Traditional software is generally built around explicit instructions and defined logic. Artificial intelligence introduces systems that learn patterns from data and generate predictions or outputs based on those learned representations.

From Explicit Instructions to Model-Based Systems

This changes how engineers approach testing and reliability. The question is no longer only whether software executed correctly. Engineers must also determine whether an AI system consistently produces useful and acceptable results.

How Testing Changes With AI

• Traditional software can often be tested against precise expected outputs.
• AI systems may require evaluation against acceptable criteria or ranges.
• Model changes can alter behavior without conventional code changes.
• Data quality can affect results as much as application logic.
• Production monitoring becomes part of the testing strategy.

What Mainframe Engineers Bring to AI

Mainframe engineers already understand systems thinking, resource constraints, performance analysis, debugging, and reliability. Those skills provide a strong foundation for modern AI engineering.

Combining Traditional Engineering With AI

The practical challenge is to combine established engineering practices with model evaluation, data quality, inference, AI application testing, and monitoring.

Continue the transition: IBM Assembler to AI · AI Reliability Engineering