"There's a vast gap between agentic AI's promise and the current implementation reality. As vendors rush to rebrand existing automation as "agentic" and marketers scramble to avoid being left behind, organizations can easily stumble into predictable traps that waste budgets and damage brands. While researching Agentic AI, decoded: A practical guide for marketers, our new MarTech Intelligence Report, I learned about the approach and mindset that successful agentic AI implementation requires."
"The pitfall: Vendors are applying "agentic" labels to traditional, rules-based automation. You think you're buying adaptive intelligence that learns and improves, but you're getting glorified if-then scripts that break when scenarios drift outside predetermined parameters. How to spot it: Ask vendors to walk through a specific decision the system made recently. If they can't show you how it reasoned through trade-offs or adapted to unexpected inputs - if every explanation sounds like "when X happens, we do Y" - you're looking at automation, not agency."
Many organizations misinterpret agentic AI, buying rebranded rules-based automation that fails when scenarios change. Vendors often label if-then scripts as agentic, so buyers must require demonstrations of autonomous, goal-oriented decision-making during proofs of concept. Agents can also overreach into unintended data fields, risking privacy and unauthorized actions. Field-level access controls and explicit data boundaries are necessary before deployment. Teams should test specific recent decisions to confirm adaptive reasoning and trade-off evaluation. Clear evaluation criteria and controlled deployments help avoid wasted budgets, brand damage, and operational surprises from premature or poorly governed agentic AI rollouts.
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