Why Interpretive AI, Not Agentic AI, Will Drive the Next Technological Revolution
By John Lester, Fast Company | August 13, 2025
As generative AI continues to reshape how businesses create content and interact with data, a new form of artificial intelligence—interpretive AI—is poised to revolutionize the way large organizations operate. While agentic AI has garnered tremendous attention for automating discrete business tasks, it is the less flashy but more fundamentally transformative interpretive AI that experts believe will unlock massive productivity gains and reshape industries over the coming decade.
Moving Beyond Generative and Agentic AI
In the current landscape, generative AI tools like text and image generators have demonstrated impressive creative capabilities and are widely used for rapid content creation. Meanwhile, agentic AI, which can autonomously execute complex workflows such as building websites with minimal human input, grabs headlines as a breakthrough in task automation. Yet both approaches exhibit inherent unpredictability, making them difficult to integrate into highly standardized, high-volume business processes.
John Lester, a partner at global consultancy Oliver Wyman, notes from his experience advising major corporations that these AI forms remain limited in delivering substantial economic value at scale. “Their workflows and outputs are often too variable and uncertain for organizations that depend on predictable and reliable processes,” Lester explains.
What Is Interpretive AI?
Interpretive AI leverages the same powerful foundational models behind today’s leading AI tools—created by firms like OpenAI and Google—but applies them in a fundamentally different way. Instead of generating open-ended responses or independently navigating flexible workflows, interpretive AI excels at understanding and systematically interpreting messy, complex, and unstructured information in consistent, algorithmically defined ways.
This capability allows organizations to automate intricate activities that were previously considered too nuanced for effective AI integration, including project management, medical diagnosis, insurance claims processing, and the interpretation of legal or operational documents.
Real-World Applications Already Emerging
Interpretive AI is already making tangible impacts in fields like healthcare. For instance, doctors save significant amounts of time as interpretive AI tools listen in on patient interactions, automatically populating electronic health records with relevant information, streamlining care tracking and billing processes.
Looking forward, Lester envisions interpretive AI enabling automated fault analysis in auto accident investigations by interpreting diverse police report formats, or monitoring employee progress by analyzing video recordings of presentations and work activities to provide real-time updates to teams. The technology’s broad applicability spans numerous sectors and operational functions.
Unlocking Major Productivity Gains
By applying interpretive AI to millions of knowledge worker tasks that involve ambiguous data, context, or language nuances, companies could realize productivity improvements ranging from 20% to 40% across the half of GDP generated by large corporations. To achieve this, however, organizations must commit to comprehensive, coordinated strategies that span multiple business units, embrace cultural change, and rigorously experiment with new workflows.
A Transformative Shift in Knowledge Work Management
Interpretive AI promises to dramatically reduce reliance on costly and slow-moving middle management functions that oversee coordination and data collection in large enterprises. By quickly interpreting complex operational data—including decades-old legacy code and buried datasets—interpretive AI can offer unprecedented transparency and efficiency in managing intricate projects and multidisciplinary teams.
Moreover, this approach allows businesses to automate monitoring of qualitative factors, such as employee interaction quality, cultural norms, and compliance with internal policies—areas previously challenging to quantify and optimize.
Complementary, Not Competitive
Interpretive AI does not replace generative or agentic AI; instead, it offers a complementary usage that taps into the underlying AI models for more predictable, scalable business automation. While not as glamorous as AI chatbots or agents performing autonomous tasks, interpretive AI aligns with the fundamental economic driver of growth: standardizing and scaling processes to be faster, cheaper, and more reliable.
The Road Ahead
As AI continues evolving, companies prepared to invest in interpretive AI technologies and embed them deeply into their operations may gain a decisive competitive advantage in productivity and efficiency.
In Lester’s words, “Done right, the obstacles are surmountable—and the payoff could be massive.”
John Lester is a partner in the digital practice at Oliver Wyman with over 20 years of experience in AI product development and implementation.
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