From AI to ROI: Closing the Enterprise Value Gap

Access experience-based perspectives from five senior AI leaders on why AI investment keeps outpacing provable value, and what it means for the decisions in front of you.

Report overview: A practical framework for closing the AI ROI gap

AI investment is moving faster than almost any capital commitment in recent memory, and the underwriting rigor that normally governs spending of this size hasn't caught up. The result is a widening gap between what AI is already doing inside individual teams and functions, and what leadership can confidently claim it's worth to the business as a whole. We convened five of GLG's most sought-after AI implementation experts, each with a different vantage point on the problem, to map exactly where that gap opens across a deployment, including:

• Why most AI initiatives never establish the baseline needed to prove they worked
• The disconnect between how vendors measure adoption and how boards measure impact, and what it costs at renewal
• Why AI's economics break the assumptions built into a decade of software ROI models
• A timeline problem: why the highest-value AI initiatives are also the slowest to show up on the P&L
• The difference between AI projects built for genuine business transformation and the ones built for AI's own sake

Contributing experts:

Yiannis Antoniou — Former Head of Data, Analytics and AI, Lab49

Divya Ashok — COO for AI, Salesforce

Mallika Iyer — CEO and Managing Partner of Arya AI, Co-Founder of MetricWolf

Dr. Ryan Mackey — Chief Medical Informatics Officer (CMIO) and AI/RCM/CDI Physician Executive, LifePoint Health

Matt Baker — Former SVP Activating AI Strategy, Dell Technologies