That tectonic rumble you hear is AI shifting the forces that drive business. Companies looking to benefit from this transformation will need to understand how AI is reshaping the economics of business—and how to navigate those economics to create value.
AI usage and shifting business dynamics mean that even while unit token costs are declining, spend continues to climb. The number one challenge for the CEO and board is how to navigate those dynamics to both manage costs and create new value.
Read more:
To better control agentic AI demand, CIOs shouldn’t simply ratchet back consumption. Instead, leaders are building financial operations, or FinOps, AI practices to predict demand and manage usage.
Read more:
How can AI inference be made economically sustainable at scale? AI infrastructure competitiveness may depend on how efficiently systems convert power, memory bandwidth, networking, and silicon into inference, making cost per token and energy per token the defining measures of AI economics.
Read more:
The decision to scale an agent is a complex and fast-changing economics decision, not a technical one. Leaders need a much better sense of what their employees and agents are doing, and what business outcomes they want to achieve.
Read more:
Leaders charged with implementing agentic programs need to get three things right: Identify where the value is, determine what drives the unit economics of AI-based workflows, and understand how to adapt to the evolving dynamics of agentic economics.
Read more:
AI can create more value by augmenting relatively high-paid, knowledge-intensive roles than by augmenting lower-wage positions. By accelerating many routine tasks that have traditionally taken up much of these professionals’ time, AI frees up their scarce expertise for higher-value work that cannot easily be delegated or automated. AI economics also vary greatly across sectors and regions.
Read more:
