Turning AI Ambition into Enterprise-scale Impact

Enterprises are funding generative AI activity at a pace, yet deployment confidence lags. This report highlights how organizations initiate many proofs of concept but stall before operationalizing them, creating an illusion of progress. It examines the friction points that derail momentum, including skills gaps across business and technology teams, cybersecurity concerns, bias and ethical use, weak data readiness for RAG, legacy integration challenges, and infrastructure complexity.

Beyond early barriers, the study identifies the scaling challenges that appear in the last mile: balancing automation with workforce impacts, establishing governance at scale, and addressing unclear AI operations and cost predictability. It then presents a disciplined approach to scaling, with clear steps and indicators that leaders can use to track progress.

The report also explores how strategic partnering helps enterprises close capability gaps. It discusses adoption model preferences and pinpoints six areas where partners create outsized impact: clarifying where generative AI drives material value, de-risking with AI-native governance and compliance, building modular production-grade platforms and data architecture, scaling through reusable components and workforce readiness, executing high-integrity pilots, and accelerating safe adoption beyond what in-house approaches can achieve. It concludes with guidance on partner selection criteria and performance metrics to enforce accountability.