In this episode, we speak with Mustafa Ghafouri - Head of Data Science at Pharmacy2U - about why so few AI prototypes make it to production, and what the ones that succeed get right. 

Mustafa left his medical degree for data science over a decade ago. Since then, he's consistently shipped models into production across healthcare and tech, including as a solution architect at Databricks and on projects with NHS Digital. He explains why operationalising AI is the most underestimated part of any project, which questions he asks before he believes a demo, and why he builds the solution before the model. We also look at what made an NHS content-moderation project work, what has held back ambitious ideas like AI scribes and automated triage, and why simple use cases done well so often beat grand plans. 

If you're about to launch your first major AI initiative, or you're stuck somewhere between promising prototype and real-world impact, this is a conversation for you. 

Drawing on Raheel’s experience building AI inside healthcare, the conversation explores the practical realities that rarely show up in demos: regulatory friction, data quality, legacy infrastructure, governance, and the assumptions that break once systems go live. They discuss why so many AI initiatives stall before production, how judgement and validation matter more than speed, and where deliberate restraint is often the most responsible technical decision.

While grounded in healthcare, the lessons apply to any mission-critical environment where systems must earn trust, operate under constraints, and perform reliably day after day. If you’re a CTO or technology leader wrestling with how to operationalise AI without compromising safety, accountability, or long-term value, this episode is for you.

In this episode, Yasin sits down with engineering leader Ryan Greenhall to unpack the realities of working alongside AI. Drawing on his experience scaling teams through crises and hypergrowth, Ryan shares how AI is reshaping roles, responsibilities, and how the best teams are using AI to amplify strong fundamentals, not replace them. Ryan also unpacks what an AI-augmented SDLC might look like – smaller teams, faster feedback loops and engineers acting as ‘tech leads’ to AI agents.

If you’re leading or working in the software industry and wondering how to prepare your teams for the age of AI, this conversation is for you.

In this episode, I talk to Yann Cluchey about what really happens on the technical side of mergers and acquisitions: from uncovering hidden risks during due diligence to the complex work of integrating systems, architectures, and cultures post-deal.

Drawing on years of experience guiding technology teams through M&A transitions, Yann shares candid insights on what to look for before you buy, how to assess engineering maturity, and why the hardest part of any merger isn’t the tech; it’s the people who build and maintain it.

If you’re an engineering or product leader facing an acquisition, or simply want to understand the unseen technical challenges behind the business headlines, this conversation is for you.

Drawing on two decades in tech, Gemma shares candid lessons from guiding startups through the messy middle of growth: when technical debt starts to bite, when processes creak under pressure, and when culture makes the difference between resilience and collapse. We also explore how AI is reshaping the challenges of scale; speeding up some problems while magnifying others.

If you’re building or leading software in a scaling environment, and wondering how to grow without breaking what already works, this conversation is for you.