My recent conversation with Andy MacMillan, CEO of leading enterprise analytics platform Alteryx, highlighted this critical yet underappreciated role in the AI revolution. As MacMillan explains, the gap between raw business data and AI-ready information requires human expertise that combines technical knowledge with deep business acumen.

"I think there's gonna be a whole set of new roles that emerge because of AI," MacMillan told me. "I think we talk a lot about the technology roles today. People that are building the large language models, people that are building the AI agents. But I think what's gonna also be needed is the business expertise."

The AI-Data Disconnect

Most organizations have spent decades organizing their data around business applications. Your CRM data is structured to support your CRM software. Your ERP data is designed for ERP functions. But AI needs something different.

"What is essentially the world's largest data prep and data transformation project is about to start," MacMillan explained. "Companies rethinking what they need to use this data for."

This mismatch creates a fundamental problem. You can't simply point an AI system at your existing business data and expect it to understand your business processes, company policies, or industry specifics. MacMillan illustrated this with a practical example of sales commissions:

"If you go ask a sales leader, what's the commission plan? They're gonna say which team are they on? What region are they in? What does their comp plan look like? What spiffs and programs are we running?" he explained. "You might have to go get their commission plan out of your commission platform. You might have to get their base salary out of your HR database. 'Cause usually, their commission is a percent of their salary."

What Exactly Is An AI Analyst?

The AI analyst bridges the gap between business knowledge and AI capability. These professionals understand both business operations and how to prepare data to make AI truly valuable.

According to MacMillan, the perfect candidate is "really that mix of data understanding, but business acumen. It's not just somebody who can do really interesting computer science or data science. You're gonna have to understand what really makes the business tick."

These analysts take their knowledge of how business processes work and translate that into data workflows that AI can use. They understand which questions need asking and what context is required to make answers meaningful.

"I think for the next decade, there's gonna be a huge opportunity for everybody that knows how to work with these data sets and understand the business," MacMillan predicted. "These are business users, not simply data scientists. These are people that understand how the data makes the business work."

The AI Data Clearinghouse

Beyond preparing data technically, organizations face governance challenges in determining what information should be accessible to AI systems.

"We have a top-down mandate to use more AI and a top-down mandate that none of our data, our IP, any of our information can be put into any AI tool," MacMillan said, describing the contradictory directions he's heard from multiple customers.

This is where the concept of an "AI data clearinghouse" becomes essential — a systematic approach to reviewing, approving, and managing what data feeds into AI systems.

"You can have a process where you go back to the clearinghouse," MacMillan explained. "You go into a data workflow like one you might build with Alteryx, and you say, okay, in this workflow, this data flow, I'm gonna pull out this information, and I'm gonna republish this workflow. I'm gonna get sign-off again."

This formal structure helps organizations balance innovation with compliance and security concerns. It creates an auditable, manageable system for making business information available to AI tools.

From Dashboards To AI-Generated Insights

The AI transformation is also changing how we interact with analytics. Instead of static dashboards requiring human interpretation, AI can deliver narrative reports highlighting the most important insights.

"Imagine instead of visualization being I go to a dashboard, I go to my big company dashboard, and it's all color-coded, and I'm clicking around trying to figure out what's going on. What if, instead, you were just getting a report that was telling you what's going on?" MacMillan suggested.

Alteryx is investing in this capability with what they call "Magic Reports" — AI-generated analyses that explain what's happening rather than just displaying data.

"My example earlier about sales being down in the Southwest is my made-up example, right? What if I was getting a report, my sales report every week, and my sales report wasn't just a set of numbers, but it was an analysis," MacMillan elaborated. "Hey, Andy, here's your report. Here's how things are going. By the way, sales are down in the Southwest; you'll notice this."

The Future Of Business Analytics

The transformation of enterprise data for AI use represents a fundamental shift in how organizations think about their information assets. While current AI implementations often focus on creative tasks using unstructured data, the real business value will come from applying AI to structured business data.

"Today, the most powerful use cases around AI tend to be, I'm using creative works to create other creative works," MacMillan noted. "That's different than asking it, what's going on in the Southwest region and having it understand how to analyze your business."

This transition raises strategic questions about what systems and capabilities organizations should own versus purchase. Will companies rely on AI agents from their existing software vendors, build proprietary systems, or create hybrid approaches?

"I think the next five years in tech are gonna be more interesting than any five years, maybe since the .com boom," MacMillan predicted. "As people really rethink what's possible."

The People Behind The AI Revolution

While many worry about AI replacing jobs, MacMillan sees tremendous opportunity, particularly for those in analytics who embrace these changes.

"For some folks, that's a pretty big concern. But I think if you're in the data and analytics space, there's gonna be a big need for a lot of knowledge on how businesses operate," he said.

The AI analyst role represents not just a new job title but a reflection of how AI is reshaping business. The most successful implementations won't be those with the most sophisticated algorithms but those that best combine technological capabilities with human business expertise.

As organizations navigate this transition, those who recognize the importance of the human element in making AI truly business-ready will gain significant advantages in turning artificial intelligence into genuine business intelligence.

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