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In this episode, I sit down with Jonathan to dissect how AI is moving from a "transformational" buzzword to a practical engine for business growth.

We dive deep into the IKEA case study, where the analysis of service desk calls didn't just lead to automation, but to the creation of a brand-new interior design service that generates millions in revenue. This shift from saving money to making money is the blueprint for modern AI implementation.

We also explore the broader impact of AI across sectors that don't always make the tech headlines.

We discuss AgTech’s role in saving billions through water and yield optimisation, and the incredible strides in Swedish healthcare where AI is detecting cancer months ahead of radiologists.

For larger enterprises, we look at the hard numbers from Alphabet and American Express, where AI is slashing fraud and generating 50% of new code.

The immediate action for any leader listening is to identify your "low-hanging fruit," such as customer support archives, and then start imagining the "10X" possibilities. AI is not just about efficiency; it is about doing what you never could before

In this deep-dive episode of the AI Moment, Jonathan Wagstaffe and me (Danny Denhard) strip away the LinkedIn hype to look at the "real" state of AI in April going into May 2026.

The conversation focuses on the widening gap between companies that are merely talking about AI and those that are aggressively retooling their operations to stay competitive.

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We explore the "Uber Effect" where companies are blowing through token budgets due to a lack of creating a flexible financial environment with how much pressure is being applied on building.

One core area we touch on (and is going to be a theme for a long time is) the critical importance of "Operational Readiness."

I argue that without a solid repository of company documentation, any attempt to deploy AI agents will likely fail or create more work than it saves.

This episode also touches on the "Compute Problem," discussing how "AI slop" and power limitations are beginning to throttle the growth of major LLMs, leading to potential price hikes and tiered service models in the near future.

Finally, we discuss the battle for model dominance. While Claude is becoming the preferred "work partner" for professionals, ChatGPT is attempting to become a consumer super-app, and Gemini is leveraging its massive search and workspace data. The takeaway for leaders is clear: 2026 is the year of separation. You must move from awareness to implementation, focusing on solving core business problems rather than just chasing the latest feature.

Here are the timestamps:

• 1:01 - Adoption Curve and its importance
• 4:45 - tokens and the importance of not buring through tokens and tokenmaxxing
• 6:15 - Ops readiness and having the right level of documentation to get the most out of AI
• 9:16 - AI Jobs Replacement, Tasks and Skills Development
• 12:29 - AI Needs Training Through Workshops & Hackathons
• 17:04 - AI Models - Why Anthropic Is Winning BIG & ChatGPT's Consumer Play Explained
• 30:49 - The Compute Problem & Is AI Slop The Actual Cause?
• 38:04 - The Latest Privacy Problems & Why You Need To As You're Now The AI Training Data

Struggling With AI?

Jonathan and I are hosting dedicated bespoke AI Workshops and AI Hackathons helping companies improve their AI capabilities and business performance, book in a time to chat to get you moving ahead

In this session, Jonathan and I explore why the frequent question "Which model should I use?" is actually the wrong place to start.

Whether you are utilising ChatGPT, Claude, or Co-pilot, the underlying engine is only as good as the context it is provided.

We discuss the psychological trap of "Model FOMO" and why business leaders should focus on developing "Prompting and Clear Thinking" as a core executive skill for the coming years.

We re-share our company context document to help you become more successful with LLMs.

We break down actionable strategies to improve your AI interactions immediately.

This includes the "GCSE" framework (Goal, Context, Sources, Expectations) and the "Interview" technique, where you allow the AI to extract the necessary information from you rather than trying to write the perfect prompt from scratch.

We also touch upon the efficiency of creating permanent context documents, such as PDFs describing your brand or ICP, to ensure consistency across all AI-generated work. The goal is to move from treating AI as a search engine to managing it like a highly capable team member.