Use Case

Improve Access to AI and ML Tools


AI and ML form a competitive advantage in any company. Using historical data to model predictions of future outcomes, you can start riding waves long before they’ve crested and enter new markets early. The key to harnessing the power of AI and ML is to bring them within the reach of knowledge workers across your organization.

Top-Line Growth

Put AI and ML tools in the hands of knowledge workers and enable actionable insights in hours, not weeks

Bottom-Line Returns

Reduce time investment and cost for data science projects

Workforce Upskilling

Increase knowledge workers’ data science skills without requiring advanced coding skills

Efficiency Gains

Reduce workload on data science teams and let them focus on high-value projects

Customer Experience

Get insights and implement business actions that impact your customers


Business Problem

In the not-too-distant past, it took a heavy lift from your data science and IT teams to derive insights from artificial intelligence and machine learning. Then it took months to operationalize those insights and start reaping the benefits. Meanwhile, other data science projects were added to the queue, creating a backlog. Some of them were overcome by events and some were dropped completely when priorities changed.

The struggle to establish AI and ML in your organization is exacerbated by the need to communicate what’s going on in the black box around data science projects. Convincing project stakeholders that the approach and the data are sound becomes half the work. An “analytics divide” opens up between the data science experts, who don’t know enough about business problems, and the knowledge workers, who don’t know enough about data science.

Alteryx Solution

Companies can bridge the analytics divide by putting AI and ML capabilities right in the hands of knowledge workers.

Analytics are designed to abstract the complexity and coding involved in AI and ML. Through easy-to-design workflows, knowledge workers can get from data sources to useful insights without needing to write code and understand computer science. Users upskilled in this way don’t have to wait on data science teams to create entire models; they can build, validate, iterate, and explore their own models. Plus, operationalization goes much more quickly as the users create visualizations and share their easily packaged results around the organization.

With Alteryx, you can:

  • Quickly create ML Pipelines directly in an Alteryx Designer workflow using Alteryx Intelligence Suite
  • Utilize the Alteryx Open-Source Library which includes trusted models such as Woodwork, Compose, Featuretools, and EvalML.
  • Scale data science across your business with Alteryx Machine Learning

Alteryx Machine Learning: ML Made Easy

Deep Feature Synthesis:

Quickly develop robust models with automated feature engineering

Open-Source Libraries:

Implement trusted models such as Woodwork, Compose, Featuretools, and EvalML

Drive Models to Production:

Integrate model predictions into any analytic process with our end-to-end analytics platform


Recommended Resources

Enable Data and Analytics Innovation for an Era of Perpetual Uncertainty
Fueled by intense and rapid political, environmental, social, and technological change: organizations face a new era of perpetual uncertainty.
  • Analytics Leader
  • Risk Reduction
  • Workforce Upskilling
Read Now
How are enterprises using technology to make decisions?
Download the report to learn how how decision intelligence will impact the future of decision-making.
Read Now
Customer Story
Nielsen Reboots Its BI Process with Alteryx and AWS
How Nielsen used AWS and Alteryx to modernize its analytics stack in less than 90 days.
  • Analytics Automation
  • BI/Analytics/Data Science
  • Alteryx Analytics Cloud
Read Now