Algorithms and automations have been buds for a decade plus. 🤝

But the old 'smart' automations were rigid. If one thing was wrong, the automation would bust. 

But with LLM-powered agents? Those automations are different. If something's wrong, the agent might just..... guess. 😳

Weak data = weaker outcomes. 

Here's how to fix it when agents come first and they're gonna finish the job, whether the data is strong or not. 

From Automation to Agents: Why Weak Data Makes AI Guess -- An Everyday AI chat with Jordan Wilson and Ed Macosky

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Topics Covered in This Episode:

1. Automation vs. Agentic Workflows: Key Differences
2. AI Agents: Data Quality and Output Risks
3. Agentification Trends in Enterprise Automation
4. Pros and Cons: Converting Automations to Agents
5. AI Agents Impact on Business Processes
6. Importance of Data Governance in AI Agents
7. AI Agent Control Towers and Observability
8. Measuring ROI: AI Agents and Data Investments

Timestamps:
00:00 "Trusting AI for Business Growth"

06:31 "Agent-Based Automation: Pros & Cons"

08:29 "AI Agents Simplify Workflow Management"

10:40 Expense Report Workflow Frustrations

15:05 AI Governance and Data Integrity

19:04 "Governance and Multi-Agent Data Strategy"

22:50 "AI ROI and Data Focus"

26:01 AI Studio: Create Apps Faster

Keywords:
AI agents, Agentification, Agentic workflows, Automation, AI-powered automation, Business process automation, Deterministic workflows, Non-deterministic workflows, Data quality, Data governance, Data management, Integration platforms, Boomi, Chief Product and Technology Officer, Large language models, Generative AI, Rigid workflows, Flexible workflows, Conversational agents, Expense report automation, Policy adaptation, AI decision-making, Human-in-the-loop, AI observability, AI traceability, Multi-agent orchestration, Ecosystem synchronization, Agent control tower, AI governance tools,

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