In our fast-paced tech world, there’s a new role you might want to keep an eye on: the AI Analyst. As businesses rush to make their data AI-friendly, AI Analysts are becoming key players in this transformation. While engineers often get the spotlight for building complex AI models, it’s the AI Analysts who are stepping up to turn raw data into insights that businesses can actually use.
Andy MacMillan, the CEO of Alteryx—a top enterprise analytics platform—recently shared some thoughts on this. He pointed out that turning business data into formats that AI systems can work with requires a mix of tech skills and a solid understanding of business. As he put it, “I think there’s gonna be a whole set of new roles that emerge because of AI. We talk a lot about the technology roles today… But I think what’s gonna also be needed is the business expertise.”
Traditionally, companies have organized their data for specific applications like CRM and ERP systems. But AI systems need a different approach, and that’s where the challenge lies. MacMillan used a sales commission example to show how AI needs to grasp company-specific processes and data sources.
AI Analysts are the solution here. They bring together data know-how and business smarts, translating business operations into data workflows that AI can handle. MacMillan described the ideal candidate as someone with “really that mix of data understanding, but business acumen.”
Beyond just prepping data, AI Analysts play a crucial role in data governance, making sure that the data used in AI systems meets security and compliance standards. MacMillan introduced the idea of an “AI data clearinghouse”—a structured way to review and manage data for AI use. This setup encourages innovation while keeping things secure and compliant.
AI’s impact stretches beyond just data prep. It’s changing how we consume analytics, moving from static dashboards to dynamic, story-driven insights. Alteryx is working on AI-generated reports, called “Magic Reports,” which offer analytical narratives rather than just data visualizations. MacMillan explained this shift, suggesting that instead of interacting with dashboards, users get reports that directly explain the insights.
Transforming data for AI applications marks a big change in how companies view their information assets. While a lot of current AI applications focus on creative tasks, the true business value lies in applying AI to structured business data. This shift is prompting companies to reconsider their tech strategies, whether that means sticking with existing vendors, developing their own systems, or going for a mix of both.
As AI continues to reshape business landscapes, the demand for AI Analysts who can blend tech and human insights will only grow. MacMillan sees this as a chance for growth, emphasizing the need for expertise in both data and business operations. The role of the AI Analyst is a perfect example of how technology and human insight can come together, offering a distinct edge to organizations that effectively leverage AI for business intelligence.
