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AUTHOR NAME
Austin P. M. Editor
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Austin P. M. is a technology futurist and educator who explores how AI and emerging technologies are reshaping finance, climate, food systems, and the bioeconomy. An IIM Bangalore alumnus and early Indian fintech founder, he runs FutureCentral, a network of specialist publications covering AI, finance, agriculture, climate, marketing and entrepreneurship. He is also a visiting faculty at several IIMs and other leading Indian business schools.
Why AI Technology Alone Won’t Save You: The AI Complementary Assets Imperative
Learn why AI complementary assets—proprietary data, process integration, and domain expertise—matter more than algorithms for competitive advantage.
The AI Flywheel Effect: How Feedback Loops Create Winner-Take-Most Dynamics
Understand the AI flywheel effect—how behavioral signals and institutional competence create winner-take-most dynamics that reward early movers.
The Four AI Economic Value Engines Transforming Enterprise Performance
Discover the four AI economic value engines—automation, augmentation, prediction, and personalization—that drive enterprise growth and advantage.
Three AI Leadership Shifts That Move AI From Experiments to Results
Big spending on AI tools won't pay off without real AI leadership shifts in how leaders think. Specifically, leaders must rethink how they fund...
Four Things That Make AI Stick: A Guide to Institutionalizing AI
Companies that escape the pilot trap aren't always those with the best data scientists or the fanciest models. Instead, they're the ones who focus...
Why Even Digital Natives Struggle to Scale AI
Many people assume that tech-native firms can skip the hard parts of AI deployment. After all, these companies have cloud-native systems built from the...
How Shell Scaled AI Across the Enterprise
Shell started its AI journey with a focused data science team set up in late 2013. Within a few years, dozens of ML projects...
Project Thinking vs Capability Thinking: Why AI Capability Thinking Wins
Most firms approach AI through a project lens. Define a problem. Build a fix. Deploy it and move on. This works for one-off wins....
Why Most AI Projects Never Leave the Lab: Escaping the AI Pilot Trap
A global consumer goods company spent big on machine learning for marketing. Their data science team built a great model in six months. In...
The Chief AI Officer Debate: Does Your Company Need a CAIO?
AI keeps climbing the corporate agenda. As a result, a key question keeps coming up in boardrooms: Does our company need a Chief AI...
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