Despite hype, true 'autonomous marketing' is not imminent. AI excels at automating the first 80-90% of a workflow, but the final, most complex steps involving anomalies, nuance, and judgment still require a human. This 'last mile' problem ensures AI's role will be augmentation, not replacement.

Criteo views the "human in the loop" not as a fallback but as a fundamental design requirement for all AI systems. Their development process explicitly focuses on identifying the correct place for human intervention and decision-making, believing that full automation is both risky and less effective.

Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.

A major risk of AI is reps will "outsource human judgment," losing the intuition that defines top performers. The correct mental model is to treat AI as a "thought partner"—a tool to accelerate research and test ideas, while the human remains responsible for strategic decisions.

In an enterprise setting, "autonomous" AI does not imply unsupervised execution. Its true value lies in compressing weeks of human work into hours. However, a human expert must remain in the loop to provide final approval, review, or rejection, ensuring control and accountability.

Rather than fully replacing humans, the optimal AI model acts as a teammate. It handles data crunching and generates recommendations, freeing teams from analysis to focus on strategic decision-making and approving AI's proposed actions, like halting ad spend on out-of-stock items.

Marketers mistakenly believe implementing AI means full automation. Instead, design "human-in-the-loop" workflows. Have an AI score a lead and draft an email, but then send that draft to a human for final approval via a Slack message with "approve/reject" buttons. This balances efficiency with critical human oversight.

The most likely future of agentic commerce involves AI handling tedious research and checkout execution, while humans remain the final arbiters. Brand preference and advertising will still matter because the human "in the loop" makes the ultimate call based on a few AI-vetted options.

A key fear of machine-to-machine commerce is that it will optimize solely for the lowest price. However, the 'human in the loop' model ensures the agent acts as a curator, presenting options for a final human decision. This preserves the importance of brand, aesthetics, and subjective value beyond pure cost.