How can the interoperability between AI and blockchain technology support a new era of industrial and digital (r)evolution

Written by Antonio Lanotte Chartered Tax Adviser/Senior Auditor/International Tax Member/Delegate Tax Technology Committee - CFE Bruxelles

Artificial intelligence (AI) and blockchain are two of the most transformative technologies of the 21st century. While AI focuses on simulating human intelligence through machines, blockchain provides a decentralised and secure way to record transactions and data. AI encompasses machine learning, natural language processing, computer vision and other techniques that enable machines to perform tasks that typically require human intelligence, while blockchain technology is a decentralised ledger technology that ensures secure, transparent and tamper-proof recording of transactions across a distributed network. In a

context of fourth industrial revolution, marked by the fusion of physical and digital technologies and so leading to smart and autonomous industrial operations, the integration of AI and blockchain spans various domains, including:

· enhancing production processes through predictive analytics, real-time monitoring and autonomous decision-making (smart manufacturing).

· improving transparency, traceability and efficiency in supply chains by utilising decentralised ledgers and AI-driven analytics (supply chain management).

· leveraging AI algorithms to predict equipment failures and schedule maintenance, combined with blockchain for secure data logging (predictive maintenance).

· ensuring product quality through automated inspections and blockchain-based certification (Quality Control).

· optimising energy consumption and distribution with AI, while using blockchain for secure and transparent energy transactions (energy management).

· AI could accelerate the transition to a large-scale circular economy.

The digital transformation could boost the energy and green transition: there is no innovation without sustainability (also see “From Artificial to Circular Intelligence: The Role of Generative AI”). In this light, the most powerful tool to combat climate change is undoubtedly carbon pricing and the use of advanced technologies like blockchain and artificial intelligence (AI) to shape circular business models that are virtuous and more sustainable (see also “A Transformative Vision for Europe: The Energy Transition and Digital Transformation”). However, despite the promising applications, several challenges hinder the adoption of AI-powered blockchain technologies in terms of scalability and performance - e.g. such as ensuring that blockchain networks can handle large volumes of transactions efficiently, and interoperability and standardisation, such as developing common standards, to enable seamless integration between different systems and platforms. In addition to this, privacy and security play a pivot role in terms of balancing the need for data privacy with the transparency inherent in blockchain, not to mention the evolving regulatory landscape to ensure compliance and foster innovation. Last but not least, the shortage of professionals skilled in both AI and blockchain technologies. Nevertheless, to overcome these challenges and harness the full potential of AI-powered blockchain technology, several opportunities must be explored, such as developing novel approaches to address scalability, interoperability and security issues. Encouraging collaboration between industry, academia and government to drive research and development; and aligning technology adoption with global sustainability objectives to ensure long-term benefits.

The technology behind the (r)evolution

Artificial intelligence (AI) refers to the development of computer systems that can perform tasks typically requiring human intelligence, such as visual perception, speech recognition, decision-making and language translation including:

· machine learning (ML) - a subset of AI that involves training algorithms to learn from and make predictions based on data.

· deep learning (DL) - a specialised form of ML that uses neural networks with many layers to analyse complex patterns in large datasets.

· natural language processing (NLP) - the capability of a computer program to understand, interpret and generate human language.

These subfields enable machines to learn from data, identify patterns and make predictions or decisions with minimal human intervention. This ability to process and analyse large volumes of data quickly and accurately is at the heart of AI's transformative potential in various applications and industries. On the other hand, blockchain is a distributed ledger technology that enables the secure, transparent and tamper-proof recording of transactions across a network of computers. This technology consists of a chain of blocks, each containing a set of transactions that are cryptographically linked to the previous block. This structure forms an immutable and auditable record of all transactions. Blockchain operates on a decentralised network which eliminates the need for intermediaries and central authorities, ensuring that no single entity has control over the entire network (decentralisation); the cryptographic linking of blocks ensures that once a block is added to the chain, it cannot be altered without changing all subsequent blocks, making the data tamper-proof (security); all participants in the blockchain network have access to the same version of the ledger, providing full transparency of transactions (transparency); and also, the decentralised nature and cryptographic security of blockchain ensure the integrity of the stored data, as it is resistant to fraud and unauthorised modifications (integrity).

Decentralised autonomous organisations (DAOs) decentralised identity and access management (DIAM)

One of the key opportunities enabled by AI-powered blockchain technology is the emergence of decentralised autonomous organisations (DAOs). DAOs are self-governing entities that operate based on a set of predefined rules encoded in smart contracts. These smart contracts automate decision-making processes through AI algorithms. In the context of the fourth industrial revolution (Industry 4.0), DAOs facilitate the creation of decentralised and collaborative networks. The main features of a DAOs are:

· eliminating the need for a central authority, DAOs enable more democratic and distributed control over organisational processes (decentralisation).

· AI algorithms automate decision-making, increasing efficiency and reducing the potential for human error (automation).

· blockchain ensures that all actions and decisions are recorded in a transparent and immutable ledger (transparency).

· DAOs allow for seamless collaboration among diverse stakeholders, promoting innovation and efficiency in industrial operations (collaboration).

Decentralised identity and access management (DIAM)

Decentralised identity and access management (DIAM) represent a transformative approach to managing digital identities and access controls in the era of Industry 4.0. By combining the strengths of AI and blockchain, DIAM systems can provide secure, efficient and user-centric solutions that enhance privacy, security and interoperability in industrial and business environments. As these technologies continue to evolve, they hold the promise of addressing some of the most pressing challenges in identity and access management. DIAM is a critical application of AI-powered blockchain technology in the context of Industry 4.0 - DIAM systems leverage the decentralised and secure nature of blockchain along with the intelligent automation capabilities of AI to manage digital identities and control access to resources efficiently and securely. Individuals and organisations can control their digital identities without relying on a central authority. This ensures privacy and reduces the risk of identity theft; identities are verified and recorded on a blockchain whereby providing a tamper-proof and transparent ledger of identity transactions. This eliminates the need for intermediaries and enhances trust; and lastly AI algorithms analyse patterns and behaviours to make real-time decisions about access permissions. This dynamic approach ensures that access controls adapt to evolving security threats and organisational policies. In fact, in smart manufacturing environments, DIAM can ensure that only authorised personnel and machines have access to critical systems and data, enhancing security and operational efficiency by verifying the identities of suppliers, partners and products throughout the supply chain, and so ensuring transparency and reducing the risk of fraud.

Quantum-resistant blockchain and AI: securing the future

As quantum computing progresses, it poses a potential threat to current cryptographic systems that underlie blockchain and AI technologies. Quantum-resistant blockchain and AI aim to safeguard these systems against the computational power of quantum computers, ensuring the longevity and security of digital infrastructures. Quantum computers can solve complex mathematical problems much faster than classical computers. This capability threatens conventional cryptographic algorithms like RSA and ECC, which secure current blockchain systems. Quantum-resistant, or post-quantum, cryptography is designed to withstand quantum attacks; quantum-resistant blockchain and AI are essential to secure the future of digital infrastructures. By adopting post-quantum cryptographic methods and developing robust AI models, we can safeguard critical systems against the impending quantum threat. As these technologies evolve, continuous research, development and collaboration among stakeholders are crucial to ensure a secure transition to a quantum-resistant future.

The convergence of AI-powered blockchain technology in Industry 4.0 presents numerous opportunities and challenges that require concerted efforts from multiple stakeholders. Researchers, industry practitioners, policymakers and other relevant parties must work together across disciplinary, organisational and sectoral boundaries to advance the state- of-the-art, develop practical solutions and create a conducive environment for technology adoption and governance. By fostering interdisciplinary research collaboration, industry-academia partnerships and multi-stakeholder collaboration and ecosystem building, stakeholders can drive the responsible and transformative implementation of AI-powered blockchain technology in Industry 4.0. This collaborative approach not only enhances technological development but also contributes to broader goals of sustainable development and societal well-being. In summary, multi-stakeholder collaboration and ecosystem building are vital for addressing the challenges and harnessing the opportunities presented by AI-powered blockchain technology. Through collective efforts, stakeholders can ensure the responsible, secure and innovative advancement of these technologies in the context of the fourth industrial revolution.