The AI-Powered Business Leader

About The AI-Powered Business Leader

Fewer Late Nights, Not Fewer Humans is about turning AI into real productivity gains - for you and for your team. Hosted by Alastair McDermott of HumanSpark, this show is for business leaders who want to work smarter and faster with AI, and then scale those wins across their team and their organisation. It is about embedding AI into how work actually gets done, in weeks rather than quarters. Episodes cover: Where AI genuinely saves time in a working week, and where it quietly costs you time instead. How to get a team using AI well without a six-month change programme. What to do about the ethical, legal and people questions that come with it. How other leaders are making it work, in their own words. You will find interviews with operators, solo episodes on what is working right now, and short pieces on single ideas worth your time. Subscribe if you would rather have fewer late nights, rather than fewer humans!

Publisher

Alastair McDermott - HumanSpark.ai

Category

business

Language

en

Explicit

No

First episode

12 Mar 2024

Latest episode

5 Dec 2024

Latest episodes

1 of 23 episodes have a transcript.

1. 

   5 Dec 2024

   The Dark Side of AI: Ethical Landmines Every Business Leader Needs to Know

   This is the final episode in the *Fewer Late Nights, Not Fewer Humans* archive - recorded in December 2024 - and it's a noticeably darker conversation than the rest of the back catalogue. Alastair sits down with Tom Murphy, an AI ethics and safety specialist he's known for nearly thirty years, to talk about the parts of AI adoption that rarely make it into the excited LinkedIn posts: the biases businesses inherit without noticing, the biases they create themselves, and the reputational damage that follows misuse. As Alastair warns at the top of the episode, some of it is mildly terrifying - and he isn't entirely joking. The conversation opens with the concept of "alignment" - whether an AI is actually pursuing the goal you think it is - and Tom explains, with a memorably simple Mario Brothers example, why we can never be fully certain that it is. From there the discussion moves through explainable versus black-box models, the limits of scenario testing, and why an AI's opacity is arguably no worse than a human employee's, except that AI operates at speed and scale. Tom then brings it firmly back to business reality: GDPR obligations around explaining automated decisions, protected characteristics, and how proxies quietly smuggle banned data back in. Car colour becomes a proxy for gender. Titles like Mr and Mrs become a proxy for gender. Postcode becomes a proxy for income, race, or simply a record of who was refused before. Most unsettling of all is his facial recognition example, in which a perfectly balanced, diligently curated training set still produced a system that learned to ignore an entire subgroup of people - because throwing them away improved the headline accuracy score. The second half turns practical. Tom outlines what subgroup testing looks like, why retraining always demands retesting, and why business leaders should insist on a baseline level of performance for every type of customer they serve rather than accepting an implicit "exchange rate for people." For listeners whose ambitions stop at "I just want to use ChatGPT and move faster," he explains what you inherit from a vendor's safety team, what you're still on the hook for, and why anything speaking to customers in your voice needs review. There's a strong case made for keeping a human in the loop, framed as a way to reassure anxious staff rather than replace them. Along the way: the US military's tank-recognition model that actually learned to identify blue skies, a jewellery insurance field that turned out to predict car crashes, thieves who avoid cul-de-sacs, the trolley problem tested live on 400 data scientists, a listener question on pushback from the anti-DEI crowd, an genuinely difficult dilemma about distributing an expensive cancer drug, and why Tesla hands control back to the driver at the exact moment a decision matters most. After this episode, the show picks up in the present. Find Tom at TomMurphyAI.com or on LinkedIn. Alastair's book, *An Absolute Beginner's Guide to Using AI*, is available at https://humanspark.ai/books/beginners-guide-to-ai/ The introduction to this episode is read by an AI-generated voice. The conversation itself is unedited archive audio.

2. 

   28 Nov 2024

   Why Your AI Strategy Is Too Complicated (And How to Fix It)

   Most AI failures aren't technology failures - they're scoping failures. In this episode from the archive (November 2024), Alastair sits down with Michael Zipursky, CEO and co-founder of Consulting Success, who has advised organisations from startups to billion-dollar corporations across more than 75 industries and is the author of *The Elite Consulting Mind* and *Consulting Success*. The conversation centres on a pattern Zipursky sees repeatedly among consulting firm owners and business leaders: the instinct to build one large, ambitious AI solution spanning five parts of the business at once, rather than solving a single narrow use case first. He is candid that his own firm made these mistakes and learned from them - and argues that unless you're a well-funded technology company with genuine project management chops, the smarter path is to pick one small use case, build it, test it, learn, and only then expand. The discussion splits AI adoption into two distinct categories: custom solutions you build yourself, and the fast-growing pile of pre-existing tools you can simply buy. Zipursky recounts an unsolicited pitch from an AI SDR service that makes phone calls while pretending to be human, and uses it to raise a question many buyers skip entirely: is this AI representing your brand the way you'd represent it? He and Alastair explore how volume-driven automation can quietly degrade response rates and brand equity, why so much automated outreach may collapse under its own weight, and the strange near-future where AI talks to AI - already visible in recruitment, where applicant-side tools now fire off hundreds of personalised applications into employer-side screening systems. The pair also look further ahead at agents, AI-optimised websites and API-first interfaces, and why science-fiction ideas from *The Jetsons* to *Star Trek* have a habit of becoming product roadmaps. On the practical side, Zipursky makes a blunt case that business owners who aren't at least actively thinking about AI's implications are being reckless - not because every industry will be transformed tomorrow, but because the odds of being the next Blockbimport, Kodak or BlackBerry are non-trivial. His guidance is deliberately unglamorous: don't try to keep up with every new tool (nobody can), don't treat it as a sprint, and instead build a culture of continuous learning where every role - marketing, research, operations, leadership - is asking how AI could make their specific work better. He also offers a neat trick for the common "I don't know what my use cases are" problem: ask the AI itself, describe your role and recurring problems, request ten options, then go deeper on one. Throughout, both agree that hallucination, context, situational awareness and expert judgement mean the human expert isn't going anywhere yet - but the expert who refuses to explore might be. **Resources mentioned:** - *An Absolute Beginner's Guide to Using AI* - https://humanspark.ai/books/beginners-guide-to-ai/ - consultingsuccess.com and the Consulting Success podcast - Connect with Michael Zipursky on LinkedIn --- The introduction to this episode is read by an AI-generated voice. The conversation itself is unedited archive audio.

3. 

   22 Nov 2024

   Why Your AI Strategy Might Be Backwards

   In this episode from the archive (originally recorded November 2024), Alastair sits down with Patrick Ward - a commercial leader with 30 years' experience, including 12 years at Microsoft, where he spent seven years with the global IoT team and reshaped the company's approach to IoT and AI engagements. Now founder of Iteria Partners and a part-time lecturer at UCD Smurfit Business School, Patrick makes a case that Alastair has repeated ever since: successful AI implementation starts with the business model, not the technology. Get that order wrong, and no amount of tooling will save the project. Patrick explains why so many IoT initiatives at Microsoft died after a successful proof of concept. The technology worked - but the moment an organisation connected its MRI scanners, blast furnaces or HVAC systems to the cloud, its value proposition, revenue model, sales incentives, channel strategy and in-house capability all had to change. Nobody had agreed to that upfront, so the project quietly got parked behind seventeen other priorities. His answer was to run a business model workshop at the outset of every customer engagement, getting the custodians of the business model - product, sales, marketing and finance - into one room. The most commonly cited benefit? Alignment. Ask ten leaders to define the value proposition and you'll routinely get ten different answers. He shares a live example of a global HVAC business struggling to get its sales team to attach £5k-per-month digital services to a multi-hundred-thousand-dollar hardware sale, and explains why bolting a digital business onto the side of an existing one rarely works. The conversation also covers Patrick's research interviewing heads of AI at 15 multinationals operating in Ireland, where the surprising finding was that scarce data science talent wasn't the biggest constraint - educating business leaders to think about AI in the context of strategy was. He walks through the AI envisioning sessions he runs for SMEs and enterprises, from competitor analysis via job postings to facilitated brainstorming on horizontal and industry-specific use cases. On productivity, his advice is blunt: don't build a business case you can't measure, roll out piecemeal rather than enterprise-wide, and invest in governance, policy, training and sharing. He closes with a memorable story about surveying a software team's real AI usage - twenty distinct use cases in a single morning, from generating synthetic French customer data to translating a Brazilian developer's written English - plus a client whose intermittent, months-long software bug turned out to be the number one finding in a ChatGPT code review. As Patrick puts it: yes, there's hype, but there's also a lot of real value, and the job is knowing the difference. **Guest:** Patrick Ward, Founder, Iteria Partners - patrick@iteriapartners.com, or find him on LinkedIn. **Mentioned:** Alastair's book, *An Absolute Beginner's Guide to Using AI* - https://humanspark.ai/books/beginners-guide-to-ai/ --- The introduction to this episode is read by an AI-generated voice. The conversation itself is unedited archive audio.

Inside a recent episode

AI Disruption: Redefining the Concept of Being an "Author"

Published 26 Mar 2024 · Transcript excerpt

[…] And, you know, that writing a book is a kind of a route to credibility and authority. And so now I can imagine that a lot of people are going to see that with less credibility and a lot more skepticism in the future. I can see the entire concept of being an author, and I hadn't thought about this until you said it, but the entire concept of being an author may mean something different. Um, like I know that, uh, you know, like if, if, if, if we set our minds to it, either of us could probably produce a book in a day that would look, it would look plausible, you know? Um, and, and it may be like, it may be like a B minus or a B plus, it might actually not be, not be that bad. […]

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