What are the most common challenges you see enterprises come up against when deploying agent-driven systems today?
Despite all the hype around agents right now, most enterprises are still really early. The big challenge is that AI doesn’t actually understand how a business works out of the the box. It can sound convincing, but without the business logic, context, and rules that analysts and operators use every day, agents are basically just fast. Not trustworthy.
A lot of that logic still lives in spreadsheets, workflows, and existing knowledge. The companies making real progress are the ones capturing that business knowledge in a way that’s flexible, auditable, and usable by AI systems.
Governance is the other major hurdle. Once agents start touching business-critical processes, companies need to understand why decisions were made, where outputs came from, and how to audit them. Otherwise, adoption stalls fast.
And finally, the best agentic systems usually aren’t built top-down by IT alone. The people closest to the workflows, analysts and business users, are the ones who know the edge cases, exceptions, and operational realities. Those are the people enterprises need to empower if they want AI that actually gets work done.
How are these challenges shaping Alteryx’s product roadmap and next set of planned launches?
Analysts and business users already use Alteryx to build workflows around how their business actually operates, without needing to become developers or wait in line for IT. Those workflows capture the business logic, rules, and context that AI systems need in order to be useful in the real world.
What we’re seeing now is that, as enterprises scale AI, the bottleneck is no longer access to models, it’s the business context those models run on. A lot of AI agents today are querying raw data directly, but they still don’t understand how the business actually works.
And too often, the logic behind those systems lives in prompts that are hard to audit, hard to govern and, honestly, kind of maddening to maintain over time.
That’s shaping a lot of how we think about the roadmap. Our focus is making it seamless for the logic already embedded in Alteryx workflows to guide and power agent-driven systems in a way that’s flexible, governed and maintainable.
The business teams closest to the workflows can own and evolve that logic, while IT still gets the visibility, governance, and control needed to support enterprise scale.
Ultimately, operationalizing agentic automation is about putting that business logic to work consistently and reliably, not just generating answers, but enabling agents to actually get work done in a way the business can trust. And you’ll hear a lot more about where we’re headed next at Inspire.
Alteryx’s Inspire conference has taking place. What themes did you see across the event’s programming and in your conversations with Alteryx users in attendance?
AI is dramatically lowering the barrier for line-of-business teams to build, automate, and evolve their own workflows without waiting in line for IT or specialized engineering resources.
That shift, putting more power in the hands of the people who actually understand how the business runs, is going to be a big theme at Inspire. And a lot of the conversation will be about how to do that in a way that’s governed, trusted, and scalable across the enterprise.
I also expect a big focus on practical AI. Less flashy agent demos, more real-world use cases where teams are actually operationalizing AI to solve business problems, automate decisions, and move faster.
Customers are learning a ton right now through hands-on experimentation, and honestly, those conversations around what worked, what broke, and what scaled are usually the most valuable part.
And of course, we expected a lot of discussion around Alteryx One. We launched it to unify the analytics, automation, and AI experience into a single platform and I’m excited to hear how customers are using it to connect cloud data, capture business logic, and build workflows that can ultimately guide and power agent-driven systems.
The creativity customers bring to Inspire is always amazing.
Since Inspire 2025, we’ve seen the conversation on AI evolve from a focus on pilots and experimentation to at-scale reinvention of business processes with AI.
Where do you see Alteryx’s role and opportunity in making that shift a success?
Since Inspire 2025, the conversation has shifted from AI experimentation to actually reinventing business processes with AI. And enterprises are realizing that this can’t be driven by centralized IT alone.
The people closest to the workflows and operational realities need to be part of shaping how these systems work.
That’s where Alteryx has a huge opportunity. The analytics workflows business users already build contain the logic, context, and decision-making that AI systems need in order to be trusted.
We make it easy for teams to build and evolve that logic without deep coding expertise, while still giving IT the governance and control needed to support enterprise scale.
Ultimately, the companies that succeed won’t just deploy more AI. They’ll be the ones where AI actually reflects how the business operates.
Finally, over the past twelve months we’ve seen Alteryx launch deeper integration capabilities with cloud data platforms. Why has this been a focus and what are customers gaining?
This has been a huge focus for us because enterprises are making massive investments in cloud data platforms like Snowflake, Databricks, and BigQuery.
But the reality is, the data that drives the business still lives everywhere – in lakehouses, spreadsheets, SaaS apps, local files, and all kinds of operational systems.
What customers need is a way to connect all of that data and put it to work in analytics and AI workflows without creating more friction for IT.
That’s why we’ve been investing so heavily in deeper cloud integrations and in-place analytics. Instead of pulling data out of those platforms, Alteryx can run workflows directly inside them, which means governance, permissions, audit trails, and cost controls stay where IT already manages them.
For customers, that’s a really important unlock. Business teams get the flexibility to work with all the data they need, while IT still gets the control and visibility required to operate at enterprise scale.
