Automation engineers do not need to become full-time data scientists to benefit from AI.
Use LLMs for language-heavy work
Documentation drafts, code explanations, SQL scaffolding, test cases and log summaries are good fits.
Use ordinary analysis first
Trend plots, distributions, correlations and data-quality checks can answer many engineering questions before ML is justified.
Use ML when the pattern deserves it
Classification, regression, anomaly detection and forecasting make sense when repeatable patterns exist.
Keep engineering discipline
Version data, record assumptions, test edge cases and separate generated suggestions from validated production logic.
Final takeaway
The engineering value comes from a measurable improvement, explicit boundaries and a design that remains understandable when the system is under pressure.