What Comes Next with Arun

Arunansu Pattanayak
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  • Technology

Most conversations about AI are either too technical for business leaders or too generic to be useful. What Comes Next with Arun fills that gap. Each episode translates real-world data and AI strategy into the language of competitive advantage — drawing on Arun’s 20+ years inside the world’s most complex enterprises, six years as a Microsoft Data & AI Executive, and his experience building Tipsora into a platform serving more than 95,000 professionals worldwide. This is not a podcast about AI tools. It is a podcast about building the organizational intelligence that makes tools matter.

Episodes

  1. 1d ago ·  Video

    How to Turn Your Data into a Product and a Revenue Stream

    If your organization burned to the ground tomorrow and you could save one thing, what would you save? The most strategic leaders always give the same answer: the data. Everything else can be rebuilt — the data is irreplaceable. So why do most organizations treat it like a filing cabinet? In this episode of What Comes Next, former Microsoft Data & AI executive and Tipsora founder Arunansu (Arun) Pattanayak makes the case that the most valuable thing you can do with your data isn't analyzing it better — it's productizing it. With the global data monetization market projected to exceed $700 billion by the end of the decade, the organizations that treat data as a business are building competitive moats no one can copy. You'll learn: The critical difference between data analytics and data as a business — and why so few companies make the leapThe three patterns that keep organizations from monetizing their data: they don't see it, they overestimate the regulatory risk, and they lack a frameworkThe three types of data products: insight products (think Bloomberg terminals and credit bureau reports), benchmark products, and platform products (the AWS model)The five-step data productization blueprint: data inventory, value mapping, compliance architecture, product design, and go-to-marketWhy governance for monetized data is different from internal data governance — re-identification risk, contractual obligations, and multi-geography regulationYour one action this week: the whiteboard exercise that starts everythingIf you lead data strategy, product, or P&L in any data-rich organization — especially financial services, healthcare, retail, or logistics — this episode is your starting blueprint. Next episode: AI and the future of work — the strategic version, not the fear version. data monetization, data products, data as a business, data strategy, data governance, enterprise AI, data productization, revenue from data, chief data officer, data compliance, agentic AI, competitive advantage, digital transformation

    12 min

  2. 1d ago ·  Video

    The Future of Work Is an Architecture Problem, Not a Threat

    AI doesn't make humans less valuable. It makes the wrong humans — people doing the wrong work — less valuable, and the right humans dramatically more valuable. The question for every leader: are you designing an organization where your people are doing the right work? In this episode of What Comes Next, former Microsoft Data & AI executive and Tipsora founder Arunansu (Arun) Pattanayak takes on the future of work conversation — not the fear version, and not the hype version, but the strategic version. Drawing on decades in financial services and enterprise AI, Arun explains why both dominant narratives tell half the truth, and why half-truths lead to whole mistakes. You'll learn: Why AI replaces tasks, not roles — and what that distinction means for workforce planningWhat happened when AI automated fraud detection, loan processing, and regulatory reporting in financial services — and why identical technology produced opposite outcomes at different organizationsThe Three-Layer Workforce Model: the automation layer, the augmentation layer, and the innovation layerThe most counterintuitive idea in enterprise AI: as AI gets better at processing information, the value of human judgment goes UP, not downThe five moves leading organizations are making right now: strategic AI literacy, workflow redesign before deployment, explicit AI governance, building "change fitness," and protecting layer-three humansWhy capability multiplier vs. headcount tool is the leadership choice that determines whether AI builds advantage or capability gapsIf you lead people, strategy, or transformation in any organization navigating AI adoption, this is the framework for designing the future of work instead of reacting to it. Next episode: a deep dive into the layers of Intelligence Architecture — the framework Arun uses to help organizations become AI-enabled. future of work, AI and jobs, AI workforce strategy, AI adoption, workforce transformation, human judgment, AI governance, change management, enterprise AI, AI leadership, augmentation, automation, organizational design, AI literacy

    12 min
  3. Why Most AI Investments Fail — Architecture, Not Tools

    1d ago ·  Video

    Why Most AI Investments Fail — Architecture, Not Tools

    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 partWhy AI is a business architecture project, not a technology project — and what happens when it's handed entirely to ITWhy AI alone creates no competitive advantage: AI is the engine, data is the fuelIntelligence Architecture: the deliberate decisions about how data is collected, governed, connected, and activated before a single model is deployedThe Data Foundation Test: three questions every leader should ask before making any significant AI investmentWhy agentic AI raises the governance bar — and how scaling AI without governance scales risk, not intelligenceOne action to take this week to assess your organization's real AI readinessWhether 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.

    13 min
  • 3 Episodes

About

Most conversations about AI are either too technical for business leaders or too generic to be useful. What Comes Next with Arun fills that gap. Each episode translates real-world data and AI strategy into the language of competitive advantage — drawing on Arun’s 20+ years inside the world’s most complex enterprises, six years as a Microsoft Data & AI Executive, and his experience building Tipsora into a platform serving more than 95,000 professionals worldwide. This is not a podcast about AI tools. It is a podcast about building the organizational intelligence that makes tools matter.

Information

  • Creator
    Arunansu Pattanayak
  • Years Active
    2K
  • Episodes
    3
  • Rating
    Clean
  • Copyright
    © 2026 What Comes Next with Arun
  • Show Website
    What Comes Next with Arun