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AI Engineering Self-Survey

How to answer
Answer based on demonstrated capabilities currently in use—not your organization’s ambition.
Select the description closest to your organization’s current state. If your organization sits between two levels, select the lower rating.

Strategy and Engineering Outcomes

Q1: How clearly does your organization connect its AI strategy to specific engineering priorities and outcomes?
Consider priorities such as shorter development cycles, improved quality, reduced rework, faster verification and validation, increased asset availability, and improved engineering capacity.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Q2: How consistently are engineering AI priorities understood and supported across business, engineering, technology, data, risk, and executive leadership?

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

AI Portfolio and Value Realization

Q3: How systematically does your organization identify and prioritize engineering AI opportunities?
Consider whether opportunities are evaluated using consistent criteria such as business value, engineering feasibility, data readiness, risk, implementation effort, and reuse potential.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Q4: How effectively does your organization measure whether engineering AI initiatives deliver their intended value?
Consider baselines, adoption, engineering hours saved, cycle-time reduction, quality improvement, cost avoidance, risk reduction, and realized financial benefits.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Engineering Workflow Transformation

Q5: To what extent has your organization identified engineering workflows that could be improved or automated with AI?
Examples may include requirements analysis, design exploration, simulation, software development, systems engineering, testing, verification and validation, technical documentation, change-impact analysis, root-cause analysis, and maintenance.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Q6: To what extent is AI already embedded in production engineering workflows and delivering repeatable improvements?
Rate operational use rather than individual experimentation with general-purpose AI tools.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

AI Solution and Agent Delivery

Q7: How mature is your organization’s ability to design, test, deploy, monitor, and improve AI-enabled engineering solutions or agents?
Consider defined delivery methods, evaluation criteria, human oversight, lifecycle ownership, monitoring, feedback, and version control.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Q8: How effectively can your organization move a successful engineering AI prototype into secure, reliable, supported production use?
Consider integration, testing, performance, documentation, support, change management, and ongoing ownership.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

AI Platform and Data Foundation

Q9: How well does your technology environment support reusable and scalable engineering AI capabilities?
Consider approved models and tools, reusable services, integration patterns, development environments, observability, identity and access management, and deployment guardrails.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Q10: How ready and accessible is your engineering data and knowledge for AI use?
Consider requirements, specifications, CAD/PLM data, simulation results, source code, test data, quality records, asset data, technical standards, and lessons learned—as well as their quality, context, traceability, security, and ownership.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

AI Assurance and Responsible Use

Q11: How consistently does your organization evaluate engineering AI solutions for accuracy, reliability, safety, security, privacy, intellectual-property risk, and regulatory compliance?

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Q12: How clearly are accountability and human decision rights defined when AI contributes to an engineering output or decision?
Consider review and approval requirements, traceability, escalation procedures, failure handling, and responsibility for final engineering decisions.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

AI Literacy, Adoption, and Scale

Q13: How prepared are engineering leaders and practitioners to use AI effectively and responsibly in their roles?
Consider role-specific training, practical guidance, prompt and context skills, output validation, risk awareness, and access to coaching or communities of practice.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

Q14: How effectively does your organization drive adoption and scale successful AI practices across engineering teams, functions, products, and locations?
Consider executive sponsorship, workflow redesign, incentives, champions, knowledge sharing, reusable patterns, adoption measurement, and continuous improvement.

1 — Not established: No defined approach, ownership, or meaningful activity

2 — Exploring: Informal experimentation or isolated pilots; results are not repeatable

3 — Developing: Defined approach with some operational use, standards, or measured results

4 — Operational: Consistently applied across multiple teams or workflows with governance and measurement

5 — Scaled: Enterprise-wide, integrated, continuously measured, and improved

Don't know / Not applicable

AI Engineering Self-Survey

Strategy and Engineering Outcomes

Q2: How consistently are engineering AI priorities understood and supported across business, engineering, technology, data, risk, and executive leadership?

AI Portfolio and Value Realization

Q3: How systematically does your organization identify and prioritize engineering AI opportunities?
Consider whether opportunities are evaluated using consistent criteria such as business value, engineering feasibility, data readiness, risk, implementation effort, and reuse potential.

Q4: How effectively does your organization measure whether engineering AI initiatives deliver their intended value?
Consider baselines, adoption, engineering hours saved, cycle-time reduction, quality improvement, cost avoidance, risk reduction, and realized financial benefits.

Engineering Workflow Transformation

Q6: To what extent is AI already embedded in production engineering workflows and delivering repeatable improvements?
Rate operational use rather than individual experimentation with general-purpose AI tools.

AI Solution and Agent Delivery

Q7: How mature is your organization’s ability to design, test, deploy, monitor, and improve AI-enabled engineering solutions or agents?
Consider defined delivery methods, evaluation criteria, human oversight, lifecycle ownership, monitoring, feedback, and version control.

Q8: How effectively can your organization move a successful engineering AI prototype into secure, reliable, supported production use?
Consider integration, testing, performance, documentation, support, change management, and ongoing ownership.

AI Platform and Data Foundation

Q9: How well does your technology environment support reusable and scalable engineering AI capabilities?
Consider approved models and tools, reusable services, integration patterns, development environments, observability, identity and access management, and deployment guardrails.

AI Assurance and Responsible Use

Q11: How consistently does your organization evaluate engineering AI solutions for accuracy, reliability, safety, security, privacy, intellectual-property risk, and regulatory compliance?

Q12: How clearly are accountability and human decision rights defined when AI contributes to an engineering output or decision?
Consider review and approval requirements, traceability, escalation procedures, failure handling, and responsibility for final engineering decisions.

AI Literacy, Adoption, and Scale

Q13: How prepared are engineering leaders and practitioners to use AI effectively and responsibly in their roles?
Consider role-specific training, practical guidance, prompt and context skills, output validation, risk awareness, and access to coaching or communities of practice.

Contact Information

First Name

Last Name

Email Address

Additional Context

What is your primary role?

Which engineering environment best describes your organization?

Approximately how many people work in engineering-related roles?

Where is your organization currently using or testing AI in engineering? Select all that apply.

What is the biggest challenge or opportunity you hope AI can address within your engineering organization?

AI Engineering Self-Survey

Strategy and Engineering Outcomes

Q2: How consistently are engineering AI priorities understood and supported across business, engineering, technology, data, risk, and executive leadership?

AI Portfolio and Value Realization

Q3: How systematically does your organization identify and prioritize engineering AI opportunities?Consider whether opportunities are evaluated using consistent criteria such as business value, engineering feasibility, data readiness, risk, implementation effort, and reuse potential.

Q4: How effectively does your organization measure whether engineering AI initiatives deliver their intended value?Consider baselines, adoption, engineering hours saved, cycle-time reduction, quality improvement, cost avoidance, risk reduction, and realized financial benefits.

Engineering Workflow Transformation

Q6: To what extent is AI already embedded in production engineering workflows and delivering repeatable improvements?Rate operational use rather than individual experimentation with general-purpose AI tools.

AI Solution and Agent Delivery

Q7: How mature is your organization’s ability to design, test, deploy, monitor, and improve AI-enabled engineering solutions or agents?Consider defined delivery methods, evaluation criteria, human oversight, lifecycle ownership, monitoring, feedback, and version control.

Q8: How effectively can your organization move a successful engineering AI prototype into secure, reliable, supported production use?Consider integration, testing, performance, documentation, support, change management, and ongoing ownership.

AI Platform and Data Foundation

Q9: How well does your technology environment support reusable and scalable engineering AI capabilities?Consider approved models and tools, reusable services, integration patterns, development environments, observability, identity and access management, and deployment guardrails.

AI Assurance and Responsible Use

Q11: How consistently does your organization evaluate engineering AI solutions for accuracy, reliability, safety, security, privacy, intellectual-property risk, and regulatory compliance?

Q12: How clearly are accountability and human decision rights defined when AI contributes to an engineering output or decision?Consider review and approval requirements, traceability, escalation procedures, failure handling, and responsibility for final engineering decisions.

AI Literacy, Adoption, and Scale

Q13: How prepared are engineering leaders and practitioners to use AI effectively and responsibly in their roles?Consider role-specific training, practical guidance, prompt and context skills, output validation, risk awareness, and access to coaching or communities of practice.

Contact Information

First Name

Last Name

Email Address

Additional Context

What is your primary role?

Which engineering environment best describes your organization?

Approximately how many people work in engineering-related roles?

Where is your organization currently using or testing AI in engineering? Select all that apply.

What is the biggest challenge or opportunity you hope AI can address within your engineering organization?

Q3: How systematically does your organization identify and prioritize engineering AI opportunities?
Consider whether opportunities are evaluated using consistent criteria such as business value, engineering feasibility, data readiness, risk, implementation effort, and reuse potential.

Q4: How effectively does your organization measure whether engineering AI initiatives deliver their intended value?
Consider baselines, adoption, engineering hours saved, cycle-time reduction, quality improvement, cost avoidance, risk reduction, and realized financial benefits.

Q6: To what extent is AI already embedded in production engineering workflows and delivering repeatable improvements?
Rate operational use rather than individual experimentation with general-purpose AI tools.

Q7: How mature is your organization’s ability to design, test, deploy, monitor, and improve AI-enabled engineering solutions or agents?
Consider defined delivery methods, evaluation criteria, human oversight, lifecycle ownership, monitoring, feedback, and version control.

Q8: How effectively can your organization move a successful engineering AI prototype into secure, reliable, supported production use?
Consider integration, testing, performance, documentation, support, change management, and ongoing ownership.

Q9: How well does your technology environment support reusable and scalable engineering AI capabilities?
Consider approved models and tools, reusable services, integration patterns, development environments, observability, identity and access management, and deployment guardrails.

Q12: How clearly are accountability and human decision rights defined when AI contributes to an engineering output or decision?
Consider review and approval requirements, traceability, escalation procedures, failure handling, and responsibility for final engineering decisions.

Q13: How prepared are engineering leaders and practitioners to use AI effectively and responsibly in their roles?
Consider role-specific training, practical guidance, prompt and context skills, output validation, risk awareness, and access to coaching or communities of practice.