After-Hours: Most AI model evaluations assume clean data. That's why pilots fail. Production is *dirty*: missing fields, varied formats, edge cases. Unless you eval against real-world chaos, your AI won't survive deployment. Design for the swamp. #AIEval #Workflow #AIautomation

Where AI initiatives stumble: neat outputs from your model won’t fit changing internal fields without adjustment. Without a clear map, you're inviting chaos. Define each destination before deploying any AI system. #AIautomation #Workflow #MCP

The 'smart' AI output that can't cleanly map to your internal system's expected fields isn't smart. It's a design failure. Your system doesn't read nuance; it needs structured data. That’s the reality of AI adoption. #AIautomation #Workflow #MCP

AI pilot gave 'great' text. Real tax? Operator hours converting that text into the *exact* structured fields your legacy system needs. AI gives options; your system needs data. That output/input format mismatch is where pilots die. Map fields first. #AIautomation #Workflow #MCP

Many teams make a crucial mistake: they treat AI outputs as standalone. Outputs need defined action paths to avoid creating chaos. If you haven’t clarified where your AI data goes, you’re setting yourself up for integration headaches. #AIautomation #Workflow #MCP

Your AI gave a 'good' summary. But if your workflow's review gate needs 'Approve' or 'Reject' + reason, and the AI delivered only nuanced text, you just shifted manual triage. AI isn't useful until the handoff is clear. #AIautomation #Workflow #AIAgents

Breakfast Bytes: A common trap is treating AI outputs as self-sufficient. Before production, outline what each output must accomplish and where it fits. This upfront clarity prevents integration chaos down the line. Don't skip this step! #AIautomation #MCP #Workflow

After-Hours: AI model evals often use pristine data. Production? Chaos. Your framework: eval sets must reflect *real-world noise*. Scramble inputs, inject edge cases. Evaluate against reality, not idealism, or your pilot fails. #AIEval #Workflow #AIautomation

This Is Where It Breaks: Treating AI outputs as plug-and-play is a costly oversight. Each output needs a defined path for action. Without this clarity, you end up with chaos instead of efficiency. Design those routes before deployment. #AIautomation #MCP #Workflow

Prompt Ain't the Process: A great prompt means nothing if your workflow lacks a clear output contract. Define not just what the AI says, but what specific fields it populates, which system receives it, and how it triggers the next action. #AIautomation #Workflow #MCP

Breakfast Bytes: Assuming AI will slot seamlessly into workflows is a mistake. Today, establish clear criteria for what any AI output needs to look like. This prevents chaos when it’s time to act. #AIautomation #MCP #Workflow