From Automation to Agents: Why Weak Data Makes AI Guess
December 11, 2025
AI Summary
5 min read🎙️ The Voices & The Context
- The Format: This episode features a dynamic interview where the host, Jordan, engages with Ed Mikowski, the Chief Product and Technology Officer at Boomi. The tone is informative and conversational, aiming to demystify AI technologies for everyday listeners.
- The Key Players:
- Jordan: The enthusiastic host who simplifies complex AI concepts for business leaders.
- Ed Mikowski: A seasoned tech executive known for his expertise in integration and automation, particularly in the realm of AI.
🗝️ Key Themes & Topics
- Transition from Automation to AI Agents: The discussion highlights the shift from traditional automation to AI agents that can make decisions independently, emphasizing the need for reliable data.
- Data Quality Importance: A recurring theme is the critical nature of data quality in determining the effectiveness of AI agents. Poor data input leads to inadequate outputs.
- Real-World Applications of AI Agents: The episode explores practical applications of AI in business processes, showcasing how agentic workflows can improve efficiencies, particularly in finance.
- Governance and Oversight of AI Agents: The speakers discuss the need for robust governance tools to monitor AI agents, ensuring they operate within defined parameters.
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What you'll learn
- 1 `(00:00)` **🎙️ Introduction: Ed Mikowski**
- 2 `(02:01)` **Transitioning from Automation to AI Agents**
- 3 `(02:42)` **Current Landscape of Enterprise Automation**
- 4 `(04:40)` **Pros and Cons of Agentification**
- 5 `(08:12)` **Understanding Agent Decision-Making**
- 6 `(10:40)` **Case Study: Expense Reports**
- 7 `(13:10)` **The Importance of Data Quality**
+ Full timestamped outline available in the app
Show Notes
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:
- Automation vs. Agentic Workflows: Key Differences
- AI Agents: Data Quality and Output Risks
- Agentification Trends in Enterprise Automation
- Pros and Cons: Converting Automations to Agents
- AI Agents Impact on Business Processes
- Importance of Data Governance in AI Agents
- AI Agent Control Towers and Observability
- 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, Expen
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