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That is a practical expert judgment shared in an interview, not the result of a controlled test of AI performance. The useful question is therefore not whether AI can be trusted in general, but whether the particular task, data, and consequences justify relying on its output.
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Where AI is a stronger fit
AI assistance is more defensible when a task recurs, the relevant cases resemble one another, and the production records are consistent enough to describe them. In that setting, a model can draw on a history that is meaningfully related to the work at hand.
Honcharuk cautions that even records for ostensibly similar operations may differ because equipment, tooling, or operators change. A model trained on mixed conditions may produce an average that does not predict what will happen on a particular order. The question is not simply whether there is a lot of data; it is whether the data represents comparable work.
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Her interview does not set a numeric minimum for an adequate dataset. Manufacturers must assess data sufficiency for the specific task rather than assume that a large history automatically makes a recommendation reliable.
Why unusual parts need more engineering judgment
Nonstandard work can combine geometry, material, tolerances, production route, and defect risk in ways that have few close precedents. These factors interact: geometry can affect which process is suitable, and that process can change the risk of defects. Recognizing a pattern in prior records does not, by itself, establish what caused the outcome or whether the same pattern applies to a new part.
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Honcharuk’s view is that AI can assist a specialist with complex, nonstandard parts, but should not replace the specialist’s review when the evidence is thin. As she puts it, “Engineering expertise stays critical not because we don’t trust the technology, but because there isn’t yet enough data for certain tasks.”
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What can go wrong when a recommendation is treated as final
An inaccurate estimate can lead a manufacturer to quote the wrong price, absorb a loss, or lose a customer. A poor process choice or risk assessment can be harder and more expensive to change once production has started. The cost of error therefore depends not only on the model’s likely accuracy, but also on when someone can catch the error and what commitments have already been made.
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Honcharuk’s deployment rule is qualitative: begin with work that has relevant historical data and where mistakes are reversible, check whether the records are consistent for that case, and have a specialist review the result before it is final. Her interview offers no universal threshold for how much data is enough or how reversible a decision must be.
A practical check before relying on AI
Use these questions to decide how much authority to give an AI recommendation on a manufacturing task:
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- Repeatability: Has the same operation or a sufficiently similar part been handled many times?
- Data consistency: Do the records reflect comparable equipment, tooling, operators, materials, and process conditions?
- Novelty and interaction: Does the job combine geometry, tolerances, materials, process choices, or defect risks in a way that has little precedent?
- Reversibility and consequence: Can an error be detected and corrected before it creates a costly quote, process, or production commitment?
- Review: Will a qualified specialist check the recommendation before it becomes a final decision?
When repeatability and consistency are strong and mistakes can be caught early, AI may be useful for support. When the work is novel, records are uneven, or a decision becomes costly to unwind, keep the output at recommendation level and require engineering review.
What reported manufacturing experience does—and does not—show
The AI Journal reports that Honcharuk designed and rolled out an internal quoting and cost-estimation system at Advanced Engineering & EDM in Poway, California, in 2024. The article says it reduced quote-request processing time and helped the company take on more complex work without adding staff. It gives no time-saved figure or independent evaluation, so this is reported company experience, not a measured result that establishes what other manufacturers can expect.
In a 2025 article, Honcharuk describes precision-manufacturing cost estimation as a layered process linking technical information to operational parameters and cost structures, including manufacturability, timing across operations, and sensitive cost drivers. That context helps explain why a quote may require process reasoning; it does not demonstrate that a particular AI system can perform those tasks reliably.
Adoption is growing, but that is not proof of reliability
The AI Journal’s 30 September 2026 article reports that 29 percent of respondents to Deloitte’s 2025 Smart Manufacturing and Operations Survey said they already used AI and machine learning at facility or network level. The article describes the survey as covering 600 executives at large U.S. manufacturers. This is a secondary report of the survey figure; the original survey was not independently checked here. Adoption indicates use, not that AI can safely make final decisions on complex or unusual manufacturing work.
Quick Recap
Sources
- The AI Journal, Nick Fuga, “Can AI Be Trusted to Manage Complex Manufacturing Work? Iryna Honcharuk Explains,” 30 September 2026.
- Universal Library of Innovative Research and Studies, Iryna Honcharuk, “Optimization of Project Cost Estimation Processes in Precision Manufacturing,” 2025.
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