How Boards can Ensure Responsible AI Use

AI in the Boardroom: What Charity Trustees Need to Do Now 🚨 Too many boards are sleepwalking into the risks, while missing the opportunities. I've just finished reading the new Institute of Directors (IoD)’s 'AI Governance in the Boardroom report' and it makes one thing clear: trustees can’t delegate this. AI is a board-level issue. Here are the key takeaways from the report every charity board should act on: 🧠 Stay Curious. Stay Learning. Boards don’t need to be technical experts, but they must understand enough to ask the right questions. Build a culture of digital curiosity at board level. ⚖️ AI = Risk AND Opportunity. Don’t just see AI as a shiny tool to save time. Trustees must weigh efficiency gains against bias, privacy, reputational harm, and compliance risks. ❓ Governance Starts with Questions. Who owns AI in your organisation? How is data being used? What safeguards are in place? Boards need simple checklists and regular oversight, not a one-off discussion. 📜 Know the Law. Regulation is tightening. - The EU AI Act is rolling out, with obligations on transparency, risk classification, and human oversight. - The UK is moving towards sector-led regulation, but trustees are still on the hook for data misuse under GDPR and the ICO’s guidance. - Trustees should be clear: ignorance won’t protect your charity from fines, reputational damage or, worst of all, harm to beneficiaries. 🎯 Impact Before Hype. Does this AI tool align with our mission, or is it just a gimmick? Focus on how tech helps people - service users, staff, and volunteers. 🛡️ Build Oversight Structures. Some boards are creating AI subcommittees or ethics groups. At the very least, AI should be a standing agenda item. Oversight isn’t optional anymore. 🔐 Data is Everything. AI governance is data governance. If your board isn’t confident on data protection, cybersecurity, and safeguarding sensitive information, that’s the place to start. The report is blunt: AI governance is now a fiduciary duty. Trustees don’t get a free pass. ✅ If you sit on a charity board, make AI part of your next meeting agenda. ✅ If you’re a Digital Trustee, help your board translate principles into practice. ✅ If you’re a CEO, empower your trustees to ask the hard questions. This is about safeguarding the people we serve, and making sure technology works for charities, not against them. 👉 If you need to find an AI, data or cyber expert for your board check out the funded Digital Trustees programme from Third Sector Lab. 👏 Thanks to all the authors of the report, including: Michael Ambjorn Phil Clare Paul Corcoran Pauline Norstrom LLB (Hons) FRSA FIoD FBCS Niran Olarinde Institute of Directors (IOD), India Institute of Directors (IoD) ❓What's your simple advice for boards looking to start their AI conversation?

To perform their duties responsibly, boards must function as Humans + AI. Adopting new working structures and evolved governance structures incorporating AI can lead to substantial performance improvement. Much of my current work with boards is on strategic framing for AI and in AI-augmented decision-making, but there is considerably more potential. A very nice HBR piece brings real-world insights to bear. The first finding was that directors and chairs largely failed to recognize the value and potential of AI in their work. However still many boards and directors are using AI in useful ways. MEETING PREPARATION Directors who use LLMs reported significantly improved understanding of agenda items and reduced workload. One director across five Danish boards uses AI to structure presentations and run simulations; another in Switzerland uses it to refine board discussion questions from the board book. SCENARIO PLANNING GenAI, used well, can be an excellent tool for rapid scenario planning. One board in Austria used an LLM to analyze geopolitical risk in an acquisition proposal. This led to it rejecting the deal, and resulted in management attaching scenario analyses to future proposals. ADDITIONAL PERSPECTIVES Boards in Finland and the Netherlands used AI to test their own strategic conclusions, finding significant overlap between AI-generated insights and their human decisions. This boosted both their confidence in the decisions and their trust in AI’s utility, particularly for validating or challenging complex judgments. IMPROVING BOARD DYNAMICS AI can offer real-time feedback on boardroom dynamics. For example, a Swiss industrial company uses AI to analyze speaking time, tone, and engagement during meetings, creating recommendations for better group engagement. The article addresses potential risks: 🔐 Information leaks. These stem not from AI itself but from poor data governance, which can be mitigated with proper access controls and security training. ⚖️ Sample bias. Regular audits and user awareness are key to avoiding flawed, discriminatory, or incomplete insights. 🧭 Anchoring in the past. AI can be overly reliant on historical data. Scenario simulations and reasoning models can help boards anticipate and adapt to future shifts. And concludes with recommendations on learning to use AI well: 1️⃣ Create engagement. Chairs should start with one-on-one conversations to assess AI literacy and follow up with tailored training to build confidence and interest. 2️⃣ Practice collective experimentation. Boards should test AI tools together in low-stakes settings, debrief their experiences, and gradually integrate AI into governance processes. 3️⃣ Maintain momentum. Chairs must lead by example, celebrate AI use regardless of outcomes, and embed AI progress into board evaluations. I am currently working on a 'GenAI in the Boardroom' mini-report that I will be sharing soon, addressing these and a range of other issues and possibilities.

4 AI Governance Frameworks To build trust and confidence in AI. In this post, I’m sharing takeaways from leading firms' research on how organisations can unlock value from AI while managing its risks. As leaders, it’s no longer about whether we implement AI, but how we do it responsibly, strategically, and at scale. ➜ Deloitte’s Roadmap for Strategic AI Governance From Harvard Law School’s Forum on Corporate Governance, Deloitte outlines a structured, board-level approach to AI oversight: 🔹 Clarify roles between the board, management, and committees for AI oversight. 🔹 Embed AI into enterprise risk management processes—not just tech governance. 🔹 Balance innovation with accountability by focusing on cross-functional governance. 🔹 Build a dynamic AI policy framework that adapts with evolving risks and regulations. ➜ Gartner’s AI Ethics Priorities Gartner outlines what organisations must do to build trust in AI systems and avoid reputational harm: 🔹 Create an AI-specific ethics policy—don’t rely solely on general codes of conduct. 🔹 Establish internal AI ethics boards to guide development and deployment. 🔹 Measure and monitor AI outcomes to ensure fairness, explainability, and accountability. 🔹 Embed AI ethics into product lifecycle—from design to deployment. ➜ McKinsey’s Safe and Fast GenAI Deployment Model McKinsey emphasises building robust governance structures that enable speed and safety: 🔹 Establish cross-functional steering groups to coordinate AI efforts. 🔹 Implement tiered controls for risk, especially in regulated sectors. 🔹 Develop AI Guidelines and policies to guide enterprise-wide responsible use. 🔹 Train all stakeholders—not just developers—to manage risks. ➜ PwC’s AI Lifecycle Governance Framework PwC highlights how leaders can unlock AI’s potential while minimising risk and ensuring alignment with business goals: 🔹 Define your organisation’s position on the use of AI and establish methods for innovating safely 🔹 Take AI out of the shadows: establish ‘line of sight’ over the AI and advanced analytics solutions  🔹 Embed ‘compliance by design’ across the AI lifecycle. Achieving success with AI goes beyond just adopting it. It requires strong leadership, effective governance, and trust. I hope these insights give you enough starting points to lead meaningful discussions and foster responsible innovation within your organisation. 💬 What are the biggest hurdles you face with AI governance? I’d be interested to hear your thoughts.

7 questions every board should ask before claiming AI readiness. Spoiler: It’s not “Do we have the tech?” Most AI failures aren’t technical. They’re cultural, political, and invisible. Boards have to lead the change, not the tools. 1️⃣ Is our culture ready for AI-scale change? About 70% of transformations stall or underdeliver. AI is even more fragile without cultural readiness. If the middle stalls, strategy dies before delivery. Fund the change, not just the tools and pilots. Reward behaviors that ship AI, not status reports. 2️⃣ Do employees trust our AI intentions? Up to 60% of employees distrust internal AI plans. Fears: layoffs, surveillance, biased decisions, errors. Distrust drains adoption, output, and brand goodwill. Give clear intent, guardrails, and shared upside. Let teams co-design workflows before rollout. 3️⃣ Can we prove our AI data is secure? 78% of breaches trace to weak controls and handling. GDPR and the EU AI Act raise the price of failure. Fines can reach 7% of global turnover. And headlines. Prove lawful data use, retention, and vendor paths. Be audit-ready before the first use case ships. 4️⃣ Will our AI use stand up to ethics tests? 56% of customers walk over perceived unethical AI. Ethics is a market signal, not a press release. Bias and opacity create legal and trust exposure. Build red lines, testing, and escalation paths. Hold the line when targets tempt shortcuts. 5️⃣ Who owns an AI mistake when it happens? Who signs their name to AI decisions that go wrong? Personal liability is moving toward executives. ‘The vendor did it’ will not survive scrutiny. Name owners, forums, and incident playbooks. Run failure drills before the real incident. 6️⃣ Can we explain any AI decision clearly? Explainability is now an investor and regulatory ask. Boards must defend a hard AI call in plain words. If you can’t explain it, you can’t defend it. Log decisions, data, and model versions by default. Practice the briefing before you need the briefing. 7️⃣ Do we control AI risk in our supply chain? About 65% of AI risk rides on third parties. Opaque models and weak clauses become your liability. Audit the stack: data, models, and human review. Contract for transparency, testing, and remedies. Replace vendors who won’t meet your standard. AI readiness is not a project. It’s a habit. Governance is a daily practice, not a deck. Lead before regulators and headlines do. Is your firm AI-ready?

𝐓𝐡𝐞 𝐦𝐨𝐬𝐭 𝐞𝐱𝐩𝐞𝐧𝐬𝐢𝐯𝐞 𝐛𝐥𝐢𝐧𝐝 𝐬𝐩𝐨𝐭 𝐢𝐧 𝐭𝐨𝐝𝐚𝐲’𝐬 𝐛𝐨𝐚𝐫𝐝𝐫𝐨𝐨𝐦𝐬? Not cyber. Not ESG. 👉 It’s AI illiteracy. Boards spent the last decade learning that cybersecurity isn’t just an IT problem - it’s a governance issue. Now the same shift is happening with AI. Here’s what AI-literate directors now ask: 1️⃣ Where are AI tools being used (officially and unofficially)? Who owns them? 2️⃣ Does this system fall into the EU AI Act’s ‘high-risk’ category - and if so, who’s liable if it fails? 3️⃣ What metrics matter? (e.g., accuracy drift, bias test results, override rates, model lineage, audit logs). 4️⃣ Do our contracts protect us on IP, data rights, indemnities, and audit rights? 5️⃣ What’s our incident playbook if an AI tool makes the wrong call? The piece most boards still miss: AI adoption isn’t just about policies. 𝐼𝑡’𝑠 𝑎𝑏𝑜𝑢𝑡 𝑝𝑒𝑜𝑝𝑙𝑒. You need to see how your teams are really using AI - and then make good practice easy, and bad practice hard. Practical moves for this quarter: ✅ Map AI use cases across the business (in-house + vendor). ✅ Define your “red lines” - the AI uses your business will not allow. ✅ Upgrade key contracts with specific AI clauses on IP, data, and liability. ✅ Run a tabletop exercise: simulate an AI failure and test your response. ✅ Build literacy with one dedicated AI board briefing per quarter. ✅ Ask the ROI question: how can we maximise real value from AI (not just experiments)? 💡 If AI misfires, the headlines won’t name the algorithm. They’ll name the board. AI literacy is the new fiduciary hygiene. 👉 Directors know they need to catch up fast. 👉 AI experts - how can directors get AI literate? 𝐃𝐫𝐨𝐩 𝐲𝐨𝐮𝐫 𝐛𝐞𝐬𝐭 𝐭𝐢𝐩𝐬, 𝐫𝐞𝐬𝐨𝐮𝐫𝐜𝐞𝐬, 𝐰𝐞𝐛𝐢𝐧𝐚𝐫𝐬, 𝐞𝐯𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐩𝐞𝐨𝐩𝐥𝐞 𝐭𝐨 𝐟𝐨𝐥𝐥𝐨𝐰 𝐢𝐧 𝐭𝐡𝐞 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 𝐛𝐞𝐥𝐨𝐰 ⬇️ #BoardGovernance #AILiteracy #RiskManagement #EUAIAct #CorporateStrategy

Stop debating #AI ethics. Start governing. If you work in boards, risk or tech, this new white paper captures where organisations truly stand today. It blends leadership insights with the Voluntary AI Safety Standard and turns ten abstract guardrails into practical action. Three takeaways that matter - Confidence and adoption still lag. The solution is not more hype. It’s accountable practice, risk clarity and capability uplift. - ESG is a powerful entry point. Use existing reporting muscles to stand up data governance, bias monitoring and human-centred design, then layer AI-specific risks on top. - Contestability is non-negotiable. Build responsibility, auditability and redressability into every AI workflow so people can challenge outcomes and you can fix issues fast. Quick guardrail checklist to start this quarter - Name an executive owner for AI, publish your approach and train your teams - Run impact-based risk assessments and test before and after deployment - Lock in data quality, provenance and cybersecurity across the supply chain - Disclose when AI is in the loop and give users clear ways to contest results - Keep an AI inventory and documentation that stands up to scrutiny If you are moving from slides to practice, this paper is worth your time. It’s practical, balanced and usable across both SMEs and large enterprises. I’ll ask for just one thing. If this piece gave you something to think about, please share it with your network or tap the like button. Your support helps me continue producing thoughtful, useful content on Responsible AI and Governance that truly serves this community. #ResponsibleAI #AIGovernance #AISafety #ESG #RiskManagement #Boards

𝗘𝘃𝗲𝗿𝘆 𝗱𝗮𝘆, 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗿𝗮𝗰𝗲 𝘁𝗼 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁 𝗔𝗜 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗯𝘂𝘁 𝗵𝗼𝘄 𝗺𝗮𝗻𝘆 𝗿𝗲𝗮𝗹𝗶𝘇𝗲 𝘁𝗵𝗲𝘆’𝗿𝗲 𝗱𝗲𝗽𝗹𝗼𝘆𝗶𝗻𝗴 𝘁𝗶𝗰𝗸𝗶𝗻𝗴 𝘁𝗶𝗺𝗲 𝗯𝗼𝗺𝗯𝘀? Why should it matter? Without robust governance, AI can amplify risks that can destroy trust, harm individuals and invite costly penalties. The Sołtysiński Kawecki & Szlęzak's whitepaper reveals key realities: • High-risk AI in hiring, credit & law enforcement faces strict EU regulations. • Prohibited practices: subliminal manipulation, social scoring, exploitative biometrics. • Limited-risk AI must clearly disclose AI-generated content. 𝗔𝗹𝗮𝗿𝗺𝗶𝗻𝗴 𝗥𝗶𝘀𝗸𝘀 𝗢𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝗙𝗮𝗰𝗲 𝗧𝗼𝗱𝗮𝘆 • Ethical & Societal: Bias, opaque decisions, environmental harm • Operational: Unpredictable models, hallucinations, bad data • Reputational: Eroded trust, social media backlash • Security & Privacy: Attacks, data misuse, re-identification 𝗧𝗵𝗲 𝗣𝗮𝘁𝗵 𝘁𝗼 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲 𝗔𝗜: 𝗞𝗲𝘆 𝗥𝗲𝗰𝗼𝗺𝗺𝗲𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 ✅ Appoint an AI Champion to lead governance ✅ Build a culture of AI literacy for all employees ✅ Ensure clear transparency in how AI makes decisions ✅ Embed strong technical safeguards to prevent misuse ✅ Maintain meaningful human oversight of high-impact AI decisions ✅ Conduct regular bias and fairness assessments ✅ Draft a simple, actionable internal AI policy aligned with the AI Act 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗜𝗻 𝗧𝗵𝗲 𝗪𝗶𝗹𝗱 Microsoft applies its own Responsible AI Standard and AETHER Committee reviews, ensuring ethical development and deployment of AI across products like Azure OpenAI and M365 Copilot. 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲 Only responsible governance can turn AI from a risk multiplier into a force for inclusive progress by embedding ethics, fairness and resilience into every system. Prof. Dr. Ingrid Vasiliu-Feltes|Helen Yu|JOY CASE|Hr Dr. Takahisa Karita|Antonio Grasso|Nicolas Babin |Alberto Espinosa Machado|Dr. Ram Kumar|Phillip J Mostert| Sara Simmonds |Anthony Rochand|Prasanna Lohar|Shalini Rao #AIForGood #EthicalAI #AICompliance #ResponsibleAI #DigitalTrust #AIGovernance #TechForGood #DigitalEquity #InclusiveInnovation

𝗧𝗵𝗲 𝗘𝘁𝗵𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜: 𝗪𝗵𝗮𝘁 𝗘𝘃𝗲𝗿𝘆 𝗕𝗼𝗮𝗿𝗱 𝗦𝗵𝗼𝘂𝗹𝗱 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 "𝘞𝘦 𝘯𝘦𝘦𝘥 𝘵𝘰 𝘱𝘢𝘶𝘴𝘦 𝘵𝘩𝘪𝘴 𝘥𝘦𝘱𝘭𝘰𝘺𝘮𝘦𝘯𝘵 𝘪𝘮𝘮𝘦𝘥𝘪𝘢𝘵𝘦𝘭𝘺." Our ethics review identified a potentially disastrous blind spot 48 hours before a major AI launch. The system had been developed with technical excellence but without addressing critical ethical dimensions that created material business risk. After a decade guiding AI implementations and serving on technology oversight committees, I've observed that ethical considerations remain the most systematically underestimated dimension of enterprise AI strategy — and increasingly, the most consequential from a governance perspective. 𝗧𝗵𝗲 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 Boards traditionally approach technology oversight through risk and compliance frameworks. But AI ethics transcends these models, creating unprecedented governance challenges at the intersection of business strategy, societal impact, and competitive advantage. 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝗶𝗰 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Beyond explainability, boards must ensure mechanisms exist to identify and address bias, establish appropriate human oversight, and maintain meaningful control over algorithmic decision systems. One healthcare organization established a quarterly "algorithmic audit" reviewed by the board's technology committee, revealing critical intervention points preventing regulatory exposure. 𝗗𝗮𝘁𝗮 𝗦𝗼𝘃𝗲𝗿𝗲𝗶𝗴𝗻𝘁𝘆: As AI systems become more complex, data governance becomes inseparable from ethical governance. Leading boards establish clear principles around data provenance, consent frameworks, and value distribution that go beyond compliance to create a sustainable competitive advantage. 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿 𝗜𝗺𝗽𝗮𝗰𝘁 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Sophisticated boards require systematically analyzing how AI systems affect all stakeholders—employees, customers, communities, and shareholders. This holistic view prevents costly blind spots and creates opportunities for market differentiation. 𝗧𝗵𝗲 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆-𝗘𝘁𝗵𝗶𝗰𝘀 𝗖𝗼𝗻𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 Organizations that treat ethics as separate from strategy inevitably underperform. When one financial services firm integrated ethical considerations directly into its AI development process, it not only mitigated risks but discovered entirely new market opportunities its competitors missed. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘷𝘪𝘦𝘸𝘴 𝘦𝘹𝘱𝘳𝘦𝘴𝘴𝘦𝘥 𝘢𝘳𝘦 𝘮𝘺 𝘱𝘦𝘳𝘴𝘰𝘯𝘢𝘭 𝘪𝘯𝘴𝘪𝘨𝘩𝘵𝘴 𝘢𝘯𝘥 𝘥𝘰𝘯'𝘵 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵 𝘵𝘩𝘰𝘴𝘦 𝘰𝘧 𝘮𝘺 𝘤𝘶𝘳𝘳𝘦𝘯𝘵 𝘰𝘳 𝘱𝘢𝘴𝘵 𝘦𝘮𝘱𝘭𝘰𝘺𝘦𝘳𝘴 𝘰𝘳 𝘳𝘦𝘭𝘢𝘵𝘦𝘥 𝘦𝘯𝘵𝘪𝘵𝘪𝘦𝘴. 𝘌𝘹𝘢𝘮𝘱𝘭𝘦𝘴 𝘥𝘳𝘢𝘸𝘯 𝘧𝘳𝘰𝘮 𝘮𝘺 𝘦𝘹𝘱𝘦𝘳𝘪𝘦𝘯𝘤𝘦 𝘩𝘢𝘷𝘦 𝘣𝘦𝘦𝘯 𝘢𝘯𝘰𝘯𝘺𝘮𝘪𝘻𝘦𝘥 𝘢𝘯𝘥 𝘨𝘦𝘯𝘦𝘳𝘢𝘭𝘪𝘻𝘦𝘥 𝘵𝘰 𝘱𝘳𝘰𝘵𝘦𝘤𝘵 𝘤𝘰𝘯𝘧𝘪𝘥𝘦𝘯𝘵𝘪𝘢𝘭 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯.

AI governance just replaced AI experimentation. The companies that treat AI as “just another tool” are creating risks they won’t discover until it’s too late. AI is no longer only an IT decision. It’s now a boardroom responsibility. The biggest shift isn’t that AI is becoming more powerful. It’s that regulators, customers, and shareholders now expect organizations to prove they can govern it safely. That means every board needs clear answers to questions like: • Who owns each AI system? • Where is company data flowing? • What access does every AI tool have? • How are AI decisions monitored? • What happens when AI is misused or compromised? Without clear ownership and visibility, organizations face more than cybersecurity risks. They also face legal exposure, compliance failures, operational disruption, and loss of customer trust. The strongest AI programs don’t start with buying better tools. They start with: → Clear ownership for every AI system → Continuous monitoring of AI activity → Vendor due diligence before deployment → Human oversight for critical decisions → Regular security testing and governance reviews AI adoption is moving faster than governance. The organizations that build governance now will be the ones trusted tomorrow. The cheat sheet below breaks down the executive priorities every board, CISO, and legal team should understand before scaling AI. P.S. If your board asked today, “Can we prove we’re governing AI responsibly?” would you have a confident answer?