As AI investments surge across industries, a critical question is emerging: are organizations optimizing for AI consumption or for business outcomes? While advances in model capabilities have made it easier to increase prompts, tokens, and workloads, leaders are increasingly being challenged to demonstrate measurable value from AI deployments. This session explores how enterprises can move beyond usage metrics and focus on productivity gains, revenue impact, operational efficiency, customer experience, and innovation outcomes. The discussion will unpack the evolving economics of AI and examine what separates successful AI scale-ups from expensive experimentation in today's competitive landscape.
Key Discussion Points: - Why rising token consumption does not automatically translate into business value
- Moving from AI activity metrics to outcome-based measurement frameworks
- The changing economics of inference, model selection, and AI operating costs
- Strategies for maximizing ROI through workflow redesign rather than model upgrades
- Balancing performance, accuracy, latency, and cost at enterprise scale
- Emerging trends in smaller domain-specific models versus large frontier models
- AI FinOps: governing spend, resource utilization, and value realization
- How leading organizations are defining success through business impact, not AI usage
- Building executive dashboards that measure value creation rather than token generation
- Lessons from enterprises shifting from AI experimentation to sustained competitive advantage
