The text discusses the evolution of generative AI and its advancement from knowledge-based applications to agentic systems capable of executing complex workflows. These systems utilize foundation models and natural language instructions, allowing them to manage multifaceted tasks across various digital platforms. The potential applications of gen AI agents span multiple industries, offering significant automation efficiencies in areas like loan underwriting, software modernization, and marketing campaigns. As these technologies mature, they promise to revolutionize business processes but will require careful implementation and oversight to address inherent challenges and risks.
| Signal | Change | 10y horizon | Driving force |
|---|---|---|---|
| Evolution from chatbots to agentic AI | From passive tools to active agents | Seamless human-agent collaboration | Increased complexity of tasks |
| Natural language for complex workflows | From code-based to language-based | Wider access to AI capabilities | Democratization of AI usage |
| Multi-agent systems enhancing workflows | From singular tasks to collaborative | Enhanced efficiency in task execution | Automation of complex processes |
| Application of agents in diverse industries | From manual processes to automation | Streamlined operations across sectors | Demand for operational efficiency |
| Enhanced AI capabilities through data | From rigid programming to adaptive AI | More personalized user experiences | Big data and insights utilization |
| Companies investing in agent technology | From nascent tools to scalable systems | Commonplace integration in businesses | Competitive advantage in tech |
| Traditional roles transformed by AI agents | From human-driven to AI-driven tasks | New job roles and responsibilities | Technological advancement |
| Control mechanisms for AI system safety | From autonomy to regulated actions | Reduced risks in AI deployment | Ethical AI development |