Revenue-Ready Data Is Not Magic, It’s Engineering

About this episode

The episode discusses the importance of data engineering and governance in successful AI initiatives and their impact on revenue generation.

Artificial intelligence is everywhere right now, in boardrooms, strategy meetings, and product roadmaps. Organisations are investing heavily in machine learning, automation, and generative AI, all with the same promise: unlock new revenue and work smarter. In the latest episode of the  Don’t Panic It’s Just Data  podcast , EM360Tech’s  Trisha Pillay  explores this challenge with Chief Technology Officer  Paul Brownell  and  Sergio Morales , Data and AI Engineering Leader from  Growth Acceleration Partners . Their discussion unpacks why so many AI initiatives fail to translate into revenue and why the real starting point isn’t the model itself, but the data, governance, and engineering practices that make meaningful outcomes possible. But here’s the uncomfortable truth and that is many AI strategies look powerful on paper, but the real financial impact is often unclear. This disconnect, called the revenue data gap, highlights an issue many organisations overlook. AI doesn’t create value on its own especially without strong data foundations, governance, and engineering discipline, even the most ambitious AI strategy will struggle to deliver…

People in this episode

Guests: Paul Brownell, Sergio Morales

Topics covered

• artificial intelligence
• machine learning
• data engineering
• revenue generation

Keywords

• AI strategies
• data governance
• revenue data gap

Mentioned in this episode

Products: machine learning, generative AI

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