Alteryx One embeds predictive AI directly into analytics workflows, so teams prepare AI-ready data, build models, and validate predictions in the same place. Predictions become part of analysis, reducing tool switching and making workflows easier to repeat and scale.
This changes how teams operate:
Predictive AI runs on the data and systems you already use, without migration or duplication, so teams move faster without disrupting existing workflows.
Connect to cloud data platforms and warehouses, including Snowflake, Databricks, BigQuery, and Redshift
Work with enterprise applications and data sources such as CRM, ERP, APIs, and flat files
Ingest upstream data and deliver downstream outputs to BI tools, dashboards, and operational systems
Predictions stay embedded within your existing data environment, so they can be applied directly in reporting, workflows, and business processes.
Teams apply predictive analytics continuously within workflows instead of rebuilding models for each request, and generate predictions as data updates so decisions reflect current conditions rather than static outputs. Models and logic are reused across teams, reducing duplication and ensuring consistent results, while predictions are acted on directly in reporting, applications, and operational processes.
The result is faster execution, broader adoption, and more reliable decisions grounded in consistent, repeatable workflows.
In Alteryx One, predictions are built on AI-ready data that is prepared, governed, and aligned before models are applied
Teams establish this foundation by shaping data into consistent, reusable inputs:
When data is prepared this way, predictions remain consistent, comparable, and grounded in AI-ready data that teams trust and can validate
In Alteryx One, predictions reflect the rules, constraints, and conditions that shape how the business operates.
Teams embed that logic directly into workflows:
When business logic is applied this way, predictions stay aligned with how work gets done, making decisions more consistent and easier to scale.
In Alteryx One, predictions are delivered where decisions are made.
Teams access and act on predictive insights directly within existing tools and processes:
When insights are delivered this way, adoption increases, decisions happen faster, and predictive outputs translate directly into action.
Alteryx One is built to meet enterprise requirements for security, governance, compliance, and transparency. Organizations rely on the platform to run analytics at scale while maintaining control, compliance, and auditability.
Predictive AI shifts from one-off analysis to continuous, system-driven execution, with predictive analytics and outputs applied as part of how the business operates.
Over time, predictive AI becomes repeatable, scalable, and durable, supporting faster rollout of new use cases and consistent decision-making.
Predictive AI operates as part of a connected system, linking data, analytics, and automation to reduce complexity and accelerate decisions.
Because these capabilities share workflows, logic, and governed controls, teams avoid stitching together separate tools or rebuilding work across systems.
Alteryx One enables teams to apply multiple modeling techniques within a single workflow using the same prepared dataset and feature set. Users configure and compare models side by side, evaluate performance against shared inputs, and select the best approach without restructuring data or creating separate pipelines. Once selected, the model remains embedded in the workflow so predictions are generated consistently using the same inputs and logic.
Alteryx One governs predictive models within shared, reusable workflows rather than as isolated assets. Teams build on common datasets, logic, and approved workflows, reducing conflicting versions. Access controls and workflow-level permissions determine who can modify or run models, ensuring centralized control while enabling reuse.
Alteryx One deploys predictive outputs by embedding them directly into workflows that connect to reporting tools, dashboards, and operational systems. Instead of exporting results or building custom integrations, predictions are written to downstream systems as part of the same workflow that generates them. Outputs update automatically as workflows run, making predictions available in the tools teams already use.
