AI in Electrical Testing is rapidly becoming a practical tool for companies performing acceptance testing, maintenance testing, commissioning, protective relay testing, power quality analysis, cable diagnostics, and other specialized electrical services.
For the NETA electrical testing industry, artificial intelligence has the potential to significantly improve how field information is collected, reviewed, documented, analyzed, and communicated.
But there is an important distinction that should be made from the beginning:
AI can assist an electrical testing technician. It cannot replace one.
Electrical testing involves much more than collecting numbers from a test instrument. It requires understanding electrical power systems, recognizing abnormal conditions, evaluating equipment condition, interpreting manufacturer requirements and industry standards, identifying safety concerns, and determining when something simply does not look right.
Those responsibilities require trained and experienced people.
The real opportunity for AI in Electrical Testing is to give those people better tools.
The Foundation Still Starts With Qualified Technicians
The electrical testing industry already has an established framework for technician competency.
ANSI/NETA ETT establishes requirements for the qualification and certification of electrical testing technicians, including experience, education, training, and demonstrated technical competency.
NETA standards also establish specifications for testing electrical power equipment and systems. These standards are used throughout the industry to help verify that electrical systems and equipment are tested using recognized procedures and evaluated in a consistent manner.
Artificial intelligence does not change that foundation.
A circuit breaker still needs to be properly inspected and tested.
A protective relay still needs to have its settings verified and protection functions tested.
A transformer still requires appropriate electrical testing and evaluation.
A medium-voltage cable test still has to be performed using the correct procedure and interpreted within the proper technical context.
AI in Electrical Testing does not eliminate any of those responsibilities.
What it can change is how effectively technicians manage the information surrounding the testing process.
How AI in Electrical Testing Can Improve Reporting
One of the most immediate applications for AI in Electrical Testing is field documentation and report preparation.
A significant amount of a technician’s time can be spent recording and organizing information such as:
- Equipment identification
- Nameplate information
- Test results
- As-found conditions
- As-left conditions
- Deficiencies
- Corrective actions
- Equipment condition
- Test instrument information
- Field observations
- Recommended follow-up actions
Historically, much of this information has been handwritten, entered into spreadsheets, or transferred from field notes into formal reports after testing is completed.
AI-assisted systems can improve this process.
Technician notes can be converted into structured information. Test results can be organized more efficiently. Missing information can be identified before the report leaves the field. Similar equipment records can be reviewed for inconsistencies. Deficiency descriptions can be standardized while still preserving the technician’s actual observations.
The purpose is not to have AI decide what the technician observed.
The purpose is to help the technician document those observations more clearly, consistently, and efficiently.
That can improve report quality while reducing the administrative burden placed on highly trained technical personnel.
Using AI to Review Electrical Test Data
Modern electrical testing can generate an enormous amount of data.
A single project may include hundreds of breakers, transformers, relays, cables, switches, meters, batteries, and other devices. Large facilities may produce thousands of individual test values.
The challenge is no longer simply collecting data.
The challenge is identifying which pieces of data deserve additional attention.
This is one of the areas where AI in Electrical Testing may provide significant value.
An AI-assisted review system could help identify:
- Test results approaching an established limit
- Significant differences between phases
- Unexpected changes from previous test results
- Equipment performing differently from similar equipment
- Missing or incomplete test information
- Possible data-entry errors
- Unusual trends across repeated maintenance cycles
- Results that require further technical review
Consider insulation resistance testing as a simple example.
A value may meet an established acceptance criterion and still represent a substantial change from previous measurements.
A knowledgeable technician who understands the equipment history may recognize that change immediately.
AI can help identify that same trend across hundreds or thousands of records and bring it to the technician’s attention.
The decision about what that trend means, however, still belongs to a qualified person.
AI in Electrical Testing Should Identify Questions, Not Manufacture Answers
This may be one of the most important principles for using artificial intelligence in the electrical testing industry.
AI should help identify what needs to be reviewed.
It should not be blindly trusted to determine what is safe.
Generative AI systems can produce answers that sound technically convincing even when the conclusion is incomplete or incorrect.
In an industry involving medium-voltage and high-voltage electrical equipment, protective systems, stored energy, arc-flash hazards, and critical power infrastructure, incorrect conclusions can have serious consequences.
For that reason, AI-generated technical conclusions should never be accepted simply because they sound reasonable.
Technicians, engineers, and other qualified personnel must remain responsible for verifying the information.
A useful philosophy is:
AI can recommend where to look. The qualified professional determines what it means.
Using AI for Historical Electrical Testing Trends
Maintenance testing becomes much more valuable when today’s results can be compared with previous results.
A facility may have ten or twenty years of maintenance records, but that information is often spread across:
- PDF reports
- Spreadsheets
- Scanned documents
- Different testing software
- Different testing companies
- Different equipment naming conventions
- Different generations of test instruments
As a result, valuable historical information often exists without being easy to use.
AI in Electrical Testing could help organize and normalize that information so equipment histories become easier to review.
Instead of looking only at today’s test result, a technician could review how the same equipment has changed over several testing cycles.
For example:
2018 → 2021 → 2024 → 2026
That comparison may help identify whether a parameter has remained stable or has begun to trend in an unfavorable direction.
Historical trending could be useful for:
- Transformer testing
- Circuit breaker timing
- Contact resistance
- Insulation resistance
- Battery testing
- Protective relays
- Cable diagnostics
- Power quality measurements
- Grounding systems
- Rotating equipment
This changes the maintenance question from:
“Did the equipment pass?”
to:
“How is the equipment changing over time?”
In many cases, that second question may be far more useful.
AI-Assisted Quality Control for NETA Testing
AI in Electrical Testing may also provide another layer of quality control before a report reaches the customer.
An automated review process could potentially identify:
- Equipment with incomplete testing
- Missing serial numbers
- Missing nameplate information
- Test values that appear inconsistent
- Duplicate equipment records
- Missing deficiency information
- Contradictory comments
- Incorrect equipment designations
- Devices shown on drawings but missing from test records
- Required tests that may not have been documented
- Missing test instrument information
- Incomplete field notes
None of these functions require AI to make the final technical decision.
Instead, AI can act as another quality-control checkpoint.
It can identify information that deserves additional human review.
For an electrical testing company, this may be one of the safest and most practical applications of artificial intelligence.
AI and Protective Relay Testing
Protective relay testing presents another interesting opportunity for artificial intelligence.
Modern protective relays can contain hundreds or thousands of settings, logic equations, protection elements, communication parameters, and programmable inputs and outputs.
AI-assisted tools may be able to help technicians and engineers:
- Compare settings files
- Identify unexpected setting changes
- Organize protection elements
- Review logic
- Compare field settings with approved documentation
- Summarize relay event information
- Identify discrepancies between revisions
- Identify areas requiring additional investigation
However, protective relaying also demonstrates exactly why human oversight remains essential.
A setting cannot always be evaluated by looking at a single number.
Its meaning may depend on:
- System configuration
- Coordination studies
- CT ratios
- PT ratios
- Equipment ratings
- Utility requirements
- Operating philosophy
- Protection zones
- Other protective devices
- System grounding
- Interlocking
- Communication-assisted protection
AI can help organize and compare the information.
Protection professionals still need to understand the system.
Better Access to Technical Information
Electrical testing technicians work with equipment manufactured across many different decades.
A technician may encounter a modern digital relay in the morning and a circuit breaker manufactured decades earlier in the afternoon.
Finding accurate technical information can consume considerable time.
AI-based knowledge systems could eventually allow technicians to search a controlled technical library containing:
- Manufacturer manuals
- Equipment drawings
- Previous test reports
- Company procedures
- Approved testing procedures
- Technical bulletins
- Equipment histories
- Project specifications
- One-line diagrams
- Relay manuals
- Test equipment manuals
Instead of manually searching through hundreds of documents, a technician could ask a specific question and be directed to the relevant source.
The key word is controlled.
There is an enormous difference between an AI system searching verified company and manufacturer documentation and an AI system generating an answer from unrestricted internet information.
For technical work, source verification is critical.
AI for Electrical Testing Project Management
Not every benefit of AI in Electrical Testing occurs while a test set is connected to equipment.
Electrical testing projects require a significant amount of planning and coordination.
Technicians must be scheduled. Equipment lists must be reviewed. Drawings and specifications must be collected. Test equipment must be prepared. Customer requirements must be communicated. Deficiencies must be tracked. Reports must be assembled.
AI-assisted project systems could help with:
- Reviewing project specifications
- Generating preliminary equipment lists
- Identifying testing requirements
- Organizing field schedules
- Summarizing project communications
- Tracking outstanding customer information
- Identifying incomplete documentation
- Preparing technician work instructions
- Organizing deficiencies
- Preparing report summaries
- Tracking project closeout requirements
Reducing administrative work allows project managers and technicians to spend more time addressing the actual technical requirements of the project.
AI Can Help With Deficiency Management
Deficiency management is another area where artificial intelligence may improve the testing process.
During a large testing project, dozens or even hundreds of deficiencies may be identified.
These deficiencies may involve:
- Mechanical problems
- Damaged equipment
- Failed test results
- Incorrect settings
- Missing labels
- Loose connections
- Inoperative trip units
- Failed control components
- Equipment condition concerns
- Documentation discrepancies
AI could help organize those deficiencies by equipment type, location, severity, or status.
It could also help identify whether similar deficiencies are appearing repeatedly across the facility.
For example, if multiple breakers of the same type exhibit similar mechanical problems, AI may help identify the pattern sooner.
Again, AI does not determine the corrective action by itself.
It helps qualified personnel recognize patterns and organize information more effectively.
Cybersecurity Risks of AI in Electrical Testing
Electrical testing companies frequently work in critical facilities.
Test reports, protective relay settings, one-line diagrams, equipment information, facility layouts, and operating procedures may contain sensitive information.
For that reason, companies adopting AI in Electrical Testing must consider where their information is going.
Important questions include:
- Is customer information being uploaded to an external AI system?
- Is that information retained?
- Can the information be used to train another model?
- Who can access the information?
- Are project files encrypted?
- Is the AI environment approved for confidential information?
- Are critical infrastructure documents appropriately protected?
- Is customer authorization required before using AI tools?
- Are employees trained on what information can and cannot be uploaded?
AI adoption without an information-security policy can create risks that have nothing to do with electrical testing itself.
The technology must be implemented responsibly.
AI Will Not Replace the Qualified Electrical Testing Technician
Whenever a powerful new technology appears, there is a tendency to assume that technical knowledge will become less important.
For electrical testing, the opposite may be true.
As AI provides technicians with increasingly powerful analytical tools, understanding the fundamentals becomes even more important.
A technician must know enough to recognize when an AI-generated response does not make sense.
That requires knowledge of:
- Electrical theory
- Power systems
- Test equipment
- Instrument transformers
- Protective relaying
- Equipment operation
- Electrical safety
- Manufacturer requirements
- Industry standards
- Field conditions
- Test methods
- Equipment limitations
AI may allow a highly qualified technician to process more information and work more efficiently.
It does not eliminate the need for qualification.
In fact, the ability to properly verify AI-generated information may become another important skill for future technicians and engineers.
The Human Still Makes the Final Decision
Electrical testing is ultimately about determining the condition and performance of real equipment operating in real electrical systems.
There are circumstances in the field that cannot always be captured by a spreadsheet or test result.
A technician may hear an unusual sound.
They may notice discoloration.
They may recognize an abnormal mechanical condition.
They may identify incorrect wiring.
They may notice that the equipment configuration does not match the drawings.
They may recognize that a test result is inconsistent with the way the equipment is actually operating.
Experienced technicians develop judgment through years of training and exposure to different equipment and situations.
AI can assist that judgment.
It cannot replace the experience behind it.
Where Electrical Test Tech Sees AI in Electrical Testing Going
At Electrical Test Tech, we believe AI in Electrical Testing has the potential to become a valuable part of the electrical testing industry when it is used responsibly.
The objective should not be to remove the technician from the testing process.
The objective should be to give the technician better information.
We see some of the greatest opportunities in:
- Field documentation
- Test data organization
- Historical trending
- Quality-control review
- Deficiency tracking
- Technical information retrieval
- Report generation
- Project planning
- Equipment history
- Data analysis
- Relay settings comparison
- Testing consistency
- Project closeout
- Maintenance trending
As these systems continue to develop, the companies that benefit most from artificial intelligence will probably not be the companies attempting to automate technical judgment.
They will be the companies that combine new technology with experienced technicians, established procedures, recognized industry standards, and strong quality control.
The Future of AI in Electrical Testing
Artificial intelligence will continue to become part of the electrical testing industry.
The question is not whether the technology will be used.
The more important question is how it will be used.
Used poorly, AI can generate incorrect information faster.
Used properly, AI in Electrical Testing can help qualified professionals find important information faster, identify trends that might otherwise go unnoticed, produce better documentation, improve quality control, and make more informed decisions.
The future of electrical testing should not be viewed as:
AI versus the electrical testing technician.
It should be viewed as:
Qualified electrical testing technicians using AI as another tool to perform better electrical testing.
That combination of experienced people, proven testing practices, established standards, quality data, and better analytical tools has the potential to improve both the reliability and safety of electrical power systems.
