Alex Miguel Meyer’s Post

Leaders should devise a pragmatic strategy for their enterprise that leverage AI for all-round value maximisation while (a) having sound governance; (b) mitigating attendant risks; (c) focusing on obtaining reasonable return on investment; (d) reskilling and upskilling people; and (e) making processes agile, sustainable, and scalable concomitant with their growth plans.

AI during instability doesn’t create chaos. It reveals it. Companies don’t fear AI. They fear what it exposes. You roll out AI during a period of uncertainty. Suddenly: - Teams feel exposed. - Leaders feel questioned. - Reports stop matching. - Processes start breaking. And the conclusion? “AI is creating chaos.” Let that sink in. AI didn’t create the chaos. It made it visible. Here’s what it typically exposes 👇 This isn’t a technology problem. It’s an organisational mirror. But here’s the truth. AI isn’t destabilising your company. It’s highlighting what was already unstable. These 3 exposure points show up every time: ✓ 𝗟𝗲𝗮𝗱𝗲𝗿𝘀𝗵𝗶𝗽 𝗴𝗮𝗽𝘀 AI forces clarity. - What exactly are we optimising for? - Who owns this outcome? - What does success look like in numbers? If leaders are misaligned, AI accelerates the tension. Conflicting priorities become visible. Vague strategy becomes measurable. Decision delays become operational friction. AI doesn’t weaken leadership. It exposes where it’s already weak. ✓ 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝗻𝗰𝗼𝗻𝘀𝗶𝘀𝘁𝗲𝗻𝗰𝗶𝗲𝘀 Automation requires standardisation. But most organisations run on: - “How John does it” - Market-by-market variations - Undocumented workarounds - Verbal approvals The moment you try to automate, you discover: There isn’t one process. There are five versions of it. AI cannot scale inconsistency. It forces you to define, document, and discipline your operations. ✓ 𝗣𝗼𝗼𝗿 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲 AI runs on data. And during instability, data cracks widen: - Duplicate records - Inconsistent KPIs - Missing fields - Different definitions across teams Suddenly: Marketing’s numbers don’t match finance. Sales dashboards conflict with operations. Board reports raise uncomfortable questions. AI didn’t corrupt the data. It exposed that the organisation was operating on fragile foundations. Pro tip. Treat AI like an operational stress test. Before deployment, ask: Is leadership aligned? Are processes standardised? Is data ownership clear? Because if those aren’t solid, AI will surface the cracks fast. And visibility feels like chaos when you’re not ready for transparency. If instability + AI feels uncomfortable… That’s not failure. That’s exposure. In my AI Impact Accelerator, I help leaders build AI systems that survive real-world operations, not just pilots. If you want to learn more, DM me IMPACT. ♻️ Repost to help leaders understand what AI really reveals. ➕ Follow Bob Young for operator-level insights on AI, leadership, and execution.

I once saw a boardroom treat an AI implementation like a cat treats a new vacuum cleaner: equal parts fascination and "I am staying exactly three rooms away from that thing." 🐱💻 The truth is, we often talk about Artificial Intelligence as if it’s a sentient oracle that arrives in a box. But if my time at CCH® Tagetik has taught me anything, it’s that the "Magic AI Button" is a myth. The real engine? It’s a 1980s psychological framework called the Technology Acceptance Model (TAM), and it’s making a massive comeback in the 2020s. A recent study published in 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 (surveying 420 brave souls across industries) confirms what we’ve suspected: 𝐏𝐞𝐫𝐜𝐞𝐢𝐯𝐞𝐝 𝐔𝐬𝐞𝐟𝐮𝐥𝐧𝐞𝐬𝐬 is the ultimate king. If your team doesn't see how AI shaves two hours off their Friday reporting, they won't use it. Period. But here’s the geeky twist: Top Management Support isn't just about signing the check. It’s the "vibe-shifter." When leadership actually backs the tech, it miraculously makes the tools feel both more useful and—get this—easier to use. It’s the placebo effect of corporate culture. However, simply "adopting" the tech is a trap. Research from the Lebanese University (analyzing 25 firms) found that "AI Adoption Level" had a near-zero impact on performance. 📉 Ouch. [1] So, what actually moved the needle? 1️⃣ 𝐄𝐦𝐩𝐥𝐨𝐲𝐞𝐞 𝐀𝐈 𝐂𝐨𝐦𝐩𝐞𝐭𝐞𝐧𝐜𝐞: This explained 48% of performance variance. If your team can't "speak machine," the machine is just a very expensive paperweight. 2️⃣ 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧-𝐎𝐫𝐢𝐞𝐧𝐭𝐞𝐝 𝐂𝐮𝐥𝐭𝐮𝐫𝐞: You need an environment where "let's break this and see what happens" is a valid Tuesday afternoon strategy. 3️⃣ 𝐑&𝐃 𝐈𝐧𝐯𝐞𝐬𝐭𝐦𝐞𝐧𝐭: You can't budget for AI like you're buying office chairs. It’s a marathon of experimentation. At Wolters Kluwer, we see this daily. We don't just build software; we’re building the bridge between human intuition and algorithmic power. 🌉 To my fellow leaders: stop looking for the "Deploy" button and start looking at your people. AI won't replace your department, but a team that knows how to leverage TAM to drive decision-making efficiency absolutely will. Are you building a culture of "perceived usefulness," or are you just buying shiny toys? Let's get weird with the data. 🤓 #ArtificialIntelligence #CorporateCulture #DigitalTransformation #TechAdoption #CCHTagetik #WoltersKluwer #TAM #FutureOfWork #ManagementScience #GeekLeadership #DataDriven #InnovationMindset References: [1] Impact of Artificial Intelligence on Organizational Performance: https://lnkd.in/dBpKrcAX [2] The Evolving Role of Leaders in the Age of AI By Gratien Mukeshimana Co-founder of 28COE: https://lnkd.in/dCWTxrBd [3] The Leader’s Guide to Transforming with AI: https://lnkd.in/dXSsqSY4

AI governance isn't your bottleneck. It's your engine. Because without a framework for accountability, AI doesn't scale, it just duplicates risk. The real competitive advantage lies in the ability to explain and defend every automated move. Read the full article from World Economic Forum: https://lnkd.in/gPt_XD54 #AI #ArtificialIntelligence #Governance #Enterprise

AI Ambition Without Strategy Is Just Experimentation Over the last 18 months, I’ve worked with several organisations and their leaders who, like most, are curious and ambitious about AI. What has been clear from those engagements is that executive teams need help in how to define, govern and communicate AI direction responsibly.   I still see many organisations mistaking AI activity for AI strategy. They are exploring potential use cases, have defined their acceptable AI use policy and of course, rolled out Microsoft Co-Pilot to a select group of users in the hope of seeing some productivity gains. There may be an AI Working Group that might include some governance and risk oversight. For many, this is considered to be the extent of their ‘AI strategy’. But activity is not strategy. There is no direction, no overarching alignment to business drivers or goals. Without a clearly defined AI purpose with intent that is anchored to business priorities, governed deliberately, and designed for scale, what looks like progress is often just experimentation that ultimately does not scale or deliver sustainable business value. AI strategy is not about doing more AI stuff. It is about deciding what and why AI really matters. It is about: ·        Being clear on ‘why’ this is good for our business goals, what business value needs to be delivered and how we embed it into the way we operate. ·        How we take our people through what will be a significant change journey with the right guardrails and understanding of risks as well as opportunities. ·        Who we need to partner with to ensure we adopt the right solutions, in the right way, with the right level of investment to get pragmatic and valuable business outcomes. If you don’t define and articulate an AI strategy grounded in strong governance you’ll likely end up with: ·        Multiple pilots for use cases that don’t scale or deliver meaningful value. ·        Growing shadow AI as employees try to do their own thing as they don’t have direction. This presents a serious data leakage problem. I’ve seen it. ·        Lack of accountability for AI initiatives and increasing risk. ·        Increasing costs with no clear ROI because business value remains unanswered. AI is ultimately a leadership and organisational cultural challenge. Without clear intent and communication: ·        Employees fear replacement instead of seeing enablement. ·        Skills atrophy creates cognitive debt. ·        Employee and customer trust potentially erodes. AI ambition is important. But without direction and intent, it risks becoming nothing more than expensive experimentation. If AI is to be material to your organisation, ask whether you truly have a strategy or you’re just managing AI activity. The organisations that will truly benefit from AI won’t be those experimenting but those deciding deliberately with clarity of purpose and intent. That’s the difference between experimentation and strategy.

AI isn’t strategic because you have it — it’s strategic because you measure and govern it. Too many leaders proudly announce AI initiatives… yet struggle to answer one simple question: 👉 “What changed because of AI last week?” If you can’t answer that, you are not managing AI — AI is managing you. In every mission-critical domain — finance, ops, risk — weekly visibility is non-negotiable. Quarterly reviews are already too late. If AI decisions are shaping outcomes daily, your governance model must keep pace. Here’s the hard truth: 📌 AI success isn’t about bold pilots or cool dashboards. 📌 ROI doesn’t magically show up on quarterly P&Ls. 📌 The real value — and real risk — shows up in weekly cadence. The leaders who win with AI do three things differently: ✅ Embed AI into workflows—not silos Weekly reviews of where AI influences decisions ensure effects are real, measurable, and intentional. ✅ Assign accountable owners, not committees Accountability isn’t diffused into thought leadership—it’s anchored to named roles with weekly signals. ✅ Track the right operational signals Focus on variance reduction, time saved, and exception rates — not vanity metrics. AI isn’t a tactical add-on. It’s a decision-shaping system. If you can’t measure it weekly, you’re flying blind. 🔍 That’s why governance cadence is the real competitive edge — and the exact discipline MyClearPathAI helps organizations operationalize. If you’re serious about transforming AI from cost center to strategic capability: Visit myclearpathai.com to explore how our frameworks and weekly governance operating models help businesses manage AI with rigor, predictability, and results. https://lnkd.in/eCtgdvga

AI will not fail at work because of technology. It will fail because of design. Most conversations about AI at work still focus on capability. What can AI do for me or my team? How quickly can we get this up and running? How much money will this save? The real risk of AI though isn’t job loss. It’s purpose loss, trust erosion, and unclear accountability. Here are a few of the implications that stood out most to me: First: the unit of change is no longer the job. It’s the human–AI interface. AI doesn’t replace roles in practice. It inserts itself at key decision points, the “moments that matter” such as recommendations, flags, drafts, and prioritization. Organizations that struggle aren’t the ones with weak models, they are the ones that never define who decide what, when, and with what authority once AI is in the loop. Second: human‑in‑the‑loop is not about ethics alone. It is about performance. Evidence increasingly points to guided AI outperforming full automation on key metrics such as quality, error reduction, and employee engagement. Keeping humans in control is not a concession to fear. It is a design choice that produces better outcomes across the board. Third: trust is becoming a first‑order operational risk. Transparency, explainability, and override rights are not soft change‑management tools. They are essential indicators of adoption, innovation, and reputation. If employees do not trust how AI is used, they will either resist it or route around it. Fourth: the future workplace creates a new privacy dilemma we have not solved yet. AI‑optimized “cognitive ecosystems” promise productivity and wellbeing, but they depend on sensing mood, behavior, and engagement, aspects that are notoriously challenging for AI to address. That is where optimization could quietly turn into surveillance unless governance is explicit and enforced. 👉 So, before scaling AI, consider redesigning a single critical workflow around decision rights and accountability. Map where humans and AI intersect. Clearly define the override rules. Make accountability unambiguous. Measure trust and quality alongside efficiency. If we get this right, AI becomes an amplifier of judgment and purpose. If we don’t, we’ll have faster processes but emptier work. I’m curious how others are approaching this challenge. Where have you seen AI clarify accountability, and where has it blurred these lines? https://lnkd.in/eACjtaam

The truth. If I had a penny for the software/AI Tools that are never implemented with care and thought

🚀 The Rise of the Head of AI: Why Organizations Are Appointing Chief AI Officers Across 2025–2026, a powerful leadership trend is reshaping the C-suite: organizations are appointing Heads of AI or Chief AI Officers (CAIOs) to lead their artificial intelligence strategies. What started as a niche technical role is now a core executive priority, reflecting AI’s expanding strategic importance. 📊 AI Leadership by the Numbers Here’s how rapidly this trend is growing: 📈 26% of organizations worldwide now have a Chief AI Officer, up from just 11% in 2023. 💼 One in three organizations already has a CAIO — and nearly half believe the role should be created. 🇬🇧 48% of FTSE 100 companies have appointed a CAIO or equivalent role, with most hires made recently. 🇮🇳 83% of Indian firms have appointed a Chief AI Officer to fast-track generative AI adoption — with another 15% planning to by 2026. 📊 A global study also found that organizations with CAIOs achieve around 10% higher ROI on AI investments — and structured AI leadership models can boost ROI by up to 36%. These figures show that AI leadership isn’t just a buzzword — it’s an emerging standard in competitive, AI-driven enterprises. 🔍 Why This Role Is Becoming Essential AI has moved beyond experimentation into core operations. Businesses now need leaders who can: ✅ Align AI with business goals AI isn’t just an IT tool — it’s a transformative enterprise-wide capability. ⚖️ Govern risk, fairness and compliance Boards increasingly demand oversight on ethics, bias, data protection and regulatory risk. 💡 Drive innovation and competitive advantage Companies with CAIOs are better positioned to scale AI for growth and efficiency. 🔗 Bridge business and technology Unlike traditional CIO/CTO roles, the Head of AI focuses specifically on strategic AI adoption across departments. 👩💼 What a Head of AI Actually Does A typical Chief AI Officer: Defines and executes enterprise-wide AI strategy 🧭 Oversees ethical, compliant AI governance 🛡️ Coordinates cross-department AI adoption 🔄 Measures ROI and business impact 📊 Builds internal AI literacy and talent 🧠 🔮 What This Means for the Future The rise of the Head of AI signals a broader shift: AI is no longer a siloed tech initiative — it’s a strategic cornerstone of modern business. As adoption grows, this role is likely to become a standard part of executive teams, much like CFOs or CMOs.

"AI won’t replace you. But someone who knows how to use AI might." That line captured the spirit of our recent IMDA x QED “AI in Action” roundtable, where CEOs, CIOs, CTOs and security leaders came together to tackle the real question: How do we move AI from pilots to enterprise-wide impact? We ran two live polls to ground the discussion in reality: 📊 Biggest barriers to scaling AI • 62% – Difficulty demonstrating ROI • 62% – User adoption & skills gaps • 31% – Data readiness challenges • 23% – Legacy integration 📊 Where AI will deliver the greatest value • 77% – Operational efficiency & cost optimisation • 54% – Better decision-making from data • 23% – New revenue models The numbers told a clear story. Everyone wants AI to deliver value. Many are still figuring out how to prove it. The discussion was refreshingly candid: • 💥ROI remains the elephant in the room. If productivity improves after deploying an AI super-app, who gets the credit, the AI tool or the person using it well? Attribution is messy. Model metrics are not business metrics. The answer lies in crystallising KPIs tied to real business problems. • 💥Context beats capability. AI is not a one-size-fits-all playbook. The “best” model means nothing without clarity on what busuness problems actually needs to be solved. • 💥Legacy data is still locked away. Extracting value from entrenched systems remains a major bottleneck. Synthetic data may support experimentation, but governance must evolve alongside innovation. • 💥Adoption is harder than deployment. Jobs may not disappear overnight, but roles will evolve. AI doesn’t replace people. It replaces outdated ways of working. • 💥Governance, testing and guardrails must be built in not bolted on. Ethics, compliance and risk management are foundational, not afterthoughts. We even had folks openly sharing business problems which AI could help solve in areas like horticulture and education! Perhaps the strongest takeaway? AI success is not about who pilots fastest. It’s about who scales smartest with clarity, discipline and leadership alignment. Grateful to IMDA and the 4 innovative Accredited/Spark companies( fileAI., Fano (Fano Labs) Silent Eight and Resaro) for enabling such an open and forward-looking exchange. 🚀 Clare Leighton Victor Li Xiaowei Sagar Rajput April Chin Wee Keong Tan Lucas Tong Joanna Wu Thanks to all the participants who made this session so thought-provoking by sharing their practitioner-led insights Mark LIM CL Audrey Ong David Tay Katherine Chong SM Lee Johnson Paul Jason Kee Kudos to the QED team for another stellar session! Caitlin ONG Ching Woon Tay @sofia yusof Ryan LIM Natasha Z.