Benoît Benatar is Director of Product Innovation at TRSB and a key contributor to the development of OTTO Research Labs’ multilingual content technology. With extensive experience in localization, language technology, and global content operations, he focuses on turning emerging AI capabilities into practical, scalable solutions for enterprise content teams. Benoît works at the intersection of product strategy, workflow design, and linguistic quality, helping organizations balance automation, governance, and human expertise. He is particularly interested in applying AI beyond machine translation to improve content risk assessment, workflow decision-making, and multilingual quality evaluation across complex global content environments.

AI Didn’t Replace You—It Promoted You to Manager

Co-presented by: Adam Goldman

Many multilingual content operations still apply the same workflow to every asset, regardless of audience, purpose, or risk. This creates unnecessary review, slows global publishing, and stretches specialist resources.

This session presents a more scalable, risk-based approach. Rather than focusing on AI as a translation engine, it explores how AI can support better decisions before and after multilingual content is produced—identifying which content needs greater oversight, assigning the right level of human involvement, and determining whether content is ready for publication or requires escalation. Attendees will learn how targeted governance can replace blanket review. The goal is not to eliminate human expertise, but to apply it where judgment has the greatest impact. By using AI as a decision layer, organizations can reduce review effort, improve consistency, accelerate global publishing, and deliver better multilingual customer experiences.

In this session, attendees will learn how to:

• Identify where one-size-fits-all multilingual workflows create unnecessary cost, delay, and review effort.
• Assess content based on audience, business impact, sensitivity, and the consequences of an error.
• Match each content type to the appropriate level of automation, human review, or specialist oversight.
• Use AI as a decision layer before and after multilingual content production—not simply as a translation engine.
• Establish clearer acceptance and escalation criteria for more consistent publishing decisions.
• Focus human expertise where it creates the greatest value.
• Pilot a risk-based workflow that improves speed, scalability, cost control, and customer experience across languages.