Abstract: The integration of artificial intelligence (AI) and blockchain combines adaptive computation with decentralized trust, cryptographic integrity, programmable transactions, and auditable data exchange. This systematic review synthesizes research on integration architectures, applications, reciprocal benefits, security, privacy, scalability, interoperability, and open challenges. Four recurring integration patterns are examined: AI enhancing blockchain operations, blockchain supporting AI systems, bidirectional AI–blockchain architectures, and decentralized or federated intelligent systems. Applications are considered across healthcare, finance, supply chains, Internet of Things, cybersecurity, energy, smart cities, and education. The review finds that AI can strengthen anomaly detection, fraud analytics, smart-contract analysis, prediction, and network optimization, while blockchain can strengthen data provenance, identity, model traceability, access control, and collaborative learning. However, combined architectures introduce additional trust boundaries involving models, oracles, smart contracts, data pipelines, and cross-system interfaces. Latency, storage growth, interoperability, privacy leakage, model manipulation, poisoning, and governance remain important barriers. The review therefore argues for selective integration: intensive AI computation and sensitive data should generally remain off-chain or in controlled edge/cloud environments, while blockchain should anchor identities, commitments, permissions, provenance, and auditable events. Future research should prioritize verifiable AI outputs, privacy-preserving decentralized learning, trustworthy oracles, cross-chain interoperability, standardized benchmarks, and governance-aware architectures.
Ekansh Tayade, Pranav Pagare, Darshan Aher et al.· Zenodo (CERN European Organi...· 0 citations
Abstract: The integration of artificial intelligence (AI) and blockchain combines adaptive computation with decentralized trust, cryptographic integrity, programmable transactions, and auditable data exchange. This systematic review synthesizes research on integration architectures, applications, reciprocal benefits, security, privacy, scalability, interoperability, and open challenges. Four recurring integration patterns are examined: AI enhancing blockchain operations, blockchain supporting AI systems, bidirectional AI–blockchain architectures, and decentralized or federated intelligent systems. Applications are considered across healthcare, finance, supply chains, Internet of Things, cybersecurity, energy, smart cities, and education. The review finds that AI can strengthen anomaly detection, fraud analytics, smart-contract analysis, prediction, and network optimization, while blockchain can strengthen data provenance, identity, model traceability, access control, and collaborative learning. However, combined architectures introduce additional trust boundaries involving models, oracles, smart contracts, data pipelines, and cross-system interfaces. Latency, storage growth, interoperability, privacy leakage, model manipulation, poisoning, and governance remain important barriers. The review therefore argues for selective integration: intensive AI computation and sensitive data should generally remain off-chain or in controlled edge/cloud environments, while blockchain should anchor identities, commitments, permissions, provenance, and auditable events. Future research should prioritize verifiable AI outputs, privacy-preserving decentralized learning, trustworthy oracles, cross-chain interoperability, standardized benchmarks, and governance-aware architectures.
Ekansh Tayade, Pranav Pagare, Darshan Aher et al.· Zenodo (CERN European Organi...· 0 citations
Abstract: Generative artificial intelligence (GenAI) is increasingly embedded in software engineering and product-development workflows, particularly through large language models that can generate code, tests, documentation, summaries, design alternatives, and natural-language explanations. This paper examines how such capabilities interact with Agile ways of working, where value is delivered through short feedback cycles, cross-functional collaboration, empirical learning, and shared ownership. A structured review of research and practitioner evidence is used to organize GenAI's effects across product discovery, requirements, backlog refinement, sprint planning, implementation, testing, review, release, and continuous improvement. Evidence indicates that AI assistance can accelerate selected development tasks and reduce friction in activities such as code generation and comprehension, while practitioner surveys also report perceived benefits in code quality, learning, and customer alignment. At the same time, faster generation can shift bottlenecks toward validation, integration, security review, architectural consistency, and decision quality. The paper therefore treats productivity as a multidimensional construct rather than a simple measure of output volume. A Human-in-the-Loop GenAI Agile Adoption Framework is proposed around five recurring controls: framing, generation, verification, decision, and learning. The framework connects product, engineering, quality, security, and governance responsibilities and introduces measurable adoption dimensions spanning delivery flow, quality, developer experience, product outcomes, and risk. The resulting model positions GenAI as an augmentation layer within Agile systems rather than as a substitute for human accountability.
Ekansh Tayade, Pranav Pagare, Darshan Aher et al.· Zenodo (CERN European Organi...· 0 citations
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