Panel
The ROI on Intelligence: Turning AI Investment into Enterprise Transformation | RAISE Summit 2026
- Organizations are expected to transition from experimental AI adoption to investment models tied to measurable value, with forward-looking shifts occurring over the course of a few years.
- Scaling AI successfully requires dynamic organizational treatment; if costs explode, companies may pull back, while successful scaling involves reaching for frontier intelligence only when necessary and utilizing cost-effective models.
- A specific hardware company case study cites a 26% reduction in AI costs over six weeks alongside a 60% uplift in code reaching production.
- Nokia plans to evolve networks into "AI native" intelligent systems and simplify workflows by treating AI as a lever for human potential rather than a simple tooling change.
- Nokia will implement soft token limits to trigger email notifications upon quota exceedance but will avoid hard token limits entirely.
- The industry is advancing into a third era of systems featuring more capable, long-running models where multiple agents operate simultaneously, necessitating system rebuilds to support individuals managing five to seven agents in parallel.
- Middle management layers focused on coordination are expected to be redefined or compressed into "part builder" roles as technical resource allocation shifts from coding to plan/design and review/test/deploy phases.
- Software development is predicted to cease being a bottleneck, prompting a shift in focus toward people, culture, and operating models, potentially shrinking engineering teams from eight to nine members to "one-pizza" teams of roughly four to reduce coordination costs.
- Organizations stuck with 35% to 45% of AI-written code reaching production must reorganize toward a systems-based approach to reach the next 10% to 50% of initiatives previously considered below the line.
- Economic impact depends on usage strategy: AI adopted solely for efficiency is projected to increase GDP growth by 0.7% annually, whereas using AI to increase the number and quality of ideas offers significantly higher potential.
- Nokia aims to free engineers from mundane tasks like CI/CD cycles to focus on innovation in physical AI applications, including AR, VR, and drones.