The Impact of Generative AI on Digital Twins

At first, those technologies don’t seem to have much in common, but the truth is that the integration of generative AI (gen AI) and digital twins can produce excellent results! And it’s already happening in several industries where such a combination allows companies to reduce production costs, optimize production performance, and accelerate deployment. In this post, we’ll show you how these two technologies can work together to create synergy, taking your production to a new level.

In a recent study conducted by McKinsey [1], almost 75% of surveyed companies have already adopted digital-twin technologies. At the same time, gen AI is also getting a lot of traction. In 2023, about 30% of companies reported that they were using this technology. In 2024, this number doubled and is now at 65%, showing a much more dynamic adoption compared to standard AI solutions.

This is not surprising as gen AI offers flexibility and usefulness at a fraction of the cost compared to “normal” AI solutions [2]. No wonder many large corporations are interested in how these technologies can revolutionize the way they work.

However, there is one potential pitfall here, as some companies develop or work on both technologies separately. From the business perspective, that’s a missed opportunity as putting them together can give you a tremendous synergy effect leading to much better results. Let’s see how digital twins and generative AI can support one another.

Discover the potential of digital twins and generative AI

Digital twins are basically virtual replicas of objects (such as machines, devices, or equipment), systems, or processes (usually via IoT sensors that gather all the required information that can be next replicated in the virtual setting) [3]. They are used to simulate and analyze the performance of their real-world counterparts without the risk of damaging them. The use of digital twins allows companies to improve their operations, spot and fix potential weak spots, improve the decision-making process, and even introduce more effective predictive maintenance.

On the other hand, generative AI is a large subset of artificial intelligence that’s all about creating new content and solutions (including digital twin models) based on available data. So, for instance, you can create the codebase for your digital twin using generative AI, thus accelerating the development process and shortening it from several months to several weeks.

The integration of those two technologies enables you to:

• Create more precise digital twin simulations (you can use gen AI to design more in-depth simulation scenarios)
• Improve predictive analytics and maintenance (gen AI can point to the elements that require additional attention)
  And even come up with new applications and solutions you haven’t considered in the past

The synergy effect thanks to Generative AI and Digital Twins

The integration of generative AI and digital twins is more than just a technological novelty. Putting these two technologies together can really improve the results you’re getting in at least three areas:

Data collection

Both technologies thrive when it comes to gathering and analyzing large-volume, real-time data. As a result, you can use gen AI to gather and organize data from various sources (e.g., maintenance logs, product images, or even videos of the assembly process). In the next step, your digital twin can analyze this data to identify patterns or anomalies. This way, you can streamline both decision-making and predictive-maintenance processes.

Better simulation scenarios

As we mentioned, earlier, because both technologies rely on large amounts of data, they can be used to design more in-depth simulation scenarios that are tailored to available datasets. You can use generative AI tools to generate ready-made scenarios for your digital twin(s), thus effectively allowing them to communicate with digital twins using natural language (in simple terms, you can ask your digital twin questions using plain English, and it will respond with answers written the same way). This way, the gen AI-digital twin tandem becomes far more accessible to less tech-savvy users.

Predictive modeling

Lastly, generative AI enhances digital twin capabilities by using real-time data from digital twins to provide more dynamic inputs. This allows digital twins to offer a secure environment where generative AI can learn and expand its prompts and outputs. By running “what if” simulations, digital twins help fine-tune generative AI, enabling it to conduct predictive modeling.

Generative AI and digital twins use cases

Even today, the combination of digital twins and generative AI offers a wide range of applications across many industries. Let’s have a look at some examples:

Smart cities

Smart cities is the first interesting application of gen AI and digital twins. This is already happening, and Singapore and Shanghai are the most famous examples of this trend [4]. Smart cities can use both technologies to simulate and create more accurate models of urban planning and development. As a result, they can save a lot of time and energy on aspects of urban planning such as traffic management or public transportation. For example, this solution can be used to optimize or introduce new bus routes that are tailored to how people move around the city.

Healthcare 

Digital twins can even be used to simulate the human body of a given patient, e.g., to see how it would respond to a specific treatment without exposing the patient to the risk of the actual treatment. Here, the role of generative AI is to gather and analyze all available patient data to provide treatment options that are fully tailored to a given patient. Another example is based on creating a digital twin of selected cells or tissues (e.g., of a tumor) to test new treatment modalities and different therapeutic approaches [5].

Manufacturing 

This is probably the most common example of how digital twins and generative AI can work together. More and more factories and production plants are using digital twins to simulate different production scenarios and identify potential improvements or weak spots that need to be fixed. This is where generative AI steps in the game, as it can analyze available simulations and suggest changes to production schedules or processes, including equipment configurations and maintenance procedures. This approach offers valuable results, such as:

• Streamlined operations
• Reduced downtime
• Improved products

Read more: Generative AI in Manufacturing. How to do Predictive Maintenance

Energy 

Digital twins and generative AI also come together perfectly in the energy sector. Such companies (including electricity and oil & gas) can create digital twins of their infrastructure (including power plants, grids, and renewable energy sources), to simulate and analyze their performance under different conditions. The role of generative AI is to analyze available data to optimize the results, e.g., by proposing new strategies for energy distribution, supply chain optimization, and resource allocation. Again, this is something that’s already happening; several large oil & gas companies are using the combination of digital twins and artificial intelligence to streamline their operations.

Final thoughts on Generative AI and digital twins

The tandem of generative AI and digital twins can be a true game-changer in your company. These technologies allow us to do things that were previously unimaginable, and since both technologies are still rapidly developing and evolving, we can expect to see many more fascinating applications in the near future.

What’s impressive about this solution is that it can be easily implemented in many different sectors, from smart cities, through healthcare, up to manufacturing. There is no doubt that more and more companies will be interested in introducing both of those technologies to their operations. The end result? New levels of efficiency, sustainability, and end-product quality.

References

[1] McKinsey. Digital Twins: The Key to Smart Product Development. URL: https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/digital-twins-the-key-to-smart-product-development, accessed on July 8, 2024
[2] McKinsey. The State of AI 2024. URL: https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai, accessed on July 8, 2024
[3] IBM. What is a Digital Twin? URL: https://www.ibm.com/topics/what-is-a-digital-twin, accessed on July 8, 2024.
[4] Palamir.com. 5 Real-World Examples of Digital Twins. URL: https://www.palamir.com/news/5-real-world-examples-of-digital-twins, accessed on July 9, 2024.
[5] Forbes.com. Digital Twin Technology Has The Potential To Radically Disrupt Healthcare. URL:  https://www.forbes.com/sites/saibala/2023/12/22/digital-twin-technology-has-the-potential-to-radically-disrupt-healthcare/, accessed on July 9,2024.
[6] Future Oil & Gas. Digital Twins in Oil & Gas. URL: https://www.futureoilgas.com/news/digital-twins-oil-and-gas, accessed on July 9, 2024.