AI system design determines whether your solution succeeds in production or fails once it leaves a controlled environment. In this part of the conversation, Matt Soltau highlights a critical shift: building AI is no longer just about capability—it's about control, adaptability, and governance.

About Matt Soltau

Matt Soltau is the Global Director of Strategy & Operations at IntelliPaaS. He specializes in helping organizations untangle complex, legacy tech stacks so they can successfully implement secure, compliant, and scalable AI and automation solutions. With a strong focus on integration and real-world execution, Matt works with companies to turn fragmented data into reliable systems that actually support AI initiatives.

AI System Design Must Balance Openness and Control

Organizations today are under pressure to:

• integrate more systems
• adopt new tools
• move faster

At the same time, they must:

• protect sensitive data
• comply with regulations
• maintain control over systems

This creates what can best be described as "controlled openness."

AI system design today requires openness at the edges and control at the core.

Companies are becoming more integrated—but also more restrictive about how that integration happens.

Security Is Built Into AI System Design

One of the clearest points in the discussion is that security is not optional.

It's foundational.

Organizations are:

• enforcing stricter governance
• requiring auditability
• limiting access to data

As Matt explains, companies are willing to say yes to innovation—but only if they can govern it.

This shifts how systems must be built from the start.

AI System Design Requires Thinking Ahead

Another key takeaway is forward-thinking design.

Teams can't just build for current requirements—they need to anticipate:

• regulatory changes
• compliance expectations
• evolving data usage

For example, when dealing with sensitive data (like HR systems), teams must:

• anonymize data
• mask personal information
• track data movement

This isn't a future concern—it's a present requirement.

The Production Failure Problem

One of the most valuable examples shared is a real-world failure.

An AI system:

• worked perfectly in testing
• delivered strong results in a controlled environment

But failed in production.

Why?

Because it wasn't connected to real-world changes:

• new regulations
• environmental factors
• shifting conditions

AI system design must account for real-world variability—not just ideal conditions.

Why Real-Time Data Matters in AI System Design

The solution to that failure was integration.

AI systems must:

• receive real-time data
• adapt to changing inputs
• evolve continuously

Without this, they become static—and quickly outdated.

This is where integration and AI intersect again:

AI is only as dynamic as the data feeding it.

Designing for Adaptability

Strong AI system design includes:

• flexible architectures
• modular integrations
• continuous data flow

This allows systems to:

• evolve with conditions
• handle new requirements
• remain relevant over time

The best AI systems aren't static—they're constantly adapting.

Conclusion

AI system design is no longer about building something that works once.

It's about building something that keeps working.

Focus on:

• governance
• real-time data
• adaptability

And your AI will survive beyond the demo.

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