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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.
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.
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").
Most companies use AI for optimization—making existing processes faster and cheaper. The greater opportunity is innovation: using AI to create entirely new forms of value. This "10x thinking" is critical for growth, especially as pure efficiency gains will ultimately lead to a reduced need for human workers.
The dominant long-term strategy isn't using AI to do the same work with fewer people (Efficiency AI). Winning companies will leverage AI to create new products, services, and capabilities, massively expanding their output and market presence (Opportunity AI).
Companies can either augment existing processes with AI for incremental efficiency (e.g., co-pilots) or completely redesign workflows. While augmentation is common, the most transformative value and disruptive business models will emerge from a clean-sheet redesign of how work is done.
The greatest value of AI isn't just automating tasks within your current process. Leaders should use AI to fundamentally question the workflow itself, asking it to suggest entirely new, more efficient, and innovative ways to achieve business goals.
True productivity gains from AI will mirror the adoption of electricity. Early factories that just replaced steam engines with electric motors saw little benefit. The revolution happened when they completely redesigned the factory floor around the new technology. Similarly, companies must reimagine entire workflows around human-AI collaboration.
Just as electricity's impact was muted until factory floors were redesigned, AI's productivity gains will be modest if we only use it to replace old tools (e.g., as a better Google). Significant economic impact will only occur when companies fundamentally restructure their operations and workflows to leverage AI's unique capabilities.
The productivity boom from AI won't materialize from workers simply using new tools. Citing historical parallels with electricity and computers, the real gains are unlocked only when companies fundamentally restructure their operations and business models around the technology.
Recent surveys suggest AI is underperforming, but the data reveals a stark divide. The 12% of companies that deeply embed AI into core processes are 3x more likely to see both cost reduction and revenue growth, creating a significant and compounding advantage over the majority who attempt superficial adoption.