Will AI Replace Engineers? – AI Implementation for Engineers
Artificial intelligence (AI) has changed the world forever. November 30th, 2022, marked the day when the ChatGPT tool was released to the world.
Introduction: AI Adoption
The release of the free tool and following tech adoption changed the world forever with the democratisation of artificial intelligence. Since the launch of this now widely used AI program, we have seen AI implementation across all industries, even creative disciplines like music and art.
But how can AI systems and deep learning revolutionise not only the business world, but the technical field of engineering?
In this article, we will explore how AI technologies are democratising and transforming the engineering industry, and how AI implementation is changing the way engineers work.
What is Artificial Intelligence (AI)?
Firstly, let's define what AI is. Artificial intelligence refers to the ability of machines to learn from data (machine learning), recognise patterns, and make decisions based on that data (i.e, fuel sloshing noise prediction to improve vehicle acoustics).
This data can come from the past (but remains unused), the present (collected during current test campaigns), and the future (through high-quality data that will be collected as new revolutionary products emerge).
All of this data can help engineers to build smarter test plans, validate test data quickly and avoid costly and time-consuming test runs, understand and predict component failure, and even calibrate highly non-linear systems to meet performance and regulatory requirements.
Essentially, artificial intelligence allows engineers to complete tasks that are often difficult to accomplish, in an accurate and fast time frame.
AI system-generated self-learning models can be viewed as the key to unlocking engineering potential. They are strong enablers for AI adoption and actual implementation, allowing engineers to capture and structure historical business data.
Moreover, new data can improve the self-learning model continuously by systematically recording the design characteristics, test conditions, and test results.
Find out more about self-learning models in this blog.
Top: The traditional, old workflow for a complex problem, based on empirical, known equations and physical models. Bottom: New workflow for an intractable (non-linear) physics problem that cannot be solved easily using the classical physics-based approach. Modelled and calibrated using Monolith’s self-learning models.
Will AI Replace Engineers?
With the above examples just mentioned, and the rapid movement of AI advancement, it is clear why some organisations and customers may be fearful about the future of engineering, and concerned about being replaced in their job roles.
If AI is able to complete these difficult tasks accurately and quicker, what is to stop AI from replacing engineers completely?
However, the opposite is true.
Traditionally, the examples mentioned above are either costly, timely, or difficult to predict. Through AI implementations and solutions, it is possible for engineers to harness this power, and automate these tasks, saving operational costs, time, and resources that help meet the organization's goals faster, ensure the business will stay competitive, and ultimately free up engineers to focus on more important tasks.
For example, around 50 years ago, technical drawings required the skills of draughtsmen to create them. Today, this task has been almost completely digitised and automated.
How Can Engineers Benefit from AI Technology & AI Implementation?
The widespread use and adoption of AI haven't made the need for engineers redundant, but has instead led to engineers being able to allocate their valuable time, resources, and focus on other tasks while using AI to solve or achieve the more tedious aspects of respective workflows, thus allowing engineers to become more efficient.
The future of engineering will be shaped by engineers who adapt and implement AI algorithms and AI technology into their workflows, and harness the power of the technology available.
There are many AI solutions being used by visionary engineers, from smart meter performance to improving vehicle acoustics, with the role of engineers being transformed.
The Ever-Changing Role of Engineers
By removing the number of tedious tasks engineers need to complete in their daily lives with AI solutions, engineers can spend more time on high-level tasks which require critical thinking, engineering experience, and engineering expertise. Not only this, but the role of engineers is becoming more fulfilling for those who implement AI and experiment with the different ways AI can benefit their specific workflows.
How Engineers Who Implement AI Can Assess Success
Engineers implementing AI are focusing on what matters most, whilst having trust in AI to help them achieve this. This results in more efficient business processes, as AI-based solutions provide continue to learn and optimise.
Additionally, AI can also help engineers to optimise designs more quickly and accurately than they could by hand, and sometimes even physics-based models that are based on complex assumptions and the engineers' expertise. Data collected from the real world does not lie, and extracting knowledge from it can save engineers a lot of time and stress. Engineers are able to extract more value from all the data they have built up at the same time, with this data being aligned with business needs.
How AI Provides Better Access To Information
Another important point to note is that AI implementation can help engineers by providing them and customers with better access to information. AI models can be trained by engineers to provide engineers with valuable insights in a manner of seconds.
Ultimately, this allows engineers to spend less time testing and more time learning from the data available, creating AI solutions to solve intractable physics problems.
AI models can even be trained to test for regulatory requirements, in order to ensure that their work meets industry standards in a matter of seconds once the AI model has been trained.
Engineers who are adopting AI are developing better quality products in half the time, all whilst having better access to information. This allows engineers to use their expertise and experience to make better decisions and create more efficient business processes.
How Engineers Can Adapt & Thrive by Implementing AI
The truth is that AI won't replace engineers. Engineers who implement AI will replace and advance beyond other engineers instead. Engineers will need to adapt by changing the way they work, implementing AI into their workflow, and using their engineering expertise on different tasks.
ChatGPT has shown that AI tools can be the perfect solution for many organisations in a huge range of industries. In the future engineers will remain the domain experts and know their products better than anyone else.
So why build a solution from scratch when a tailored Monolith already exists where more than 20 people in the R&D department work on a day-to-day basis to facilitate the work of engineers working in the future?
Using test data, engineers can quickly adopt Monolith to build highly accurate self-learning AI models that instantly predict the performance of systems in a wider variety of operating conditions.
Monolith AI software allows you and your team of engineers to spend less time testing and more time learning.
Monolith In Two Minutes 👇
How AI Can Improve Business Processes
Businesses that implement AI will see the productivity of their product development process massively increase. AI models allow engineers to go through fewer design & simulation iterations.
At the same time, businesses can empower their engineers by creating effective test plans informed by historical data. This increases the value of data that businesses are using in the product development process.
Through Monolith's no-code AI software, businesses can make their processes more efficient and less costly.
Conclusion: AI Implementation for Engineers
AI won't replace you, but engineers who implement AI will! AI will usher in the “golden age” when engineers will be able to focus on the "fun things", and AI software programs and tools will support them.
Explore three ways to identify good AI use cases in engineering, and take the first step to democratise AI for your team of engineers!