How to address the challenges of AI-driven digital twins

Digital twins for sustainable business model innovation

Sustainable business model innovation involves new ways to conduct business that generate profit and have a positive impact on the environment, society and stakeholders. The model focuses on innovative strategies that ensure long-term viability while minimizing negative effects on the planet and society.

Digital twins, enhanced by artificial intelligence, allow companies to test and optimize manufacturing processes in virtual environments before implementing them in the real world. While AI is energy-intensive in its own right, companies are working globally to innovate sustainable options for data centers.

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Organizations can use AI-driven digital twin technology to model and then implement plans to reduce CO₂ emissions, improve resource efficiency and optimize their environmental footprint.

These digital twins can also help companies minimize energy usage on production lines and in other industrial contexts. Predictive maintenance and asset management digital twins reduce waste and downtime and extend asset life -- all beneficial from a sustainability perspective. Modeling of circularity of materials, product designs and the wider effects of infrastructure projects allow companies to identify and select optimal solutions.

Digital twins are also helping organizations adhere to environmental regulations by providing precise data on emissions and resource usage. These applications aid real-time environmental monitoring and compliance reporting.

8 common barriers and how to solve them

Digital twins can have a positive effect, but problems might stop organizations and their CIOs from getting fully on board with the tech. This is especially the case in well-established sectors or companies that are learning to transform to more sustainable business models.

The tech industry must resolve barriers to digital twin adoption. This could accelerate use, driving a steeper growth curve, and maximize the potential of AI-driven digital twins in the fight against climate change.

These challenges can include the following.

1. Not knowing where to start

The multitude of use case possibilities can overwhelm teams, leading to indecision and inertia.

One way to address this barrier is to pick a small project to begin with, supported by a progressive roadmap over time. Deciding and getting started are the most important actions. These actions break analysis paralysis, create momentum and reduce opportunity cost. An Agile approach to development can give companies scope to adapt and pivot as new findings emerge.

2. Inadequate data

For many organizations, poor quality data is a persistent issue with tech implementation.

Have a clearly defined and targeted project, at least initially, can help. Organizations can work to improve data, close any gaps and avoid scope creep. Take the time to develop a strategy to address data issues. Moreover, ensure that the team has the capability and capacity to shape and deliver that strategy.

3. Legacy systems

The integration of legacy systems with digital twins can be expensive and suboptimal.

To address this barrier, assess the ROI and limitations of working with an existing system versus replacing it. If your legacy system is getting in the way of progress, the sooner you replace it, the better. Keeping the legacy system could lead to diminishing returns.

4. Vendor selection

AI-driven digital twin providers tend to have specific areas of focus, and the vendor options are changing as the market grows. It can be challenging to assess how well a new vendor will perform, especially in the still relatively early stages of developments in the technology.

To solve this, clearly define the criteria for project success. Consider factors such as expertise, track record, scalability and support services. Start by commissioning a proof of concept or pilot before you commit to the full project. Use first-hand client references to inform decision-making.

5. Data legalities

Data legalities can slow things down substantially at the contracting stages of a digital twin project.

Consult legal experts specializing in data protection and intellectual property law to assess potential risks and develop appropriate safeguards for all parties. Set up comprehensive data governance policies and procedures to ensure compliance with relevant regulations and standards. Prioritize transparency and obtain informed consent from data subjects regarding the collection, processing and use of their data for digital twin applications.

6. Present bias

Present bias is the human tendency to value short-term gain over greater long-term benefits.

To overcome this, construct the project and investment case so that the company can see some quick wins from the early stages, even from the proof of concept. Demonstrating early on what the digital twin can achieve in the future will help generate support and increase momentum.

7. The fear of missing out

The rapid pace of technological innovation often leads to the fear of missing out on using new tech. As a result, organizations might feel pressured to adopt digital twins without fully understanding their relevance or potential effects. Company leaders might also feel concerned that a new, better iteration will be available in the future, so they wait to decide indefinitely or make no decision at all.

To counter this problem, stay informed about emerging technologies through training programs, industry conferences and knowledge-sharing initiatives. Collaborate with technology partners, research institutions and industry experts to gain insights into trends and best practices.

8. Cybersecurity questions

Addressing cybersecurity concerns is key to ensure the confidentiality, integrity, and availability of sensitive data and critical systems.

Create a cybersecurity strategy as an integral part of your project from the outset. Conduct regular, comprehensive threat assessments to identify potential cybersecurity risks and vulnerabilities associated with AI-driven digital twin implementation. Encrypt data both in transit and at rest, enforce data access controls and regularly audit data handling practices to ensure compliance with relevant regulations and standards.

Furthermore, comprehensive cybersecurity training and awareness programs to educate employees about potential threats and best practices for mitigating risks can address this issue.

Another challenge to address is assessing and managing cybersecurity risks associated with third-party vendors and partners involved in the development, deployment and maintenance of AI-driven digital twin tools. Conduct due diligence assessments, establish contractual obligations regarding cybersecurity and regularly review third-party security practices to ensure alignment with organizational standards.

While the barriers to commissioning and implementing AI-driven digital twins are multifaceted, the tech presents new opportunities for sustainable innovation and growth.

With a proactive and strategic approach, organizations can navigate these obstacles. The result is using the transformative potential of AI-driven digital twins to drive operational efficiency and enhance decision-making capabilities, while delivering tangible, sustainable value to stakeholders.

About the author

Jo North, Ph.D., CEO of business consulting firm The Big Bang Partnership, is a university lecturer and expert on sustainable innovation within technical industries. She is the author of Leading Sustainable Innovation: A Roadmap for Technical Environments, available from Kogan Page in Fall 2024. More information about the book is available from Kogan Page.