ThinkData Podcast

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 episode
Why Dries left The Access Group to build meet DWIGHT
The problem meet DWIGHT is trying to solve with AI-powered digital workers
Why the timing was right for a new generation of enterprise automation
How early-stage AI companies identify genuine product-market fit
How to position an AI company when virtually everyone is talking about AI
The biggest GTM lessons from scaling meet DWIGHT
Hiring lessons from building an early-stage AI company
What Dries looks for when hiring into a startup
What success looks like for meet DWIGHT over the next 12–24 months
How 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 episode
What AI sprawl means for enterprise technology teams
Why organisations are struggling to measure AI ROI
How businesses should think about AI token budgets
Why AI spending could eventually be managed more like payroll
The shift from standardised software towards intent-driven workflows
Why enterprises may need to own more of their AI “harness”
AI tools vs underlying intelligence
How smaller teams can achieve more with AI agents and copilots
Why AI investment needs to balance experimentation with measurable returns
What 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 Mungil
Nikhil 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 ThinkData
ThinkData 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 episode
Rowan’s journey from BCG X to Transcend
Why companies are struggling with AI governance
Why governance becomes harder when it is bolted on later
How startups can move quickly without creating unnecessary risk
What “responsible AI” actually means in practice
The role data foundations play in trustworthy AI
Where responsibility for AI governance should sit inside an organisation
The commercial and reputational consequences of getting it wrong
Whether regulation slows innovation or can actually enable it
How 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 trust
Why AI should improve—not replace—the hiring process
The biggest misconceptions around AI recruitment
Why bias remains one of the hardest problems to solve
Product-market fit and scaling an AI startup
What 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.