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What separates AI that looks impressive from AI that businesses can actually trust? Alex Hutchings welcomes Cassie Harman, Chief Product Officer at Nitro, to the ThinkData Podcast for a conversation about practical AI, enterprise adoption, and genuine product innovation. With nearly two decades of experience building products across financial services, healthcare and energy, Cassie brings a perspective shaped by industries where trust, security and compliance matter. Topics include the value of everyday AI applications, the challenges of moving beyond demos, AI-washing, Nitro’s approach to embedding AI into its products, and the tasks AI could quietly eliminate in the years ahead. Watch and subscribe: https://www.youtube.com/@thinkdatapodcast Learn more about Dataworks: https://www.mydataworks.io/

AI is increasingly moving beyond tools that simply help people work faster. The next step could be AI-powered digital workers capable of taking responsibility for entire business processes. In this episode of ThinkData, Alex Hutchings is joined by Dries De Coster, Founder & CEO of meet DWIGHT, a company building AI-powered digital workers to automate middle and back-office processes. After around 20 years in the HCM industry, including leadership roles at SAP and The Access Group, Dries launched meet DWIGHT just over three years ago. The conversation explores why he believed the timing was right for a new approach to automation, how the company identified genuine product-market fit, and what it takes to build and position an AI company in one of the noisiest technology markets we have seen. In this episodeWhy Dries left The Access Group to build meet DWIGHTThe problem meet DWIGHT is trying to solve with AI-powered digital workersWhy the timing was right for a new generation of enterprise automationHow early-stage AI companies identify genuine product-market fitHow to position an AI company when virtually everyone is talking about AIThe biggest GTM lessons from scaling meet DWIGHTHiring lessons from building an early-stage AI companyWhat Dries looks for when hiring into a startupWhat success looks like for meet DWIGHT over the next 12–24 monthsHow the digital workforce could evolve as AI technology matures

AI is supposed to make businesses more productive. But what happens when AI itself becomes one of the biggest expenses? In this episode of ThinkData, Alex Hutchings is joined by Nikhil Mungil, Head of AI R&D at Cribl, to explore the growing challenge of AI sprawl, the true cost of enterprise AI adoption, and how businesses should think about AI investment as usage continues to accelerate. Nikhil explains why the next phase of enterprise AI isn't simply about buying more tools or accessing better models. The real competitive advantage could come from how businesses harness that intelligence, integrate it into their own workflows, and measure whether it is actually creating value. They discuss why AI budgets may eventually need to be managed more like payroll, with different teams receiving different levels of AI resource depending on their workloads and the value they generate. Nikhil argues that trying to measure AI ROI across an entire organisation can be misleading — the real measurement needs to happen much closer to individual teams, tasks and workflows. Alex and Nikhil also explore how AI is already changing the structure of technical teams, with smaller groups increasingly supported by AI agents, copilots and AI-assisted development tools. In this episodeWhat AI sprawl means for enterprise technology teamsWhy organisations are struggling to measure AI ROIHow businesses should think about AI token budgetsWhy AI spending could eventually be managed more like payrollThe shift from standardised software towards intent-driven workflowsWhy enterprises may need to own more of their AI “harness”AI tools vs underlying intelligenceHow smaller teams can achieve more with AI agents and copilotsWhy AI investment needs to balance experimentation with measurable returnsWhat CEOs should consider before investing millions into AI One of Nikhil's central arguments is that businesses should “own as much of the harness as possible” — maintaining control over the workflows, evaluation and business logic surrounding AI, while retaining the flexibility to change underlying intelligence providers. About Nikhil MungilNikhil Mungil is Head of AI R&D at Cribl. His background spans companies including Substack, Splunk and ThoughtWorks, with much of his career focused on observability, security and large-scale machine data. At Cribl, Nikhil established its AI research and development organisation across engineering and product, working on models for telemetry data alongside agentic products designed to help users turn huge volumes of machine data into useful insights. About ThinkDataThinkData brings together founders, executives and technology leaders shaping the future of data and AI. Hosted by Alex Hutchings and brought to you by Dataworks, the podcast explores what it really takes to build, launch, and scale companies at the forefront of artificial intelligence and data. About Dataworks Dataworks helps Seed–Series B AI companies across the US and Europe build GTM, engineering, and data teams. Visit Dataworks

AI is moving faster than almost any technology we’ve seen before. But is governance keeping up? In this episode of the ThinkData Podcast, I sat down with Rowan Stewart, AI & Data Safety Product Leader at Transcend, and discussed what happens when organisations rush AI products to market without the right data, privacy and governance foundations in place. Before joining Transcend, Rowan spent around six years at BCG X, helping organisations build and launch new technology. Today, she works with companies including Robinhood, Brex and Groupon as they navigate the increasingly complex world of AI and data governance. We discussed why so many businesses are still treating AI governance as something to solve later, whether regulation really has to slow innovation, what responsible AI actually looks like in practice, and the consequences when businesses get it wrong. We also explored one of the biggest challenges facing startups today: how do you continue moving at startup speed while ensuring the AI products you build are trustworthy, compliant and capable of scaling? In this episodeRowan’s journey from BCG X to TranscendWhy companies are struggling with AI governanceWhy governance becomes harder when it is bolted on laterHow startups can move quickly without creating unnecessary riskWhat “responsible AI” actually means in practiceThe role data foundations play in trustworthy AIWhere responsibility for AI governance should sit inside an organisationThe commercial and reputational consequences of getting it wrongWhether regulation slows innovation or can actually enable itHow AI governance is likely to evolve as adoption accelerates

AI is transforming every industry, but few are changing as quickly as recruitment. This week, I'm joined by Arsham Ghahramani, Co-Founder and CEO of Ribbon, an AI-powered hiring platform helping companies interview every applicant through conversational AI. Before founding Ribbon, Arsham led machine learning teams at Amazon and completed a PhD focused on AI bias and model stress-testing. Since launching Ribbon, the company has grown to more than 500 customers, raised $8 million in funding, and was recently named Fast Company's #1 AI recruiting platform. In this episode, we discuss: Building AI that recruiters and candidates can trustWhy AI should improve—not replace—the hiring processThe biggest misconceptions around AI recruitmentWhy bias remains one of the hardest problems to solveProduct-market fit and scaling an AI startupWhat hiring could look like in five years' time Whether you're a founder, recruiter, hiring manager, or simply interested in where AI is taking the future of work, this is a conversation you won't want to miss.

What happens when AI can browse, understand, and interact with the web just like a human? This week on the ThinkData Podcast, I'm joined by Abhishek Das, Co-founder and Co-CEO of Yutori. Before founding Yutori, Abhishek was a research scientist at Meta, where he worked on AI agents long before the recent explosion in generative AI. Today, Yutori is building specialised AI models designed specifically for web agents—systems capable of navigating websites, completing tasks and operating autonomously in complex online environments. In this episode we discuss: • Why specialised AI models outperform general-purpose LLMs for web automation • The biggest technical challenges of building reliable AI agents • Why AI demonstrations often fail in production • Product-market fit in one of AI's fastest-moving markets • What founders consistently underestimate when building AI companies • How AI agents could fundamentally change the way businesses and consumers use the internet If you're interested in AI infrastructure, startups, product, engineering, or the future of autonomous software, this is an episode you won't want to miss.

Enterprise data has never been more valuable, yet most companies still struggle to answer simple business questions. This week, I sat down with Ethan Ding, Co-Founder and CEO of TextQL, to discuss why traditional analytics tools weren't built for the age of AI, how autonomous agents are changing enterprise infrastructure, and what happens when AI starts generating thousands of queries where humans once generated dozens. We discuss: • Why today's BI tools are reaching their limits • The biggest technical challenges behind enterprise AI • How TextQL is rethinking the modern data warehouse • Why data analysts aren't disappearing—but their jobs are changing • Finding product-market fit in one of AI's most competitive markets • What enterprise software looks like over the next five years If you're building, investing in, or buying AI products, this is a conversation you won't want to miss.

Agentic engineering is quickly becoming one of the most important shifts in software development. In this episode, I sit down with Scott Breitenother, Co-Founder & CEO of Kilo Code, to discuss why individual coding assistants won't drive the future of software development, but by autonomous AI agents capable of planning, building, testing, and shipping software. Scott shares the journey from building and selling Brooklyn Data Company to launching Kilo, an open-source agentic engineering platform designed to help developers become dramatically more productive in the AI era. We discussed product-market fit, engineering adoption, the realities of competing with Cursor, Copilot, and Claude Code, and what engineering leaders should be thinking about as AI fundamentally changes how software teams operate. If you're a founder, engineering leader, developer, or simply interested in the future of AI-powered software development, this is an episode you won't want to miss.

In this episode of the ThinkData Podcast, I welcome back Dimer Health Co-Founders Carrie Hodge, Sarig Reichert, alongside Chief Medical Officer Dr. David Feldman. Fresh off the announcement of their $13.5 million Series A, the team discusses why the 30 days following hospital discharge remain one of the most broken parts of healthcare, and how Dimer is using clinician-led AI to transform patient outcomes. The conversation covers scaling after fundraising, building culture during hypergrowth, the realities of operating at Series A, and what it takes to create a new layer of healthcare infrastructure.

Today, we’re getting into where AI actually fits in the finance stack and why most teams are getting it wrong. I’m joined by Deepak Bapat, CTO and co-founder of Tabs, an AI-native platform automating contract-to-cash end-to-end. We cover: The difference between AI-native vs AI-layered productsWhy finance has near-zero tolerance for AI errorsThe technical challenge of automating contract-to-cash workflowsIntegrating AI into fragmented ERP ecosystemsWhat finance teams may look like in the next 3–5 years as AI agents take over operational workflows A really interesting conversation around where AI creates real operational leverage, and where the hype still outweighs the reality.

Most enterprise AI isn’t failing because of the models… It’s failing because no one trusts the output. In this episode of the ThinkData Podcast, we’re joined by Uri Knorovich, CEO of Nimble, to unpack the growing trust gap in enterprise AI and why bad, delayed, and fragmented data is becoming the biggest blocker to real AI adoption. We explore: • Why AI works in demos but breaks inside enterprises • The hidden risk of AI outputs without traceable data lineage • Why trusted, live data infrastructure matters more than model speed • The shift from “is the AI fast?” to “can we trust the decisions?” • Real-world examples where AI and live data are already driving value A great conversation on the future of enterprise AI, data infrastructure, and building systems companies can actually rely on.

On today’s episode, we’re joined by Sadegh Salehi, Director of Research and Principal Scientist at Overjet, a Series C AI-powered dental platform spun out of MIT. Overjet is using computer vision and AI to analyse dental X-rays, helping dentists, insurers, and patients make more accurate, transparent, and data-driven care decisions. In this episode, we explore: • The hardest machine learning challenges in dental imaging • Building AI systems clinicians and insurers can trust • Why Overjet expanded from software into hardware • What it really takes to drive AI adoption inside clinical workflows • The future of AI in dentistry over the next 5–10 years A fascinating conversation on applied AI, healthcare infrastructure, trust, and the future of medical imaging.

Most AI today is intelligent… but it doesn’t feel human. In this episode, I sit down with Vishnu Hari, founder of Ego AI and former AI researcher at Meta, to unpack what’s missing and why “humaneness” might be the biggest unlock in AI. We cover: Why AI still feels roboticThe gap between intelligence and behaviourWhy most AI companies are solving the wrong problemAnd where the real opportunity sits If you think AI is already “good enough”, this will challenge that.

Today’s guest is Anjan Kundavaram, Chief Product Officer at Fivetran, a company at the heart of the modern data stack. In this episode, we unpack one of the biggest questions in AI right now Who actually controls customer data We explore what data ownership really means in the age of AI, how API restrictions are shaping what companies can and cannot build, and what happens to innovation if data becomes harder to access We also dig into how Fivetran thinks about “data freedom” and what enterprises should be doing today to stay ahead If you are building, investing, or operating in AI, this is one you do not want to miss

In this episode of ThinkData, I sit down with Wiley Jones, CEO and Co-Founder of Doss. Doss is a Series B, hypergrowth startup rethinking ERP from the ground up. They are building a modern, AI-native operations stack for physical product companies, designed to replace spreadsheets, legacy systems, and slow, painful implementations. We cover: Where legacy ERP vendors like NetSuite and SAP have fundamentally missed the markWhy now is the moment for a new category of operations softwareWhat makes Doss a credible replacement, not just another layerHow AI is actually being applied inside ERP todayWhy traditional ERP implementations fail and how Doss is changing thatThe hardest product and GTM challenges they are solving right now If you are building or scaling an operations-heavy business, this is a must-listen.