Why Most AI Investments Fail — Architecture, Not Tools
Show notes
Only 14% of CFOs report measurable ROI from their AI investments — yet 66% of business leaders expect significant AI impact within two years. Why is nearly everyone betting on AI while so few are seeing it work?
In this debut episode of What Comes Next, former Microsoft Data & AI executive Arunansu (Arun) Pattanayak draws on 20+ years of building enterprise data and AI systems for organizations including EY, KPMG, Deloitte, Citibank, JPMorgan Chase, and Credit Suisse to answer that question — and the answer isn't "move faster."
You'll learn:
- The three assumptions that quietly kill enterprise AI ROI — including why deploying AI is the easy part and building the data foundation is the hard part
- Why AI is a business architecture project, not a technology project — and what happens when it's handed entirely to IT
- Why AI alone creates no competitive advantage: AI is the engine, data is the fuel
- Intelligence Architecture: the deliberate decisions about how data is collected, governed, connected, and activated before a single model is deployed
- The Data Foundation Test: three questions every leader should ask before making any significant AI investment
- Why agentic AI raises the governance bar — and how scaling AI without governance scales risk, not intelligence
- One action to take this week to assess your organization's real AI readiness
Whether you're a CEO, CIO, CDO, or founder planning your AI strategy, this episode gives you a working edge in the language of strategy, not speculation.
Next episode: how to turn your organization's data from a cost center into a competitive product.
Book call with Arun:
https://go.tipsora.com/meet-with-arun