I spent two days in Chicago at Boomi World 2026.

After hundreds of conversations with enterprise leaders, CDOs, and AI founders, one question keeps coming up: Our AI works in the demo. Why does it fail before production?

The answer is almost never the model.

What breaks is everything around it:

Most enterprises are solving these problems separately.

Different tools. Different teams. No shared layer. That is why progress stalls. What Boomi presented this year is not a feature update. It is a layered approach to fixing the exact reasons enterprise AI fails before production.

The Model Context Protocol is becoming the standard for how AI connects to tools and systems.

But MCP without governance creates risk.

If every team can connect any AI tool to any system, you lose visibility and control.

Boomi Connect solves this at the infrastructure layer.

Every connection is:

No shadow integrations. No custom security work per connection.

The MCP Registry adds a controlled catalog of approved tools across Boomi, partners, and external ecosystems. Platform teams control access. Developers build on approved systems. This is how you prevent AI sprawl before it becomes a governance problem.

Why this matters

Most enterprises today do not have visibility into how AI tools are connecting to production systems. That is a real risk. This solves governance at the connection layer, not as a policy.

My take

This is one of the most important shifts. Governance cannot be a checklist. It has to be enforced in infrastructure. That is what makes this meaningful.

Before building anything, enterprise AI projects spend weeks aligning:

This is the hidden cost most teams underestimate. Boomi Orchestrate compresses this entire phase.

You describe the problem.

It generates:

All aligned to your actual environment. Agent SIM then allows teams to test and validate agents before deployment. Every test creates auditable records. That matters more than people think.

Why this matters

40–60% of enterprise AI timelines are spent before development even starts. This directly attacks that problem.

My take

Agent SIM stands out. Most projects do not fail technically. They fail at compliance approval. If you can generate validation evidence automatically, you unblock production.

Context is where enterprise AI quietly fails. The model works. The answers look correct. But they are wrong because the data is inconsistent.

Different teams define the same data differently. Data is outdated. No shared context layer exists.

Boomi addresses this through:

This creates:

Why this matters

AI is only as good as the data it retrieves. Without governance, retrieval becomes a risk. Without consistency, accuracy collapses.

My take

Most enterprises are underinvesting here. The winning teams are not just choosing better models. They are building better context layers.

One of the biggest blockers to enterprise AI is simple:

Where does the data go?

In regulated industries, sending data to external AI systems is not acceptable.

Distributed Agent Runtime solves this.

AI runs where the data lives:

No data movement required.

Multi-Region Instances extend this globally with region-specific deployment.

Why this matters

Compliance is one of the biggest blockers to scaling AI. If you cannot explain where data goes, deployment stops. This removes that blocker at the infrastructure level.

My take

This is critical for industries like finance and healthcare. Many teams have budgets approved but cannot deploy. This directly addresses that gap.

The developer experience for enterprise integration has been outdated.

Developers now expect:

Boomi Companion integrates directly into developer workflows.

It:

Agentstudio APIs allow agents to be invoked via REST.

Embedding Agents makes deployment simple.

You can deploy agents with minimal effort.

Why this matters

Speed of development and deployment defines adoption.

If deployment takes weeks, usage never scales.

My take

Embedding is the unlock. Building agents is hard. Deploying them has been just as hard. Reducing that friction changes how fast enterprises can scale AI.

A key insight shared at the event:

Most AI use cases require deep access to enterprise systems. That matches what I hear every week. The problem is not model capability.

It is:

Boomi’s approach connects all of these layers:

The enterprises that will win in AI will not be the ones with the best models.

They will be the ones that:

That is the shift I saw clearly at Boomi World 2026. And it is the direction the market is moving.

More coverage coming from The Ravit Show.

The Bigger Picture

Boomi cited a finding from their 2026 Scaling Agentic AI report: 96% of agentic use cases depend on deep access to core enterprise systems. That number is consistent with what I hear every week. The challenge is never model capability. It is the gap between what the model can do and what the enterprise will allow it to reach, the quality of context it has access to, and whether the infrastructure supporting it survives a compliance review.

What Boomi put forward at Boomi World 2026 is a coherent, layered answer to each of those gaps. Governed connectivity at the MCP layer. Verified context at the knowledge and metadata layer. Compliance-grade infrastructure for sovereignty and deployment. And a developer experience designed for teams building with AI-assisted tools.

The enterprises that win at AI over the next three years will not be the ones who found the best model. They will be the ones who built the infrastructure to put that model to work inside real enterprise systems, at scale, with governance that holds up under scrutiny. That is the bet Boomi is making. And from what I saw in Chicago, they are building for it seriously.

More coverage coming. Reply with what you want me to dig into next.