Announcing StarCIO's AI Strategy & Governance Workshops - 15 Advisories to select from & evolve your roadmap. We guide the leadership team on a pragmatic AI vision, define clear guardrails, and establish a responsible path to value.
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RT Workflows become collaborations. Employee onboarding, quote-to-cash, contract management - non-linear, multi-system processes - turn into coordinated teamwork between people and AI agents through a single interface.
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Agent sprawl: One CDO told me his company has already deployed 1,000 AI agents. Do you have visibility into what your AI agents do, what they access, or when to retire them? Manage agents like interns.
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RT Here's the part the C-suite is missing: cut off the flow of entry-level talent, and you're building a leadership debt. Who will challenge an AI agent's assumptions when no one new ever learned the business?
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The takeaway for CIOs: you've spent years centralizing data. The next question isn't where the data lives. It's whether the right agents are sitting on top of it for the right disciplines.
#Data#AI#CIOyoutube.com/watch?v=SZ1YmV1O…
RT My advice to the C-suite: hire the 2026 grads. They've endured more than most generations ? and they've used AI more than half your current workforce. The "AI Natives" you're searching for just graduated.
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CIOs are stuck with too many AI POCs and not enough governance. AI strategy and governance aren't opposite sides of the same coin - they need to be implemented in parallel, not after.
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Process debt: deploying AI agents on broken business processes is RPA's worst lesson, repeated. Reimagine how the work could be optimally done before you generate a single line of code.
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On the Snowflake Summit floor weren't just AI agents for productivity - there were agents to run the business. That's a much bigger CIO conversation than workflow optimization.
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Snowflake CoWork: analytics agents purpose-built for different business disciplines, sitting directly on top of centralized data. The dashboard era was self-service. CoWork is co-work.
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RT Building AI agents is the easy part. Lifecycle management is where tech debt accumulates - stale prompts, buggy skills, vulnerable integrations sitting in production.
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The hidden cost of data management debt isn't infrastructure. It's trust. When business users stop believing the primary data assets, every data-driven initiative slows down, AI included.
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