What is an AI coworker?

Summary

• An AI coworker is a data-smart AI assistant that automates work and takes agentic actions across business tools and workflows.
• Effective AI coworkers combine real-time data access, governance-enforced security, and business context to execute trusted actions.
• AI coworkers embed in Slack, Teams, email, and mobile, and integrate with systems like Jira to enable work at point of need.
• Governance and security are foundational: trustworthy AI coworkers enforce source-native access controls and provide transparent cost tracking.
• The unified context layer connecting data, tools, and business logic transforms an AI assistant into a genuine coworker.

What is an AI coworker?
An AI coworker is a data-smart AI assistant designed to automate work and take agentic actions across your business systems. Rather than just answering questions, it understands your business data and tools well enough to execute tasks on your behalf: scheduling meetings, updating records, creating documents, and routing decisions. The best AI coworkers operate with full visibility into your data and enforced security controls, so they make decisions you can trust and trace.

What separates an AI coworker from a general-purpose chatbot?

A general-purpose AI assistant answers questions in isolation. An AI coworker adds three critical layers: it connects to your actual business data and tools so it reasons over real information, not generic knowledge; it takes action in your systems on your behalf instead of just telling you what to do; and it operates within governance guardrails so every action respects your organization's security and compliance.
An effective AI coworker knows how your business operates, what your priorities are, and what information matters for the decisions you face. It integrates into how you already work: in the tools and systems where decisions happen, not in a separate interface.

Key capabilities that make a trustworthy AI coworker

Capability	Why it matters
Real-time data access	Answers grounded in current business data, not stale reports. Connects across your entire data estate, including Lakehouse-federated sources and connected SaaS tools.
Agentic reasoning and action	Executes tasks autonomously: creates documents, schedules actions, updates systems, routes work. Reduces busy work and enables faster decision cycles.
Embedded into daily workflows	Lives in Slack, Teams, email, and mobile. Available where work actually happens, no context-switching needed.
Governed security	Enforces your source-native access controls automatically; users only see insights they're authorized for.
Transparent cost management	Provides visibility into how much compute and token usage each interaction costs so you can optimize.
Business context understanding	Knows key concepts, definitions, and relationships across your organization to follow reasoning across systems.

How Databricks approaches AI coworkers

Databricks offers two complementary products. Genie One is the data-smart AI coworker that embeds into Slack, Teams, email, and mobile to answer questions and take actions directly in the tools you use. It connects to your Lakehouse and federated data sources so it reasons over real data and executes tasks across your business systems.
Genie Agents is the agentic platform behind it. You can create custom agents that automate specific workflows: generating reports, monitoring data quality, sending alerts, updating Jira tickets, or triggering processes. Your team can share agent skills with colleagues, turning automations into repeatable, consistent workflows.
Both rely on Genie Ontology, a unified business context layer that extracts and reasons over structured and unstructured data. The Ontology acts as a living map of how your business operates: it knows your key concepts, how they relate, and where they live across systems. This context enables Genie to make decisions that business users recognize and trust.
Governance is enforced by design. Genie operates through Unity Gateway, which centralizes authentication, budget management, and cost tracking. Every interaction respects your source-native access controls, so an analyst only sees data they're already authorized for. You get detailed cost visibility so you understand exactly what agentic tasks cost.

Use cases

In finance, an AI coworker monitors spend, flags exceptions in real time, and routes them to reviewers without waiting for a weekly report. In sales, it summarizes customer interactions, tracks deal progress, and surfaces risk signals before they become problems. In operations, it automates routine approvals, aggregates status updates across systems, and alerts the team when thresholds are crossed. The AI coworker knows the business context, operates within security boundaries, and saves time on routine decision-making.

FAQs

Can an AI coworker work with Slack and email?

Yes. Genie One embeds natively into Slack, Teams, email, and iOS and Android mobile apps. You can mention Genie in Slack channels or ask it in email threads without switching platforms. It integrates with systems like Jira and can send alerts or updates back through those channels.

What happens if an AI coworker doesn't have permission to access some data?

Genie respects your organization's access controls automatically. If you ask about data you don't have permission to see, it won't show it to you, just like a human coworker wouldn't. Unity Gateway enforces this, so there's no privilege escalation or accidental oversharing.

Can I create custom AI agents for my team?

Yes. With Genie Agents, you build custom agents that automate specific work: generating reports, monitoring data quality, routing approvals, or triggering processes. You can share agent skills with your team so workflows stay consistent across the organization.

How do I know what my AI coworker is doing and what it costs?

Databricks provides transparent cost tracking through Unity Gateway so you see exactly what each agentic task costs in compute and tokens. This helps you make informed trade-offs. You also get visibility into which data sources the coworker is querying and how it's reasoning.