, interpret company-specific terms and acronyms, and generate human-readable responses. Glean's semantic search is adapted to your company's data and language to understand your unique context.

Machine learning and deep learning are the intelligence behind enterprise AI systems. They train search algorithms to understand your company's unique language and acronyms, build knowledge graphs that model relationships between people and projects, and enable risk management through sensitive content detection. Machine learning also improves over time as employees use AI systems. Glean's machine learning models learn from usage patterns and organizational context to deliver increasingly relevant, personalized results.

RAG (Retrieval-Augmented Generation) pulls relevant context at query time and feeds it to the model. Fine-tuning retrains the model on your data. RAG is faster to implement, easier to update, and better for most enterprises. Fine-tuning suits specialized, stable domains. Glean uses RAG paired with its knowledge graph for grounded, up-to-date responses.

Advanced platforms enforce permissions at query time — users only see data they already have access to in source systems. They respect existing systems' access controls, sync permission changes in real time, and never expose data across permission boundaries, even when generating AI responses. Glean's permissions enforcement reflects source system changes immediately.

Yes. Look for platforms with SOC 2 Type II, HIPAA, GDPR, and ISO 27001 certifications. Check for single-tenant or private cloud deployment options, data residency controls, and sensitive content detection. Strong AI apps also offer AI-specific guardrails against prompt injection and jailbreaks. Glean Protect offers all of these plus partnerships with security vendors.

Track time saved per employee or workflow. Measure improvements in resolution times, win rates, or onboarding speed. Calculate reduction in "AI tax" from consolidating fragmented tools. The strongest signal is whether usage grows organically after pilots — that indicates tangible business value, not just novelty. Glean customers see 93% adoption in just 2 years, and companies typically recover their investment in under 6 months.