Artificial intelligence is reshaping how businesses operate, but success doesn’t start with models or algorithms — it begins with data readiness.

As enterprises rush to adopt AI, many overlook a fundamental challenge: Their data isn’t ready. Fragmented, siloed and unstructured data can hinder AI’s effectiveness, making preparation a critical first step in realizing AI’s full potential. Alteryx Inc., under its new Chief Executive Officer Andy MacMillan (pictured), has embraced this reality, focusing on helping businesses bridge the gap between AI’s aspirations and the data challenges that stand in the way.

MacMillan and Tom Davenport, distinguished professor at Babson College, recently spoke with theCUBE’s John Furrier about the importance of AI and analytics, emphasizing that achieving AI success requires more than just enthusiasm — it demands a solid data foundation. Leading off the discussion, Furrier highlighted that while enterprises are accelerating AI adoption, simply deploying AI tools isn’t enough.

TheCUBE Research’s John Furrier on the set at the AI & Analytics: Shaping the Future with Alteryx event

“When you get into AI, you’re seeing people throw AI at the enterprise, and AI in the enterprise is one of the hottest categories right now because only 1% of the spend has even been realized yet,” he said. “You can’t just throw AI at the enterprise, because there’s a lot of knobs and buttons in the enterprise that you got all kinds of domain-specific configurations, you’ve got architectures. It’s so nuanced in the enterprise.”

The discussion took place during the “AI & Analytics: Shaping the Future With Alteryx” event, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. The event explored the evolving role of data in AI initiatives, the growing importance of business analysts and the challenges of measuring AI’s return on investment. (* Disclosure below.)

Here are three key insights you may have missed from theCUBE’s coverage:

1. AI success starts with data readiness — not just better models.

AI’s potential is only as strong as the data that fuels it. Many companies rush to adopt AI without first ensuring data readiness, meaning their data is properly structured, integrated and accessible. AI models fall short of delivering meaningful insights without a strong foundation, leading to inefficiencies and missed opportunities.

Alteryx has positioned itself as a key player in solving this challenge by helping organizations improve data readiness for AI-driven insights. The company focuses on integrating disparate data sources, enabling business users to access and manipulate data effectively without requiring advanced coding skills, according to MacMillan. This approach democratizes data analytics, allowing more employees to leverage AI without needing deep technical expertise.

“We talk about AI in three layers at our company right now,” MacMillan said during the event. “There’s the way you interface our product — everybody’s creating an AI interface to their product, [and] we are too. And how you get output from the product. But the third one that, I think, is unique to us is how the product is actually used to help companies get ready for AI.”

For AI to succeed at scale, businesses must eliminate data silos and improve accessibility, according to MacMillan. Many companies still grapple with fragmented data spread across multiple platforms, each governed by different rules and access permissions. This lack of integration makes it difficult to generate meaningful insights and train AI models effectively.

“The biggest area we’re investing in is … connectivity in [the] cloud,” MacMillan said during the event. “Data warehouses continue to be a big driver of our business as people want to get more value out of the investments they’ve made in that infrastructure. The second one is exactly in line with our discussion here, which is [that] our product as it exists today is the product that solves this problem. This is not a future state we have to get to; people use our product to do data prep. This is the new data prep.”

2. AI is reshaping the role of business analysts, making them key drivers of automation.

As AI transforms enterprise operations, the responsibilities of business analysts are expanding. Once focused primarily on reviewing past trends, analysts are now instrumental in shaping how AI is integrated into decision-making processes.

This shift requires analysts to move beyond traditional reporting roles, taking on responsibilities that directly impact AI implementation. Instead of simply analyzing historical trends, they are now structuring data for AI-driven workflows and ensuring models are built on high-quality, accessible data, according to MacMillan.

Alteryx’s Andy MacMillan talks with theCUBE about how Alteryx helps companies prepare their data for AI-driven insights.

“One of the things we’ve done since the founding of the company was this idea that no matter how much you instrument something, for example, with a dashboard, you’re still going to get asked questions and have to iterate and work,” he said during the event. “That was pretty inefficient. Now they do it in Alteryx, and that might be pulling some data out of the cloud data warehouse and might be pulling in third-party data.”

As AI adoption accelerates, business analysts play a growing role in ensuring data readiness — structuring and preparing data so AI models can generate meaningful insights. Their responsibilities are expanding beyond data preparation to include optimizing AI systems and ensuring they align with business priorities, Analysts who understand both business needs and data mechanics are uniquely positioned to drive AI initiatives forward, ensuring that AI applications are aligned with real-world business objectives, according to MacMillan.

“I think what we’re going to see now is a bunch of new requests coming in [from companies] who are … rolling out AI … [and are] going to turn to that same analyst and say, ‘Go figure out how the AI thing is going to know how to do this’ for a very long time,” he said. “That analyst is going to have to figure out how to pull this data together, systematize it, turn it into a workflow and build that out.”

Here’s the complete video interview with Andy MacMillan:

3. Measuring AI’s return on investment remains challenging, making analytical AI a more immediate business driver.

Despite AI’s promise, many organizations struggle to see measurable returns — often because they underestimate the importance of data readiness in operationalizing AI models at scale, according to Davenport. The difficulty of operationalizing AI models at scale remains a major hurdle because companies must integrate AI into existing workflows, train employees and modify business processes.

“The big issue, whether it’s generative or analytical AI, has always been how we get to production deployments,” Davenport told theCUBE Research’s Dave Vellante during an interview at the event. “It’s easy to do a proof of concept, a pilot or a little experiment — but putting something into production means you have to train the people who will be using this system. You have to integrate it with your existing technology architecture; you have to change the business process into which it fits. It’s getting better, I think, with analytical AI.”

Many organizations find it difficult to quantify AI’s impact, often because their foundational data readiness isn’t where it needs to be. While generative AI has generated excitement, analytical AI offers clearer and more immediate paths to revenue generation, most notably in targeted marketing, pricing strategies, and fraud detection. While generative AI has generated excitement, analytical AI offers clearer and more immediate paths to revenue generation, most notably in targeted marketing, pricing strategies and fraud detection, according to Davenport.

“If [revenue] is your objective, then analytical AI is probably going to get you there more easily than generative AI because you can target the right customers, you can figure out what’s the best price to charge all those sorts of things,” Davenport said during the event. “I think generative AI has been more oriented to productivity kinds of improvements, but most organizations haven’t seriously measured the productivity gains.”

Here’s theCUBE’s complete video interview with Tom Davenport:

To watch more of theCUBE’s coverage of the “AI & Analytics: Shaping the Future With Alteryx” event, here’s our complete video playlist:

(* Disclosure: TheCUBE is a paid media partner for the “AI & Analytics: Shaping the Future” event. Neither Alteryx Inc., the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)

Image: SiliconANGLE