When Algorithms Cross the Line: Understanding Real-World AI Incidents

The Digital Transformation Playbook

#Tech #pKieran Gilmurray #Artificial Intelligence #Generative AI #Data Analytics #Intelligent Automation #Emerging Technologies #Technology #Innovation #Robotic Process Automation #Ai Content #Cloud Computing #Digital Transformation #Business

42:14

Episode Description: Jessica B. Harris may have been born and raised in New York City, but she has Tennessee roots through her father and has spent much of her life split between homes in the Northeast and the South – specifically New Orleans. For more than fifty years, she has been a college professor, a writer, and a lecturer, and her many books have earned her a reputation as an authority on food of the African Diaspora, as well as a lifetime achievement award from the James Beard Foundation. A few years back, Netflix adapted her book, High on the Hog: A Culinary Journey from Africa to America , into a 4 part docuseries. And I’m very proud to say that she’s a longtime contributor to Southern Living with a regular column called The Welcome Table. This episode was recorded in the Southern Living Birmingham studios, and Sid and Jessica talked about her mother’s signature mac and cheese, the cast-iron skillet she’d be sure to save if ever her house were on fire, and her dear friend, the late New Orleans chef Leah Chase. For more info visit: southernliving.com/biscuitsandjam Biscuits & Jam is produced by : Sid Evans - Editor-in-Chief, Southern Living Krissy Tiglias - GM, Southern Living Lottie Leymarie - Executive Producer Michael Onufrak - Audio Engineer/Producer Jeremiah McVay - Producer Learn more about your ad choices. Visit podcastchoices.com/adchoices…

about a year ago 20:04

MP3•Episode home

When AI goes wrong, who pays the price? Our deep dive into recent research uncovers the troubling realities behind AI privacy breaches and ethical failures that affect millions of users worldwide.

TLDR:

Research analyzed 202 incidents tagged as privacy or ethical concerns from major AI incident databases
Four-stage framework covers the entire AI lifecycle: training, deployment, application, and societal impacts
Nearly 40% of incidents involve non-consensual imagery, deepfakes, and impersonation
Most incidents stem from organizational decisions rather than purely technical limitations
Only 6% of incidents are self-reported by AI companies, while the public and victims report 38%
Current governance systems show significant disconnect between actual harm and meaningful penalties
Recommendations include standardized reporting, mandatory disclosures, and stronger enforcement
Individual AI literacy becoming increasingly important to recognize and resist manipulation

Drawing from an analysis of over 200 documented AI incidents, we peel back the layers on how privacy violations occur throughout the entire AI lifecycle—from problematic data collection during training to deliberate safeguard bypassing during deployment. Most concerningly, nearly 40% of all incidents involve non-consensual deepfakes and digital impersonation, creating real-world harm that current governance systems struggle to address effectively.
The findings challenge common assumptions about AI incidents. While technical limitations play a role, the research reveals that organizational decisions and business practices are far more influential in causing privacy breaches than purely technical failures. Perhaps most troubling is the transparency gap: only 6% of incidents are self-reported by AI companies themselves, with victims and the general public being the primary whistleblowers.
We explore the consequences ranging from reputation damage to false accusations, financial loss, and even wrongful arrests due to AI misidentification. The research highlights a critical disconnect between the frequency of concrete harm and the application of meaningful penalties—suggesting current regulations lack adequate enforcement teeth.
For professionals and everyday users alike, understanding these patterns is crucial as AI becomes increasingly embedded in our daily lives. The episode offers practical insights into recognizing manipulation, protecting personal data, and joining the conversation about necessary governance reforms including standardized incident reporting and stronger accountability mechanisms.
What role should you play in demanding transparency from the companies whose algorithms increasingly shape your digital experience? Listen in and join the conversation about creating a more ethical AI future.

Research Study Link

Support the show

𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses.
☎️ https://calendly.com/kierangilmurray/results-not-excuses
✉️ kieran@gilmurray.co.uk
🌍 www.KieranGilmurray.com
📘 Kieran Gilmurray | LinkedIn
🦉 X / Twitter: https://twitter.com/KieranGilmurray
📽 YouTube: https://www.youtube.com/@KieranGilmurray

Chapters

1. Understanding AI Privacy Incidents (00:00:00)

2. Research Methodology and Framework (00:01:55)

3. Training and Deployment Problems (00:03:25)

4. Application and Societal Impacts (00:05:40)

5. Root Causes Behind AI Incidents (00:08:34)

6. Responsibility and Disclosure Sources (00:11:06)

7. Consequences and Governance Gaps (00:14:13)

8. Recommendations for Better AI Governance (00:18:17)

118 episodes

When Algorithms Cross the Line: Understanding Real-World AI Incidents

The Digital Transformation Playbook

published about a year ago

MP3•Episode home

When AI goes wrong, who pays the price? Our deep dive into recent research uncovers the troubling realities behind AI privacy breaches and ethical failures that affect millions of users worldwide.

TLDR:

Research analyzed 202 incidents tagged as privacy or ethical concerns from major AI incident databases
Four-stage framework covers the entire AI lifecycle: training, deployment, application, and societal impacts
Nearly 40% of incidents involve non-consensual imagery, deepfakes, and impersonation
Most incidents stem from organizational decisions rather than purely technical limitations
Only 6% of incidents are self-reported by AI companies, while the public and victims report 38%
Current governance systems show significant disconnect between actual harm and meaningful penalties
Recommendations include standardized reporting, mandatory disclosures, and stronger enforcement
Individual AI literacy becoming increasingly important to recognize and resist manipulation

Research Study Link

Support the show

Chapters

1. Understanding AI Privacy Incidents (00:00:00)

2. Research Methodology and Framework (00:01:55)

3. Training and Deployment Problems (00:03:25)

4. Application and Societal Impacts (00:05:40)

5. Root Causes Behind AI Incidents (00:08:34)

6. Responsibility and Disclosure Sources (00:11:06)

7. Consequences and Governance Gaps (00:14:13)

8. Recommendations for Better AI Governance (00:18:17)

118 episodes

All episodes

The Digital Transformation Playbook

1
The Future Isn't Just Gen AI - It's Ethical, Orchestrated Intelligence 18:13

17 hours ago18:13

18:13

From academic curiosity to business transformation - Peter van der Patten's journey through AI spans decades, beginning in 1989 when he first explored the fundamental nature of intelligence. As Lead Scientist and Director of Pega's AI Lab, Peter brings a unique perspective that bridges theoretical understanding with practical implementation. [Sponsored] The conversation reveals how AI has evolved from isolated research projects to become deeply integrated into enterprise operations. Peter articulates a vision where intelligence isn't merely a technological overlay but woven into every customer interaction. This philosophy has guided Pega's approach: creating a horizontal platform where different AI technologies - from process mining to speech recognition, decisioning engines to generative models - work in concert within broader business ecosystems. What emerges is a nuanced view of enterprise AI implementation. The most successful deployments blend multiple AI approaches, combining the predictability of traditional decisioning with the flexibility of generative capabilities. Take insurance claims processing: automated systems can assess approval likelihood or fraud risk while agentic components gather information and synthesize findings - all within structured workflows that maintain regulatory compliance. This orchestrated approach addresses one of generative AI's central challenges: hallucination. By embedding large language models within established decisioning frameworks and governance structures, organizations can harness creative capabilities while maintaining accuracy and accountability. Peter emphasizes that ethical principles like explainability and transparency become even more critical as AI systems grow more complex and autonomous. Looking toward the horizon, Peter identifies research agents as an early "sweet spot" for agentic implementation - tools that gather and synthesize information across sources while posing relatively low risk. The next frontier? Multi-agent collaboration, where AI systems must negotiate, understand each other's goals, and work cooperatively across organizational boundaries. Join us for this fascinating exploration of AI's past, present and emerging future - and discover why effective implementation demands more than just advanced technology. 🌎 Watch Peter's interview on YouTube - https://youtu.be/W01zmYX-K8A #PegaWorld2025 #AI #AgenticAI #PegaWorld #AILiteracy Pegasystems #a d #Sponsored Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Digital Minions or Digital Dream Teams? The Future of AI Collaboration 26:09

23 hours ago26:09

26:09

The distinction between AI agents and agentic AI might sound like semantic hair-splitting, but it represents one of the most significant evolutionary leaps in artificial intelligence development. While interest in both has exploded since late 2022, understanding their fundamental differences unlocks a clearer vision of where AI technology is heading. TLDR: AI agents are autonomous software programs designed for specific tasks with minimal human supervision Agents leverage powerful foundation models like LLMs and LIMs as their cognitive engines Agentic AI represents a leap forward through coordinated teams of specialized agents Multiple agents working together can tackle complex problems through goal decomposition Real-world applications range from customer support to medical decision support Current limitations include lack of causal understanding and difficulties with long-horizon planning The evolution from single agents to collaborative teams mirrors human approaches to complex tasks AI agents function as autonomous software programs designed for specific tasks in digital environments. They operate independently with minimal human oversight, excel at narrowly defined jobs, and can adapt to changing conditions. Powered by foundation models like GPT-4 and DALL-E, these digital workers become even more capable when "toolmented" – connected to external tools and APIs that expand their abilities beyond their internal knowledge. Agentic AI takes this concept to an entirely new level. Rather than a single agent juggling everything, agentic systems deploy teams of specialized agents collaborating toward shared complex goals. Think of it as the difference between a lone smart thermostat managing temperature and an orchestrated smart home ecosystem handling everything from weather forecasting to security, energy optimization, and scheduling through coordinated specialist agents. Both approaches are already transforming industries. Individual AI agents excel at customer service automation, email management, personalized recommendations, and scheduling assistance. Meanwhile, agentic AI systems tackle significantly more complex challenges – coordinating robotics in automated warehouses, providing collaborative medical decision support, automating research processes, and managing adaptive workflows for legal or cybersecurity applications. Despite their impressive capabilities, significant challenges remain. From limited causal understanding and planning difficulties in individual agents to amplified complexity and unpredictable emergent behaviors in agentic systems, researchers are actively pursuing solutions through improved memory architectures, better coordination frameworks, and stronger ethical guardrails. The potential implications for scientific discovery, global project management, and human-AI collaboration are profound – if we can navigate the technical hurdles responsibly. What aspects of this collaborative AI evolution do you find most promising or concerning? Join the conversation and share your thoughts on the future of AI agent technology. Research: AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Legacy Transformation: How Pega's Blueprint Revolutionizes Enterprise Workflows 13:22

1 day ago13:22

13:22

Legacy transformation just got a whole lot faster. In this fascinating conversation with Matt Healy, who leads go-to-market for the Pega platform, we explore the remarkable evolution of Pega's Blueprint - a revolutionary SaaS product that's redefining how enterprises approach workflow automation and legacy system migration. [Sponsored] Blueprint has become the fastest-adopted capability in Pega's history, with over 150,000 applications generated in just 12 months. The latest advancement? AI agents that analyze legacy assets - from screen recordings to documentation - and transform them into cloud-ready applications. The results are staggering: Vodafone Networks has compressed their workflow deployment timeline to just 40 hours, dramatically accelerating their digital transformation journey. At the heart of Pega's approach is what Healy describes as "center-out business architecture," allowing enterprises to define processes once and deploy them across all channels. This unified approach ensures consistency as customers navigate between mobile, web, and contact centers, while maintaining full context throughout their journey. We also dive deep into Pega's unique approach to agentic AI, balancing innovation with enterprise requirements for governance and compliance. While research shows fewer than 20% of enterprises are realizing ROI from agentic AI investments, Pega's "predictable AI agents" wrap structured workflows with AI layers, ensuring compliant, auditable processes that deliver measurable value. Looking ahead, Healy predicts 2025 will be "the year of adoption" as enterprises integrate AI agents into core operations. Through partnerships with cloud providers and system integrators, Pega is focused on helping organizations escape legacy constraints and embrace an AI-powered future. Ready to transform your legacy systems? Try Blueprint today at pega.com/blueprint and experience the difference for yourself. 🌎 Watch the full interview on YouTube - https://youtu.be/rD-HKW6m00g #PegaWorld2025 #AI #AgenticAI #PegaWorld #AILiteracy Pegasystems #a d #Sponsored Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
The Future of MarTech Doesn't Have to Be Complex or Creepy 15:35

1 day ago15:35

15:35

The relationship between artificial intelligence and marketing has reached a fascinating inflection point. Rather than sparking fear about job displacement, AI presents an unprecedented opportunity for collaboration—enhancing human capabilities while handling data-intensive tasks that previously consumed valuable time. Tara DeZeo, Pega's product marketing leader for customer decision hub, frames this shift perfectly: "If we think about AI as a collaboration partner versus a replacement for marketers, that's more how it's being used in marketing organizations and that's how it should be used." This view resonates with forward-thinking marketers who recognize that automation of routine tasks creates space for more strategic, creative work. The conversation delves into one-to-one marketing—an approach that personalizes experiences at scale using real-time data signals. Unlike traditional segmentation that places customers into predetermined buckets, this AI-powered approach delivers contextually relevant communications when they matter most. Consider the banking app that recognizes when you've entered a wildfire zone and offers emergency resources instead of credit card promotions. That's not just smart marketing; it's empathetic engagement that builds lasting trust. As third-party cookies fade into obsolescence, marketers face a critical inflection point. The solution isn't to abandon personalization but to reimagine it through first-party and "zero-party" data—information willingly shared by customers. This shift demands transparency about how data will be used, creating a foundation for more meaningful relationships. As DeZeo notes, "We weren't very transparent about how we were using third-party cookies... we just obfuscated that from the consumer to the point where it became a problem." Looking ahead, Pega's Customer Engagement Blueprint represents a significant advancement in visualizing AI-powered marketing journeys, helping even traditional marketers evolve their approaches while identifying gaps in current programs. The vision for tomorrow centers on "scaling personalization without complexity"—activated data, unified experiences, and genuine connections that feel helpful rather than intrusive. Try Blueprint for yourself at pega.com/blueprint and discover how AI can transform your marketing from merely transactional to truly transformational. 🌎 Watch Tara's interview on YouTube - https://youtu.be/pGg_S9h5mIk Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Your AI Butler Might Have Left the Back Door Open 16:32

3 days ago16:32

16:32

What lurks beneath the impressive capabilities of your AI assistants? Security vulnerabilities that could put your data and systems at risk. TLDR: Privacy leakage becomes a major concern as sensitive data may become part of the LLM's memory Local vulnerabilities include file deletion, unauthorized access, and resource overconsumption AI agents can become unwitting accomplices in attacks against remote services Effective defences include proper session isolation, robust sandboxing, and encryption techniques The security of AI agents must be designed in from the beginning, not added as an afterthought While we marvel at AI agents writing scripts, querying databases, and browsing the web, security researchers have identified critical weaknesses in how these systems operate. This AI agent created podcast episode dives deep into ground breaking research on the hidden dangers of LLM-powered AI agents and why they matter to anyone using or developing this technology. We explore how poor session management can lead to information leakage between users, causing privacy breaches or mixed-up actions. We unpack the concept of model pollution, where malicious or unwanted data gradually corrupts an AI system's responses. The conversation tackles privacy risks illustrated by real-world incidents like Samsung's code leak through ChatGPT, showing how sensitive information can become embedded in model memory. The most eye-opening segment examines how AI agents can become security liabilities through local vulnerabilities (deleting files, accessing private data) and remote exploits (becoming unwitting participants in attacks against other services). Your helpful assistant could potentially become part of a botnet or leak your sensitive information—all while appearing to function normally. But there's hope. We detail promising defense strategies including proper session isolation, robust sandboxing techniques, and advanced encryption methods that allow agents to work with sensitive data without exposing the actual content. The episode emphasizes that security cannot be an afterthought but must be woven into AI systems from the beginning. As these powerful AI tools become increasingly embedded in our digital lives, understanding their security implications isn't just for tech experts—it's essential knowledge for everyone. Listen now to gain crucial insights into keeping your AI interactions secure and your data protected . Research: Security of AI Agents Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
The AI Within: Human Psychology and Chatbot Interactions 20:43

8 days ago20:43

20:43

A revolutionary study has uncovered unexpected truths about our growing relationships with AI chatbots, revealing complex patterns that challenge conventional wisdom about human-AI interactions. Listen in as Google NotebookLMs AI voice generated agents talk to this excellent piece of research. TLDR: Nearly 1,000 participants exchanged over 300,000 messages with GPT-4 across nine different interaction conditions Using chatbots generally reduced feelings of loneliness but also led to less socialization with real people Longer daily usage consistently linked to negative outcomes including increased loneliness and emotional dependence Diving deep into data from nearly 1,000 participants who exchanged over 300,000 messages with GPT-4, we explore the fascinating psychological effects of daily chatbot use. The results paint a nuanced picture: while AI interactions generally reduced feelings of loneliness, they simultaneously led to decreased real-world socializing. Most notably, longer daily usage consistently predicted negative outcomes across all interaction types – a finding that should give us pause as these technologies become increasingly embedded in our lives. The study's most surprising revelation challenges our assumptions about voice versus text interactions. While voice-based chatbots initially seemed to produce better psychological outcomes, these benefits disappeared or even reversed with extended use. Meanwhile, text interactions showed higher emotional engagement and more supportive AI responses than their more human-sounding counterparts. Even more unexpectedly, using AI for practical, factual conversations – rather than personal ones – was linked to greater emotional dependence with prolonged use. Four distinct interaction patterns emerged from the research, from "socially vulnerable" users who form emotional bonds with AI to "casual" users who maintain healthy boundaries. Your own characteristics – from pre-existing loneliness to how you perceive the AI – significantly influence which pattern you might fall into and what psychological effects you'll experience. As we navigate this new frontier of digital relationships, these findings raise critical questions about responsible AI design and usage. How can we harness the benefits of these technologies while preserving genuine human connection? The balance we strike today may shape the future of our social wellbeing in an increasingly AI-integrated world. What patterns do you recognize in your own AI interactions? Share your thoughts and join the conversation about finding healthy boundaries with our digital companions. Link to research: How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Randomized Controlled Study Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Your Competitors Are Already Using Autonomous AI Agents. Are You? 27:13

10 days ago27:13

27:13

What happens when AI doesn't just respond to prompts but takes initiative, makes decisions, and collaborates with other AI agents to solve complex business problems? Simon Torrance, a global authority on business transformation with over three decades of experience, opens our eyes to the revolutionary world of agentic AI – and why it matters right now. TLDR: AI with agency can make independent choices, take actions, and solve complex problems Agentic AI differs from automation by creating end-to-end solutions rather than just following processes Five key elements of an agent: LLM capability, persona, reasoning ability, memory, and action capability Leaders should understand the technology, develop a strategy, and identify high-value, low-risk areas to start Approximately 50% of knowledge worker roles will be disrupted by agentic AI Mental model shift: recognizing AI can do work we currently believe only humans can do Forget what you thought you knew about automation. Agentic AI represents a fundamentally different capability: artificial intelligence with genuine agency that can act independently, reason through problems, and execute solutions without human intervention. As Simon explains, this technology allows organizations to create synthetic workforces that can dramatically expand operational capacity at a fraction of the cost of human teams. The examples Simon shares are nothing short of remarkable. A team of 20 AI agents completing complex risk analysis in one hour that would take human consultants six weeks. Ten AI agents running an entire insurance operation – brainstorming on Slack, managing claims, ensuring compliance – and delivering higher customer satisfaction than human teams. These aren't future possibilities; they're happening now with technology that's already a year old. For business leaders, the implications are profound. The strategic risk isn't just falling behind technologically – it's being outcompeted by rivals who triple their operational capacity while slashing costs. Simon makes a compelling case that we're approaching a watershed moment where AI capabilities will match or exceed human cognitive abilities in many domains within the next decade. This requires a fundamental shift in how we think about work, organizational structure, and competitive strategy. Ready to explore how agentic AI could transform your organization? Then lets chat - ☎️ FREE introductory meeting: https://calendly.com/kierangilmurray/30min Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
The ChatGPT Education Test 18:09

10 days ago18:09

18:09

The educational landscape is rapidly evolving with AI tools, but what does the evidence actually tell us about ChatGPT's effectiveness in learning environments? Moving beyond the hype and confusion, we dive deep into a ground breaking meta-analysis that examined 51 different research studies conducted between 2022-2025. Listen in as Google NotebookLMs voice generated podcast hosts explain. TLDR: ChatGPT shows large positive impact on learning performance (effect size 0.867) Greatest impact on critical thinking occurs in STEM fields when ChatGPT acts as an intelligent tutor Effectiveness depends on thoughtful integration by educators Works best when designed to support deeper learning rather than providing quick answers The results are striking. Students using ChatGPT showed significant improvements in learning performance with an effect size of 0.867 – considered large in educational research. The impact on learning perception and higher-order thinking skills was moderate but still consistently positive. When we examine where ChatGPT shines brightest, the patterns become clear: skills-based courses saw the strongest positive effects, while problem-based learning environments (effect size 1.113) created the ideal conditions for ChatGPT to enhance student performance. Timing matters too. Performance benefits peak during 4-8 weeks of use, suggesting a sweet spot between the initial learning curve and potential over-reliance. Interestingly, positive attitudes toward learning continue to grow the longer students use ChatGPT. For developing critical thinking skills, STEM fields benefit most, especially when the AI functions as an intelligent tutor rather than a passive tool. These findings have profound implications for how we think about AI integration in education. ChatGPT demonstrates effectiveness more than double that of traditional AI assessment tools previously studied, pointing to its versatility as both a learning assistant and potential tutoring companion. However, thoughtful implementation remains essential – the technology works best when deliberately structured to support deeper learning rather than simply providing quick answers. How can educators and students leverage these insights? Consider strategic implementation in problem-solving contexts, especially for skills development and STEM subjects. Use ChatGPT as an interactive tutor rather than a passive reference tool when possible, and be mindful of optimal duration periods. Beyond the technology itself, these findings invite us to reconsider what uniquely human elements of education become even more crucial as AI tools continue to evolve. Listening to this episode and want to try implementing these evidence-based strategies yourself? We'd love to hear about your experiences using AI in learning environments – share your thoughts and join the conversation! Link to research: The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Robot Writes Review, Human Falls Asleep 13:17

13 days ago13:17

13:17

The promise of generative AI is tantalizing – better quality work, produced faster and with less effort. But what happens when that efficiency comes at the cost of something vital: our intrinsic motivation? TLDR: AI-assisted work produces better quality outputs - longer, more analytical performance reviews and warmer, more personable emails When AI handles cognitive heavy lifting, we miss out on the satisfaction of solving problems ourselves People feel reduced ownership and control when AI generates content, leading to disengagement This motivation dip persists even when returning to non-AI tasks Organizations risk workforce disengagement, higher turnover, and potential skill stagnation Blend AI and human contributions rather than letting AI produce final outputs Design engaging solo tasks to counterbalance AI-assisted work Make AI collaboration transparent to maintain sense of partnership Rotate between AI-assisted and independent tasks throughout the workday Train employees to use AI mindfully, developing critical evaluation skills Drawing on fascinating research involving over 3,500 participants, my AI agents (non human Google Notebook LM) dive into this compelling tension at the heart of workplace AI adoption. The data reveals a striking pattern: while AI collaboration significantly improves performance metrics (think more analytical performance reviews and warmer, more effective emails), it simultaneously triggers a roughly 20% drop in intrinsic motivation and a similar rise in feelings of boredom. We explore the psychology behind this motivation decline, examining how AI can inadvertently hijack the cognitively demanding aspects of work – the very parts that often provide the greatest sense of accomplishment and ownership. When AI handles the heavy mental lifting, we lose those satisfying moments of wrestling with problems and finding solutions. This disengagement doesn't just affect AI-assisted tasks; it lingers even when we return to independent work. For organizations embracing generative AI, these findings offer crucial guidance. Rather than avoiding this powerful technology, the research points to five specific strategies for thoughtful integration: blending AI and human contributions, designing engaging solo tasks, maintaining transparency about AI's role, rotating between AI-assisted and independent work, and training employees to use AI mindfully as partners rather than passively accepting its output. Ready to harness AI's productivity benefits without sacrificing engagement? Listen now to discover how you can find the sweet spot where technology enhances rather than diminishes your sense of purpose and satisfaction at work. Research: https://hbr.org/2025/05/research-gen-ai-makes-people-more-productive-and-less-motivated Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Your Digital Partner: Why Agentic AI Will Change Everything We Do 19:27

15 days ago19:27

19:27

Ever feel buried under emails, alerts, and to-do lists that seem to have lives of their own? That productivity paradox—being frantically busy yet accomplishing little of real value—sits at the heart of our exploration into Agentic AI. Google Notebook LMs agents tell the story for us. TLDR: Self-directed artificial intelligence learns your patterns and anticipates needs Reclaims significant time by handling email management and scheduling complexities Functions as an intelligent information filter, cutting through digital noise Creates personalized learning experiences that adapt to individual learning styles Serves as a creative collaborator for writing, design, music, and other creative pursuits Enhances health management through personalized monitoring and preventative insights Unlike traditional AI that passively waits for commands, Agentic AI functions as an autonomous collaborator. These self-directed systems understand context, reason independently, and take initiative toward achieving your goals without constant supervision. Through reinforcement learning and sophisticated language models, they develop the ability to adapt to new situations and devise creative solutions. The result? Technology that genuinely works for you rather than demanding perpetual management. The practical applications are transformative across multiple domains. For productivity, imagine email agents that intelligently prioritize messages, schedulers that eliminate booking complexities, and systems that handle digital paperwork automatically—potentially reclaiming hours of your week for deep work. As information filters, these AI companions learn your preferences to deliver personalized content briefs rather than forcing you to wade through irrelevant noise, whether for news, research, or social connections. The personalization extends further into education, where AI tutors analyze individual learning patterns and adjust delivery methods to match your specific style—visual, auditory, or kinesthetic. For creativity, they serve not as replacements but as digital muses, offering unexpected ideas or starting points while you maintain creative direction. In health and accessibility, the impact could be profound: from preventative health insights based on subtle biometric patterns to real-time assistive technologies that provide unprecedented independence for people with disabilities. What would you do with a genuinely intelligent digital partner that understood your needs, preferences, and goals? How might it transform your productivity, learning, health, and home environment? The potential is extraordinary, but realizing it requires thoughtful implementation guided by strong ethical principles. Subscribe now to join our ongoing exploration of how emerging technologies can enhance human potential and create more fulfilling lives. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Silicon vs Carbon: Why Your Next Colleague Might Be an Algorithm 33:38

17 days ago33:38

33:38

The technological revolution is here, and it's powered by artificial intelligence. In this riveting conversation with AI expert Kieran Gilmourie, we explore the urgent need for businesses to embrace AI technologies before competitors leave them behind. TLDR: AI is being rapidly adopted across all business sectors, company sizes, and government departments worldwide European companies need strong AI policies to compete globally and maintain knowledge economy advantages When using public AI models like ChatGPT, protect sensitive data by turning off training features and avoiding sharing confidential information No profession is entirely immune to AI disruption - from therapy to art, law to healthcare Technology should handle automatable tasks, freeing humans to focus on areas where they uniquely add value Continuous learning and adaptability are essential skills for thriving in an AI-powered workplace Rather than diminishing human connection, AI can enhance it by removing mundane tasks and expanding creative possibilities "For the love of goodness, can every Irish business please grasp this as quick as they can?" Kieran urges, highlighting that companies across every sector and level are rapidly investing in AI capabilities. This isn't a wait-and-see scenario – organisations implementing AI are experiencing transformative improvements in productivity, creativity and competitive positioning. We take a global perspective, examining how different regions approach AI adoption and regulation. While the US leads in investment and China follows closely, smaller nations like Saudi Arabia are making remarkable strides by integrating AI across government services and business operations. Europe's regulatory approach aims to balance innovation with necessary guardrails – what Kieran describes as maintaining "infinite pace and infinite patience" simultaneously. The conversation turns practical with essential tips for using public AI models safely. From turning off training features to exercising discipline with sensitive information, Kieran provides actionable guidance for protecting intellectual property while still leveraging powerful tools like ChatGPT. He explains why effective prompting techniques dramatically improve AI outputs, comparing these systems to brilliant interns who need clear direction to deliver their best work. Perhaps most thought-provoking is our exploration of AI's impact on traditional roles. Kieran challenges the notion that certain professions are immune to disruption, suggesting that continuous adaptation is the only sustainable strategy. "Concentrate on the bits that are uniquely you," he advises, urging listeners to embrace lifelong learning while allowing technology to handle automatable tasks. Far from diminishing human creativity or connection, Kieran believes AI enhances our humanity by removing the mundane: "Let the technology take the robot out of us so that we can be the most human we have been in the last 50 or 70 years." Ready to transform how your organisation leverages AI? Subscribe today and join the conversation about leadership in the age of artificial intelligence. Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
How to Make ChatGPT Your Legal Assistant Without Going to Jail 15:59

17 days ago15:59

15:59

The legal profession stands at the threshold of transformation as generative AI reshapes how lawyers work. But how do you harness this technology effectively? The secret lies in mastering prompt engineering—the art and science of crafting instructions that guide AI to produce valuable legal outputs. TLDR: Generative AI represents an unprecedented shift in how lawyers work, with Microsoft's legal team reporting 32% faster task completion and 20% better accuracy Legal applications span contract review, dispute resolution, litigation support, M&A due diligence, regulatory compliance, and client communications Start experimenting with these techniques on your own work to discover practical applications and efficiency gains Microsoft and the Singapore Academy of Law just released a 30+ page guide on using GenAI in legal practice. Our AI generated conversation dives deep into the recently published guide "Prompt Engineering for Lawyers," extracting actionable insights for legal professionals eager to leverage AI without getting bogged down in technical complexity. We explore the four fundamental pillars of effective prompting: crafting clear goals, providing rich context, setting explicit expectations, and referencing specific sources. The potential benefits are substantial. Microsoft's legal team reported tasks completed 32% faster with 20% better accuracy when using AI tools properly. From contract review and litigation support to regulatory compliance and client communications, we walk through real-world examples of prompts tailored for different legal scenarios. Each example demonstrates how thoughtfully constructed instructions can yield powerful results across practice areas. Yet we don't shy away from critical ethical considerations. The discussion emphasizes that AI serves as a co-pilot, not autopilot—the lawyer always bears ultimate responsibility for the work product. We address essential questions about client confidentiality, data security, and the importance of verification when working with AI-generated content. Whether you're already experimenting with AI tools or just beginning to explore their potential, this episode provides a practical foundation for developing this increasingly essential legal skill. Start applying these prompt engineering techniques today and discover how they can elevate your practice while freeing you to focus on the strategic work that truly requires your expertise. Read my top ten prompts for legal professionals - here Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
The Agent Company Benchmark: Evaluating AI's Real-World Capabilities 32:45

18 days ago32:45

32:45

The dizzying pace of AI development has sparked fierce debate about automation in the workplace, with headlines swinging between radical transformation and cautious scepticism. What's been missing is concrete, objective evidence about what AI agents can actually do in real professional settings. TLDR: The Agent Company benchmark creates a simulated software company environment to test AI agents on realistic work tasks AI agents must navigate digital tools including GitLab, OwnCloud, Plane, and RocketChat while interacting with simulated colleagues Even the best-performing model (Claude 3.5 Sonnet) only achieved 24% full completion rate across all tasks Surprisingly, AI agents performed better on software engineering tasks than on seemingly simpler administrative or financial tasks Common failure modes include lack of common sense, poor social intelligence, and inability to navigate complex web interfaces Performance differences likely reflect biases in available training data, with coding having much more public data than administrative tasks The gap between open source and closed source models appears to be narrowing, suggesting wider future access to capable AI systems The Agent Company benchmark offers that much-needed reality check. By creating a fully simulated software company environment with all the digital tools professionals use daily—code repositories, file sharing, project management systems, and communication platforms—researchers can now rigorously evaluate how AI agents perform on authentic workplace tasks. The results are eye-opening. Even the best models achieved just 24% full completion across 175 tasks, with particularly poor performance in social interaction and navigating complex software interfaces. Counterintuitively, AI agents performed better on software engineering tasks than on seemingly simpler administrative or financial work—likely reflecting biases in available training data. The specific failure patterns reveal fundamental limitations: agents struggle with basic common sense (not recognizing a Word document requires word processing software), social intelligence (missing obvious implied actions after conversations), and web navigation (getting stuck on routine pop-ups that humans dismiss without thinking). In one telling example, an agent unable to find a specific colleague attempted to rename another user to the desired name—a bizarre workaround that highlights flawed problem-solving approaches. For anyone trying to separate AI hype from reality, this benchmark provides crucial context. While showing meaningful progress in automating certain professional tasks, it confirms that human adaptability, contextual understanding, and social navigation remain essential in the modern workplace. Curious to explore the benchmark yourself? Visit theagentcompany.com or find the code repository on GitHub to join this important conversation about the future of work. Research: THEAGENTCOMPANY: benchmarking llm agents on consequential real-world tasks Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
From Drowning in Tasks to Thriving with AI: Your Personal Digital Agent Has Arrived 19:28

19 days ago19:28

19:28

Ever felt like you're drowning in a sea of tasks while nothing truly gets done? Agentic AI is here to change that. Unlike conventional AI systems that simply respond to commands, these digital agents function as autonomous collaborators—understanding your needs, anticipating problems, and executing tasks independently within boundaries you set. TLDR: Unlike regular LLMs that wait for prompts, agentic AI can function independently within defined boundaries Automating mundane tasks like email sorting and scheduling gives back hours for creative problem-solving AI agents can enhance creativity by offering inspiration and collaboration in writing, design, and music Responsible development focusing on ethics and privacy is essential for agentic AI to reach its full potential Agentic AI represents a revolutionary approach to reclaiming your most precious resource: time. By handling mundane tasks like email management, scheduling, and data entry, these systems free up your mental bandwidth for creative problem-solving and strategic thinking. They function as personalized information filters, cutting through the noise to surface only what matters to you, turning information paralysis into focused clarity. But the true power of agentic AI extends beyond productivity. These systems transform how we learn through personalized educational experiences that adapt to individual strengths and learning styles. They serve as creative partners, offering inspiration for writers, artists, and musicians without replacing human ingenuity. In healthcare, they provide proactive monitoring and personalized wellness recommendations, while accessibility features make technology more inclusive for people of all abilities. Even our living spaces become more responsive through smart home agents that learn our routines and preferences. The future isn't about humans versus machines—it's about thoughtful collaboration that enhances our capabilities while respecting our autonomy. With careful guidance and a commitment to ethical principles, agentic AI can help us build richer, more intentional lives where technology truly serves human flourishing. Ready to experience what happens when AI works for you, not the other way around? Read this chapter in full: Agentic AI: Your Personalized Power-Up for a Better Life Support the show 𝗖𝗼𝗻𝘁𝗮𝗰𝘁 my team and I to get business results, not excuses. ☎️ https://calendly.com/kierangilmurray/results-not-excuses ✉️ kieran@gilmurray.co.uk 🌍 www.KieranGilmurray.com 📘 Kieran Gilmurray | LinkedIn 🦉 X / Twitter: https://twitter.com/KieranGilmurray 📽 YouTube: https://www.youtube.com/@KieranGilmurray…

The Digital Transformation Playbook

1
Smart Assistants Aren't So Dumb Anymore: The AI Agent Ecosystem Explained 43:52

22 days ago43:52