AI is boosting blockchain’s evolution
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Over the past few years, blockchain has gradually become mainstream as more businesses have adopted it for various use cases in sectors such as government, transport and logistics, finance and healthcare.
Across these sectors it has been underpinning trust and integrity of various systems – which feature a lot of personal and financial information – as well as offering a tool to track goods.
According to Gartner’s hype cycle, some elements of blockchain are slowly wading out of the “trough of disillusionment” as it becomes more firmly entrenched in different businesses and starts crawling up the “slope of enlightenment.”
Blockchain’s journey is now set to receive a boost from a technology that has hit the headlines and fueled myriad discussions across the globe over the past 24 months: artificial intelligence (AI).
Just as with other technologies and approaches, AI is undeniably becoming a prerequisite in ICT setups, either for integration to enhance existing technologies or as part of existing ICT infrastructure that helps improve processes, drive service innovation and bring greater operational insights.
Convergence and synergies
Interestingly, there is a growing synergy between blockchain and AI.
While certain use cases of blockchain are getting enhanced by AI, various AI applications are also leveraging aspects of blockchain.
Each has unique strengths: AI excels at identifying patterns and making predictions, while blockchain offers transparency, security and decentralized transactions.
Some examples of this synergy include the following:
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AI can help with shortcomings in blockchain like the need to analyze huge amounts of data to uncover insights and predict patterns that help in decision making.
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Inherent in AI are challenges like data security and bias which can be addressed through blockchain by ensuring data integrity – for example for training AI models using immutable data to remove bias and ensure transparency and which in turn assures reliable insights and predictions.
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The data integrity offered by blockchain also allows audits to be undertaken along a decision-making process which are crucial in sectors like healthcare and finance.
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In instances where data sharing is needed across organizations (e.g. for credit ratings) and where such sharing needs to be anonymized, blockchain can ensure this as well.
Overall, this synergy offers great scope for further innovation and improvements to existing application of either technology, and which enhancements would certainly deliver increased efficiency, greater security and improved decision-making processes.
Innovation opportunities
Some of the areas where AI is already being deployed to enhance existing blockchain use cases include the automation of smart contracts for applications in real estate, insurance, finance and supply chains.
Aside from helping speed up processes, AI can also help where contracts include variables (e.g. the cost of fuel) and need to be adaptive by ensuring the correct and current data is applied in contracts.
AI can also enhance secure data sharing in various blockchain applications in areas like supply chains (between producers, agribusinesses, logistics companies and manufacturers), healthcare (between an ecosystem of service providers and suppliers) and the financial services sector (between credit rating agencies, merchants, banks, fintechs, telcos and insurers).
In the finance space, decentralized apps (dApps) are becoming more mainstream, with various players needing collaboration to innovate on services and share information.
dApps feature shared data and back-ends that allow different entities to share and process information and remove the need for third party intermediaries, which also helps speed up processes and reduce costs.
As cybersecurity remains a constant but evolving issue, the integrity offered by blockchain is now even more assured by using AI for advanced security to manage risks and help detect threats early.
AI algorithms can be deployed to help identify patterns within blockchain data and render analyses that help identify weaknesses and potential threats in systems.
Asset tokenization is another area that is gradually emerging where various real-world assets (RWAs) are converted into digital tokens.
These include finance (e.g. treasury bills, equities, fiat money), intellectual property, art, real estate and precious commodities which can now be tokenized to denote clear ownership.
Asset tokenization ensures transparent transactions and tamper-proof records that guarantee ownership of these assets.
Hurdles and challenges
The individual and shared challenges that affect the adoption of both AI and blockchain now need to be examined together in a new light to pave the way for further innovation and usage.
The adoption of AI in Africa is currently encumbered by some issues which are peculiar to AI only, such as compute power and data availability, while some of the shared challenges include skills, regulation, data governance, data protection, electricity availability and network coverage.
Outside of these challenges, and where the nature of deployments and usage is regional, more attention will need to be paid to harmonizing policies between countries for elements like cross-border payments and contracts.
Currently continent level policies and guidelines on AI are gradually emanating from individual countries, regional economic communities (RECs) and the Africa Union (AU) which should help provide a shared roadmap for all countries.
However, there is a long way to go to consolidate approaches to both these technologies and align policies even more.
.jpg?width=1280&auto=webp&quality=80&disable=upscale "3D rendering of Bitcoin digital pathways.")
Some blockchain use cases are being enhanced by AI, while AI applications are also leveraging aspects of blockchain. (Source: Freepik – AI-generated)
There may also be a need to devise sector-specific approaches that recognize the issues that lie between AI and blockchain, and more so for sectors like healthcare and finance where huge amounts of personal and financial information reside.
Outlook
As these technologies continue to evolve, it can be expected that even more technologies will start to converge at the intersection of AI and blockchain, and these include:
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The Internet of Things (IoT) – information collection and sharing between devices or network nodes e.g. for supply chains, in agriculture and in healthcare (wearables).
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Greater adoption of digital identities and digital identity management as both AI and blockchain will help address current concerns like data protection and privacy. Digital identities will also pave the way for more innovation in digital services, including payment systems and e-government services.
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New approaches to machine learning can also be expected in which blockchain is used to assure the completeness and integrity of data, as well as aiding in the process of auditing predictions and newly generated information.
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Electronic payments – whether from person-to-person, business-to-consumer or consumer-to-government – will most certainly be enhanced by an AI/blockchain convergence and will help further shore up financial and digital inclusion for both businesses and individuals.
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The speed and integrity of cross-border payments also stand to be improved through this convergence to allow for more dynamic regional trade and a more evolved financial ecosystem.
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To address the issue of costs, businesses may consider open-source options for both these technologies.
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