By Carlo Giovine and Vasu Gupta
Over the past decade, we’ve seen big changes happening for enterprise artificial intelligence (AI). Organizations have invested in chatbots and copilots designed to help workers with productivity, but these ambitions have moved from assistance to autonomy. Leaders now want to deploy systems that can plan and launch complex workflows all on their own.
Today, organizations are moving toward an elastic enterprise model, designed to reshape tasks, systems, and teams in real time. In an elastic enterprise, business processes become fluid, bringing together services, data, and human expertise to resolve business outcomes.
Scaling Agentic Capacity with Extended Reasoning
One of the biggest drivers for this change is the evolution of frontier AI models. Early AI operated on a one-step, immediate-response model. But today’s models use extended reasoning, giving them the ability to plan, self-correct, and evaluate their work before final output.
