What Comes Next with Arun

Send us Fan MailYour AI agent can book a flight, move money, and open accounts on your behalf. But it can't take a selfie or hold a driver's license — so how does anyone know it's really acting for you?Arun sits down with Ravi Bijlani, founder and CEO of KYXStart.Ai, who is building the trust and verification layer for the agentic era. Ravi spent 24 years in identity and payments before betting that the real bottleneck in AI wouldn't be intelligence — it would be trust.In this conversation: – Why "card-not-present" is becoming "human-not-present" – KYC for AI agents (KYCA): verifying who — or what — is on the other end of a transaction – Binding an agent to a device and a verified human, with guardrails ("don't book a flight over $500") – The coming protocol wars between Google, Stripe, Mastercard, and Visa — and why KYXStart sits above all of them – How the business actually makes money when the underlying standard is open – The financial-inclusion endgame: identity with nothing but a phone number and a selfieChapters 00:00 – Most organizations aren't behind on AI — they're behind on the thinking 00:41 – Meet Ravi Bijlani and KYXStart.Ai 02:50 – The gap Ravi saw: 24 years in identity and payments 05:10 – Why agents need a trust layer 06:27 – The scale: 180+ countries, billions of identities 07:50 – How this differs from managed identity and RBAC 09:30 – Static identity → signal-based identity; "human-not-present" 11:20 – Guardrails and the $500 flight: KYC for agents 13:50 – Accountability and the agent registry 15:00 – Is there a global standard? The protocol wars 18:00 – Where KYXStart plays: the trust layer on top 21:00 – Monetizing an open standard 26:40 – Diversifying: agent onboarding for banks today 30:00 – Turning it into a platform: AML and risk scoring 33:25 – Team structure: 21 people, founder's mentality 38:20 – The R&D vision: a golden record and financial inclusion 42:00 – Partnerships, the ecosystem, and the tipping point 46:20 – CloseSubscribe to What Comes Next with Arun and leave a 5-star review — it genuinely helps new listeners find the show.🎧 Apple: https://podcasts.apple.com/us/podcast/what-comes-next-with-arun/id6788060249 🎧 Spotify: https://open.spotify.com/show/033KRXb9RHJJIDSoE2KjYF ▶️ YouTube: https://www.youtube.com/playlist?list=PLDBiRxvJWaSA 🌐 Website: https://www.arunansupattanayak.com/ 📘 Future-Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKCConnect with Ravi Bijlani: https://www.linkedin.com/in/ravibijlani/  Company site: https://www.kyxstart.ai/Support the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailHigh earners rarely lose money because they make bad decisions. They lose it in the gaps — between a financial advisor who only sees stocks and bonds, a CPA who shows up 60 days before the deadline, an insurance agent working a different angle, and an estate plan that's either missing or out of date. Nobody is coordinating the whole picture.In this episode, Arun sits down with Lord Munjal, Founder & CEO of Alpheva AI, who is building a financial operating system for high earners — an "AI family office" for the people who earn well but were never big enough to afford the real thing.Using the five stages of the Double Advantage Cycle from Arun's book Future Proof Your Business, they trace how Alpheva found its nest, diversified into education, opened its platform to advisors, built an ownership-minded team, and is now moving toward agentic execution of your money.In this episode: 00:00 Cold open: behind on AI, or behind on the thinking? 00:38 Meet Lord Munjal and Alpheva AI 01:08 The Double Advantage Cycle framework 02:46 Finding the niche: the origin of Alpheva 03:53 The hidden problem — high earners lose money in the silos 07:05 Democratizing the family office for $400K+ earners 11:55 How the AI and human experts actually coordinate 15:56 AFI vs AGI: vertical intelligence, not general 16:29 Diversifying into education and a future social layer 22:26 Opening the platform to CPAs and advisors 25:18 Build Your Own Alpha: an ownership-minded team 28:02 R&D: agentic execution of your money 31:08 Crypto, metals, and surfacing opportunities 33:00 Shaping the external environment 35:13 Where to find Lord and a listener offer🔗 Connect with Lord Munjal LinkedIn: https://www.linkedin.com/in/lordmunjal/ Alpheva: https://alpheva.com Listener offer: use code Arun100 for 10% offSubscribe and leave a 5-star review — it helps more builders find the show.Support the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailMost conversations about AI in finance stop at "we use AI." This one goes deeper.Aidan McConnell started as a data science intern at Sezzle in 2019, worked his way up to Head of AI/ML, then left to build QuantLink AI — a vertically integrated data and analytics platform for investors. Think premium market data, quant workflows, and institutional-grade charts, made accessible enough that a first-time investor can actually use them.In this episode, Arun and Aidan trace the build using Arun's five-stage framework for sustainable success — and Aidan gets specific about the decisions most founders gloss over:Why 100% of QuantLink's software is written by AI — and the ontology and deterministic validation that stops it from hallucinating "slop"The gap he saw in a market already crowded with Bloomberg, Merrill, and SchwabWhy exposing all of their code became a trust strategy for a young company with no institutional track recordThe surprise: customers didn't come for the AI — they came for the charts and the reportsWhere the platform goes next: an open marketplace for data, models, agents, and templatesHis bet on the future of AI architecture — deterministic graphs over generated codeChapters 00:00 — The thinking gap: why organizations fall behind on AI 00:28 — Meet the guest: Aidan McConnell, QuantLink AI 01:08 — Arun's five-stage framework for sustainable success 02:34 — Finding the niche: the gap in a saturated market 05:49 — Flipping the game: letting users build their own signals 08:30 — The B2B shift and user responsibility 09:51 — Transparency as a trust strategy 11:27 — Building the core: AI-first, human layer on top 12:24 — Ontologies and deterministic validation 16:00 — The eval harness: data-fetching vs. product layer 21:30 — Diversification: charts, reports, and the "boring" wins 28:28 — Becoming a platform: the open marketplace vision 31:20 — People and the ownership mindset 35:10 — Affiliates and incentives 36:37 — R&D: vertical models and the future of AI architecture 41:03 — The external environment: regulation and consumer education 44:21 — What's shipping next + how to try itListen, then send it to one person building in the intelligence economy.Subscribe and leave a 5-star review — it genuinely helps the show reach more builders.🎧 Apple: https://podcasts.apple.com/us/podcast/what-comes-next-with-arun/id6788060249 🎧 Spotify: https://open.spotify.com/show/033KRXb9RHJJIDSoE2KjYF ▶️ YouTube: https://www.youtube.com/playlist?list=PLDBiRxvJWaSA 🌐 arunansupattanayak.com 📘 Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKCGuest: Aidan McConnell QuantLink AI (https://www.quantlink.ai/) · LinkedIn: https://www.linkedin.com/in/aidan-mcconnell-341259108/ · X: https://x.com/Aidan_mcconnellSupport the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailIn the finale of this series, Arunansu Pattanayak brings together every framework from the show into a single worldview — one he argues is the most important strategic idea for any leader heading into the next decade.The claim is simple and uncomfortable: the winners won't be the organizations with the best AI. They'll be the ones that build the most intelligent architecture underneath it. Tools are commoditized. Buying the same software as your competitor gets you the same capability as your competitor. What compounds — and what can't be quickly copied — is the intelligence architecture beneath the tools.This episode names that idea directly: competitive intelligence architecture — the invisible infrastructure of the intelligence economy, as decisive now as electricity and transportation networks were in the 20th century.In this episode:Why more AI alone doesn't make you more competitiveThe one shift every leader should make: from "what tools?" to "what architecture?"Technology-first vs. architecture-first thinking — and why the reversal mattersThe three sources of durable advantage that can't be reverse-engineeredThree concrete things to do after nine episodes of frameworksChapters00:00 — Behind on the thinking, not the tools00:38 — One complete worldview01:08 — Pulling the threads together (the series recap)02:25 — Naming it: competitive intelligence architecture03:07 — Intelligence as the new invisible infrastructure03:35 — The one shift: from tools to architecture04:14 — Durable advantage: three things that can't be copied05:27 — Three things to do now06:29 — The question underneath everything07:23 — Where to go nextSubscribe and leave a 5-star review — it's the fastest way to help another leader find the show.🎧 Apple: https://podcasts.apple.com/us/podcast/what-comes-next-with-arun/id6788060249 🎧 Spotify: https://open.spotify.com/show/033KRXb9RHJJIDSoE2KjYF ▶️ YouTube: https://www.youtube.com/playlist?list=PLDBiRxvJWaSA 🌐 https://www.arunansupattanayak.com/Support the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailMost organizations aren't behind on AI. They're behind on the thinking required to use it.In this solo episode, Arunansu Pattanayak makes a prediction: if your AI strategy is built around specific tools, platforms, and vendors, most of that document will be obsolete within 12 months — not because your team planned badly, but because you built on the fastest-moving layer of the stack instead of the slowest.Arun breaks down why vendor-driven thinking feels like strategy but quietly hands your direction to someone else's roadmap, why technology roadmaps fail for a structural reason, and how to reframe the whole thing around durable capabilities: evaluating and integrating new tools fast, governing your data well no matter the platform, and retraining teams as workflows shift.You'll walk away with a one-exercise action item you can run on your current strategy doc this week — and a simple test for whether you have a real strategy or just a procurement list.Build the long-term intelligence system first. Let the tools rotate through it.Subscribe and leave a 5-star review.Support the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailMost organizations bought the AI. What they skipped was the thinking required to make it matter.In this solo episode, Arunansu Pattanayak sets aside the frameworks and tells one story — the moment, after 20+ years inside Merrill Lynch, Citibank, Credit Suisse, JP Morgan, TD Bank, and Microsoft, when he finally understood what actually breaks in transformation. It was never the technology.He unpacks why technically excellent systems fail to change anything: people keep using old workarounds no one asked about, leadership announces "data-driven decisions" while the culture keeps making the same instinct-driven calls, and departments quietly guard their own version of the truth because controlling the data means controlling influence. None of those are technology problems. They're organizational behavior problems wearing a technology costume.The episode closes on the realization that reshaped how Arun works: intelligence architecture isn't a technical framework — it's a sequencing philosophy. Governance, culture, and decision-making readiness, built deliberately in the right order, alongside the technical build rather than bolted on after.This week's action item is inside.Subscribe and leave a 5-star review if this shifted how you think about AI in your organization.Support the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailMost organizations aren't behind on AI. They're behind on the thinking required to use it.In this solo episode, Arunansu Pattanayak makes an uncomfortable argument for anyone who has ever built a five-year strategic plan: future-proofing, as most people define it, is a myth. Complex systems don't behave predictably over multi-year horizons — and the further out you forecast, the more confident the prediction sounds and the less likely it is to be right.That combination of high confidence and low accuracy is what makes long-range prediction dangerous as a strategic foundation. When leadership bets three years of investment on a confident call, they don't just risk missing a target. They allocate resources, build org charts, and architect systems around an assumed future — and end up with a structure that actively resists the future that actually arrives.The alternative is what Arun calls change fitness. Borrowed deliberately from physical fitness: you don't train for one specific challenge you're certain is coming. You build general strength, flexibility, and conditioning so your body can respond to whatever shows up.In this episode:Why forecasting and adaptability are different questions that lead to different investmentsThe strategic rigidity trap — how confident predictions get hard-coded into org charts and architectureWhat change-fit organizations actually build: modular architecture, short feedback loops, distributed decision authorityWhy strategic plans should be treated as living hypotheses, not fixed commitments to be defendedThe one question worth more than any five-year forecastThe question to sit with: What capability would make us stronger — regardless of what happens next?Subscribe and leave a 5-star review to help more leaders find the show.Get certified and explore what we're building at tipsora.com Connect with Arun: arunansupattanayak.comSupport the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailMost organizations are not behind on AI. They are behind on the thinking required to use it.In this solo episode, Arunansu Pattanayak — ex-Microsoft Data and AI executive and CEO of Tipsora — makes an argument that might sound strange coming from someone who has spent his career in AI: more AI is not going to make your organization more competitive.The large language models, copilots, and agentic systems everyone is racing to adopt are becoming commodities. Your competitor can license the same tools, read the same case studies, and hire the same talent. If you can buy it, it cannot be the source of your edge.Arun breaks down what actually creates durable competitive advantage in an intelligence-driven economy:Why tools, use cases, and even talent are copyable — often within a single quarterThe three things competitors cannot replicate: proprietary data, decision culture, and architectureThe first-mover myth: why being first with a new AI tool buys you headlines, not advantageThe difference between organizational intelligence and AI dependency — and why one is durable while the other is rentedA practical action item: how to identify the one advantage AI alone cannot create for your competitorsIf this episode shifts how you think about AI strategy, share it with someone who needs to hear it — and subscribe and leave a 5-star review so more leaders can find the show.Connect with Arun:Website: https://www.arunansupattanayak.com/LinkedIn: https://www.linkedin.com/in/arunansuspeaks/AI certifications for you and your team: tipsora.comSupport the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailMost organizations don't have an AI problem — they have a sequencing problem. They're building layer four before laying layer one.In this solo episode, Arunansu Pattanayak, ex-Microsoft Data & AI executive and CEO of Tipsora, draws on 20+ years across financial services, enterprise technology, and beyond to lay out a five-layer framework for building an organization that actually wins in an intelligence-driven economy:Layer 1: Data governance and architecture — the unsexy foundation everything else inheritsLayer 2: Intelligence infrastructure — ending the "three departments, three versions of the truth" problemLayer 3: Data as a business model — turning data from cost center to profit centerLayer 4: AI-driven decision culture — a human transformation, not a technical oneLayer 5: Competitive durability — the outcome you can't buy, only buildPlus the one question every leader should answer before this episode ends: which layer is your organization's weakest link?Ready to move from thinking to building? Get certified at tipsora.com or connect at arunansupattanayak.com.If this episode shifted how you think about AI readiness, subscribe and leave a 5-star review.Support the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailAI 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 literacySupport the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailIf 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 transformationSupport the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC

Send us Fan MailOnly 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.Support the show Arun's book, Future Proof Your Business: https://www.amazon.com/FUTURE-PROOF-YOUR-BUSINESS-Strategic-Framework-ebook/dp/B0H8MKQTKC