Discover how AI and blockchain are reshaping finance and business, with real-world use cases and emerging challenges from the 2025 AI Rush conference.

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Convene at 155 Bishopsgate, a modern venue in London’s financial district, served as the location for the AI Rush conference. Photo: AI Rush

AI Rush London highlighted a pivotal shift: artificial intelligence (AI) and blockchain are no longer buzzwords, but practical tools revolutionizing asset management and business strategy. The event showcased how these technologies are converging to create new ecosystems, operational models, and opportunities, while also surfacing real-world challenges and limitations.

AI + Crypto + Asset Management: Convergence or Collision?

The fusion of AI and blockchain is driving a fundamental shift in asset management. AI’s analytical power enables the rapid processing of massive datasets, uncovering trends and optimizing portfolios in real time. Machine learning models can now detect complex market patterns, forecast volatility, and identify investment opportunities that would be invisible to traditional systems.

Alpha Stage panel, from left to right: Andrew Grill (The Actionable Futurist), Staci Warden (Algorand), Miguel Avalos (LinkedIn), Sammy Newman (Vinci Construction), Benjamin Hickey (IBM). Photo: compoundY

Blockchain, meanwhile, brings transparency, security, and decentralization. Every asset transaction is recorded immutably, eliminating the need for intermediaries and reducing both costs and settlement times. Tokenization is at the heart of this transformation-bonds, securities, and even real-world assets can be represented as digital tokens, held and traded seamlessly in blockchain wallets. This not only improves liquidity through fractional ownership but also automates compliance and record-keeping, making markets more accessible and efficient.

Read More: Explore decentralized finance (DeFi), lending with flash loans, and a new era of finance

Distributing Intelligence on the Blockchain

A key insight from the conference: distributing AI intelligence across blockchain networks is transformational. If AI agents-autonomous software entities-can operate on decentralized blockchains, they can execute tasks, make decisions, and transact value without human intervention. Each agent can have its own wallet, receive payments for completed work, and interact with smart contracts, creating a new paradigm for digital labor and commerce.

Network diagram illustrating AI agents distributed on a blockchain network with digital wallets, smart contracts, and asset flow. Photo: compoundY

For example, in asset management, AI agents could autonomously analyze markets, execute trades, and rebalance portfolios, all while recording their actions on the blockchain for full transparency and auditability. This model reduces operational friction and increases trust, as every action is verifiable and tamper-proof.

AI Agents: From Automation to Autonomy

AI agents are poised to transform business processes at every level. The conference explored a three-tiered model of agentic AI:

Level 1: Basic agents that answer questions or retrieve information.
Level 2: Agents that can search the internet, compare options, and provide actionable recommendations.
Level 3: Fully autonomous agents that pursue goals, execute complex tasks, and transact value-often requiring a crypto wallet to operate independently.

This evolution is only possible with blockchain infrastructure, which provides the programmable, secure environment needed for agents to transact and collaborate

IBM watsonx Orchestrate’s Orchestrator Agent is redefining workplace productivity. Serving as a multi-agent system enhanced by intelligent automation, it dynamically chooses optimal actions, brings forward the most relevant tools, and effortlessly connects various AI agents and assistants-all within a single, streamlined chat interface.

Real-World Use Cases and Emerging Challenges

Tokenized Asset Management

Tokenization is enabling the management and trading of a wide range of assets-bonds, shares, real estate, and more-directly from a digital wallet. Smart contracts automate interest payments, compliance checks, and ownership transfers, reducing costs and errors. Fractional ownership lowers barriers for retail investors, while instant settlement (T+0) enhances liquidity and market responsiveness.

Composability and Decentralized Governance

Platforms like Bittensor are pioneering composability, where multiple AI models can be trained to work together in decentralized networks, enhancing innovation and scalability. Decentralized governance models, such as DAOs (Decentralized Autonomous Organizations), are emerging as frameworks for regulating AI agents and protocols. These models use blockchain voting mechanisms and reputation systems to ensure transparent, participatory decision-making, with all actions recorded immutably.

AI in Business: Beyond the Hype

The conference also addressed the practical realities of deploying AI in business. While large language models (LLMs) are powerful, they have limitations: high computational costs, lack of true understanding of time, and the risk of generating inaccurate or “hallucinated” responses. Techniques like Retrieval-Augmented Generation (RAG) are being used to ground AI outputs in real data and improve reliability.

Read More: Discover how RAG AI is shaping the future of financial workflows and its role in financial decision-making.

Explainability is another critical issue-businesses and regulators need to understand how AI models reach their conclusions to ensure accountability and trust. In highly regulated sectors like finance and marketing, this is especially important, as compliance and transparency are non-negotiable.

Data, Regulation, and Adoption

Data remains the foundation of AI’s value, but regulatory constraints-especially in Europe under GDPR-limit the collection and use of customer information. Companies must articulate the value and cost of the data they leverage and inspire users to adopt AI-driven solutions by demonstrating tangible benefits and maintaining trust.

The Road Ahead: Opportunities and Open Questions

The convergence of AI and blockchain is ushering in a new era of business and finance, characterized by:

Greater efficiency, transparency, and automation in asset management
The rise of autonomous AI agents capable of executing complex business processes and transactions
New governance models that distribute decision-making and compliance across decentralized networks
Enhanced liquidity, accessibility, and innovation through tokenization and smart contracts

However, challenges remain-scalability, data privacy, explainability, and regulatory compliance will require ongoing attention. As these technologies mature, the winners will be those who can navigate complexity, build trust, and deliver real-world value.

The AI Rush made it clear: the future of business is not just digital, but autonomous, decentralized, and data-driven. The convergence of AI and blockchain is not a collision, but a catalyst for the next wave of innovation.

Read More: Secure Your Crypto: A Simple Guide to Buying Bitcoin and Ethereum Directly with Ledger Live

Topics Include: SaaS, Cybersecurity, Software Engineering, Machine Learning, NLP, AI Governance, Regulation and Compliance, Banks, Asset Management, Insurance.

Disclaimer

The information presented in this article is based on insights and discussions from the AI Rush conference held in London in May 2025. The content reflects the views and opinions shared by speakers and participants during the event and is intended for informational purposes only. While every effort has been made to ensure accuracy and relevance, the rapidly evolving nature of AI and blockchain technologies means that some details may change over time. Readers are encouraged to independently verify any critical information before making business or investment decisions. The article does not constitute financial, legal, or professional advice.

About the Author

Razvan Chiorean is a published author of compoundY and a cutting-edge researcher in quantum computing, AI-ML, and blockchain technology. Through his #AIResearch handle, Razvan continues to conduct research, blog, and educate, bridging cultures and inspiring technological progress while consistently sharing his findings and insights. He collaborates with leading tech companies, contributes to open-source projects, and is dedicated to fostering ethical standards and inclusivity in technology, ensuring a future where advancements benefit everyone.