Professional and Career Topics

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

On the flip side, I think a lot of people are too enthusiastic to implement AI because it is perceived as the latest and greatest, so much so that they fail to take the proper steps to test it. AI is not infallible, and there need to be checks and balances in place to make sure the data it is spitting out is trustworthy. AI can very convincingly give inaccurate outputs. And younger employees still need to know what the AI is doing, similarly to how we still learn to do math by hand before using a calculator and how to do designs on paper before using design software. You have to understand what it is doing to be able to check the outputs.

As with any new technology, there is some benefit to the resistance of some when there are legitimate pitfalls that would be overlooked with an overeager implementation.

Articles and news segments centered on the need to get on board with AI right now before you're left behind make me nervous. Many platforms are forcing AI integration where it hands down worsens the end-user experience. AI summaries on searches are laughably inaccurate with a high frequency. When they forced its integration with the "assistant" on my phone, I asked it to call my sister (as I did previously with no issues) and it called a random store location of a retail chain that has her name as part of the name. It didn't even ask if that was what I meant before it found a number online instead of using the contact in my phone with that name. It didn't make it better; it made it worse. But they brag about how they're at the forefront of AI integration. 

I know that there are AI tools that work better than the LLM that are being integrated in so many places that can be more tailored to a specific function. However, this ill-thought-out, forced implementation of AI where it doesn't belong is most likely having a direct impact on the willingness of many to look into other places where it may be better used in industry.

(For the record, I'm a younger engineer)

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Heidi and Renn make super points and clearly don't suffer from old folks disorders.

I'm one of the old folks. I use AI selectively, always wary of black box syndrome. In the 1990s I used a neural net technique to create boundary condition time series for a numerical model and it produced decent but not wonderful results. Since then I've used it a few times when data gaps made other techniques fall short. It's helpful, but not a panacea. I understand that AI can produce good computer code but I can't testify personally to its reliability. Testing to ensure the code reproduces known accurate results is essential both to use and to convincing us older folks that it can be trusted. I enjoy writing programs, so I'm not tempted to try.

So far, my testing of LLM's on deeply technical subjects that I understand has shown them to often hallucinate. When that happens, it's not only fear of the new that inhibits AI's use, it's technology that still contains bugs, like Windows 1.0 did.

Bill Mc

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Dear Dr. McAnally,

Thank you for your thoughtful and honest perspective. I fully agree that a healthy skepticism-especially regarding "black box" models-is essential, particularly when it comes to technical disciplines where precision matters. Your early experience with neural networks in the 1990s is fascinating, and I appreciate your continued commitment to careful validation and testing. As someone who enjoys programming myself, I also find that human oversight remains irreplaceable, especially in high-stakes engineering applications.

I wonder, from your perspective as a seasoned expert, do you think there's value in developing specialized adaptation or training programs for older professionals as automation and AI continue to expand in our field? Would it make sense to differentiate these learning paths-perhaps by experience level, role, or even personal motivation-to ensure they are more accessible and effective?

Heidi and Renn make super points and clearly don't suffer from old folks disorders.

I'm one of the old folks. I use AI selectively, always wary of black box syndrome. In the 1990s I used a neural net technique to create boundary condition time series for a numerical model and it produced decent but not wonderful results. Since then I've used it a few times when data gaps made other techniques fall short. It's helpful, but not a panacea. I understand that AI can produce good computer code but I can't testify personally to its reliability. Testing to ensure the code reproduces known accurate results is essential both to use and to convincing us older folks that it can be trusted. I enjoy writing programs, so I'm not tempted to try.

So far, my testing of LLM's on deeply technical subjects that I understand has shown them to often hallucinate. When that happens, it's not only fear of the new that inhibits AI's use, it's technology that still contains bugs, like Windows 1.0 did.

Bill Mc

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

I am an "older" Engineer/ Project Manager" and agree with the sentiment of a cautious approach. AI is an integral part of the of the world in which we operate. My concern is where to get introductory papers/ documents which will provide an objective and balanced view of where to start with understanding and applicability. Thanks for the thought provoking articles and responses.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

I am an "older" Engineer/ Project Manager" and agree with the sentiment of a cautious approach. AI is an integral part of the of the world in which we operate. My concern is where to get introductory papers/ documents which will provide an objective and balanced view of where to start with understanding and applicability. Thanks for the thought provoking articles and responses.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Thank you very much for your feedback and support. It is very encouraging to receive responses from experienced colleagues, especially when it comes to such an important and sensitive issue as adapting the older generation to AI.

Regarding your questions - to start understanding and studying AI, I would recommend focusing on review articles and introductory programs on AI and machine learning. It is important to choose materials that explain not only the technical aspects but also provide real-world examples of AI applications in your industry.
In my research, I also try to highlight the practical aspects of AI implementation for senior professionals, to help them adapt more easily and overcome fears of new technologies.

I would be glad to keep the dialogue going and exchange experiences!

I am an "older" Engineer/ Project Manager" and agree with the sentiment of a cautious approach. AI is an integral part of the of the world in which we operate. My concern is where to get introductory papers/ documents which will provide an objective and balanced view of where to start with understanding and applicability. Thanks for the thought provoking articles and responses.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Thank you very much for your feedback and support. It is very encouraging to receive responses from experienced colleagues, especially when it comes to such an important and sensitive issue as adapting the older generation to AI.

Regarding your questions - to start understanding and studying AI, I would recommend focusing on review articles and introductory programs on AI and machine learning. It is important to choose materials that explain not only the technical aspects but also provide real-world examples of AI applications in your industry.
In my research, I also try to highlight the practical aspects of AI implementation for senior professionals, to help them adapt more easily and overcome fears of new technologies.

I would be glad to keep the dialogue going and exchange experiences!

I am an "older" Engineer/ Project Manager" and agree with the sentiment of a cautious approach. AI is an integral part of the of the world in which we operate. My concern is where to get introductory papers/ documents which will provide an objective and balanced view of where to start with understanding and applicability. Thanks for the thought provoking articles and responses.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

I am an "older" Engineer/ Project Manager" and agree with the sentiment of a cautious approach. AI is an integral part of the of the world in which we operate. My concern is where to get introductory papers/ documents which will provide an objective and balanced view of where to start with understanding and applicability. Thanks for the thought provoking articles and responses.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Darya – your point is well taken. Indeed it does – doesn't it?

People talk about generation-gap, etc, but I like to see it as a generational transformation. A gap indicates a void within – but there's no such void – there's just transformation, a graduation from one stage to the next. It happens without us being aware. As we mature and gather more experience – our brain tends to look more for 'why' rather than 'what'.

A case in point, I had a recent experience of staying in an airbnb suite. Somehow, the previous guest switched the smart TV to a position, I couldn't figure it how to get back to work. The manager came, he couldn't do it, and was thinking of asking a technician to come. Our co-host neighbor came with his 8-year old son, he couldn't do it. The 8-year old took the remote and activated the TV in a second. We said, in loud voice 'what!'. 

I take help of my son and daughters now and then on all kinds of electronic gadgets. Many of them change so fast, we begin to ask 'why' it's needed. The previous set-up was already working fine. Yet, things are changing fast – many of them are powered by AI chips. (more in Artificial Intelligence – the Tool of No Limit).

I had the similar experience in Water Modeling. I have been told quite a few times that – this or that guy knows how to run the Software, but do not understand hydraulics. Indeed, if you teach an 8-year old how to run a modeling software – he or she can do it very fast, without ever understanding 'why' something happens.

There have been quite an interest in AI and Civil Engineering in Collaborate discussions before (at least three of the titles are: AI in Practice Today; AI Applications in Engineering and Construction; Human-AI Team). Many issues came up there – one discussion leading to another. The last thread, quoting 2022 NAP #26355 Human-AI Teaming discussed about HSI in the context of MDO.

In the end, there is an age-related transformational issue – to find the right balance between 'why' and 'what'. Professionals talk about we need some grey-haired here to lead. Then the grey-hairds need some 'what' assistance – that's how the profession of secretaries come into relevance. And all organizational hierarchy works that way.

I have read a Scientific American article recently – titled 'Criminal AI is Here . . .' There comes the question of exponential proliferation of AIPPS and Ethics (also more in AI Essay). Once I saw a caption quite a while ago on a High School billboard saying something like, a course is being offered how to hack a computer . . .. I thought to myself how such a course could be offered to high school kids. What the school board and administration have been thinking? I thought to myself . . . this is another symptom of societal degradation . . . luring our youngs to head in the wrong direction.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Darya – your point is well taken. Indeed it does – doesn't it?

People talk about generation-gap, etc, but I like to see it as a generational transformation. A gap indicates a void within – but there's no such void – there's just transformation, a graduation from one stage to the next. It happens without us being aware. As we mature and gather more experience – our brain tends to look more for 'why' rather than 'what'.

A case in point, I had a recent experience of staying in an airbnb suite. Somehow, the previous guest switched the smart TV to a position, I couldn't figure it how to get back to work. The manager came, he couldn't do it, and was thinking of asking a technician to come. Our co-host neighbor came with his 8-year old son, he couldn't do it. The 8-year old took the remote and activated the TV in a second. We said, in loud voice 'what!'. 

I take help of my son and daughters now and then on all kinds of electronic gadgets. Many of them change so fast, we begin to ask 'why' it's needed. The previous set-up was already working fine. Yet, things are changing fast – many of them are powered by AI chips. (more in Artificial Intelligence – the Tool of No Limit).

I had the similar experience in Water Modeling. I have been told quite a few times that – this or that guy knows how to run the Software, but do not understand hydraulics. Indeed, if you teach an 8-year old how to run a modeling software – he or she can do it very fast, without ever understanding 'why' something happens.

There have been quite an interest in AI and Civil Engineering in Collaborate discussions before (at least three of the titles are: AI in Practice Today; AI Applications in Engineering and Construction; Human-AI Team). Many issues came up there – one discussion leading to another. The last thread, quoting 2022 NAP #26355 Human-AI Teaming discussed about HSI in the context of MDO.

In the end, there is an age-related transformational issue – to find the right balance between 'why' and 'what'. Professionals talk about we need some grey-haired here to lead. Then the grey-hairds need some 'what' assistance – that's how the profession of secretaries come into relevance. And all organizational hierarchy works that way.

I have read a Scientific American article recently – titled 'Criminal AI is Here . . .' There comes the question of exponential proliferation of AIPPS and Ethics (also more in AI Essay). Once I saw a caption quite a while ago on a High School billboard saying something like, a course is being offered how to hack a computer . . .. I thought to myself how such a course could be offered to high school kids. What the school board and administration have been thinking? I thought to myself . . . this is another symptom of societal degradation . . . luring our youngs to head in the wrong direction.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Darya – your point is well taken. Indeed it does – doesn't it?

People talk about generation-gap, etc, but I like to see it as a generational transformation. A gap indicates a void within – but there's no such void – there's just transformation, a graduation from one stage to the next. It happens without us being aware. As we mature and gather more experience – our brain tends to look more for 'why' rather than 'what'.

A case in point, I had a recent experience of staying in an airbnb suite. Somehow, the previous guest switched the smart TV to a position, I couldn't figure it how to get back to work. The manager came, he couldn't do it, and was thinking of asking a technician to come. Our co-host neighbor came with his 8-year old son, he couldn't do it. The 8-year old took the remote and activated the TV in a second. We said, in loud voice 'what!'. 

I take help of my son and daughters now and then on all kinds of electronic gadgets. Many of them change so fast, we begin to ask 'why' it's needed. The previous set-up was already working fine. Yet, things are changing fast – many of them are powered by AI chips. (more in Artificial Intelligence – the Tool of No Limit).

I had the similar experience in Water Modeling. I have been told quite a few times that – this or that guy knows how to run the Software, but do not understand hydraulics. Indeed, if you teach an 8-year old how to run a modeling software – he or she can do it very fast, without ever understanding 'why' something happens.

There have been quite an interest in AI and Civil Engineering in Collaborate discussions before (at least three of the titles are: AI in Practice Today; AI Applications in Engineering and Construction; Human-AI Team). Many issues came up there – one discussion leading to another. The last thread, quoting 2022 NAP #26355 Human-AI Teaming discussed about HSI in the context of MDO.

In the end, there is an age-related transformational issue – to find the right balance between 'why' and 'what'. Professionals talk about we need some grey-haired here to lead. Then the grey-hairds need some 'what' assistance – that's how the profession of secretaries come into relevance. And all organizational hierarchy works that way.

I have read a Scientific American article recently – titled 'Criminal AI is Here . . .' There comes the question of exponential proliferation of AIPPS and Ethics (also more in AI Essay). Once I saw a caption quite a while ago on a High School billboard saying something like, a course is being offered how to hack a computer . . .. I thought to myself how such a course could be offered to high school kids. What the school board and administration have been thinking? I thought to myself . . . this is another symptom of societal degradation . . . luring our youngs to head in the wrong direction.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Dear Dr. Barua,

Thank you very much for your thoughtful and insightful response. I truly appreciate the depth of your reflections and the vivid examples you shared-they strongly resonate with the key themes I'm exploring in my research.

Your perspective on "generational transformation" rather than a "generation gap" is truly inspiring. Indeed, the shift from asking "what" to asking "why" reflects not only cognitive evolution but also the value of accumulated experience. The story about the smart TV and your young assistant made me smile-it beautifully illustrates the quiet yet powerful transition between generations when it comes to technology.

I am currently studying the cognitive and emotional responses of older workers to automation and AI, and your observations are highly relevant. Your example from hydraulic modeling is exactly the type of real-world tension I aim to examine-balancing deep domain expertise with rapid technological adaptation.

If possible, I would be deeply grateful if you could share your opinion with me directly in the form of a short letter addressed to me-Darya Stanskova, an engineer with over ten years of experience in the energy and construction sectors, currently working as a cost estimator and project manager, and living in the United States. It would mean a great deal to me, as perspectives like yours are essential in understanding the broader value and relevance of this topic.

Please feel free to include your full name, country of residence, current position, and professional background at the end of the letter, ideally in PDF format.

Additionally, I would love to hear your thoughts on lifelong learning. In one of the case studies I'm analyzing, several professionals expressed a desire to retire early after realizing that adapting to new technologies would require continuous learning rather than a one-time effort. I would be interested to know how you view this shift-do you see lifelong learning as a natural part of modern professional life, or as a burden, especially for experienced professionals?

Thank you once again for your meaningful and inspiring words. Your voice adds valuable depth to this important conversation.

Warm regards,
Darya Stanskova
Engineer | Cost Estimator | Project Manager
United States

Darya – your point is well taken. Indeed it does – doesn't it?

People talk about generation-gap, etc, but I like to see it as a generational transformation. A gap indicates a void within – but there's no such void – there's just transformation, a graduation from one stage to the next. It happens without us being aware. As we mature and gather more experience – our brain tends to look more for 'why' rather than 'what'.

A case in point, I had a recent experience of staying in an airbnb suite. Somehow, the previous guest switched the smart TV to a position, I couldn't figure it how to get back to work. The manager came, he couldn't do it, and was thinking of asking a technician to come. Our co-host neighbor came with his 8-year old son, he couldn't do it. The 8-year old took the remote and activated the TV in a second. We said, in loud voice 'what!'. 

I take help of my son and daughters now and then on all kinds of electronic gadgets. Many of them change so fast, we begin to ask 'why' it's needed. The previous set-up was already working fine. Yet, things are changing fast – many of them are powered by AI chips. (more in Artificial Intelligence – the Tool of No Limit).

I had the similar experience in Water Modeling. I have been told quite a few times that – this or that guy knows how to run the Software, but do not understand hydraulics. Indeed, if you teach an 8-year old how to run a modeling software – he or she can do it very fast, without ever understanding 'why' something happens.

There have been quite an interest in AI and Civil Engineering in Collaborate discussions before (at least three of the titles are: AI in Practice Today; AI Applications in Engineering and Construction; Human-AI Team). Many issues came up there – one discussion leading to another. The last thread, quoting 2022 NAP #26355 Human-AI Teaming discussed about HSI in the context of MDO.

In the end, there is an age-related transformational issue – to find the right balance between 'why' and 'what'. Professionals talk about we need some grey-haired here to lead. Then the grey-hairds need some 'what' assistance – that's how the profession of secretaries come into relevance. And all organizational hierarchy works that way.

I have read a Scientific American article recently – titled 'Criminal AI is Here . . .' There comes the question of exponential proliferation of AIPPS and Ethics (also more in AI Essay). Once I saw a caption quite a while ago on a High School billboard saying something like, a course is being offered how to hack a computer . . .. I thought to myself how such a course could be offered to high school kids. What the school board and administration have been thinking? I thought to myself . . . this is another symptom of societal degradation . . . luring our youngs to head in the wrong direction.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Darya:

Thanks for kind words. You're on my contact list, so don't hesitate to ask if you have something on mind.

My professional profile is there on my signature links. If not seen already, they are in: ORCiD; Profile Summary; also in my ASCE profile. I live in Vancouver, Canada.

I will send you a pdf letter as you have asked – perhaps somewhat polished (!) to help your endeavor.

Again, make some time to go through NAP #26355 to learn some fundamentals on human-AI teaming; also some of our Collaborate discussions. It is very important to understand some basics about generational transformation – how best to incorporate capabilities, sensitivities, adaptability, etc. They have many goodies there.

Wishing you all the best.

* * *

Renn:

On your comment, the answer is: perhaps yes, perhaps no.

If the title of the course would have been, how to recognize when your computer is hacked or how to prevent hacking – then your comment is perfectly understandable. But, a course title, how to hack a computer has many reasons to raise eyebrows. Let's not forget that some high school kids harassing, bullying and doing online violence on one another – perhaps even also to public is not something unknown. Is there a connection?

But, you are right in a sense that – in the end, students grasp whatever they are capable of. Some get good out of what are taught, others just don't get it.

* * *

Stephen:

In the image on my AI Essay – I have tried to capture some key characteristics of AI basics: adaptive algorithm, data/information to train and build the machine, and the end-delivery. Further on understanding BNN to create machine ANN, and addressing the most important Ethics questions.

To understand AI basics – perhaps one can begin by asking how does one train a child to learn and to do things. Machines are just children, but an obedient one – programs are designed to let them learn and then perform accordingly. But, then some questions arise, like:

What materials to feed in to let them learn?

How can they be made intelligent to let them answer correctly?

How to adapt answers to different kinds of needs and queries?

How and when should they stop when something unethical or illegal happen?

Anyway here are some links to some sources to keep you busy.

Microsoft AI Fundamentals for Juniors

Microsoft Slide Deck on AI

A McGraw Hill Book

MIT AI Concepts

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Dear Dr. Barua,

Thank you very much for your thoughtful and insightful response. I truly appreciate the depth of your reflections and the vivid examples you shared-they strongly resonate with the key themes I'm exploring in my research.

Your perspective on "generational transformation" rather than a "generation gap" is truly inspiring. Indeed, the shift from asking "what" to asking "why" reflects not only cognitive evolution but also the value of accumulated experience. The story about the smart TV and your young assistant made me smile-it beautifully illustrates the quiet yet powerful transition between generations when it comes to technology.

I am currently studying the cognitive and emotional responses of older workers to automation and AI, and your observations are highly relevant. Your example from hydraulic modeling is exactly the type of real-world tension I aim to examine-balancing deep domain expertise with rapid technological adaptation.

If possible, I would be deeply grateful if you could share your opinion with me directly in the form of a short letter addressed to me-Darya Stanskova, an engineer with over ten years of experience in the energy and construction sectors, currently working as a cost estimator and project manager, and living in the United States. It would mean a great deal to me, as perspectives like yours are essential in understanding the broader value and relevance of this topic.

Please feel free to include your full name, country of residence, current position, and professional background at the end of the letter, ideally in PDF format.

Additionally, I would love to hear your thoughts on lifelong learning. In one of the case studies I'm analyzing, several professionals expressed a desire to retire early after realizing that adapting to new technologies would require continuous learning rather than a one-time effort. I would be interested to know how you view this shift-do you see lifelong learning as a natural part of modern professional life, or as a burden, especially for experienced professionals?

Thank you once again for your meaningful and inspiring words. Your voice adds valuable depth to this important conversation.

Warm regards,
Darya Stanskova
Engineer | Cost Estimator | Project Manager
United States

Darya – your point is well taken. Indeed it does – doesn't it?

People talk about generation-gap, etc, but I like to see it as a generational transformation. A gap indicates a void within – but there's no such void – there's just transformation, a graduation from one stage to the next. It happens without us being aware. As we mature and gather more experience – our brain tends to look more for 'why' rather than 'what'.

A case in point, I had a recent experience of staying in an airbnb suite. Somehow, the previous guest switched the smart TV to a position, I couldn't figure it how to get back to work. The manager came, he couldn't do it, and was thinking of asking a technician to come. Our co-host neighbor came with his 8-year old son, he couldn't do it. The 8-year old took the remote and activated the TV in a second. We said, in loud voice 'what!'. 

I take help of my son and daughters now and then on all kinds of electronic gadgets. Many of them change so fast, we begin to ask 'why' it's needed. The previous set-up was already working fine. Yet, things are changing fast – many of them are powered by AI chips. (more in Artificial Intelligence – the Tool of No Limit).

I had the similar experience in Water Modeling. I have been told quite a few times that – this or that guy knows how to run the Software, but do not understand hydraulics. Indeed, if you teach an 8-year old how to run a modeling software – he or she can do it very fast, without ever understanding 'why' something happens.

There have been quite an interest in AI and Civil Engineering in Collaborate discussions before (at least three of the titles are: AI in Practice Today; AI Applications in Engineering and Construction; Human-AI Team). Many issues came up there – one discussion leading to another. The last thread, quoting 2022 NAP #26355 Human-AI Teaming discussed about HSI in the context of MDO.

In the end, there is an age-related transformational issue – to find the right balance between 'why' and 'what'. Professionals talk about we need some grey-haired here to lead. Then the grey-hairds need some 'what' assistance – that's how the profession of secretaries come into relevance. And all organizational hierarchy works that way.

I have read a Scientific American article recently – titled 'Criminal AI is Here . . .' There comes the question of exponential proliferation of AIPPS and Ethics (also more in AI Essay). Once I saw a caption quite a while ago on a High School billboard saying something like, a course is being offered how to hack a computer . . .. I thought to myself how such a course could be offered to high school kids. What the school board and administration have been thinking? I thought to myself . . . this is another symptom of societal degradation . . . luring our youngs to head in the wrong direction.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

The "hack a computer" title could be interpreted several ways. 

1. Hacking software is the more traditional interpretation of "hacking".  Usually a malicious activity in accessing someone else's private computer or software.  But could also be learning ways to defend your computer or software against malicious hacks or security breaches.
2. To Millennial or Gen Z, it might suggest a class on how to build or optimize computer hardware/software. Like the infamous overclocking hack of computer processing capacity resulting in the need for additional or stronger fans to dissipate the heat inside the computer desktop case. Or upgrading the video card, adding RAM or other hardware upgrades (mainly to run the latest gaming software in its full glory).  My youngest in college ran a monthly class at the Filipino Community Center on how to build a computer from spare parts for $10 (mainly for the older folks who could not afford a computer). While my oldest in middle school built 20+ gaming computers for friends & family from discarded old computers parts from the South Seattle recycling center.  Both shared how to hack certain pieces of computer hardware and the issues of getting the software associated with the new or refurbished hardware to work together reliably.  So hacking a computer in High School may mean upgrading it to a gaming computer. 

As for learning new AI Tools or software, it can be a challenge for older engineers (myself included).  The younger engineers I formally mentored always shared one new cell phone app, software upgrade or AI tool at each lunch.  So I came away with a new skill or knowledge from each mentoring encounter, which I could play with later that day or week. 

But just hearing the AI stories from my "Techbro" sons also helps. One nephew tried to write a software program for his HS Computer class using AI - it did not go well. He spent twice as much time frantically trying to get my sons to help him fix it as it would have taken him to write it himself.  Learning lesson for all involved. 

Cheers from a Lifelong learner,

Jennifer

Darya:

Thanks for kind words. You're on my contact list, so don't hesitate to ask if you have something on mind.

My professional profile is there on my signature links. If not seen already, they are in: ORCiD; Profile Summary; also in my ASCE profile. I live in Vancouver, Canada.

I will send you a pdf letter as you have asked – perhaps somewhat polished (!) to help your endeavor.

Again, make some time to go through NAP #26355 to learn some fundamentals on human-AI teaming; also some of our Collaborate discussions. It is very important to understand some basics about generational transformation – how best to incorporate capabilities, sensitivities, adaptability, etc. They have many goodies there.

Wishing you all the best.

* * *

Renn:

On your comment, the answer is: perhaps yes, perhaps no.

If the title of the course would have been, how to recognize when your computer is hacked or how to prevent hacking – then your comment is perfectly understandable. But, a course title, how to hack a computer has many reasons to raise eyebrows. Let's not forget that some high school kids harassing, bullying and doing online violence on one another – perhaps even also to public is not something unknown. Is there a connection?

But, you are right in a sense that – in the end, students grasp whatever they are capable of. Some get good out of what are taught, others just don't get it.

* * *

Stephen:

In the image on my AI Essay – I have tried to capture some key characteristics of AI basics: adaptive algorithm, data/information to train and build the machine, and the end-delivery. Further on understanding BNN to create machine ANN, and addressing the most important Ethics questions.

To understand AI basics – perhaps one can begin by asking how does one train a child to learn and to do things. Machines are just children, but an obedient one – programs are designed to let them learn and then perform accordingly. But, then some questions arise, like:

What materials to feed in to let them learn?

How can they be made intelligent to let them answer correctly?

How to adapt answers to different kinds of needs and queries?

How and when should they stop when something unethical or illegal happen?

Anyway here are some links to some sources to keep you busy.

Microsoft AI Fundamentals for Juniors

Microsoft Slide Deck on AI

A McGraw Hill Book

MIT AI Concepts

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Dear Dr. Barua,

Thank you very much for your thoughtful and insightful response. I truly appreciate the depth of your reflections and the vivid examples you shared-they strongly resonate with the key themes I'm exploring in my research.

Your perspective on "generational transformation" rather than a "generation gap" is truly inspiring. Indeed, the shift from asking "what" to asking "why" reflects not only cognitive evolution but also the value of accumulated experience. The story about the smart TV and your young assistant made me smile-it beautifully illustrates the quiet yet powerful transition between generations when it comes to technology.

I am currently studying the cognitive and emotional responses of older workers to automation and AI, and your observations are highly relevant. Your example from hydraulic modeling is exactly the type of real-world tension I aim to examine-balancing deep domain expertise with rapid technological adaptation.

If possible, I would be deeply grateful if you could share your opinion with me directly in the form of a short letter addressed to me-Darya Stanskova, an engineer with over ten years of experience in the energy and construction sectors, currently working as a cost estimator and project manager, and living in the United States. It would mean a great deal to me, as perspectives like yours are essential in understanding the broader value and relevance of this topic.

Please feel free to include your full name, country of residence, current position, and professional background at the end of the letter, ideally in PDF format.

Additionally, I would love to hear your thoughts on lifelong learning. In one of the case studies I'm analyzing, several professionals expressed a desire to retire early after realizing that adapting to new technologies would require continuous learning rather than a one-time effort. I would be interested to know how you view this shift-do you see lifelong learning as a natural part of modern professional life, or as a burden, especially for experienced professionals?

Thank you once again for your meaningful and inspiring words. Your voice adds valuable depth to this important conversation.

Warm regards,
Darya Stanskova
Engineer | Cost Estimator | Project Manager
United States

Darya – your point is well taken. Indeed it does – doesn't it?

People talk about generation-gap, etc, but I like to see it as a generational transformation. A gap indicates a void within – but there's no such void – there's just transformation, a graduation from one stage to the next. It happens without us being aware. As we mature and gather more experience – our brain tends to look more for 'why' rather than 'what'.

A case in point, I had a recent experience of staying in an airbnb suite. Somehow, the previous guest switched the smart TV to a position, I couldn't figure it how to get back to work. The manager came, he couldn't do it, and was thinking of asking a technician to come. Our co-host neighbor came with his 8-year old son, he couldn't do it. The 8-year old took the remote and activated the TV in a second. We said, in loud voice 'what!'. 

I take help of my son and daughters now and then on all kinds of electronic gadgets. Many of them change so fast, we begin to ask 'why' it's needed. The previous set-up was already working fine. Yet, things are changing fast – many of them are powered by AI chips. (more in Artificial Intelligence – the Tool of No Limit).

I had the similar experience in Water Modeling. I have been told quite a few times that – this or that guy knows how to run the Software, but do not understand hydraulics. Indeed, if you teach an 8-year old how to run a modeling software – he or she can do it very fast, without ever understanding 'why' something happens.

There have been quite an interest in AI and Civil Engineering in Collaborate discussions before (at least three of the titles are: AI in Practice Today; AI Applications in Engineering and Construction; Human-AI Team). Many issues came up there – one discussion leading to another. The last thread, quoting 2022 NAP #26355 Human-AI Teaming discussed about HSI in the context of MDO.

In the end, there is an age-related transformational issue – to find the right balance between 'why' and 'what'. Professionals talk about we need some grey-haired here to lead. Then the grey-hairds need some 'what' assistance – that's how the profession of secretaries come into relevance. And all organizational hierarchy works that way.

I have read a Scientific American article recently – titled 'Criminal AI is Here . . .' There comes the question of exponential proliferation of AIPPS and Ethics (also more in AI Essay). Once I saw a caption quite a while ago on a High School billboard saying something like, a course is being offered how to hack a computer . . .. I thought to myself how such a course could be offered to high school kids. What the school board and administration have been thinking? I thought to myself . . . this is another symptom of societal degradation . . . luring our youngs to head in the wrong direction.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.

Darya:

Thanks for kind words. You're on my contact list, so don't hesitate to ask if you have something on mind.

My professional profile is there on my signature links. If not seen already, they are in: ORCiD; Profile Summary; also in my ASCE profile. I live in Vancouver, Canada.

I will send you a pdf letter as you have asked – perhaps somewhat polished (!) to help your endeavor.

Again, make some time to go through NAP #26355 to learn some fundamentals on human-AI teaming; also some of our Collaborate discussions. It is very important to understand some basics about generational transformation – how best to incorporate capabilities, sensitivities, adaptability, etc. They have many goodies there.

Wishing you all the best.

* * *

Renn:

On your comment, the answer is: perhaps yes, perhaps no.

If the title of the course would have been, how to recognize when your computer is hacked or how to prevent hacking – then your comment is perfectly understandable. But, a course title, how to hack a computer has many reasons to raise eyebrows. Let's not forget that some high school kids harassing, bullying and doing online violence on one another – perhaps even also to public is not something unknown. Is there a connection?

But, you are right in a sense that – in the end, students grasp whatever they are capable of. Some get good out of what are taught, others just don't get it.

* * *

Stephen:

In the image on my AI Essay – I have tried to capture some key characteristics of AI basics: adaptive algorithm, data/information to train and build the machine, and the end-delivery. Further on understanding BNN to create machine ANN, and addressing the most important Ethics questions.

To understand AI basics – perhaps one can begin by asking how does one train a child to learn and to do things. Machines are just children, but an obedient one – programs are designed to let them learn and then perform accordingly. But, then some questions arise, like:

What materials to feed in to let them learn?

How can they be made intelligent to let them answer correctly?

How to adapt answers to different kinds of needs and queries?

How and when should they stop when something unethical or illegal happen?

Anyway here are some links to some sources to keep you busy.

Microsoft AI Fundamentals for Juniors

Microsoft Slide Deck on AI

A McGraw Hill Book

MIT AI Concepts

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

Dear Dr. Barua,

Thank you very much for your thoughtful and insightful response. I truly appreciate the depth of your reflections and the vivid examples you shared-they strongly resonate with the key themes I'm exploring in my research.

Your perspective on "generational transformation" rather than a "generation gap" is truly inspiring. Indeed, the shift from asking "what" to asking "why" reflects not only cognitive evolution but also the value of accumulated experience. The story about the smart TV and your young assistant made me smile-it beautifully illustrates the quiet yet powerful transition between generations when it comes to technology.

I am currently studying the cognitive and emotional responses of older workers to automation and AI, and your observations are highly relevant. Your example from hydraulic modeling is exactly the type of real-world tension I aim to examine-balancing deep domain expertise with rapid technological adaptation.

If possible, I would be deeply grateful if you could share your opinion with me directly in the form of a short letter addressed to me-Darya Stanskova, an engineer with over ten years of experience in the energy and construction sectors, currently working as a cost estimator and project manager, and living in the United States. It would mean a great deal to me, as perspectives like yours are essential in understanding the broader value and relevance of this topic.

Please feel free to include your full name, country of residence, current position, and professional background at the end of the letter, ideally in PDF format.

Additionally, I would love to hear your thoughts on lifelong learning. In one of the case studies I'm analyzing, several professionals expressed a desire to retire early after realizing that adapting to new technologies would require continuous learning rather than a one-time effort. I would be interested to know how you view this shift-do you see lifelong learning as a natural part of modern professional life, or as a burden, especially for experienced professionals?

Thank you once again for your meaningful and inspiring words. Your voice adds valuable depth to this important conversation.

Warm regards,
Darya Stanskova
Engineer | Cost Estimator | Project Manager
United States

Darya – your point is well taken. Indeed it does – doesn't it?

People talk about generation-gap, etc, but I like to see it as a generational transformation. A gap indicates a void within – but there's no such void – there's just transformation, a graduation from one stage to the next. It happens without us being aware. As we mature and gather more experience – our brain tends to look more for 'why' rather than 'what'.

A case in point, I had a recent experience of staying in an airbnb suite. Somehow, the previous guest switched the smart TV to a position, I couldn't figure it how to get back to work. The manager came, he couldn't do it, and was thinking of asking a technician to come. Our co-host neighbor came with his 8-year old son, he couldn't do it. The 8-year old took the remote and activated the TV in a second. We said, in loud voice 'what!'. 

I take help of my son and daughters now and then on all kinds of electronic gadgets. Many of them change so fast, we begin to ask 'why' it's needed. The previous set-up was already working fine. Yet, things are changing fast – many of them are powered by AI chips. (more in Artificial Intelligence – the Tool of No Limit).

I had the similar experience in Water Modeling. I have been told quite a few times that – this or that guy knows how to run the Software, but do not understand hydraulics. Indeed, if you teach an 8-year old how to run a modeling software – he or she can do it very fast, without ever understanding 'why' something happens.

There have been quite an interest in AI and Civil Engineering in Collaborate discussions before (at least three of the titles are: AI in Practice Today; AI Applications in Engineering and Construction; Human-AI Team). Many issues came up there – one discussion leading to another. The last thread, quoting 2022 NAP #26355 Human-AI Teaming discussed about HSI in the context of MDO.

In the end, there is an age-related transformational issue – to find the right balance between 'why' and 'what'. Professionals talk about we need some grey-haired here to lead. Then the grey-hairds need some 'what' assistance – that's how the profession of secretaries come into relevance. And all organizational hierarchy works that way.

I have read a Scientific American article recently – titled 'Criminal AI is Here . . .' There comes the question of exponential proliferation of AIPPS and Ethics (also more in AI Essay). Once I saw a caption quite a while ago on a High School billboard saying something like, a course is being offered how to hack a computer . . .. I thought to myself how such a course could be offered to high school kids. What the school board and administration have been thinking? I thought to myself . . . this is another symptom of societal degradation . . . luring our youngs to head in the wrong direction.

Dilip

---------

Dr. Dilip K Barua, Ph.D

Website Links and Profile

The integration of Artificial Intelligence (AI) technologies is rapidly transforming the industrial processes in the energy and construction sectors. As industries increasingly utilize AI for asset management, planning, and operational optimization, adapting the workforce to these technologies has become a significant challenge. According to IBM's report, more than 75% of industrial enterprises have implemented AI in key operational areas. Similar findings by Deloitte highlight that energy and construction sectors are among the leading adopters of AI technologies, driven by the need for greater efficiency and automation. However, the age composition of the workforce significantly affects the success of such transformations. Failure to consider the psychological and professional characteristics of older employees may lead to resistance and reduced effectiveness.

Understanding how older employees perceive and adopt Artificial Intelligence (AI) technologies requires consideration of established models of technology acceptance and user behavior.

One foundational model is the Technology Acceptance Model (TAM), which posits that technology adoption depends on two key factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Among older workers, PEOU often declines because of their reduced familiarity with emerging technologies. This has increased the need for targeted training, simplified interfaces, and supportive onboarding.

Relevance to Older Employees and AI Adoption.

Older employees (typically aged 45 and above) tended to fall into the Late Majority (40%) and laggard (20%) categories. This means that 60% of aging employees are hesitant or slow to adopt AI technology. Their resistance often stems from the following factors:

– Lack of familiarity with digital tools compared with younger colleagues.

- Fear of AI replacing their jobs, leading to uncertainty and reluctance.

- Higher learning barriers that, require customized training and mentoring.

This distribution highlights why specialized AI training programs for older employees are essential: they help build confidence, reduce resistance, and ensure smooth workforce adaptation.

Successful AI integration in industrial settings requires:

- Transparent communication about AI's role and benefits of AI.

- Personalized training approaches that address the needs of older workers.

- Inclusive adaptation strategies involving staff throughout the process.

By supporting older employees and fostering trust in AI, industries can ensure smoother and more effective digital transformation, benefiting both the workforce and the organization as a whole.