AI, Alteryx, or Both? Run the Numbers

Natural language interfaces make AI feel easy. Ask a question, get an answer, move on. But when analytics work has to be repeatable, accurate, and cost-effective, the picture gets more complicated.

Many teams are now weighing different approaches: manual work, traditional analytics workflows, AI-assisted workflows, and agent-based solutions. The question is no longer whether AI is useful. It is where it delivers value, where it introduces cost, and where a governed analytics approach still wins.

In this session, we will walk through a practical cost-benefit analysis of a standard analytics process completed in multiple ways, including Alteryx, AI-driven approaches, and combined solutions. We will compare effort, credit consumption, and reliability so teams can make smarter decisions about where AI belongs in their analytics stack.

If your organization is trying to balance innovation with cost control, this conversation will help you frame that trade-off more clearly.

What you will learn:

• How the cost of prompt-based analytics compares with governed workflow-based approaches
• Where AI can speed work up and where it can create unpredictable credit usage
• Why reliability, consistency, and business context matter in analytics tasks
• How to decide when to use AI, when to use Alteryx, and when a hybrid approach makes the most sense