Overview
- On Wednesday, panels and reporting from Argentina and Peru described a clear shift: firms are moving away from broad, license-led AI rollouts toward targeted projects that must show measurable return on investment.
- Consultants and executives identified three common early mistakes: adopting AI as a buzzword, distributing licenses without integration plans, and failing to name accountable owners for each system.
- Speakers stressed that poor data management blocks AI value and must be treated as a strategic asset by cleaning sources, standardizing metrics, and building governance before scaling models.
- Industry voices warned that models will become commoditized, so proprietary customer knowledge and human talent that can frame and verify AI outputs are the durable differentiators.
- Legal-sector commentators urged 'humanized' AI practices to protect junior training by preserving hands-on tasks, adding structured mentorship, and using AI mainly to verify work rather than replace formative experience.