In my work with enterprise technology leaders, I’ve seen so many companies lay claim to AI rapidly (through pilots, tests and cross-functional experiments) that the enterprise AI moment increasingly feels like a gold rush. If it is, then leaders should take a lesson from history, from the real Gold Rush. During that time, the most durable wealth didn’t go to just any prospector. It didn’t even go mainly to the ones who struck gold. It went to the ones who built the railroads (and the equipment, the supply chains and the financial systems). It went to the builders of infrastructure.
, it becomes fuel for everything that follows, and it improves the context by which AI agents can reliably scale. In this way, it enables agentic support, enterprise copilots and intelligent orchestration. In short, knowledge is more than a side project. It is infrastructure itself.
2. Transform ITSM from workflow optimization to outcome optimization
Across industries, service desks face a common challenge: they must handle growing ticket volumes and expectations. Yet they often must keep their headcount the same. They can mitigate some of these pressures through traditional ITSM platforms, which help to optimize their workflows. But mitigation is about as far as it goes.
The real solution lies in supplementing their ITSM with AI, which can unlock the kind of nonlinear support that changes the very economics of their work. Typically, this supplementing comes about in stages, each introducing a new category of ITSM-related, AI-powered use-cases: intelligent intake and classification, automated routing, embedded knowledge retrieval, guided self-service and ultimately,
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