As nearly 90% of organizations adopt large language models, competitive advantage will come from hard-to-copy AI systems, workflows, and infrastructure rather than using off-the-shelf models alone.

AI tools become indispensable when embedded in core operations and customer journeys, transforming them from conveniences into essential components for competitive pricing and quality.

Amazon’s competitive edge relies on proprietary data from customer activity—searches, purchases, views, and ad responses—driving superior recommendations, forecasting, and marketplace performance.

AI is driving cost reductions in cognitively intensive sectors as it becomes embedded in core operations and workflows, enabling scalable, low-cost improvements.

Microsoft Dragon Copilot is deployed in 150 hospitals, drafting clinical notes in electronic health records and cutting documentation time by about half, helping reduce clinician burnout.

Organizations that sustain faster learning and development velocity in AI, through experimentation and data-driven learning, build stronger long-term moats.

Like the early digital transformation era, enduring value will stem from scalable AI integration into business models, not just technology adoption.

Resolution Life illustrates impact by using AI to automate actuarial and financial tasks, shrinking claim processing times from weeks to around 15 seconds and increasing processing capacity.

Boards should monitor AI strategy indicators such as transaction costs, the share of customer interactions handled by AI, and improvements in operational efficiency as AI becomes central to operations.

The strategic moat comes from turning cognitive work into scalable infrastructure—data pipelines, fine-tuned models, integrated workflows, and governance layers that are hard for rivals to replicate.

Long-term success will come from integrating AI deeply into customer journeys and core business operations, creating assets that are hard for competitors to replicate, rather than mere adoption.