Stephen O'Meara’s Post

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.

If you're building BI at scale, the Analytics & BI track at hashtag #DataAISummit 2026 has sessions worth adding to your schedule. Leaders from Atlassian, HP, Virgin Atlantic and more will cover the work that is moving their organizations forward: — Scaling AI-powered BI from pilot to production — Modernizing semantic layers with AI — Building trusted generative BI Join us this June to learn how to turn data into impact: https://lnkd.in/gd_2Kw76

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

Currently, we’re working with a client who's facing a challenge that many growing businesses can relate to: Their operations were expanding, but their systems weren’t keeping up. The key issues were: • Slow response times affecting client experience • Repetitive manual tasks consuming valuable team hours • Workflow inefficiencies creating operational delays • Rising costs without proportional efficiency gains This is a common problem. As businesses scale, processes that once worked manually start becoming bottlenecks. As the Co-Founder at BroadDigix, my current focus is helping solve this through strategic AI implementation. Our solution includes: • Automating repetitive workflows • Optimizing response and support processes • Improving internal operational efficiency • Creating smarter systems that can scale with growth The goal is simple: Not just to implement AI for the sake of innovation but • To use AI where it creates measurable business impact. • Better efficiency. • Better client experience. • Lower operational friction. This project is another reminder that AI creates the most value when it solves a clear operational problem. For businesses currently exploring AI, that’s usually the best place to start. Don’t fall behind, get your AI assessment right now: https://lnkd.in/d5atwpDF #AI #ArtificialIntelligence #Automation #ClientExperience #BusinessGrowth #DigitalTransformation #Operations #CEO #Founder

Observe most organizations today and you’ll see the same thing: Teams using AI, but still working the same way they always have. Result? A bit of efficiency, maybe some marginal gains... but far from the value that's possible (or even measurable). Our latest World Economic Forum report helps breaks down what it actually takes to get there (and the behavioral changes required to make it stick). https://lnkd.in/gqqSth-K

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

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

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

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.