Business organizations are not rejecting AI. They reject business volatility.
This is the shift that many founders still misunderstand – and it’s becoming one of the defining realities that separate enterprise AI companies that scale from those that stall after early momentum.
In recent years, AI startups have benefited from a market driven by experimentation. A strong demonstration, an impressive model and a strong vision were often enough to generate business interest, pilot programs and investor enthusiasm.
But business AI is entering a different phase now, where businesses are no longer evaluating whether AI is exciting. They are evaluating whether widespread deployment is safe.
At TechCrunch Disrupt 2026to be held October 13–15 at Moscone West in San Francisco, Arsalan Tavakoli-Shirajico-founder and vice president of field engineering at Databricks, will unpack this shift during the AI Stage session, “The Enterprise Isn’t Broken. Your Assumptions About It Are.”
Disrupt will bring together 10,000+ founders, investors and operators to explore the technologies and operational pressures that are changing the way companies are built and scaled. The three-day event will feature 250+ sessions across six stages, led by the technology leaders driving the industry today.
Explore the sessions featured on the Disrupt AI Stage. Up to $410 in ticket savings ends May 29 at 11:59 PM PT. Register here.
The pilot was never the hard part
The business AI market is full of successful pilots that never became real deployments. Not because the technology failed. But because the organization could not absorb the operational consequences of its adoption.
Now, the reality founders have to face is that AI startup deals rarely die because the model didn’t underperform. They die because the business lost confidence in what growth would require.
This is the gap that the Tavakoli-Shiraji session is designed to explore. Most businesses don’t just evaluate whether an AI product works. They rate:
An AI product can perform exceptionally well in a controlled environment and still fail commercially if its deployment creates instability in the business.
This distinction is important to founders because many AI startups are still optimizing for the wrong outcome. They are built for initial excitement rather than long-term business adoption. And businesses are becoming much more disciplined about recognizing the difference.
Join Disrupt to hear how enterprise AI leaders assess what actually survives beyond the pilot phase. Lock in ticket savings of up to $410 when you sign up by May 29 at 11:59 p.m. PT.
Business AI becomes an operational trust problem
AI startups that are gaining traction in large organizations increasingly share one thing in common: They reduce uncertainty.
They integrate more cleanly into existing systems. They create less friction in the workflow. They are easier to govern, easier to explain internally and easier for organizations to trust over time.
This sounds less exciting than groundbreaking demos or model benchmarks. But it’s quickly becoming the difference between AI startups that generate attention and those that generate steady revenue.
The market is maturing. Business buyers are asking different questions now:
- What happens after deployment?
- How much functional change is required?
- How does this affect governance?
- Can teams realistically adopt it at scale?
- What happens when the model fails?
These concerns are no longer secondary. In many organizations, they have become the core of the purchase decision itself. For AI founders selling to the enterprise, this session breaks down what really drives adoption after the pilot phase ends. View session details and save $410 ticket to learn what to prioritize to gain traction with AI business deals.
Because Tavakoli-Shiraji sees the market differently
Tavakoli-Shiraji brings an unusually relevant perspective to this conversation because his background spans both business strategy and deeply technical systems architecture.
Before joining Data brickswas an associate director at McKinsey & Company, advising businesses, technology vendors and public sector organizations on cloud computing, next generation IT and business transformation strategy. He also received a PhD in computer science from UC Berkeley, with an emphasis in networking and distributed systems.
This lens is valuable to startups because business AI success increasingly depends on more than just strong engineering. Founders must now understand how technical systems interact with organizational behavior, infrastructure realities, procurement processes, governance issues, and operational risk.
The startups that succeed in business AI over the next several years may not necessarily be the ones with the most advanced models. They may be the ones who best understand how businesses actually absorb change.
That’s the kind of business pressure Tavakoli-Shiraji and other speakers are making AI Stage at Disrupt will explore. Presented by Google Cloud, the stage examines how AI agents and generative AI are reshaping SaaS, enterprise adoption, the software economy, security and operational infrastructure — including Tavakoli-Shiraji’s session on why business AI success increasingly depends on operational trust rather than simple technical performance.
Throughout the stage, founders will learn how and why the focus is shifting away from AI innovation and toward the real challenges of developing, governing, and scaling AI systems within real organizations.
Two days left to save on enterprise AI information
Explore the Disrupt agenda and learn how founders, investors and business 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 cards.
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