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Mature enterprises are moving beyond using AI for operational efficiency. They are now leveraging their unique, proprietary data to train custom models. This allows them to build differentiated services and products that competitors cannot replicate, creating new top-line revenue opportunities rather than just improving bottom-line savings.

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A PwC study reveals the leading 20% of companies capture 75% of AI's economic gains. They focus on using AI to identify new growth opportunities and reinvent business models, rather than simply improving efficiency on existing tasks.

The most successful organizations will view AI not as a tool for cost-cutting (doing the same with less) but as an expansionary technology. This mindset focuses on using AI to create new products, enter new markets, and dramatically increase scope, rather than just incremental efficiency gains.

The key for enterprises isn't integrating general AI like ChatGPT but creating "proprietary intelligence." This involves fine-tuning smaller, custom models on their unique internal data and workflows, creating a competitive moat that off-the-shelf solutions cannot replicate.

Focusing on AI for cost savings yields incremental gains. The transformative value comes from rethinking entire workflows to drive top-line growth. This is achieved by either delivering a service much faster or by expanding a high-touch service to a vastly larger audience ("do more").

Michael Dell identifies the next frontier for enterprise AI as applying models to vast stores of private, unused data. The winning strategy involves taking standard models and retraining them on this proprietary data, creating a unique competitive advantage and organizational knowledge that cannot be easily copied.

Since LLMs are commodities, sustainable competitive advantage in AI comes from leveraging proprietary data and unique business processes that competitors cannot replicate. Companies must focus on building AI that understands their specific "secret sauce."

Contrary to the popular belief that AI's main purpose is to replace humans for less money, user data shows its primary benefit is enabling entirely new functions. As AI costs rise, the focus will shift from simple cost-cutting to strategic investments in capabilities that were previously impossible.

Focusing AI efforts on efficiency and cost reduction offers limited, short-term benefits. The truly transformative approach is to invest in AI to create new revenue streams, enhance product offerings, and grow the business exponentially.

A PwC study shows a stark divide in AI returns. Leading companies aren't just deploying more AI; they are twice as likely to redesign workflows and pursue new revenue opportunities. This focus on "opportunity AI" for growth, rather than just "efficiency AI" for cost-cutting, separates leaders from laggards.

Since all competitors can access public data through common AI tools, it offers no sustainable advantage. To drive more pipeline and revenue, companies must seek out and integrate proprietary or non-public data sources aligned with their Ideal Customer Profile (ICP), creating a unique data asset for their AI to leverage.