Balancing AI Ambitions With Realistic Goals

After years working in AI an ML, I've learned that chasing 100% accuracy can do more harm than good. Real-world data is messy and ever-changing. Our models should embrace that diversity, not hide from it. Sure, near-perfect accuracy sounds nice in theory, but that can simply mean overfitting to the noise and quirks of one dataset. The real test is how a model handles new, unseen data. And not all applications need ultra-high accuracy. For movie recommendations? 70% is solid. For medical diagnosis? We clearly need to aim higher. Benchmarking to reasonable targets for each industry is key. There are financial trade-offs too. Endlessly squeezing out smaller accuracy gains costs exponentially more. The latest model with 99.5% accuracy might not justify 10x the resources over the 98% accurate one. We have to work smarter. The path forward is balancing accuracy, efficiency and impact. Rather than chasing the illusion of perfection, we should focus on moving the needle in practical, meaningful ways. That's how we responsibly advance AI and drive real change. I've learned you can't force accuracy through brute computation alone. It takes nuanced human judgement to apply AI ethically and effectively. We still have much to learn together on that front. But I'm excited for the future, as long as we stay grounded. #ArtificialIntelligence #MachineLearning #DataScience #AIEthics #TechForGood

Too many AI strategies are being built around the technology instead of the business challenges they should solve. The real value of AI comes when it is directly tied to your goals. I have arrived at seven lessons on how to align your AI strategy directly with your business goals: 1. Start with the "why," not the "what." Before discussing models or tools, ask what business problem you need to solve. It could be speeding up product development, or cutting operational costs. Let that answer be your guide. 2. Think in terms of business outcomes. Measure AI success by its impact on metrics like revenue growth or employee productivity not by technical accuracy. 3. Build a cross-functional team. AI can't live solely in the IT department. Include leaders from all relevant departments from day one to ensure the strategy serves the entire business. 4. Prioritize quick wins to build momentum. Identify a few small, high-impact projects that can deliver results quickly. This builds organizational confidence and makes people ready to take on larger initiatives. 5. Invest in data foundations. The best AI strategy will fail without clean and well-governed data. A disciplined approach to data quality is non-negotiable. 6. Focus on change management. Technology is the easy part. Prepare your people for new workflows and equip them with the skills to work alongside AI effectively. 7. Create a feedback loop. An AI strategy is not a one-time plan. Continuously gather feedback from users and analyze performance data to adapt and refine your approach. The goal is to make AI a part of how you achieve your objectives, not a separate project. #AIStrategy #BusinessGoals #DigitalTransformation #Leadership #ArtificialIntelligence

AI Economics: Bold Predictions, Harsh Realities Remember PwC's bold prediction that AI would contribute $15.7 trillion to the global economy by 2030? Meanwhile, the current reality portrays a different picture. An MIT study reveals that 95% of corporate GenAI pilots are failing. Also, the FT’s three-part AI series highlights ballooning capex, power constraints, and shaky unit economics around data centers. And the AI Index 2025 notes uneven productivity gains. Let's pause and examine what's actually happening on the ground. The MIT study reveals that 95% of corporate generative AI pilots are failing to deliver measurable business impact. Despite $30-40 billion in enterprise AI investment, only 5% of initiatives achieve rapid revenue acceleration. The culprit isn't the technology; it's flawed integration and misalignment with existing workflows. This reality check comes as various reports, including analyses from major financial publications, highlight the growing disconnect between AI promises and practical outcomes. We're witnessing "GenAI Divide", a stark gap between expectations and execution. The path forward, in my opinion, requires honest recalibration: ✔️ Start small, think workflow-first: Integrate AI into existing processes rather than forcing wholesale changes ✔️ Measure what matters: Define clear success metrics beyond tech demos; focus on P&L impact ✔️ Invest in change management: 95% failure rate suggests this is more about people and processes than algorithms  ✔️ Build gradually: Successful companies are treating AI as a marathon, not a sprint ✔️ Ship safely: policy, auditability, and human-in-the-loop by default. The trillion-dollar AI revolution might still happen, but it won't be through blind faith in shiny pilots. It'll come from organizations that approach AI with strategic patience, clear objectives, and ruthless focus on real-world value creation. Ambition is good. But disciplined execution, not hype, will determine who captures real AI value. #AI #TechReality #InflatedExpectations

I’ve seen AI budgets burn with nothing to show—here’s the mistake no one talks about. Technology alone won't get you far. If AI isn't tied to your strategic vision, you're just chasing trends. Too many teams jump into AI with no clear direction. The result? Wasted time. Lost chances. Frustrated teams. Senior executives: Set your north star first. Know your real goal. Map your AI roadmap to it. Keep things simple—focus on what works, not what's shiny. Build resilience and clarity in your people. Empower teams to adapt and keep learning. Measure real impact, not just activity. AI is a tool, not the goal. Only alignment with your vision unlocks its real value. So, does your AI plan match your north star? What's your take?

💰 A CFO told me over coffee yesterday: “The gap between AI hype and AI economics is wider than anyone admits.” He’s caught in a pincer movement: the board is demanding “AI transformation” while the P&L is demanding “margin protection.” Here’s how he actually decides what gets funded (spoiler: it’s not about the coolest technology): 1. Real economic impact The first question: does this measurably improve unit economics? If an AI pilot just “speeds up” a process but doesn’t lower cost per transaction or increase lifetime value, it doesn’t get funded. They focus on areas like Trust & Safety, where automation directly reduces fraud friction and improves conversion. 2. Built to scale globally In a global marketplace, local point solutions might not pay off. They back AI initiatives that can scale across multiple categories and regions. If a solution can’t be rolled out across the ecosystem, it becomes a capital drain rather than a competitive advantage. 3. Shifting talent to higher-value work The question isn’t always whether automation makes things cheaper. It’s also important to assess whether it allows human talent to move from manual work to high-judgement, high-impact work. The goal is making teams meaningfully more effective, not necessarily marginally cheaper. It’s the discipline of saying “No” that makes the “Yes” powerful. The goal is making AI a true business partner that delivers measurable ROI, not just a headline. How are you distinguishing between strategic AI investment and expensive distraction in your organisation?

💡 The AI honeymoon is over, and most organizations have little to show for it. After years of pilots, proof-of-concepts, and innovation theater, BCG reports only 26% of companies have deployed working AI products—and a mere 4% see meaningful returns. The problem isn't technology. It's the absence of disciplined strategy married to human purpose. I've spent three decades watching brilliant technologies fail not from technical shortcomings, but from organizational incoherence. AI is no different. What separates companies that generate real value from those burning resources on experiments that go nowhere? Two things: strategic discipline and portfolio thinking. In our recent Harvard Business Review articles, we explore how organizations can move beyond the chaos: First, balance innovation with governance using practical frameworks. Our OPEN and CARE framework provide structured ways to ask the right questions early — questions that align AI with genuine business priorities while protecting against risks that emerge when we automate without thinking. This isn't about slowing down or creating bureaucratic bottlenecks. It's about moving forward with intention, ensuring every AI initiative serves both business value and human purpose. Second, treat AI as a portfolio, not a collection of pet projects. Organizations like Northrop Grumman, PepsiCo, and Lloyds Banking Group have proven that structured portfolio management—complete with prioritization frameworks, resource allocation discipline, and clear buy/sell/hold decisions—transforms AI from cost center to strategic asset. When you combine these approaches, something fundamental shifts. AI stops being something bolted onto strategy and becomes inseparable from it. The result: better returns, less waste, and organizations that remain distinctly human even as they become more technologically capable. The question isn't whether to invest in AI. It's whether you're managing those investments with the same rigor you'd apply to any other strategic portfolio. 🔗 Read further @ 📍 "Two Frameworks for Balancing AI Innovation and Risk" → https://lnkd.in/edHnUzGK 📍 "Manage Your AI Investments Like a Portfolio" [with/ Tom Davenport, Paul Scade, PhD, Erik Nelson] → https://lnkd.in/gEJ_WnyM What's blocking your organization from moving AI from experiments to enterprise value? I'm curious what you're seeing.

Buy or build? One trajectory from ambition to pragmatism is worth a thousand AI strategies. Singapore's SEA-LION AI model started by pretraining from scratch. By v2 and v3, the team shifted toward adapting open pretrained models with region-specific data and post-training. By v4, they deliberately scaled down from 70B to 27B, because deployment constraints and inference costs, not pride, were binding in Southeast Asia. The paper behind that story, by Lu, Xu, Tjhi, Li, Bosselut, Koh, and Kankanhalli, lays out the acquisition pathways governments actually face. API access and managed services at one end. Hybrid approaches in the middle (RAG, sovereign cloud partnerships, fine-tuning). Full pretraining at the other end. It then compares the trade-offs across sovereignty, privacy and security risk, cost, domestic capability building, sustainability, and national context fit, with an explicit nod to how fast the cost-capability frontier is moving. Two reasons I really like this paper. First, it is not written from the balcony. It draws on practitioner accounts from the teams behind SEA-LION and Switzerland's Apertus, including what turned out to be harder than expected: data acquisition, infrastructure software, and talent retention. Most AI-for-government writing either stays at the level of slogans or disappears into architectures. This one connects strategic choices to operational reality. Second, the idea of capability debt. When governments outsource everything, they do not just defer effort. They compound the eventual cost of catching up, because organizational learning accrues through iteration, not procurement. The paper is also clear on the other side of the argument: buying is not a lesser choice. For many use cases and economies, the most responsible path is to consume models as a managed service, while investing in data governance, integration, evaluation, and contractual controls. That is how states retain real control, through data rights, audit and safety requirements, portability, and credible exit options, even when they are not building the underlying foundation models themselves. That last point aligns with what I keep seeing in work on sequencing AI deployment. The real gap will not be between countries that build models and countries that do not. It will be between the small set of governments that choose a deployment pattern they can sustain, and the larger set that reach for one they cannot maintain.

AI is forcing CIOs into a new kind of balancing act: deliver meaningful cost efficiency and create credible paths to new revenue all at the same time. In my recent conversation with CIO News, I shared the framework to strike that balance. The era of chasing shiny ideas is over. https://lnkd.in/gVRszZk7 Today, every AI initiative must be grounded in strategy, data, and ROI timing. Here are the three lenses I apply: ROI Horizon Short-term pressure? AI for automation and cost takeout wins. 12–24 month runway? AI for sales, product growth, and new markets makes sense. Data Maturity AI only performs as well as the data feeding it. • Strong operational data → automate, reduce cost • Rich customer data → drive revenue and experience Business Priority If margin pressure is the priority, focus AI on operational efficiency. If the business is chasing growth, point AI toward revenue. Always start with the real business pain point cost, noise, or compliance. But strategy alone isn’t the blocker. Most challenges come from: • Data everywhere and nowhere lineage, quality, governance • Underestimating AI’s ongoing cost cloud meters never stop running • Immature security & governance models zero-trust AI is still evolving The biggest trap? Waiting for “perfect conditions.” You won’t get a nirvana state where data is pristine, governance is flawless, and platforms are fully mature. Progress comes from disciplined, iterative execution. Start small. Start real. Start now. AI rewards momentum—not perfection. Thanks #CIONews.com

My AI Journey, Chapter 1: From Ambitious Goals to Tangible Impact in IT VMO A couple of years ago, our CIO laid down a challenge that truly ignited my AI journey: "50% of all IT work is AI-powered" and "Reduce employee task friction by 50%." Bold goals, right? But as Leader of IT VMO, I saw an immediate opportunity to tackle a persistent pain point that many of us in operations face. Our IT VMO team was constantly fielding the same questions from stakeholders. While we had meticulously documented answers in SharePoint, training sessions, and various forums, the sheer volume of repetitive queries was a significant manual burden. This wasn't just friction; it was a drain on our capacity to focus on strategic VMO initiatives. That's when we decided to build our own solution. Inspired by tools like Cisco IT's BridgeIT (which leveraged GPT 3.5 at the time), we developed a specialized AI chatbot for our stakeholders - VIVA (VMO Integrated Virtual Assistant). The premise was simple: stakeholders could ask questions in natural language, and our Generative AI would respond with clear, concise, and easy-to-understand answers, pulling directly from our existing knowledge base. The impact? Revolutionary. This simple chatbot has given my team back invaluable time. We've shifted from being reactive answer-providers to proactive strategic partners, focusing our expertise only on those complex matters that truly require human guidance. The numbers speak for themselves: a remarkable 60% of stakeholder questions are now answered autonomously by our AI chatbot. The remaining 40% are handled by our always-on, always-available team, who can now dedicate their energy to higher-value tasks. This isn't just a story about a chatbot; it's a living testament to how I eliminated significant manual overhead, accelerated access to information, and freed our talent to innovate. For those who fear GenAI will take away jobs, or for those who hear industry leaders say AI will enable us to do more with limited time – this is what that reality looks like. It's about augmenting human potential, not replacing it. It's about empowering teams to achieve more impactful work. This is just the first chapter in my AI journey, and I'll be sharing more insights, challenges, and successes in upcoming posts about my usage of GenAI and Agentic AI in the VMO space. What repetitive tasks are currently burdening your teams? How are you leveraging AI to transform operations and truly empower your workforce? I'd love to hear your thoughts and experiences. Let's learn from each other how we can collectively drive this AI-powered future forward. #AI #GenerativeAI #AgenticAI #ITOperations #VMO #DigitalTransformation #Efficiency #Innovation #FutureOfWork #CiscoIT #AITransformation

If your organization is serious about implementing AI responsibly, the first step isn’t to rush toward deployment; it’s to clearly understand what the technology can and cannot do well. Knowing both the capabilities and the limitations of today’s models is crucial for building realistic expectations. Next, you must ask a harder question: Is AI even the optimal solution for the business problem you’re trying to solve? Too often, organizations fall into the trap of using AI because it’s the latest buzzword, rather than because it’s the right tool. In some cases, traditional analytics or rule-based systems may serve your goals more effectively, with less complexity and risk. Finally, recognize that AI is never a “one-size-fits-all” proposition. The ecosystem of models, products, and services is diverse, and choosing the right approach requires careful comparison, rigorous evaluation, and alignment with your organization’s risk tolerance, compliance requirements, and long-term strategy. In short: understand the technology, validate its suitability, and select wisely, because in AI, the context matters as much as capability. #AI