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.

• 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.

• 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.

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.

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.

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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?

• 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.

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 products
• Why finance has near-zero tolerance for AI errors
• The technical challenge of automating contract-to-cash workflows
• Integrating AI into fragmented ERP ecosystems
• What 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.