Scaling AI Responsibly in Life Sciences and Enterprise

Responsible AI is a decision discipline, not a compliance layer. This is Part 6 and the finale of a series on AI governance: solving the decision problem, not just the infrastructure problem. The prevailing enterprise assumption is that governance slows AI down. Poor governance can but well-designed one does the opposite, it creates the conditions for speed. Demonstrating where AI operates, who is accountable, and when human intervention is required eliminates the friction of scaling. Through evidence and reliable performance, the board, customer, and regulatory confidence support an organisation's ability to move AI beyond experimentation into core business processes. To turn governance into an operating advantage, every board agenda must integrate four core disciplines: • Executive AI Fluency: Building a common Board and C-suite vocabulary to evaluate capability, limitations, risk, and commercial return without falling for vendor hype. Fluency is the ability to interrogate purpose, evidence, and accountability, knowing precisely when a system should be constrained or stopped. • Scope & Sweet Spot: Prioritising use cases where material value and acceptable risk can both be evidenced. The objective is not AI everywhere: it is AI where capability, commercial value, and enterprise risk appetite genuinely intersect. • Capital Allocation Filter: Requiring every investment to name its operating outcome, baseline improvement, and value metric. AI must compete for capital like any other strategic asset. • Non-Transferable Accountability: Assigning named accountable owner for every material AI system, supported by clearly allocated responsibilities across its lifecycle. Distributed oversight must never become diluted accountability. These disciplines create speed only when operationalised. Low-risk uses need a fast lane. Material uses require rigorous evidence, testing, and explicit intervention thresholds. Deployed systems require traceability, monitoring, and defined conditions for suspension or retirement. Governance becomes an advantage when controls are reusable and embedded directly into the workflow, rather than rebuilt as a bespoke approval exercise for every proposal. Infrastructure questions are real, but they are not the hardest ones. The harder questions are about decisions. So the final strategy question is simple: what is your organisation's AI filter, the test every material use case must pass before it earns the right to scale? If the answer is "we don't have one," that is more than a governance gap. It is the difference between experimenting with AI and building an organisation capable of scaling it. #AIGovernance #BoardOversight #EnterpriseStrategy #CapitalAllocation #CFOLeadership #EUAIAct #CPS230 #DirectorDuties #ExecutiveLeadership #DigitalTransformation

Anthropic just gave outside reviewers a company badge and gave up the right to stop them from publishing. That's the most interesting AI story this week, and it isn't about a model. Trust in AI is moving from "take our word for it" to "let someone else check." Three signals from the last 48 hours: 👉 Anthropic onboarded six researchers from METR, a nonprofit AI evaluator, with internal access roughly on par with its own risk teams. METR can publish what it finds. Anthropic can only redact narrow categories like customer data, and METR can say publicly whether a redaction was fair. 👉 The Simons Foundation, OpenAI and Anthropic committed $15M to help mathematicians check AI-assisted results. The labs are paying for the human review their own output has outpaced. 👉 Proofpoint agreed to buy Varonis for about $6.3B, pitched on one idea: the data an AI agent can reach decides both its value and its risk. The pattern → the hard part of AI isn't producing more. It's proving what got produced is right, and controlling what it can touch. For any team shipping AI this quarter: → Who outside the team can review it, and can they say no? → Can you list every system your agents can read and write? → Is there budget for checking, or only for building? If the people building frontier models are inviting outsiders in, "we tested it ourselves" won't hold up for long. Who checks your AI's work? #AI #FutureOfWork #TechNews #Leadership #Innovation

𝗧𝗵𝗲 𝗦𝗶𝗹𝗲𝗻𝘁 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗶𝗻 𝘁𝗵𝗲 𝗔𝗴𝗲 𝗼𝗳 𝗔𝗜 AI makes noise cheap. A convincing report, a polished deck or a confident answer takes seconds, and every week brings a new tool and a new prediction. The people who create the most value are rarely the loudest. I attended the launch of Becoming A Silent Professional by Marc Van Obberghen, with a foreword by Lt.-Col. Christophe Comhair, former commander of the Belgian Special Forces Group. The foreword defines a silent professional by intent: discipline without rigidity, humility without hesitation, confidence without ego. Their silence is focus, not absence. Applied to AI: 𝟭. 𝗙𝗼𝗰𝘂𝘀. Organisations announce an AI strategy before they know which problem to solve. Pick one process, understand it, test whether AI improves it, measure the result. 𝟮. 𝗛𝘂𝗺𝗶𝗹𝗶𝘁𝘆. Models sound sure, even when they are wrong. Treat every output as a draft and check it before it reaches a customer or a regulator. "Good enough" arrives in seconds. The silent professional asks if it is right. 𝟯. 𝗔𝘁𝘁𝗶𝘁𝘂𝗱𝗲 𝗯𝗲𝗳𝗼𝗿𝗲 𝗰𝗿𝗮𝗳𝘁𝘀𝗺𝗮𝗻𝘀𝗵𝗶𝗽. Tools change every quarter. Curiosity, critical thinking and accountability last. I'd rather build on someone who questions an output and owns the result than on someone who knows the latest feature. 𝟰. 𝗠𝗼𝗿𝗮𝗹 𝗰𝗼𝘂𝗿𝗮𝗴𝗲. Telling a sponsor the data isn't good enough, stopping a pilot that impresses but delivers nothing, flagging a bias risk the day before launch. Many failed AI initiatives had someone who saw the problem early and stayed silent. 𝟱. 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽. Extreme ownership means owning the outcome, including what a model produced. Every automated process needs a named owner. "The algorithm decided" is not an answer. 𝟲. 𝗗𝗲𝗰𝗲𝗻𝘁𝗿𝗮𝗹 𝗰𝗼𝗺𝗺𝗮𝗻𝗱 𝗻𝗲𝗲𝗱𝘀 𝗮 𝘀𝗵𝗮𝗿𝗲𝗱 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻. You can only push decisions to the edge if everyone knows how models work, where they fail and what data may go in. Without that baseline you get shadow AI. 𝟳. 𝗦𝘁𝗮𝘆 𝗳𝗮𝗿 𝗮𝗵𝗲𝗮𝗱. Build the unglamorous things before you need them: data quality, access controls, evaluation, incident procedures. When the next model or regulation arrives, you adapt instead of starting over. The book is a workbook, so a test for your own team: who owns each AI use case, by name? If the model is wrong tomorrow, who notices, and how fast? Thanks to Marc Van Obberghen for the session. #AITransformation #AIGovernance #Leadership #Ownership

Hey Fanuel Dewever, This is what I like to call a to-the-point and fast recap. I've appreciated your presence and time-focused intervention. Unfortunately, an unforeseen technical issue got everybody kicked out of the session. That is why I send the material upfront so we don't need to do technical "mambo jambo", throughout the session. Luckily, Hilary Harris and Carla Daniëls rejoined when I reopened the session. Anyway, the recap you made here just below, based on the limited knowledge you have of the book at this stage (hence the material sent to everybody and the voice-over itself), is fun to read and indeed takes some of the key reflections away, which are deepened in the book. The fact that you pull this forward to AI & technical-related programs is a happy relief to bring "Human" thinking and steering as well as a grounded "We" approach, back into your kind of programs. If everybody would do this in each kind of Organization & Business Transformation-related program, things would work way swifter. Keep on taking care (where you can, Fanuel), walk tall and stay safe, Marc Van Obberghen Founder & Partner: Tribus Montana, Vade Ad Cor, Inscriptio and Cuspis Hastae Editores Publisher LI Newsletter: Infinite Believers Author: The Inspiration Booklet & Becoming A Silent Professional Mobile: 0032 (0) 497 / 401234 #BecomingASilentProfessional

AI success starts with trusted data. As organisations look to scale AI initiatives, unstructured data must become a governed, traceable, and reusable asset. Without a strong data foundation, the risks increase and the value AI can deliver becomes limited. Looking to build the right foundations for AI? Speak with our team: https://lnkd.in/gY2n8j97

Are you a leader who knows AI is an essential part of your organisation's future success but is struggling to know where to start or how to avoid the big mistakes? Informotion can help you and your AI leaders turn unstructured data from an obstacle into a genuine asset. The starting point is making that data dependable, well understood, traceable, and reusable, so outputs can be generated consistently and relied upon wherever they are applied. Unstructured data needs to sit inside a governed and reusable data foundation alongside structured enterprise data, metadata, lineage, and the tools and capabilities AI systems draw on to act on data in a repeatable way. Lacking that foundation, every move towards scale adds risk and chips away at confidence in the outputs, ultimately capping the value AI can deliver. Read more in the McKinsey & Company article below. If you're exploring how to prepare your organisation for AI at scale, we'd be happy to start a conversation. https://lnkd.in/gja-XFZy #Informotion #AgenticAI #DataGovernance #AILeadership

I just completed the AI Governance Intensive from the Harvard Data Science Initiative — a executive program built around the S.T.E.E.R. framework: Situate, Tension-test, Establish, Evidence, Roll out & Review. The deliverable wasn't a quiz. It was a full AI governance playbook written against a real organization, and building it changed how I think about this work. Three things stuck with me: 1. You're governing people, not models. The program's core argument is that AI is sociotechnical. You aren't governing the AI — you're governing the human practices, decisions, and organizational arrangements that make the AI consequential. Once that clicked, governance stopped feeling like a compliance exercise and started being a design problem. 2. Shadow AI is a symptom, not the disease. People route around approval paths that are slow. Enforcement-first doesn't fix that. Publishing clear rules and a named tool list before anyone is held to them does. So does honest discovery, and making a new-tool request genuinely easy to submit. The goal is an accurate inventory, not a disciplinary record. 3. Accountability has to be scheduled, not stated. A named owner on a slide isn't ownership. Confirm the owner in writing with their leader, name a backup, put reviews on calendars with actual dates instead of a vague "quarterly," and report missed reviews as exceptions — so silence is visible rather than assumed to be compliance. The line from my own playbook I keep coming back to: success isn't how much AI you deploy. It's that every deployment is one you'd be comfortable explaining to a customer, a regulator, and your own board. Thanks to the HDSR faculty and to a genuinely sharp cohort. Now the real work starts. Certificate: https://lnkd.in/giZpaYd6 #AIGovernance #ResponsibleAI #NISTAIRMF #AILeadership

Most AI strategies don't fail in the tech stack they fail in the culture. Organizations are pouring effort into AI-ready data but forgetting the people who have to use, trust, and act on it every day. Without that shift, even the best governance policies stay on paper.   Swipe through to see why culture not budget is the real blocker to AI governance, and what it takes to fix it.   Building your AI foundations? VEGA INTELLISOFT can help you get the data and the culture right. #DataGovernance #AIStrategy #DataCulture #DigitalTransformation #AIReadiness #EnterpriseAI #DataDrivenCulture #AIGovernance #DataLeadership #FutureOfWork #TechLeadership #VegaIntellisoft #AITransformation #DataManagement #BusinessGrowth

AI governance does not scale by creating a list of approved and prohibited tools. It scales when people understand how to make good decisions about AI usage. This week, I had the opportunity to participate in a members-only roundtable discussion titled "Stop Tooling Around," hosted by Kyle David PhD at the AI Governance Collective, with fellow practitioners Fred Descloux and Emily Dorlack. We discussed how risk-based, people-centric AI governance helps organizations scale and grow. Here are the key takeaways: 1. Assess the use case, not just the tool. AI risk depends on the business purpose, the data involved, the outputs, and the consequences, not simply on which model or platform is used. Our governance principles should not need to change every time a new model or tool appears. 2. AI literacy must build judgment, not just awareness. Employees need to understand why guardrails exist, the risks behind them, and the impact of their decisions, which will build their confidence to make responsible choices independently. 3. Governance should enable responsible adoption. The goal is not to create an ever-growing list of approved and prohibited tools. Employees need practical guidance on which capabilities match specific tasks, which data they can use, and when additional review is required. 4. Shadow AI can be a governance signal. Instead of focusing only on detection and blocking, organizations should ask why employees are using unapproved tools. The answer may reveal gaps in approved alternatives, policy, accessibility, functionality, or business processes. 5. Start small and iterate. Organizations don't need to solve every AI governance challenge before taking action. Start with a few prioritized use cases, learn from them, and refine the governance approach over time. This comes back to the fundamentals. Before asking “Which AI tool can we approve?” we should be asking: - What business problem are we solving? - What data is entering the system? - What happens to the output? - What could happen if the AI gets it wrong? - What guardrails and human review are needed? - Do employees understand why those guardrails exist? AI governance practitioners have an opportunity to build an operating model that supports adoption while maintaining appropriate oversight. That means connecting governance to business value, making risk understandable, and giving people a clear path to use AI responsibly. #AIGovernance #AIRiskManagement #ResponsibleAI #AILiteracy #DataGovernance #Privacy #RiskManagement #ResponsibleInnovation

Thank you, Prabhmeet Kohli and Fred Descloux for such an engaging conversation. I love opportunities like this to connect with fellow AI practitioners, exchange perspectives, and think through how we put AI governance into practice. One of the things I value about the AI Governance Collective is the space it creates for practitioners with different experiences and perspectives to learn from one another. I am glad to be part of these conversations and the community we are building!