Evolving Alteryx focusing on AI-ready data, logic for agents

Without relevant, AI-ready data, agents are doomed to fail.

Andy MacMillan

In response, as agentic AI became the focal point of many enterprises' development initiatives over the past couple of years, longtime data preparation specialist Alteryx made its mission under CEO Andy MacMillan, who took over as the company's leader in December 2024, to provide users with the canvas they need to make data AI-ready.

Since then, however, Alteryx has expanded its ambitions under MacMillan.

The emergence of AI over the past few years as a means of generating insights and automating business processes has forced many data management and analytics vendors to evolve. For example, data platform vendors Databricks and Snowflake now provide full-featured AI development environments. Database providers such as MongoDB and Couchbase similarly aim to become AI development platforms in addition to their historical focus. And analytics specialists including GoodData and Tableau are building on longtime semantic modeling capabilities to turn their platforms into context layers within agentic workflows.

Alteryx, which struggled to keep up with the pace of change before being sold to a private equity firm in December 2023 and taken private to reorganize away from the public spotlight, is likewise evolving.

However, beyond shifting from a data preparation platform for BI to a focus on AI-ready data, the vendor aims to become a critical part of agent workflows by enabling business users to add business logic -- rules, workflows and analysis -- to agents to help give them the contextual awareness they require to properly carry out their specific work.

Unlike many AI development platforms that provide developers and engineers with capabilities for building agents, Alteryx's new Agent Studio is designed to empower Alteryx's user base of business analysts -- the experts in their domains with first-hand experience -- to connect data, logic and governance with AI.

Its decentralized approach to development could be a differentiator. But, with only 11% of respondents to a recent Alteryx survey expecting responsibility for AI workflows to move to line-of-business domains over the next three years, this approach could be risky, according to consultant Donald Farmer, founder and principal of TreeHive Strategy, who noted that 11% is not a transformative number.

In a recent interview before the start of Alteryx Inspire, the vendor's user conference in Orlando, Fla., MacMillan discussed the vendor's shift from a focus on data preparation for BI to AI-ready data.

In addition, he spoke about Alteryx's different approach to developing agents, the struggles he sees from customers as they attempt to become agentic enterprises, and what might be major themes in data and AI when Alteryx hosts its user conference in 2027.

Editor's note: This Q&A has been edited for clarity & conciseness.

When we last talked shortly after you took over as CEO, you spoke about evolving Alteryx so it becomes the canvas for customers to prepare data for AI -- how is making data AI-ready different than making it ready for BI?

Andy MacMillan: They're not entirely different. But what is different about it is the speed that agents are going to interact with data and the governance around it. With BI, you had snapshots. Everyone could look at the same dashboard for a week, and you could audit it. Now, if there is a Model Context Protocol endpoint for an agent, and anyone can ask that agent a question, there's a different level of predictability that's needed.

Now, instead of going to a Tableau or Qlik dashboard as I might have five years ago, I'm going to go to ChatGPT. But I want ChatGPT to give me answers with the same certainty and clarity that I would have gotten from those dashboards, and I also want ChatGPT's new capabilities, which are the analysis and probabilistic nature that can do the reasoning. Right now, I think people feel like they're trading those off. They feel like they have the consistency of their dashboard or the reasoning of the agent. Our goal is to make sure there isn't a tradeoff.

When customers use Alteryx to prepare data, are there different things they need to do differently when getting it ready for AI than they did when preparing it for BI?

MacMillan: I don't think there are things they have to do differently. I think there are just things overall that we all have to make sure that we're doing better, such as the governance around it to make sure there's visibility and understandability and thinking through the permutations of how people are going to interact with the data can be different.

Our ambitions have gotten bigger than simply data preparation. Now, [our ambitions] include providing some of the calculations and business logic that you could maybe argue is data prep, but is more than just cleaning data.

What are different are the capabilities to build the workflow. Three years ago -- even two years ago -- Alteryx users could drag and drop tools onto a canvas and solve a problem, but you still had to know how to drag and drop all those tools. Now, you can type in what you're trying to do and watch it put the tools on the canvas that help you build the workflow. There's also the ability to use AI to interrogate the requirements. … At its core, the value proposition is empowering the person who knows the process the best, and knows the data the best, to be responsible for building [AI tools], and empowering them to keep it up to date.

What does it require to be AI-ready?

MacMillan: I think I have a different take than a lot of folks. There's been a lot of talk about a semantic layer. That's not bad -- putting labels on data and making sure it's clean is all reasonable. But I think to be AI-ready, data has to have gone through the operational understanding of what and how to use the data and how to pull it together for use cases, and then the agent has to know which use cases to use the dataset for.

When I talk to folks about this, I point out that when you talk to the best people in your company about a topic, and you ask them a question, they usually ask you questions back. If I say, 'What was the revenue for our stores in California?', they respond, 'Do you want all the stores, or do you want the online sales that came through California?' Or maybe they point out that there were two stores in California that are no longer in business, but were in business during the first quarter and ask if that should be included. That's not a semantic layer problem. That's a business logic problem. Getting data AI-ready is not just having a semantic layer. It's also having business logic in a callable place, where that logic is in the calculation and the AI can call that and get the answer. That business logic is the missing piece.

How does business logic get connected with AI to inform agents?

MacMillan: There's a misconception that business logic is in all of an organization's old applications, and, having worked at some of these big SaaS providers, I don't know that it always is. What we don't want to do is consume AI only through those applications. You want to be able to get to the data and pull it all together so you're not orchestrating AI on top of applications, but instead building logic that talks directly to data. Maybe the data came from those applications, but you're implementing that logic at the data layer in a visible, understandable, repeatable, auditable environment and connecting it with AI.

Now, with business logic, there's a powerful platform for making AI work and understand the actual business.

Where is Alteryx in its evolution toward becoming the canvas for getting data AI-ready?

MacMillan: We're definitely there as we launch the capabilities that we're shipping.

But I would say that our ambitions have gotten bigger than simply data preparation. Now, [our ambitions] include providing some of the calculations and business logic that you could maybe argue is data prep, but is more than just cleaning data. We're helping the analysts and ops people of the world put their knowledge to work with the data in a bunch of different ways. Data prep is one of those ways, but building agents in Agent Studio is a lot more than data prep. That's taking prepared data and logic and activating it with AI.

Beyond getting data AI-ready, what's a specific new role that Alteryx hopes to play?

MacMillan: I think it's this business logic layer. What we're talking to customers about is making building AI agents simpler. Everyone today has access to AI, and most people that we talk to have access to a bunch of data. What we're trying to do is help them simply put that data to work for AI, and do that without trying to run through an application stack and without having some big orchestration project with 20 different platforms to get an agent to do the most basic sales and marketing things.

All the data from sales and marketing applications is in a cloud data warehouse, and the sales and marketing operations team knows what that data means. We're letting them describe the logic, expose that logic to ChatGPT and create an agent. We're just trying to provide our users a canvas to do that.

When you meet with customers, what are the biggest concerns you hear from them as they strive to become agentic enterprises?

MacMillan: Data governance is a big one -- the tension between IT, DataOps and the business. A lot of customers have a pristine data warehouse, but no one is allowed to use it, so that's not super helpful. Other customers have the opposite, where data is everywhere and they're struggling to manage it.

The other big one that we're seeing is that the budget has shifted from AI being an IT-driven initiative to being a line-of-business initiative. With that shift has come responsibility, and with the responsibility has come a mandate to start using AI to solve actual problems, so customers are asking how to do that. They can't just use AI to write better emails. They have to use it to [improve operations]. That's the pressure I'm hearing at the moment from every one of our customers. That shift has happened quickly, and we're trying to be helpful as companies go through that shift.

Things are evolving incredibly fast, so predicting the future is perhaps more difficult now than ever, but what do you think will be the major trends in data and AI a year from now?

MacMillan: I think we're going to be talking about people modernizing their business logic away from their application portfolio and into an agent portfolio. That doesn't mean applications all go away, but I talk to so many people today that are constrained by their logic being kept in their enterprise resource planning and customer relationship management applications, and that constraint on their agentic growth is clearly going to be a problem.

People are going to ask how to get to the data layer under that and go fast. People are going to realize they can build an agent when they get [constraints] out of the way and can just implement logic and go fast. We're going to move to an era when we agentify business logic, make it visible, understandable, repeatable, auditable, trusted and business-owned but running on the IT infrastructure environment. That's where we're headed.

Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.