Agentic AI Implementation Runs Through Change Control | Focused Labs
The article discusses the challenges of implementing Agentic AI in enterprise settings. It highlights the misconception that Agentic AI is similar to traditional software enablement, emphasizing the need for a change control framework. The author argues that as AI agents become integrated into workflows, organizations must address ownership and accountability for changes driven by these agents.
- ▪Agentic AI implementation is often misrepresented as software enablement, which fails when agents alter workflows.
- ▪A change record is essential for managing the impact of AI agents on business processes and accountability.
- ▪Organizations must rethink their approach to AI adoption, focusing on the changes agents will drive rather than just usage metrics.
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try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 305023) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Austin Vance for Focused Posted on May 17 • Originally published at focused.io Agentic AI Implementation Runs Through Change Control | Focused Labs #ai #programming There’s been a big mis-selling in Agentic AI implementation. People compare its implementation to software enablement. But this breaks when the agent can change a workflow. The agent approves a refund, opens an incident, updates a customer record, begins onboarding for a new customer, or escalates a support ticket.
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