Evolution of AI in Enterprises: A Tale of How We Started Using AI for Business

TOCThe Enterprise AI Timeline: A Civilisation StoryBuilding Block of AI: A Foundational PhasePermutations & Combinations of AI: An Experimental PhaseThe Arrow Pullback: A Modern & Emerging PhaseThe Foundational Problems: What Did Agents Solve in The Evolutionary Scheme?The Trajectory of Agentic GrowthThe Predominance of Static Rule-Based SystemsThe Help from LLMs and RAG ModelsA Step Single Agent Architectures with Learning CapabilitiesThe Current State: A Leap to Autonomous Multi-Agent ArchitectureHow Does the Multi-Agent Architecture Refine the AI Landscape?PrologueThe dream of creating a self-operating consciousness (or AI-like capabilities) dates back to ancient philosophers and inventors. Tread back to the automatons of 19th-century visionaries like Samuel Butler’s Erewhon (an 1872 Novel), where machines were imagined to surpass human intelligence. Fast forward to today, AI is no longer just a philosopher’s dream but a tangible blessing reshaping enterprises.The question many ask is: why did AI gain significant traction in enterprises only recently, given its long history? The answer is in the rapid evolution of execution power behind AI capabilities combined with a shift in enterprise needs. In the past, AI systems were clunky, costly, and difficult to integrate into existing infrastructure.