Scaling AI-Powered BI at DataAISummit 2026

Excited to share a piece I recently wrote for Nava on something I’ve seen make a real difference when testing AI-powered tools: working with participant advisory councils (PACs). While AI systems are often judged by metrics like accuracy and speed, what ultimately determines success is whether people trust them, understand them, and can use them in their day-to-day work. That’s where PACs come in. In this article, I break down 5 reasons why PACs are so valuable—from uncovering workflow friction to surfacing risks that traditional testing can miss. Grateful to our Comms team for sharing this, and to everyone who’s contributed their time and insights to this work. Would love to hear how others are thinking about testing and trust when it comes to AI 👇 https://lnkd.in/gZnEvTcU

AI Illiteracy Is Becoming a Leadership Liability Artificial intelligence illiteracy is rapidly emerging as a material leadership risk. MIT has released ten high-quality AI courses at no cost, yet many executives are likely to overlook them, an oversight with significant strategic implications. While technical coding skills are not required, approving AI-related budgets without a foundational understanding leaves decision-makers exposed to vendor bias, inflated claims, and poorly aligned implementations. Leaders who are successfully navigating this space are asking more disciplined and critical questions: which processes still require human judgment, where AI systems fail at scale, what data underpins model outputs, and whether there is a demonstrable return on investment. This level of inquiry is quickly becoming a baseline expectation rather than a differentiator. The MIT course portfolio offers a practical and accessible pathway to build this capability, covering everything from foundational concepts to applied AI strategy and system design. The ten courses include: 1. Foundation Models and Generative AI https://lnkd.in/gNwgbtaE 2. AI 101 https://lnkd.in/gyJz7whc 3. Artificial Intelligence https://lnkd.in/ggneRvcZ 4. Introduction to Machine Learning https://lnkd.in/gT5HcRs5 5. Introduction to Deep Learning https://lnkd.in/gq8PwrnP 6. Understanding the World Through Data https://lnkd.in/gVdj_EhG 7. Machine Learning with Python https://lnkd.in/gUdHfAhx 8. How to AI Almost Anything https://lnkd.in/ghfgKgsx 9. Introduction to Algorithms https://lnkd.in/g2P-3ptd 10. AI in K-12 Education https://lnkd.in/gs9Fesqy By 2026, the designation “non-technical” will no longer shield executives from accountability in AI-driven decisions.

𝗧𝗵𝗲 𝗽𝗮𝗻𝗲𝗹 𝗶𝘀 𝗻𝗼𝘄 𝗰𝗼𝗺𝗽𝗹𝗲𝘁𝗲... 𝗔𝗻𝗱 𝗶𝘁'𝘀 𝗮 𝘀𝘁𝗿𝗼𝗻𝗴 𝗼𝗻𝗲! When we set out to build 𝗧𝗵𝗲 𝗔𝗜 𝗜𝗺𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 — 𝗪𝗵𝘆 𝗡𝗼𝘄 𝗜𝘀 𝘁𝗵𝗲 𝗠𝗼𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝗜𝗿𝗶𝘀𝗵 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀, we wanted a room where the conversation goes beyond the hype — into what AI is actually doing to how organisations are built, run, and compete. We think we've got that room. 𝗕𝗼𝗮𝗿𝗱𝗿𝗼𝗼𝗺 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀 | 𝗧𝗵𝘂𝗿𝘀𝗱𝗮𝘆 𝟭𝟭 𝗝𝘂𝗻𝗲 𝟮𝟬𝟮𝟲 📍 The Stephen's Green Club, Dublin 🔹 𝗚𝗿𝗲𝗴 𝗛𝗲𝗮𝘀𝗹𝗶𝗽 𝗣𝗵𝗗 — AI Adoption Consultant & Serial Entrepreneur Cutting through the noise on where Irish business actually stands — and what to do about it. 🔹 𝗠𝗮𝗿𝗶𝗮 𝗠𝗰𝗖𝗮𝗻𝗻 — CEO, Future Focus | Organisational Psychologist The human side of AI adoption. Because if your people aren't with you, your AI strategy isn't working. 🔹 𝗩𝗲𝗿𝗼𝗻𝗶𝗰𝗮 𝗥𝗮𝗵𝗻𝗲𝗯𝗲𝗿𝗴 — Founder, Innovera | AI Governance & GTM Strategist Former EY Partner. Trinity Business School. Boards, the EU AI Act, and why governance is a growth strategy — not a blocker. 🔹 𝗥𝗲𝗴𝗶𝗻𝗮 𝗦𝘁𝗮𝗽𝗹𝗲𝘁𝗼𝗻, 𝗖𝗵𝗿𝗶𝘀 𝗣𝗼𝘄𝗲𝗹𝗹 — Argon & Co, one of Europe's leading business design and transformation specialists. Regina as Managing Partner brings enterprise transformation depth; Chris brings something additional — a practitioner's view of AI applied directly to procurement. They'll address the question most organisations haven't asked yet: 𝘪𝘴 𝘺𝘰𝘶𝘳 𝘰𝘳𝘨𝘢𝘯𝘪𝘴𝘢𝘵𝘪𝘰𝘯 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 ready 𝘵𝘰 𝘶𝘴𝘦 𝗔𝗜? 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗿𝗶𝗴𝗵𝘁 𝗻𝗼𝘄 Most leaders are asking "should we adopt AI?" The smarter question is "do we have the capability, the structure, and the people to make it stick?" That's the conversation happening on 11 June. Procurement. Org design. Governance. Adoption. The full picture — from people who've done it at scale. And it won't just be coming from the panel. We're expecting a number of AI practitioners in the room — people already doing interesting things in the space. The conversation will be as good as the company, and the company is shaping up well. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂'𝗹𝗹 𝗹𝗲𝗮𝘃𝗲 𝘄𝗶𝘁𝗵: ✅ Where AI is already reshaping procurement, operations and organisational design ✅ A practical framework for building AI capability — not just deploying AI tools ✅ How to govern AI before regulation makes that decision for you ✅ The questions your board should be asking — and almost certainly isn't 🎟️ Spaces strictly limited — invite-only for business leaders, owners, directors and advisors 👉 𝗥𝗲𝘀𝗲𝗿𝘃𝗲 𝘆𝗼𝘂𝗿 𝗽𝗹𝗮𝗰𝗲: https://lnkd.in/dX3TM99E 𝘉𝘰𝘢𝘳𝘥𝘳𝘰𝘰𝘮 𝘊𝘰𝘯𝘷𝘦𝘳𝘴𝘢𝘵𝘪𝘰𝘯𝘴 — 𝘋𝘶𝘣𝘭𝘪𝘯'𝘴 𝘭𝘦𝘢𝘥𝘦𝘳𝘴𝘩𝘪𝘱 𝘯𝘦𝘵𝘸𝘰𝘳𝘬 #AI #ArtificialIntelligence #IrishBusiness #BoardroomConversations #AIGovernance #OrgDesign #Procurement #DigitalTransformation #Dublin #TheAIImperative

Most AI conversations in boardrooms right now are about tools. The harder question is whether the organisation underneath can actually use them. On 11 June I'm joining Regina Stapleton, Greg Heaslip, PhD, Maria McCann and Veronica Rahneberg at Boardroom Conversations in Dublin to get into this topic. My angle: AI is already reshaping how work gets done, and also where most organisations discover their data, processes and people aren't ready for what they've bought. I'll talk about what's actually working, what's quietly failing, and what procurement and supply chain teams should be doing in the next twelve months. Invite-only, spaces limited. Details in Stephen O'Meara's post below.

Array is hosting a short masterclass on May 26th focused on practical review workflows and defensibility. AI is rapidly changing document review, which is exciting until someone suggests replacing legal judgment with a prompt and a prayer. The more interesting conversation is where AI actually improves workflows, where it creates risk, and how teams are balancing speed with defensibility in the real world. Looking forward to hearing the Array team cut through some of the noise around AI and talk about what is actually working in practice including optimizing the tried and true workflows. Accelerating Review Without Sacrificing Defensibility: What Works in Practice May 26th | 2:00 PM – 2:45 PM ET Register here: https://lnkd.in/gUNdAfgA #DocumentReview #LegalTech #ArtificialIntelligence #GenerativeAI #TrustArray #eDiscovery #PracticeInnovation

I am seeing a lot of AI certifications and digital badges popping up on my feed lately. It is great to see people learning the terminology, but we need to have a candid conversation about what those certificates actually mean. Passing a multiple-choice test on a third-party API does not make you a master. It just proves you know what the ingredients are. It does not prove you know how to run the kitchen. Right now, the industry is stuck in a "microwave mindset." Too many people are building fragile wrappers around probabilistic models, serving pre-packaged intelligence, and calling it innovation. True technical leadership requires moving away from the hype cycle and focusing on vanilla engineering principles. It requires building systems where you actually own the logic and the outcome. I wrote Artificial Intelligence: A Cook's Perspective for the developers, founders, and CTOs who are ready to move beyond the certificate. This book is a manifesto for the digital kitchen. It covers exactly how to transition from a passive consumer of tools into an active architect of robust Artificial Intelligence systems. In the book, we break down: The Commodity Trap: Why relying on public "processed" data gives you zero competitive advantage. The Price per Plate: How to manage token economics and inference costs so your successful product does not bankrupt your business. The Sovereign Kitchen: Why protecting your proprietary data and local context is the only way to build a lasting intellectual moat. The Executive Tasting: How to orchestrate multi-agent systems without losing the human domain expertise that actually guarantees quality. A certificate is just the beginning. The foundational models are a commodity, but your domain expertise, your scars, and your ability to build deterministic systems are the masterpiece. If you are ready to stop serving microwave meals and start building infrastructure that lasts, it is time to step up to the pass. The kitchen is open. Grab your copy here: 🇮🇳 India: https://lnkd.in/dQBwghJ3 🌎 Global: https://lnkd.in/dmktzjjN

Excited to be co-hosting an upcoming Master Series session on Real-time Voice Agents for a modern customer experience. Customer expectations have moved on—fast, natural, personalized support is now the baseline. But many contact centers are still constrained by traditional IVR: rigid menus, limited context, and fragmented systems. In this session, we���ll explore how real-time voice agents in Microsoft Dynamics 365 are changing that—bringing together generative AI, intent understanding, and unified customer data to deliver more natural, end-to-end interactions. We’ll cover: • Designing conversational, AI-driven voice experiences • Handling intents, knowledge, and actions in real time • Seamlessly transitioning between AI and human agents with full context • Modernizing IVR without ripping and replacing your existing contact center If you’re thinking about how to evolve your voice strategy—balancing experience, control, and cost—this should be a valuable session. Register here: https://lnkd.in/eNJtHH7U #MicrosoftMasterSeries #Dynamics365 #AI #VoiceAI #ContactCenter #CustomerService #CopilotStudio #DigitalTransformation #AgenticAI #Microsoft Sonel R.

🚨 Zero → AI Engineer in 6 Months (But Not the Way You Think) Everyone wants to “learn AI.” But most people are just: Watching tutorials Trying random tools Copy-pasting prompts And calling it progress. It’s not. Real AI engineers are not built by consuming content. They’re built by thinking in systems + building consistently. So if you’re serious, here’s the roadmap I recommend 👇 🗓️ Month 1 Foundations (No shortcuts here) Before GenAI, you need to think like an engineer. • Python (data structures, OOP) • Math for ML (linear algebra, probability) • How models actually work 👉 Start here: https://lnkd.in/gdSCtXJv 🎯 Output: Small ML scripts + GitHub repo 🗓️ Month 2 Machine Learning Core This is where most people quit. This is also where real engineers are made. • Supervised & unsupervised learning • Feature engineering • Model evaluation & bias 🎓 Stanford CS229 – Andrew Ng 👉 https://lnkd.in/gDc9siUC 🎯 Output: End-to-end ML project 🗓️ Month 3 Deep Learning Systems Now you stop “using models” and start understanding them. • Neural networks, CNNs, RNNs • Training deep models properly • Debugging failures 🎓 CS230 👉 https://lnkd.in/guaavVdk 🎓 CS231n 👉 https://lnkd.in/gVJZ-xMq 🎯 Output: Deep learning project with real training logic 🗓️ Month 4 LLMs & GenAI Now GenAI actually makes sense. • Transformers & attention • Embeddings + vector search • NLP foundations 🎓 CS224N 👉 https://lnkd.in/gQdFAZXM 🎓 CS224U 👉 https://lnkd.in/gRsWMfA6 👉 Practice repo: https://lnkd.in/gRb53YAF 🎯 Output: LLM-powered system (not just prompts) 🗓️ Month 5 AI Systems & MLOps This is the difference between: “Cool demo” vs “Real product” • APIs + deployment • Vector databases • Monitoring + evaluation • CI/CD for ML 🎯 Output: Deployed AI system 🗓️ Month 6 Real AI Engineering Now you think like an engineer, not a learner. • Data pipelines • Scale & performance • Security & privacy • Real-world case studies 🎓 CS221 👉 https://lnkd.in/gM9ndUCf 🎓 CS234 👉 https://lnkd.in/gVvz87Hc 🎯 Output: Production-ready AI system 💡 Most people rush to tools. Great AI engineers master foundations → then build systems that survive production. If you’re starting from zero, start now. Not perfectly. Just consistently. 6 months later, you won’t recognize your own skillset. ♻️ Repost if you’re building serious AI systems in 2026 to help others learn. 📌 Save this you’ll come back to it

Anthropic co-founder Chris Olah just stood at the Vatican and told the world his own company can't be trusted to govern itself. Not in those exact words. But close. At the presentation of Pope Leo XIV's first encyclical on AI today, Olah said: "Every frontier AI lab... operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing." He said this made outside scrutiny essential. This is the most candid thing I've heard from an AI company executive in years. It's not a regulator saying AI labs can't self-police. It's one of the people building the technology saying it. The Pope's encyclical - 83 pages, titled "Magnifica Humanitas" - called for governments to slow down AI development, protect workers, and limit private control over AI data. Those are the expected calls. Olah's presence at the Vatican, and his words, are the actual news. Competitive pressure, capital pressure, geopolitical pressure - all of it pushes AI labs in one direction. He knows it. He said it publicly. For enterprise buyers who've been wrestling with AI governance for two years, this matters. The trust problem isn't theoretical anymore. One of the people closest to the technology just confirmed it's real. https://lnkd.in/dDhAN7aY