KNIME Data Summit New York

For years, centralized data teams have been the ultimate paradox: the primary innovators of the enterprise and its most frustrating bottleneck. But AI is throwing a wrench into that equation. As natural language interfaces dismantle technical barriers, the bottleneck is finally dissolving—only to be replaced by a high-stakes governance challenge.

How do you catalyze the raw ingenuity that results from widespread data access without the seemingly unavoidable side effects of security leaks and "agent debt"? This talk explores the shift toward data entrepreneurship, where the data team’s role evolves from gatekeeper to architect. We’ll discuss how to scale lightning in a bottle, de-risk decentralized innovation, and build the "paved paths" necessary for a self-sustaining, AI-ready ecosystem in 2027 and beyond. 

Trevor Kaufman (CEO, KNIME)

AI agents are no longer a concept — they're transforming core business processes. But LLMs alone crash out on real enterprise complexity. They need rich context, domain-specific tools, and robust data infrastructure to deliver.
That's where AI platforms come in. Purpose-built to design, develop, and deploy agents that combine the power of LLMs with real-world analytics, decisioning logic, and enterprise connectivity, they enable organizations to tackle even the most complex use cases — at scale, with governance built in.
In this session, Forrester VP & Principal Analyst Mike Gualtieri shares his industry perspective on enterprise AI agents and outlines the seven non-negotiable capabilities to look for in an AI platform.

Mike Gualtieri (VP, Principal Analyst, Forrester) 

André Stoll is a Solutions Architect at AWS in Zurich where he helps customers in Switzerland build innovative, cloud-native solutions. With a background in software engineering and robotics, he has extensive expertise in building highly reliable, scalable solutions and AI technologies. His current focus is helping FinTech and regulated software companies make sense of AI in the cloud — while satisfying the compliance and governance requirements their banking customers demand.

André Stoll, AWS

Building AI-ready organizations isn't just a technology challenge — it's a people, process, and systems challenge all at once.
Drawing on her experience as CTO at Etsy, Rachana Kumar explores the three dimensions every organization must address: closing the ownership gap between citizen developers and engineering teams; evolving AI literacy across the entire business, not just technical functions; and shifting culture toward faster experimentation and learning from failure.
She introduces a five-stage production readiness model — and tackles the critical gap where most AI projects stall before reaching scale. Finally, she maps the evolution from Systems of Record to Systems of Coordination, and where platforms like KNIME fit in enabling that transition.

Rachana Kumar (Former CTO, Etsy)

At AMD, KNIME has become the connective tissue between disparate operational data sources, business users, and Snowflake — and KNIME Business Hub is what turned a collection of desktop workflows into a governed, enterprise-grade analytics platform. This session walks through our migration from legacy 4.x workflows running on individual machines to modernized workflows deployed on Business Hub, now orchestrating roughly 80% of our data pipelines. We'll cover how Hub-managed credentials eliminated plaintext secrets, how scheduled executors and the WebPortal removed the person-at-machine bottleneck, and how dual-mode debugging lets engineers diagnose issues without touching production. Through three real workflows — self-service forecast ingestion, automated scheduled loaders, and a complex Open POs production pipeline — we'll show how Business Hub delivers secure automation, self-service access for non-technical users, and the accountability layer driving AMD's operational excellence and data maturity. 

Deepshikha Shekhawat (Business Intelligence Program Manager, AMD)

Aadesh is responsible for managing DTCC’s Internal Audit function, with enterprise-wide responsibility for evaluating and strengthening processes, governance, and risk management to support the firm’s compliance with relevant laws and regulations. Under his leadership, DTCC’s Internal Audit function has undergone a significant digital transformation and was recognized two years in a row at the IIA Great Audit Minds conference through the Protiviti Audit Innovator Awards.

He has two decades of leadership and expertise in internal audit and compliance. Prior to DTCC, he served for 6 years as Managing Director and Head of Audit for IT, Operations and Latin America business at Société Générale Americas (SG).

Aadeh Gandhre (Chief Audit Executive, DTCC)

Delivering on AI's promise requires more than data collection — it demands real Data Product Management. The differentiator isn't data volume, but the integration of domain knowledge through clear semantics and a unified ontology.

In this session, Kirk Thieme, Head of Data Product Management (CRM Excellence) at Siemens Healthineers, shows how non-technical teams bridge the gap between raw information and AI-ready assets. Using KNIME to orchestrate complex workflows — from web scraping to human-in-the-loop verification — and Snowflake as their data foundation, he walks through a live use case: building a Customer Ontology that powers the vision of an AI-driven Sales Buddy.

Key takeaways: why clear data semantics are the secret to making data understandable for both humans and AI, how to bridge technical infrastructure and business expertise through intuitive workflows, and how KNIME and Snowflake work together to deliver governed, high-quality data products at scale.

Kirk Thieme (Head of Data Product Management (CRM Excellence), Siemens Healthineers)

AI is evolving faster than most organizations can adapt — leaving leaders caught between urgency and uncertainty. Should you build, buy, or partner? Double down on platforms or hedge across vendors? Move fast or wait for the dust to settle?

In this panel, three enterprise leaders share how they're navigating these decisions in practice. From mapping processes on a value-effort matrix to running "human intelligence" experiments before committing to AI, from designing for modularity and LLM-agnosticism to knowing when your secret sauce is too valuable to hand to a third party — the conversation gets refreshingly practical.

They also tackle the harder questions: how do you balance experimentation with measurable business impact? How do you avoid lock-in when the best model today may be obsolete in six months? And what's the biggest knowledge gap still holding organizations back from real AI adoption?

Kirk Thieme (Head of Data Product Management, Siemens Healthineers), Deepshikha Shekhawat (BI Program Manager, AMD), Ben Garden (Managing Director, IRIS North America), Moderated by Lisette Johnson (Knime)