待翻譯：What is an AI Assistant?

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What is an AI Assistant? | Databricks Blog Skip to main content An AI assistant is software that uses artificial intelligence to understand natural language, process requests, and complete tasks, ranging from answering questions to executing multi-step workflows. The technology behind AI assistants spans large language models, retrieval-augmented generation, and agentic frameworks, each suited to different enterprise needs from data analysis to autonomous pipeline management. Choosing the right AI assistant requires evaluating data integration depth, governance controls, extensibility, and alignment with your team's existing workflows and skill levels. AI assistants use language models, data retrieval, and reasoning to understand requests and take action on behalf of users. For enterprise data teams, this means generating SQL, building dashboards, troubleshooting pipelines, and automating repetitive work without requiring everyone to write code. The market reflects how quickly organizations have moved from experimentation to production: the global AI assistant market was valued at $19.1 billion in 2025 and is projected to reach $114.1 billion by 2035. What separates a useful assistant from a novelty is depth of integration: one that understands your data catalog and respects your governance policies is fundamentally different from one that generates generic text in isolation. How AI assistants work AI assistants combine several technologies into a pipeline that transforms a user's natural language input into a meaningful output, whether that's a written answer, a generated query, a visualization, or an executed action. Understanding this pipeline helps you evaluate which assistants are genuinely capable and which are surface-level wrappers around a single language model. Core technologies behind AI assistants Large language models (LLMs): Large language models that power the assistant's core language capabilities, with enterprise platforms offering both proprietary (GPT, Claude, Gemini) and open-source (Llama, DeepSeek) options. Natural language processing (NLP): The broader set of techniques for parsing, tokenizing, and interpreting user input, including entity recognition, sentiment analysis, and language translation. Machine learning and deep learning: The training paradigms that allow models to improve over time through exposure to data. Knowledge graphs and retrieval-augmented generation (RAG): Systems that ground the assistant's responses in verified, up-to-date information by retrieving relevant documents or metadata at query time rather than relying solely on what the model memorized during training. Speech recognition and computer vision: Input modalities that extend AI assistants beyond text, enabling voice commands, image interpretation, and video analysis. Agentic frameworks and tool integration: Architectures that allow an assistant to plan multi-step workflows, call external tools and APIs, and execute actions autonomously. The AI assistant process step by step User input capture: The assistant receives a request through text, voice, or a structured interface. Intent recognition and context parsing: The system identifies what the user is asking for and gathers relevant context. Data retrieval and knowledge grounding: The assistant queries knowledge bases, data catalogs, or documentation to retrieve facts and metadata that inform its response. Response generation: The language model produces an output, whether that's a natural language explanation, a SQL query, a code block, or a structured recommendation. Action execution or output delivery: Depending on the assistant's capabilities, it either presents the response for review or executes it directly, such as running a query, creating a dashboard, or triggering a pipeline. Feedback loop and continuous learning: User corrections, approvals, and usage patterns feed back into the system to improve future responses. Types of AI assistants Not all AI assistants serve the same purpose. The differences matter when you're deciding what to adopt, because an assistant built for consumer scheduling has almost nothing in common with one designed to generate production data pipelines. TypeWhat it doesCommon examplesBest suited for Voice assistantsRespond to spoken commands, control smart devices, answer general knowledge questionsSiri, Alexa, Google AssistantConsumer convenience, hands-free interaction Conversational chatbotsHandle text-based dialogue for customer support, FAQ resolution, and simple task completionWebsite chat widgets, support botsCustomer service, lead qualification General-purpose AI assistantsGenerate text, summarize documents, write code, and answer open-ended questions across domainsChatGPT, Gemini, ClaudeKnowledge work, writing, research, brainstorming Domain-specific AI assistantsOperate within a specialized field such as healthcare, legal, or finance with tailored knowledge and compliance awarenessClinical documentation assistants, legal research toolsRegulated industries requiring domain expertise Data and developer assistantsGenerate code, build queries, create visualizations, debug errors, and automate workflows within a data or engineering platformGenie Code, GitHub CopilotData teams, software engineers, ML practitioners Autonomous AI agentsPlan and execute multi-step tasks independently, calling tools, making decisions, and monitoring outcomes without continuous human inputAgentic frameworks, Genie CodeComplex enterprise workflows, production pipeline management The industry is moving rapidly from general-purpose assistants toward autonomous agents. According to Zapier, ChatGPT is the most commonly used AI app in the workplace at 71%, more than twice as popular as Google's AI search or Gemini. But workplace adoption is increasingly shifting toward assistants that do more than answer questions. They execute work. Key benefits of AI assistants for modern teams Faster time to insight through automated analysis AI assistants compress the cycle from question to answer. Instead of writing queries from scratch, waiting for a data engineer to build a report, or manually exploring unfamiliar datasets, a team member can describe what they need in plain language and get a working analysis in seconds. Databricks' own data shows the impact: according to a Databricks research survey, over 72% of Genie Code users reported saving at least 30% of their time on any given task. Reduced operational overhead and manual effort Repetitive tasks like writing boilerplate ETL code, formatting dashboards, or debugging common errors consume hours of skilled engineering time. AI assistants handle these tasks reliably, freeing data teams to focus on architecture decisions, model development, and strategic analysis. When 7-Eleven built a GenAI creative assistant on Databricks, the company automated content workflows that previously required significant manual coordination across marketing teams. More accessible data for non-technical stakeholders One of the most persistent problems in data-driven organizations is the gap between the people who have questions and the people who can query databases. AI assistants bridge this gap by letting business analysts, product managers, and operations leads interact with data through natural language rather than SQL or Python. This doesn't replace data teams. It reduces the volume of ad hoc requests they field daily. Scalable decision support across the organization A single data team can only serve so many stakeholders manually. AI assistants scale decision support by making it self-service. When an assistant is grounded in a governed data catalog like Unity Catalog, every user gets answers drawn from the same authoritative sources, with the same access controls applied consistently. Challenges to consider before adoption AI assistants are not without real constraints, and understanding them upfront prevents costly missteps. Hallucination and accuracy risks: Language models can generate plausible but incorrect outputs. Data privacy and security exposure: An AI assistant that processes sensitive data must operate within your organization's governance framework. Integration complexity: If the assistant can't access your data catalog, understand table relationships, or respect column-level permissions, its outputs will be unreliable or incomplete. Over-reliance and skill erosion: Teams that defer entirely to AI-generated code without reviewing it risk introducing subtle bugs into production systems. Bias in training data: Models inherit the biases present in their training corpora. For enterprise use cases involving hiring, lending, or customer segmentation, this requires active monitoring and evaluation to ensure outputs meet fairness standards. Cost at scale: Token-based pricing for large language models can escalate quickly when an assistant handles high query volumes or processes large context windows. Read now How to choose the right AI assistant for your organization With 84% of enterprise leaders planning to increase AI agent investments over the next 12 months, according to a Zapier/Centiment survey, the question is no longer whether to adopt an AI assistant but which one fits your environment. CriteriaWhat to evaluate Data integrationNative connections to your data catalog, schema awareness, cross-source querying without manual config Security & governanceRole-based access, audit trails, data lineage, and dataset-level restrictions built into the assistant layer ExtensibilitySupport for custom tool integrations, multiple LLM providers, and domain-specific agent development Team fitMatch the interface and capability level to the users — code-first for engineers, conversational for analysts Evaluate data integration depth and platform compatibility The most capable AI assistant is the one that already understands your data. Look for assistants that connect natively to your data catalog, respect table schemas and column descriptions, and can query across data sources without requiring manual configuration. An assistant built into your existing platform will outperform a standalone tool that requires separate connectors and authentication layers. Assess security, governance, and compliance controls Enterprise AI assistants must enforce the same access controls that govern your data platform. That means role-based permissions, audit trails, data lineage tracking, and the ability to restrict which datasets the assistant can access. Databricks' approach to internal AI assistant security illustrates how governance should be built into the assistant layer, not bolted on after deployment. Consider extensibility and customization options Your needs will evolve. An AI assistant that only handles predefined tasks today will become a bottleneck tomorrow. Evaluate whether the assistant supports custom tool integrations, works with multiple LLM providers, and allows your team to build domain-specific agents on top of it. Platforms like Agent Bricks let teams build with preferred frameworks like LangChain or LlamaIndex, then deploy to serverless compute, avoiding lock-in to a single model or vendor. Align capabilities with team skill levels and workflows An assistant designed for data engineers writing Spark pipelines serves a different purpose than one built for business analysts exploring dashboards. The right choice depends on who will use it most and where they work. A data science agent that operates inside notebooks serves ML practitioners directly, while a conversational Genie interface serves analysts who need answers without writing code. Where AI assistants are headed: trends shaping the future The trajectory is clear: AI assistants are becoming agents. The shift from tools that respond to prompts toward systems that plan, reason, and execute autonomously is already underway. Accor [truncated for AI cost control]