AI-Readiness Assessment

Find out what the next step is towards your successful AI journey!

Answer this survey to understand your AI maturity level, and equally important, get the resulting report to ensure you take the right next step towards successful AI operations.

Typical Stakeholder: VP, Director, Manager for Maintenance, Operations, Plant, Fleet, and Service management

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* 1. Your information

Name

Company

Email Address

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* 2. Is this your first AI initiative? How well adopted is AI-tech within your company?

We have built a strong AI-aware culture, and we have many AI models running 24/7/365 in production environments with AI-Operations under continuous and semi-automated enhancement data pipelines.

We have a strong pipeline of AI projects at various levels of maturity.

Many departments have run AI proofs of concepts, and a few are in production with manual updates.

Only a few AI proof-of-concept projects have been tried but without success.

AI proofs of concepts are planned but not yet started.

We have no AI proof of concept or AI project planned at the moment.

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* 3. How many of your current assets are monitored in near real time?

Almost all (95%+) of our current assets are monitored today.

Most (80%+) of our current assets are monitored today.

Some (40%+) of our current assets are monitored today.

Few (5%+) of our assets are monitored today.

Almost none (less than 5%) of our assets are monitored today.

We are still manually collecting data from our assets today.

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* 4. How clean and reliable is the data you are collecting today (manually or automatically)?

We have built data pipelines and maintain them to ensure reliability and data cleanliness over time.

We have built some threshold-based filtering to keep bad data out of our decision-making practice.

We manually check a portion of the data on a regular basis to ensure data quality.

We rely on end users to let us know when they see bad data on their dashboards.

We don't have a process to manually or automatically check data reliability and quality.

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* 5. When a system or asset failure occurs, what statement below best represents your organization?

Few failures occur because we have a prescriptive maintenance system in place preventing failures.

Few failures occur because we have a conservative scheduled-based maintenance program in place.

Some failures occur because we are reducing our maintenance slowly until we start seeing failures.

Some failures occur and we have little visibility as to why they happen.

We are constantly fighting fires.

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* 6. Has your organization implemented information standards processes to manage engineering and operational information?

We have well-defined company-wide standards to manage our real-time and operational data.

We follow best practices most of the time when it comes to managing real-time and operational data.

We have some level of asset-based information management, which allows for relatively quick access to real-time and operational data.

We have a few (typically siloed) systems to manage our real-time and operational data.

We do not have standards in place to manage our real-time and operational data.

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* 7. Do you have the easy access and visibility you need on all your assets? Select the sentence below that best describes your current situation.

We have complete visibility on maintenance events, real-time telemetry, and technical documentation related to all our assets.

We have some visibility on maintenance events, real-time telemetry, and technical documentation related to all our assets.

We have partial visibility on real-time telemetry and technical documentation related to most of our assets.

We have partial visibility on real-time telemetry related to some of our assets.

We have little to no visibility on operational data from our assets.

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* 8. Do you use mobile technologies to communicate to the shop floor or remote workers? Select the sentence below that best describes your current situation.

Yes, we extensively make use of mobile technologies to disseminate the right information to the right employee, at the right time, and in a cyber-secure way.

Yes, we have some systems that make use of mobile technologies to disseminate the right information to the right employee, at the right time, and in a cyber-secure way.

Yes, we have a few systems that make use of mobile technologies to disseminate the right information to the right employee, at the right time, and in a cyber-secure way.

Yes, we are in the process of deploying our first mobile app to disseminate the right information to the right employee, at the right time, and in a cyber-secure way.

No, we do not make use of mobile technologies to disseminate operational information.

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* 9. Do you use edge devices for either collecting operational data or acting on operational data to improve your process in real time or near real time?

Yes, we use edge devices for both collecting and acting locally on the operational data through AI-based algorithms.

Yes, we use edge devices for both collecting and acting locally on the operational data by feeding back information using traditional algorithms.

Yes, we use edge devices only for collecting operational data.

No, we do not use edge devices.

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* 10. Up to what level are the operational KPIs or metrics distributed and aggregated?

KPIs and metrics are aggregated with the ability to drill down from the top corporate level to single assets.

KPIs and metrics are aggregated with some ability to drill down from individual plants to single assets.

KPIs and metrics are aggregated with some ability to drill down from individual production areas to single assets.

KPIs and metrics are aggregated with no drill-down capabilities.

KPIs and metrics are not aggregated.

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* 11. How do your teams and employees collaborate?

They collaborate using easy-to-use mobile-based software solutions available 24/7/365.

They collaborate using easy-to-use mobile-based software solutions that are up and running most of the time.

They use somewhat unreliable but easy-to-use mobile-based software solutions to collaborate.

They use static or desktop-based software solutions to collaborate.

They use radio, old-fashioned phones and other non-software solutions to collaborate.

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* 12. How well understood are the terms AI and machine learning in general within your organization?

All employees have a solid understanding of what AI and machine learning can do for us today.

Most employees have a basic understanding of what AI and machine learning can do for us today.

Some employees have a basic understanding of what AI and machine learning can do for us today.

A few data scientists and engineers have a solid understanding of what AI and machine learning can do for us today.

Very few people truly understand what AI and machine learning can do for us today.

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* 13. How would you best define "success" as it relates to an AI initiative?

After moving an AI model into production, the targeted business metrics and KPIs are continually improved as, or better than, your initial business goals, and can be compared to a pre-AI operations baseline.

After moving an AI model into production, the targeted AI performance metrics (F1-Score, false negative, false positives, and/or overall accuracy) are close to the AI training performance values.

After moving an AI model into production, no human interaction is needed.

All the infrastructure costs are absorbed by the returns from our first AI initiative.

I am unclear how to measure AI success.