Databricks co-founder at TechCrunch Disrupt 2026: what kills enterprise AI deals

Enterprise organizations aren't rejecting AI — they're rejecting operational instability.

This shift is one many founders still misunderstand — and it's becoming a defining reality that separates enterprise AI companies that scale from those that stall after early momentum.

For the past few years, AI startups have benefited from a market driven by experimentation. A strong demo, an impressive model, and a powerful vision were often enough to spark enterprise interest, pilot programs, and investor enthusiasm.

But enterprise AI is entering a new phase — one where companies are no longer asking if AI is exciting, but whether it's safe to deploy at scale.

At TechCrunch Disrupt 2026, happening October 13–15 at Moscone West in San Francisco, Arsalan Tavakoli-Shiraji, co-founder and SVP of field engineering at Databricks, will unpack this shift during his AI Stage session, “The Enterprise Isn’t Broken. Your Assumptions About It Are.”

Image Credits:TechCrunch

Disrupt will gather 10,000+ founders, investors, and operators to explore the technologies and operational pressures reshaping how companies are built and scaled. Over three days, the event will feature 250+ sessions across six stages, led by tech leaders who are shaping the industry today.

Explore the sessions on the Disrupt AI Stage. Ticket savings of up to $410 end May 29 at 11:59 p.m. PT. Register here.

The pilot was never the difficult part

The enterprise AI market is filled with successful pilots that never turned into actual deployments. Not because the technology failed, but because the organization couldn't absorb the operational consequences of adoption.

The reality founders must face is that AI startup deals rarely fail because the model underperformed. They fail because the enterprise lost confidence in what the deployment would entail.

That's the gap Tavakoli-Shiraji’s session aims to explore. Most enterprises aren't just evaluating whether an AI product works; they're evaluating:

An AI product may perform exceptionally well in a controlled environment yet still fail commercially if its deployment creates instability within the business.

This distinction is important for founders because many AI startups are still optimizing for the wrong outcome. They are building for initial excitement rather than long-term operational adoption. And enterprises are becoming far more disciplined in recognizing the difference.

Register for Disrupt to hear how enterprise AI leaders evaluate what truly survives beyond the pilot phase. Lock in your ticket savings of up to $410 when you register by May 29 at 11:59 p.m. PT.

Enterprise AI is becoming an operational trust challenge

The AI startups gaining traction inside large organizations increasingly share one common trait: they reduce uncertainty.

They integrate more cleanly into existing systems, create less workflow friction, and are easier to govern, explain internally, and trust over time.

That may sound less exciting than breakthrough demos or model benchmarks, but it's quickly becoming the difference between AI startups that generate attention and those that generate durable revenue.

The market is maturing. Enterprise buyers are now asking different questions:

Those concerns are no longer secondary. In many organizations, they have become core to the buying decision itself. For AI founders selling into the enterprise, this session breaks down what actually drives adoption after the pilot phase ends. Check out the session details and get your $410 ticket savings to learn what to prioritize to gain traction with enterprise AI deals.

Why Tavakoli-Shiraji sees the market differently

Tavakoli-Shiraji brings an unusually relevant perspective to this conversation because his background spans both enterprise strategy and deeply technical systems architecture.

Before joining Databricks, he was an associate principal at McKinsey & Company, advising enterprises, technology vendors, and public-sector organizations on cloud computing, next-generation IT, and enterprise transformation strategy. He also earned a PhD in computer science from UC Berkeley, focused on networking and distributed systems.

That perspective is valuable for startups because enterprise AI success increasingly depends on more than just strong engineering. Founders now need to understand how technical systems interact with organizational behavior, infrastructure realities, procurement processes, governance concerns, and operational risk.

The startups that succeed in enterprise AI over the next several years may not necessarily be the ones with the most advanced models. They may be the ones that best understand how enterprises actually absorb change.

That is the kind of operational pressure that Tavakoli-Shiraji and other speakers on the AI Stage at Disrupt will explore. Presented by Google Cloud, the stage examines how AI agents and generative AI are reshaping SaaS, enterprise adoption, software economics, security, and operational infrastructure — including Tavakoli-Shiraji’s session on why enterprise AI success increasingly depends on operational trust rather than simply technical performance.

Across the stage, founders will learn how and why the focus is shifting away from AI novelty and toward the real-world challenges of deploying, governing, and scaling AI systems within real organizations.

Two days left to save on enterprise AI insights

Explore the Disrupt agenda and learn how founders, investors, and enterprise operators are managing the next phase of AI adoption. Register by May 29 at 11:59 p.m. PT to save up to $410 on your passes.

Image Credits:Slava Blazer Photography / Flickr (opens in a new window)