In this episode, Jonathan and I dive into the practical applications of voice AI that go far beyond simple voice-to-text.

We start by looking at the "surprise moments" in our workshops, where leaders realise they can use their phones to simulate high-pressure business environments.

We discuss how a hotel group used ChatGPT to train receptionists by role-playing as a complaining customer, providing immediate coaching on how to improve the interaction.

We also explore the "unlock" of using voice for strategic documentation. I share a personal example of how Jonathan helped a sales leader turn a quick conversation into a full strategy document using Gamma, reducing a day’s work to mere minutes.

We look at the broader business impact, such as how IKEA used voice analysis to discover a massive demand for interior design services, turning a cost-saving exercise into a new revenue stream.

Finally, we address why voice is such a powerful tool for those who find writing a barrier, including individuals with dyslexia, and how tiny lapel mics are becoming a new norm in the London startup scene to facilitate constant AI collaboration.

Enjoy the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight.

Left wanting more? Subscribe to our newsletter at https://aimomentpodcast.substack.com/subscribe for deeper dives and intelligence delivered with every podcast episode.

In this episode of The AI Moment, I sat down with Jonathan Wagstaffe to tackle one of the most pressing questions for modern business leaders: Is it time to build your own company LLM? We move past the hype of "building from scratch" to discuss the practical realities of the Rent, Buy, vs. Build framework.

We explore why context is the new currency in AI. It is no longer enough to simply use a public model; to gain a competitive edge, businesses need to integrate their own operating procedures and product ecosystem into the AI's workflow. However, this isn't without significant risk. We discuss the "dull, boring" but essential issue of data quality, noting that messy or fragmented data will undermine even the most sophisticated AI ambitions.

The conversation highlights Yahoo Scout as a leading example of the "hybrid model"—taking a powerful base like Claude and layering specific data on top to create a specialist tool. For leaders, the takeaway is clear: be mindful of the exploding costs of token usage and the scarcity of AI expertise. Instead of chasing a "naked LLM," focus on building the proprietary guardrails and intelligence layers that turn a generic tool into a powerful business asset. As the enterprise space evolves rapidly toward the summer of 2026, staying agile with a hybrid approach is your best bet to avoid being "cleaned out" by rapid platform shifts

In this episode of The AI Moment, Jonathan Wagstaffe and me (Danny Denhard) tackle the most pressing question facing modern executives: How do we actually measure the success of AI?

As businesses move past the initial excitement of generative tools, the challenge is to move away from "instinctive" utility and towards rigorous, actionable KPIs that satisfy the boardroom.

The discussion centres on moving the goalposts from measuring the AI itself to measuring the impact on existing business metrics. Danny introduces a robust four-pillar framework for leaders to adopt: Velocity, Quality, Economic, and Strategic. This includes looking at "Keep-Me-Out-Of-Jail" metrics like hallucination rates and the "Human-in-the-Loop" (HITOR) rate—measuring how much human intervention is required to make AI output viable.

>> We have go deeper in sharing more insights on our newsletter Subscribe to our newsletter at ⁠https://aimomentpodcast.substack.com/subscribe⁠ for deeper dives and intelligence delivered with every podcast episode.

Jonathan and Danny also explore the departmental nuances of these KPIs, noting that success in Sales looks very different from success in Support or Operations. Whether it is reducing proposal turnaround time or decreasing product decay rates, the message is clear: AI is a lever for business outcomes, not an outcome in itself.

Key Takeaways for This Week:

• Identify the "business results" you want before deploying the tool.

• Track "saved time" through the lens of what that time is reinvested into.

• • Establish "Trust and Reliability" metrics to manage hallucination risks.

• Enjoyed the episode? Subscribe and follow the podcast on your favourite platform to never miss an insight.Want more?

  
  

•