
Enterprise AI investments frequently succeed at the pilot stage and collapse at scale, not because the technology fails, but because the organizational conditions for adoption were never established. In this episode, Darko Todorovic, CTO at HTEC Group, examines why most AI ROI gaps originate in poor problem definition and inadequate change management, and outlines how senior leaders can build the baselines, KPIs, and organizational readiness needed to measure and sustain real returns. The conversation covers practical guidance on assessing technological and organizational maturity, avoiding POC-to-production pitfalls, and selecting the right AI tools for specific business contexts. This episode is sponsored by HTEC. In this episode we cover how enterprise leaders can measure and prove AI ROI after deployment. To go deeper on this topic and learn how to identify real AI trends by tracking where venture funding is flowing, and by listening to how leading CEOs describe risk and competitive strategy, download our free PDF report, "3 Ways to Discover AI Trends in Any Sector" at emerj.com/ait1
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Fixing the Decision Speed Gap in Modern Supply Chains - with Joris Wijpkema of Optilogic

Modernizing Targeting to Close the Field Execution Gap - with Damion Nero of Daiichi Sankyo

How Unified Context Turns AI Into Real Enterprise Performance - with Ravi Marwaha of Arango

AI Models as a Commodity and Why Data Foundations Decide Who Wins - with Guillermo B. Vazquez of SAP
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