AI Operating Model Shifts

AI agents are the new aggregators. Just as Google aggregated web content and Facebook aggregated social connections, AI agents will aggregate services and workflows. The company that owns the agent layer -- the interface between the user and all the services they use -- captures the majority of the value. This is why every platform company is racing to be the default agent.

Act One of generative AI was about model capabilities and the infrastructure to serve them. Act Two is about applications that generate sustainable revenue. We are now seeing the first generation of AI-native applications reach $100M ARR, and they share common traits: they solve specific workflows, they improve with usage data, and they create switching costs through accumulated context.

Our annual survey of 1,800 enterprises shows that 72% now have at least one AI application in production, up from 55% a year ago. However, only 18% report having AI embedded across multiple business functions. The primary barriers remain: data quality (cited by 64%), talent shortages (58%), and unclear governance frameworks (52%).

Every major tech company is now shipping agent capabilities. Google has agents in Workspace, Microsoft has them in 365 Copilot, Salesforce has Agentforce. The difference between these and last year's chatbots is that they actually do things -- book meetings, file expense reports, update CRM records. The question is how much autonomy you want to give them.

The organisations succeeding with AI share a common pattern: they treat AI as an operating model change, not a technology project. This means restructuring decision rights, creating AI product management roles, establishing federated governance, and measuring outcomes rather than deployments. The technology is 20% of the challenge; the organisation is 80%.