AI didn’t arrive all at once – although in many cases it might seem it did. It evolved in distinct phases, each unlocking new capabilities and changing how work gets done:
𝟭. 𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗔𝗜 (𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗜):
- Systems powering rule-based models and statistical inference to detect fraud, recommend investments, and process documents - all in response to human prompts.
- Financial Services (FS) example: Credit scoring models and fraud detection engines improved efficiency, but remained passive tools waiting on human input.
𝟮. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 (𝗚𝗲𝗻𝗔𝗜):
- LLMs and foundation models that brought language fluency and contextual understanding. These systems can create, explain, and summarize - moving from data crunching to content generation.
- FS example: Chatbots that summarize regulatory filings, generate client reports, or support advisors with contextual investment narratives.
𝟯. 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜:
- Systems that can interpret goals, plan actions, and operate independently within constraints. These agents shift the human role from executing tasks to defining intent.
- FS example: AI agents that autonomously rebalance portfolios based on client preferences and market movements - no human intervention required.
𝟰. 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 (𝗠𝗔𝗦):
- MAS represent the next leap. Multiple agents - each specialized - work together, negotiate, and adapt in real time to achieve shared outcomes across environments.
- FS: Agents handling client onboarding, AML checks, credit assessment, and regulatory filings collaborate seamlessly to approve new clients in minutes.
𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀:
MAS enable distributed, intelligent systems that can self-organize, learn continuously, and respond dynamically to change. They reduce operational bottlenecks and shift digital architectures from static pipelines to living systems.
𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗳𝗼𝗿 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀:
- Efficiency: MAS collapse multi-day processes into seconds - from KYC to loan origination.
- Mass hyper-personalization: Real-time tailoring of product decisions across customer journeys and risk contexts.
- Resilience: Distributed agents can recover from local failures, reroute tasks, and maintain service continuity without manual intervention.
- Compliance: Agents track regulatory changes and trigger operational updates autonomously.
MAS aren’t just the next step in AI - they’re how AI starts to really work like a system. The real transformation won’t be about bigger models anymore, but about smarter collaboration between them.
Opinions: my own, Graphic source: Capgemini
𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐦𝐲 𝐧𝐞𝐰𝐬𝐥𝐞𝐭𝐭𝐞𝐫: https://lnkd.in/dkqhnxdg
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