Alteryx CEO: Building Trust in AI for Enterprise Value

Tell us a bit about you and your role

I’m the CEO of Alteryx. I joined in December of 2024 as the company made a significant shift in our strategy towards AI data prep and agentic analytics. 

My career started on the technical side. I was a Java developer during the height of the dot-com era. That foundation has stayed with me. I’m deeply curious about how things work and I care about building products that solve real problems for customers. 

After earning my MBA, I moved into product leadership at Stellent, which was later acquired by Oracle, where I served as a VP. I then went to Salesforce to lead the data.com division. From there, I stepped into CEO roles at Act-On Software and later UserTesting.

I’ve spent most of my career in data and enterprise software, so I knew Alteryx well before joining. What drew me in was the opportunity to help shape the future of a company that sits at the centre of how enterprises operationalise data and AI through the lens and expertise of business functions. Organisations are moving from experimentation to execution; from IT-driven proof of concept to line-of-business driven use cases – and Alteryx plays a critical role in making that possible. That is an exciting place to be. 

Give us a brief overview of Alteryx 

Alteryx is a business user, no-code/low-code platform used by more than 8,000 companies around the world to put their data to work. Over the past two decades, we’ve evolved from helping people work more effectively with spreadsheets on their desktops, to enabling organisations to operationalise analytics and AI at scale through automated data workflows. 

What hasn’t changed is our mission: to make advanced analytics and AI accessible beyond the need for deep coding expertise. Business users need an intuitive space to work with the messy realities of their data, automate processes, reimagine what’s possible in AI-enabled world and revolutionise how they operate. That’s exactly what Alteryx delivers. 

Why do most AI pilots fail to scale into production? 

Alteryx’s latest research shows that most AI pilots never make it into production – fewer than one in four do. Common barriers include trust issues, legacy systems, siloed data and gaps in the data stack. 

The reason often comes down to how AI is deployed today. Too many enterprises are using raw data without the proper business context or logic, which leads to hallucinations, inconsistent answers and results that change from one query to the next. This is both a technical and organisational challenge. On the technical side, poor data quality and weak governance erode trust. On the organisational side, AI initiatives are often led centrally without business teams’ input, so they miss the use cases where AI can deliver real impact. 

The companies that succeed are those that invest early in equipping their teams to build AI workflows with strong governance and high-quality, verifiable data. When you combine the creativity of generative AI with deterministic rules and proper business context, trust grows and real business value follows. 

How can companies build trust in AI for strategic decisions? 

Trust starts with transparency. Executives are only comfortable using AI for strategic decisions when they can see that outputs are based on governed, high-quality data. That means knowing what data went in, what rules were applied and having the ability to quickly adjust both as circumstances change. 

Embedded governance is critical to earning the confidence of senior leaders and line of business teams. Equally important, business users need to trust AI to identify and drive the right use cases. That comes from making sure AI workflows are infused with business context, grounded in a company’s own rules and data, not just general industry knowledge. When AI reflects the reality of the business, it is both more effective and easier to trust.  

What practical steps can businesses take to improve data governance and accessibility? 

Strong governance depends on clarity. That means consistent definitions, shared metrics and clear ownership across the organisation. It’s telling that 28% of leaders plan to prioritise governance improvements this year. Those efforts are most effective when governance is visual and human readable. If a compliance team can’t look at a data pipeline and understand what it does, they can’t approve it. Governance also needs to be embedded in the workflows powering AI, not added as an afterthought.

Accessibility is just as important. The most effective way to drive it is by giving users no-code, drag-and-drop tools to build AI workflows using the business logic they already know. This approach breaks from the traditional IT-driven model, where teams waited for business intelligence to build workflows. In today’s fast-moving AI environment, that model is far too slow. 

What are the risks and benefits of decentralising AI workflows? 

Decentralisation is gaining momentum. Business leaders told Alteryx they expect responsibility for AI workflows to shift to specific lines of business, rising from 22% today to 33% by 2028. This shift has big advantages. Sales operations, finance and supply chain teams have the context to solve the problems they face daily with AI. Not central IT teams. 

That said, decentralisation without governance carries risk. Without clear standards and rules, organisations can end up with duplicated logic, conflicting models or shadow systems that undermine trust. The right approach gives business teams self-service capabilities while maintaining central oversight for governance, data quality and platform consistency. 

How can organisations make generative AI outputs more reliable? 

Reliability begins with the data and logic behind the model. Generative AI becomes far more dependable when it is grounded in structured, trustworthy data. Many of the issues we hear about, like hallucinations, come from layering AI on top of raw data sources without proper context. 

To make AI outputs reliable, enterprises can combine the technology’s creativity with deterministic workflows that enforce accuracy, repeatability and alignment with business definitions. 

How can businesses ensure AI investments deliver measurable business value? 

AI delivers value when it is tied directly to business outcomes. That means defining clear objectives for each use case, whether it is driving revenue, improving efficiency or reducing risk, and designing workflows that connect to the operational processes that influence those outcomes. 

Our research shows that budgets are increasing. Eighty-nine per cent of organisations plan to maintain or grow AI investment this year and AI platforms are on track to make up more than half of the data stack. But investment alone is not the differentiator. The companies seeing the greatest returns are the ones embedding AI into day-to-day operations, grounding it in high-quality data and giving both IT and business teams the ability to shape and refine workflows. 

When outputs are reliable and governance is strong, teams gain the confidence to scale. That is when AI moves beyond pilots and becomes a true driver of performance across the business.