The Hidden Costs: Data, Oversight, and Your Time

Hidden costs of automation: uncovering the true cost of AI tools and low-code RPA platforms Why most AI automation pitfalls come from data, oversight time, and hidden workflow design risks Learn how to forecast total cost of ownership, avoid low‑code automation risks, and design sustainable, human-in-the-loop AI

What You'll Learn:

• How to break down the true cost of AI tools and RPA beyond license fees, including data cleaning costs and ongoing maintenance
• How to estimate the real total cost of ownership for automation, including human oversight time and exception handling in automation
• A simple verification checklist (scope, data quality, edge-case frequency, escalation path, re-audit interval) to vet any automation idea before you build it
• How to spot low-code automation risks in your current workflows and avoid creating fragile, one-person-dependent systems
• Practical ways to design human in the loop AI so people catch failures early without becoming full-time babysitters of bots
• How to factor in data acquisition, prompt/workflow design, and retraining when budgeting for AI automation projects
• A quick method to identify where automation truly saves time vs. where it just shifts work to data prep and verification
• A 3-step reflection and action process to capture key insights, find one relevant area in your work, and take a small action this week