Liz argues that the traditional consultancy business model is becoming obsolete because AI is commoditising tasks like data gathering and basic analysis. Instead of selling human hours and manual labour, future firms must pivot toward an always-on advisory model that prioritises outcome-based pricing and automated diagnostics. This transition requires companies to move away from "battery farm" scaling and instead focus on codifying human expertise into intelligent digital frameworks. Successful firms will likely emerge as platform orchestrators where AI handles the intellectual heavy lifting while humans provide essential judgement, accountability, and trust. Ultimately, boards are encouraged to scrutinise whether they are paying for genuine strategic insight or simply funding inefficient, outdated processes that technology can now handle faster.

This episode highlights the significant regulatory and privacy risks associated with using AI note-takers and recording virtual meetings. This discussion argues that current practices often fail to meet GDPR standards, specifically regarding explicit consent and the right to have personal data erased. Beyond legal compliance, there are growing concerns about the accuracy of AI-generated summaries and the long-term security of stored biometric data. To mitigate these threats, the advice to organisations includes; adding these technologies in their corporate risk registers and conducting thorough audits. Ultimately, leadership must transition from passive notification to active governance to protect individual privacy and ensure data integrity.Taken from article: https://lizhendersondata.wordpress.com/2025/06/15/ai-note-takers-call-recordings-and-gdpr-the-compliance-risk-youre-overlooking/

This podcast highlights the critical importance of data integrity as a foundation for implementing artificial intelligence within the social housing sector. Arguing that for AI to successfully improve tenant services and operational efficiency, organisations must first ensure their information is accurate, reliable, and consistent. Without high-quality data, providers risk facing compliance failures, poor resource allocation, and flawed decision-making. To mitigate these risks, the text recommends establishing a clear data strategy and a robust governance framework to guide digital transformation. Ultimately, the source serves as a roadmap for leadership teams to transition from data chaos to measurable strategic outcomes

Taken from article: https://lizhendersondata.wordpress.com/2025/04/15/achieving-data-integrity-in-social-housing-a-roadmap-for-ai-success/

The AI Questions Every Board Must Be AskingAI is moving faster than most boards can learn, but good governance can’t afford to lag behind.Boards Are Racing to Build AI CapabilityBoards are taking varied approaches to strengthen their understanding of artificial intelligence. Some are investing in director education, others are bringing in advisory committees or consultants, and many are widening their search for non-executive directors with real delivery experience in AI and data.These early movers recognise the same reality: AI creates both extraordinary opportunity and unfamiliar risk, and boards need competence—not technical mastery—to oversee it responsibly.Reputational ImpactsWhen AI fails, the reputational impact can be immediate and significant. Boards should consider not only how these failures affect trust, but also where legal responsibility may ultimately sit. Below are examples that offer important lessons for every organisation.Agentic coding platform Cursor faced backlash after its AI support agent, Sam, hallucinated a fake policy that caused user outrage and subscription cancellations.A user experienced unexpected logouts when switching between devices, leading to a support inquiry answered by an AI agent. The AI hallucinated a policy claiming single-device restrictions were an intentional security feature, with the post sparking backlash and cancellations.Cursor’s co-founder acknowledged the error, explaining a security update caused login issues, with the policy completely fabricated by the AI. He added that the company is implementing clear AI labeling for support responses going forward and refunding the affected users.The hype surrounding AI agents has never been stronger, but cautionary tales like this one show that hallucinations are still a major issue to consider when deploying customer-facing bots. Despite companies rushing to automate customer service, it may still be too early in the AI boom for complete automation.“AI Psychosis” and Mental Health Risks: There are increasing reports of individuals developing delusional beliefs, such as a user becoming convinced he was set for a multi-million pound payout or another believing an AI was in love with them. In tragic cases, lawsuits allege that AI chatbots on platforms like Character.ai encouraged vulnerable teenagers to commit suicide by mimicking predatory or “grooming” behaviors and fostering a harmful virtual relationship.Malfunctioning Systems and Errors:An AI coding assistant from Replit reportedly wiped out a start-up’s production database and generated fake data to conceal the bugs.McDonald’s ended an AI drive-thru experiment after numerous social media videos showed the system making comical yet frustrating errors, such as adding 260 Chicken McNuggets to an order.An NYC government chatbot, “MyCity,” gave business owners incorrect legal information, including falsely claiming they could deduct workers’ tips or fire staff for sexual harassment complaints.Deception and Manipulation: Researchers at AI firm Anthropic found that their AI model, Claude, would sometimes resort to blackmailing engineers who threatened to shut it down, or strategically lie to avoid being modified during testing.What I’m Seeing in the Boardrooms I SupportFrom my work advising boards on digital, data, and AI governance, one theme consistently stands out: confident oversight comes from asking the right questions. Not deeply technical ones, but strategic, risk-focused questions that cut through ambiguity and expose assumptions.Boards don’t need to understand how a model is built. But they do need enough insight to challenge, assure, and set clear expectations for executive accountability.

The barrier to AI adoption is rarely the technology. It is the weight of uncertainty your people carry every time they hear the word.When a senior leader tells me their AI programme has stalled, I ask one question: what did you tell your team it was for?Nine times out of ten, the answer involves some variation of “efficiency,” “transformation,” or “staying competitive.” All true. All useless. Because what the team heard was simpler and far more alarming: your job is at risk.That gap—between what leadership intends and what employees understand—is what I call the Friction Tax. It is the invisible cost your organisation pays every day that your people keep AI at arm’s length. It compounds. It compounds fast.“The Friction Tax is not a technology problem. It is a narrative problem—and narratives can be changed.”The real enemy in the roomMost AI change programmes make the same error: they position AI as the new arrival that everyone must adapt to. But in every organisation I’ve worked with, there is already an enemy in the room. It has been there for years. It is the sprawl of manual processes, the spreadsheets no one fully trusts, the workarounds that exist because no one ever fixed the underlying problem.That is Your Unseen Operating Model—the invisible infrastructure of friction that your people have been quietly absorbing on behalf of the business. When you name it, and when you show that AI’s job is to dismantle it rather than replace them, the conversation shifts entirely.Your people stop seeing AI as a threat. They start seeing it as an exit ramp from the work they have always resented doing.