Quickbase AI can help you move from a plain-language prompt to a working application, or an incoming request to an automated decision. Our latest webinar, From Prompt to Power: Automating Work With Quickbase AI Features, showed how teams can let AI take on that kind of building and decision-making inside governed workflows.

Charlie Kleiner (Customer Marketing Manager, Quickbase) joined Alex Pederson (AI Forward Deployed Engineer, Quickbase) and Georgi Georgiev (Quickbase Product Manager) to explore the Intelligence Package and how it combines conversational AI, background automation, predictive insights, app documentation, and administrative controls within the Quickbase platform.

Read on to see how a service request becomes an actionable record, and a preview of what's next.

Quickbase AI Brings Operational Context to Work Already in Your Apps

The Quickbase Intelligence Package connects AI with the data, workflows, app structures, and permissions that support your operations.

• AI Agent: Build, explore, analyze, and update work through plain-language conversation.
• AI Actions: Add AI-powered extraction, classification, summarization, and generation to Pipelines.
• Data Analyzer: Build predictive models from historical app data and identify factors that influence outcomes.
• AI App Intelligence: Generate exportable documentation for an app's purpose, structure, relationships, and connected Pipelines.
• AI Control Center: Manage AI access by feature, user, or group.

Unlike general-purpose AI assistants, the Quickbase Intelligence Package is designed around the way work already happens inside Quickbase. It combines AI with your operational data, application structure, business rules, and permissions. As a result, builders, end users, and administrators each gain value without sacrificing governance.

AI Agent Handles the Conversation; AI Actions Keep Work Moving

AI Agent serves as foreground AI. A user starts a conversation, asks a question, requests a change, or explores operational data. During the webinar, Alex built a demo application entirely through AI chat, including its tables, fields, relationships, and sample data (the same app used in the walkthrough below). AI Agent can also create or update records, analyze permitted data, surface unusual results, and provide Quickbase product help.

AI Actions serve as background AI. They run inside Pipelines after a defined trigger and return results that later steps can use. Common examples include extracting invoice details, classifying requests, summarizing descriptions, drafting content, and generating structured field values.

Use Agent when a person needs to direct or explore work. Use Actions when the same interpretation should happen automatically.

One Incoming Request Became Three Usable Decisions

The walkthrough started with a service request about three actuators sticking during operation and delaying the production line. The record had a subject and description, but no category, status, owner, or completed priority assessment.

The Intake Pipeline Converted Free Text Into a Triage Record

The first Pipeline asked an AI Action for a category, priority, confidence score, and short summary, then wrote the results back to the record.

The demo returned a 95% confidence score, categorized the issue as a product defect, moved it into triage, and summarized the production impact. Consistent fields can help teams sort queues, report on demand, and route urgent work without asking someone to interpret every submission first.

The Resolution Pipeline Reused Knowledge From Closed Requests

The second Pipeline searched the Requests table before asking AI to draft a resolution. Its query limited the context to closed requests with a matching category and related products.

The AI Action used those historical records and resolutions to create a suggestion for the new request. That design grounded the output in the organization's experience and reduced the chance of an unsupported answer.

The same pattern can support maintenance issues, customer cases, quality events, or safety reports.

The Assignment Pipeline Balanced Expertise With Workload

The third Pipeline queried a roster with team membership, open-request counts, and record IDs. It prompted AI to choose one qualified owner and prefer the least-loaded person on the right team.

The AI returned a record ID and one-sentence rationale. A later step could map both values into Quickbase fields, which supports faster assignment, balanced workloads, and clearer routing decisions.

Five Decisions That Make AI Workflows More Dependable

1. Keep fixed logic deterministic. Let Pipelines handle triggers, queries, filters, and field updates. Use AI for interpretation, comparison, or generation.
2. Give AI a role and one specific job. Define the perspective, task, decision criteria, and expected output.
3. Filter context before the AI step. Search for relevant records first. The resolution workflow narrowed its source material by status, category, and product.
4. Add your business rules. Don't take a requester's "urgent" or "ASAP" at face value. A prompt can require AI to assess operational impact, safety, affected users, or another approved standard instead.
5. Request structured output, confidence, and reasoning. Define the values the AI should return and set a human-review threshold that fits the risk.

These choices place AI judgment inside a predictable workflow. The Pipeline controls when the process runs, which information the AI receives, and where the final values go.

Governance Stays Attached to the User and the Action

Quickbase AI follows existing app roles and permissions. Access to an AI feature does not grant new rights to data or actions.

If a user asks AI Agent to delete records, the Agent checks permissions, shows what it plans to delete, and requests explicit confirmation. AI Control Center lets Realm Administrators enable selected capabilities, grant access to users or groups, and expand adoption gradually.

A team could start with AI Agent access for a small pilot group, evaluate the results, and expand access as its governance model matures.

What's Coming Next for Quickbase AI

The roadmap moves AI beyond records, into company knowledge, app structure, and full workflow building.

Knowledge Layer will let AI Agent answer questions with approved company documents, including SOPs, onboarding materials, repair procedures, checklists, and compliance content. The team expects the first version to launch before year-end.

Schema Management will expand conversational changes to reports, dashboards, forms, relationships, tables, and other app components.

Workflow Agent will ask clarifying questions, generate a complete Pipeline, validate and simulate it, and prepare it for review before activation.

Ready to Build an AI Workflow on Quickbase?

Choose one repeatable process, define what success looks like, and test it with the people closest to the work. Use what you learn to refine the workflow before expanding AI to other teams or use cases.

Watch From Prompt to Power: Automating Work with Quickbase AI Features on demand to see the demo and prompting approach. Bring AI Agent, AI Actions, and the rest of the Intelligence Package into your own apps. Existing customers get 60 days free.