Practical AI For Real Businesses

AI is getting smarter by the month, but it is still perfectly capable of giving you a confident answer that is flat-out wrong. 

That tension sits at the heart of our conversation with Kieran Gilmurray, an award-winning author and board-level strategist who works at the sharp end of AI, automation, data analytics, and organisational change. 

We start with a simple truth that applies to both humans and machines: progress comes from mistakes, but only if we are willing to spot them, admit them, and refine what we do next.

TL;DR / At A Glance

• why admitting mistakes is a strength in business and in AI adoption
• how generative AI answers anyway and why that can mislead
• where AI bias comes from and how training data shapes outcomes
• what digital transformation means beyond installing new software
• starting points for AI in a company, from chatbots to marketing and retention analytics
• choosing a lighthouse project that is meaningful but delivers value quickly
• a real retention model story that raises ethical questions about pricing and vulnerable groups
• responsible AI habits, from looking around corners to keeping humans accountable

From there, we unpack what “digital transformation” really means. For us, it is not a tech shopping spree. It is a practical shift towards better customer outcomes and stronger business performance, with digital technology as the enabler. 

Kieran explains how to slow down, get clear on the decisions your organisation needs to make, build a data-centric strategy, and invest in people so the tools actually improve the way work gets done.

We also dig into the most common AI stereotype, the chatbot, and why modern conversational AI can be a powerful first step when it is trained, measured, and continuously improved.

The conversation takes a sharp turn into ethical AI with a real pricing and retention model that delivered big gains yet created uncomfortable consequences. That story becomes a blueprint for responsible AI: look around corners, test for bias, expect unintended outcomes, and stay accountable even when the model is “working”. 

If you care about generative AI for business, AI governance, customer experience, and using data to make better decisions, this one will give you both momentum and caution. 

Subscribe, share with a colleague, and leave a review with the biggest AI question you are wrestling with right now.

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𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.

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