We are living through one of the loudest, most chaotic moments in the history of enterprise technology.
Right now, in boardrooms across the globe, the mandate is identical: Deploy AI immediately. Drive exponential growth. Do not get left behind. The pressure is immense, but the execution is deeply flawed. Caught in the relentless marketing wave of the generative AI boom, small and medium enterprises are being pressured to act without a framework, a roadmap, or a baseline understanding of their own data hygiene.
The result is a culture of "spray and pray."
Companies are rushing to slap AI wrappers onto legacy products, greenlighting fragmented Proof of Concepts (POCs), and launching isolated features, hoping that one of them will magically trigger the exponential valuation they’ve been promised. It is innovation theater, and it is burning through capital.
The harsh reality is that you cannot buy your way out of decades of tech debt, and you cannot mandate AI transformation from the top down without understanding the unglamorous plumbing required to make it work.
I do not say this lightly. My journey with machine intelligence did not begin with last year's hype cycle; it began in 1995, writing early "fuzzy logic" programs when AI was confined to raw code and research labs.
Over the last three decades, I have architected the intelligence layers for some of the world’s most complex platforms. I built the machine learning models powering insider threat detection for Microsoft’s Office 365 cloud. I helped engineer the global recommendation algorithms for Amazon Video. As CPTO at Visible, we didn't just deploy a chatbot—we built an intelligent agent-assist platform that fundamentally restructured our unit economics and reduced OpEx by 70%. Later, at Meta, I managed the massive data and compliance pipelines that make these systems possible at a global scale.
Today, through my capital fund investment groups, I sit on the other side of the table. I observe firsthand how CTOs and executive teams are attempting to make critical infrastructure choices under immense pressure. I see exactly what happens when theoretical data science collides with the brutal physics of enterprise scale and capital allocation.
What I see is a desperate need for a rubric. Enterprises need a rigorous, methodical way to measure their true readiness, identify structural gaps, and tie their engineering motion directly to financial outcomes.
To solve this, I am launching a new 3-book series: Building Purposeful AI for Enterprises.
This series is designed specifically to bring a maturity framework approach to AI implementation, moving leaders out of the POC quagmire and into systemic execution.
Next month, I will be releasing Volume 1: How to Build an AI-Enabled Enterprise. Inside, I introduce the NoARE framework—the exact diagnostic playbook needed to stop digging ditches and start building bridges.
Stay tuned.
— Himanshu Niranjani
Recovering CTO, corporate stoic, and reluctant plumber of the AI revolution.