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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.

Thanks for listening! Remember we have an accompanying newsletter with every podcast - where we go a bit deeper - https://aimomentpodcast.substack.com/subscribe

In this episode of the AI Moment, Jonathan and I tackle the "value gap" many business leaders face when first experimenting with AI.

We dive into the critical differences between free and paid AI subscriptions, highlighting why sticking with free models might actually be hindering your organisation's progress.

We discuss the psychological hurdle of the $/£20 per month subscription and why this "SaaS tax" is negligible compared to the cognitive lift the tools provide.

Jonathan explains that free models often result in "bland" outputs because they lack the sophisticated reasoning of the more advanced versions. I share my personal journey of transitioning from free Claude to paid Gemini, specifically how features like "Gems" unlocked a higher level of utility for my daily operations.

Our conversation concludes with actionable advice for leaders: try it yourself first, experiment in a domain you understand well to judge quality accurately, and use "meta-prompts" to break down recurring business problems. We believe that once you experience the "magic moment" of a full-powered model, you will never look back.

Welcome back everyone. I'm proud to say we bring you a special episode today.

I interviewed Dr. Kelly Monahan, a researcher (and former leader at Upwork and Meta) and advisor to Fortune 1000 companies, about AI’s impact on workplace culture and the future of work.

Keep an ear out for my 7 hidden gems in this podcast

1. The "Power Paradox" of AI Adoption

2. The Risk of "AI Peer Pressure"

3. "Digital Exhaust" as a Competitive Advantage

4. The Threat to "Expertise Dignity"

5. Playing "Checkers vs. Chess" with Headcount

6. The "Pilot" vs. the "Checkout" Model

7. The $1-for-$1 Investment Rule

These are packed full of leadership advice and positive steps!

The areas you will love:

Rethink Business: Kelly argues generative AI should prompt leaders to rethink business purpose beyond shareholder maximisation, warning of a crossroads between human flourishing and inequality-driven displacement.

AI Is Transformation: She emphasises AI adoption is primarily a prioritisation and change-management challenge, not just tooling, and uses her “elevator/skyscraper” analogy to push leaders toward workflow redesign rather than doing the same work faster with fewer people.

Human First Approach: We discuss preserving human decision-making (e.g., pilots, human checkout), risks of agentic AI increasing complexity and governance/legal exposure (e.g., healthcare claims), and research showing heavy AI users may trust AI over colleagues, potentially eroding workplace connection.

The AI Business Metrics: Kelly advises defining AI skills, measuring readiness, focusing on growth metrics like revenue per employee, clarifying company purpose and AI principles, and investing in upskilling alongside technology.

Kelly also has a forthcoming book coming out called “Reclaim the Plot” and it sounds like the perfect way to address work issues.

Please connect with Kelly below

• Personal site - https://drkellymonahan.com/
• Company site - https://www.beyondthedesk.com/
• LinkedIn - https://www.linkedin.com/in/kelly-monahan-ph-d-18879413/

As it is an AI Moment interview here are the chapters to go through if you are short on time:

00:00 Meet Dr Kelly

01:03 AI Purpose and Workforce

03:06 Adoption Guardrails and Priorities

05:41 Elevator Moment Workflow Redesign

07:11 Human in the Loop and Agentic Risks

17:05 Remote Work Politics and Power

27:04 Reclaim the Plot

29:30 HR Takes Back the Layoff Story

32:34 Metrics That Value People

38:47 Align on Purpose and Principles

41:34 Human And/Plus AI To Improve Work

43:30 Three Leadership Principles

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.

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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. I introduce 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.

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We 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:

1. Identify the "business results" you want before deploying the tool.
2. Track "saved time" through the lens of what that time is reinvested into.
3. Establish "Trust and Reliability" metrics to manage hallucination risks.

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