Evaluation of Technology Solutions

Ever heard of the Lippitt-Knoster Model for Managing Complex Change? It's a classic in the change management world, laying out the essential pieces needed to navigate big transformations. Taking a cue from that, I've adapted it to fit the world of digital transformation. There are seven key elements you can't afford to miss: Vision, Strategy, Objectives, Capabilities, Architecture, Roadmap, and Projects & Programs. Skip any one of these, and you're asking for trouble. Here’s why each one matters: • 𝐕𝐢𝐬𝐢𝐨𝐧: This is the 'what' of your transformation. A clear vision gives everyone a target to aim for, aligning all efforts and keeping the team focused. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Think of this as the 'why' and 'how.' A solid strategy explains the logic behind your vision, showing how you plan to get there and why it's the best route. It’s designed to guide everyone in the company on how to make decisions that support the vision, aligning all efforts and keeping the team focused. • 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞𝐬: These are your milestones. Clear, specific objectives make it easy to measure success and ensure everyone knows what's important. Without them, you can easily veer off course and waste resources. • 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬: These are what your company will now be able to do that it wasn't able to before in order to achieve the objectives. These can be organizational capabilities (like improved decision-making), technical capabilities (such as real-time operational visibility), or other types like enhanced customer engagement or streamlined processes. • 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞: A robust architecture ensures all your tech works together smoothly, preventing inefficiencies and costly headaches. This includes various types of architecture such as data architecture, IT infrastructure architecture, enterprise architecture, and functional architecture. Effective architecture is central to reducing technical debt and aligning software with broader business transformation goals. • 𝐑𝐨𝐚𝐝𝐦𝐚𝐩: Your roadmap is the game plan. It lays out the sequence of actions, helping you avoid uncertainty and missteps. It's your guide to getting things done right. • 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 & 𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐬: These are where the rubber meets the road. Actionable projects and programs turn your strategy into reality, making sure your plans lead to real, tangible outcomes. From my experience, I think '𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬' and '𝐑𝐨𝐚𝐝𝐦𝐚𝐩' are the two most overlooked. What do you think? ******************************************* • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

𝗠𝗶𝗻𝗶𝗻𝗴 𝗶𝘀 𝗵𝗼𝘁 𝗳𝗼𝗿 𝗮 𝗿𝗲𝗮𝘀𝗼𝗻 - 𝗶𝘁’𝘀 𝗯𝗲𝗶𝗻𝗴 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲𝗱. Mining has long sat in the background of capital markets. Often lumped in with the broader commodity markets and seen as slow, capital-intensive, and lacking technological progression. Important, but not investable. Strategic, but stagnant. Well, that narrative is breaking. Critical minerals, while always seen as national security assets, have ascended to a top national priority. Electrification, AI infrastructure, and defense supply chains are all colliding with a system that historically took 10+ years to deliver a new mine - if delivered at all. Meanwhile, discovery rates are collapsing while permitting timelines are stretching, further compounding capital risk. Due to this growing demand gap and market tailwinds, we spent the last few months mapping where the real bottlenecks and areas of venture-scale opportunity across the mining value chain sit, touching on: ⛏️ Exploration and feasibility - the binding constraints 🤖 Use of AI - sensing are collapsing the drill → data → decision loop ⏱️ Time-to-value matters - often more than technical novelty 💰 Moving multiples - how tech can move assets from “mining multiples” to “growth industrial” outcomes 📊 Business model innovation - why royalty-like, equity-linked models may matter as much as the tech itself The result is a framework for evaluating mining-tech opportunities via capital intensity vs. time-to-value, with a focus on cycle-time compression, risk reduction, and scalable value capture. And the best part? This isn’t just theory, we’re already seeing signals in OEM offtake behavior, upstream verticalization, and a new generation of founders treating mining as a potentially data-rich industry ripe for transformation. If you’re building, investing in, or navigating mining, minerals, or industrial AI — give it a read and let’s compare notes! As they say these days, the [VCs] yearn for the mines ⛏️ [Link to full piece in comments, also drop a comment if you want the spreadsheet backup to the market map] CC: Cathay Innovation, Simon Wu, Elijah Yi, Rose Yuan, Jaclyn H., Daniela Caserotto Leibert #Mining #CriticalMinerals #IndustrialTech #AI #EnergyTransition #VentureCapital #Reindustrialization

I am an Engineering Manager working at Google with almost 20 years of experience. If I could sit down with a Jr. Software Engineer, here are 50 cheat codes I would share with them that I learned from my experiences. [1] Ask why this system even needs to exist ➤ Before a single line is written, challenge the core purpose, “Is this a business problem or just a tech exercise?” Real systems solve pain, not boredom. [2] Redraw the lines, define what’s “inside” and “outside” your system ➤ Figure out where your service starts, stops, and how it talks to the world. 80% of future headaches come from blurred boundaries. [3] Don’t chase new tech for the resume, use what your org supports ➤ That AWS Lambda demo looks cool until your team tells you there’s a 10-year-old Jenkins server already scheduled to do that job. Proven > Shiny. [4] System design isn’t “one size fits all”, context is everything ➤ YouTube and interview videos show perfect worlds. Your system will live in mess, legacy, and compromise. Embrace it. [5] Optimize for “how easy to change?” not “how cool is this?” ➤ You won’t get it perfect first time. Make it so anyone (even you) can swap out parts later, with minimal pain. [6] Start with use cases, not tech ➤ Interview solutions start with “put Kafka here.” Real solutions start with “who will use this and how?” [7] Know your real users, not just your APIs ➤ Customers, PMs, even other devs, all are “users” with needs. If your “system” forgets one, it’s doomed. [8] Design for the traffic you have, not the traffic you dream of ➤ Every engineer who overbuilt for ‘Google scale’ at a 10K user startup has regretted it. Scale when you must. [9] Understand your company’s default tech stack, don’t fight it ➤ Don’t propose a NoSQL database if everyone else is running Postgres unless you have a bulletproof case. [10] Pick the boring solution if you want peace ➤ Every time I chased “the best tech,” maintenance bit me back. The system you forget about is the most stable one. [11] Get the team’s buy-in before you architect a “masterpiece” ➤ Don’t be a solo hero. Feedback from PMs, ops, QA, other engineers, all of it will expose what you missed. [12] Refactor and cleanup aren’t “nice to haves”, they’re your real job ➤ Every shortcut you leave will double your pain in 6 months. [13] Read logs and production metrics every week ➤ Production is where truth lives. Ignore at your own risk. [14] Test how things break, not just how they work ➤ Simulate failing databases, crashing services, weird user flows, assume chaos is coming. [15] You’ll be asked to fix code you didn’t write, embrace it ➤ Legacy code is half your career. Treat it with respect and curiosity, not blame. [16] Never let a diagram go out without clear boundaries ➤ Always show what’s external, what’s internal, and what’s a dependency, otherwise, no one will know what breaks what.

The Italian Fire Prevention Code, and other international regulations allow the application of alternative solutions and innovative systems to ensure fire safety, provided that they are supported by a risk assessment and demonstrate that they achieve a level of safety equivalent to or higher than traditional solutions. This approach can also be applied to photovoltaic systems, which, as we know, can represent a risk in certain conditions. This is true for new installations but especially for existing systems where the new installation and design rules can hardly be applied. The adoption of innovative technologies can significantly improve the fire safety of photovoltaic systems. - Intelligent Monitoring Systems Real-time monitoring: data analysis platforms can detect anomalies such as overheating, short circuits or electrical arcs, sending alarms in real time. - Failure Prediction: The use of artificial intelligence (AI) algorithms allows to predict potential failures before they occur, reducing the risk of fires. (SIMON System Intelligent Monitoring) Integration with fire systems: Monitoring systems can be connected to automatic shutdown devices to intervene immediately in case of emergency. - Fireproof Materials Fire-resistant photovoltaic modules: The use of panels certified according to fire resistance regulations (for example, UNI 9177) can reduce the risk of flame propagation. Fireproof wiring and components: The adoption of materials with high resistance to heat and fire can prevent the ignition of fires. - Digital Twin for Fire Safety Virtual models: The creation of a digital twin of the photovoltaic system allows to simulate fire scenarios and evaluate the effectiveness of safety measures. Design optimization: The digital twin can be used to identify critical points and optimize the arrangement of components to reduce risks. Integration with predictive systems: The digital twin can be connected to predictive monitoring systems to simulate and prevent risk situations. #fireprevention #safety #solarpanel #solarplant #energysafety

Most organizations are looking at ROI the wrong way when it comes to XR technologies. The real return is not just in equipment savings or technology acronyms added to your institution. It is in building confident, prepared teams who drive improvements into the workforce and perform better either in school or the professional workplace. ⇝ The strongest workforce investments focus on outcomes. Confidence, safety, and retention create greater long-term value than simply buying more equipment. ⇝ Simulation-based training goes beyond teaching skills. Repeated practice in realistic scenarios builds instinct, trust among team members, and better decision-making when lives are on the line. ⇝ Retention is now more important than recruitment. Hiring more staff does not solve shortages if they leave within a year. A confident workforce is a stable workforce. If you want to reduce turnover and improve patient outcomes, shift your focus. Invest in building confidence, trust, and preparedness in your budding professionals, because they will push those improvements into the marketplace That is the real ROI. VRpatients #HealthcareTraining #WorkforceDevelopment #SimulationTraining #EmployeeRetention

Common Sense Media recently released a comprehensive risk assessment of AI teacher assistants/lesson planning tools. Their findings reveal that while these tools promise increased productivity and creative support, they're also creating "invisible influencers" that could fundamentally undermine educational quality. Unlike GenAI foundation model chatbots, these tools are specifically designed for instructional planning and classroom use and are rapidly being adopted across districts. Key Concerns from their report: • "Invisible Influencers" in Student Learning: AI-generated content directly shapes what students learn through potentially biased perspectives and historical inaccuracies that teachers may miss; evidence also shows these tools suggest different approaches and responses based on student race/gender • “Outsourced Thinking" Problem: Tools make it dangerously easy to push unreviewed AI instructional content straight to classrooms, while novice teachers lack experience to spot subtle errors and biasses • High-Stakes Outputs: IEP and behavior plan generators create official-looking documents that could impact student educational trajectories even though these plans should be human-generated (and in the case of IEP goals are mandated to be human generated) • Undermining High-Quality Instructional Materials: Without proper integration, these tools fragment learning and can undermine coherent, research-backed curricula Recommendations from the report: • Experienced educator oversight required for all AI-generated educational content • Clear district policies and guidelines for AI teacher assistant implementation • Integration with existing high-quality curricula rather than replacement of established materials • Robust teacher training on identifying bias and evaluating AI outputs • Careful oversight of real-time AI feedback tools that interact directly with students We'd also recommend foundational AI literacy for teachers before they begin using GenAI teacher assistants, so that they are aware of the potential limitations. While AI teacher assistants aren't inherently problematic, they require the same careful implementation and oversight we'd expect for any tool that directly impacts student learning. The potential for enhanced productivity is real, but so are the risks to educational equity and quality. This report underscores the urgent need for GenAI EdTech tool makers to provide evidence of how their tools mitigate these issues along with evidence-based policies and professional development to help educators navigate AI tools responsibly. All of which underline how important AI Literacy is for the 2025-2026 school year. Link in the comments to check out the full report. Also check out our 5 Questions to Ask GenAI EdTech Providers resource in the comments if you are planning to implement any of these tools in your school or district. #AIinEducation #ailiteracy #Education #K12 AI for Education

"We need an AI strategy!" 𝘙𝘦𝘤𝘰𝘳𝘥 𝘴𝘤𝘳𝘢𝘵𝘤𝘩 Hold up. That's the wrong question. The right question? "What business problem are we actually trying to solve?" I've sat in countless board meetings where executives demand AI initiatives – not because they've identified a problem AI can solve, but because they're afraid of being left behind. This FOMO-driven approach is precisely how companies end up in what I call "perpetual POC purgatory" – running endless proofs of concept that never see production. Here's the uncomfortable truth: Your goal isn't to use AI for the sake of AI. Your goal is to solve real business problems. Sometimes the best solution is a regular hammer, not a sledgehammer. So when leadership pushes AI without purpose, redirect the conversation: → "What business outcome are we trying to drive?” → “What’s the actual problem we’re solving?” → “Is AI the most effective tool for that — or just the most exciting one?” Next, how do you determine if AI is the right solution? I recommend this straightforward approach that keeps business problems at the center: 1. 𝗗𝗲𝗳𝗶𝗻𝗲 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗽𝗿𝗲𝗰𝗶𝘀𝗲𝗹𝘆 - What specifically are you trying to solve? The more precisely you can articulate the problem, the easier it becomes to evaluate whether AI is appropriate. 2. 𝗖𝗼𝗻𝘀𝗶𝗱𝗲𝗿 𝘁𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 𝗳𝗶𝗿𝘀𝘁 - Could existing technology or processes handle this faster, cheaper, and more reliably? 3. 𝗟𝗲𝗮𝗻 𝗼𝗻 𝗲𝘅𝗽𝗲𝗿𝘁𝘀 - If the problem seems AI-suitable, validate it with people who’ve delivered outcomes — not just hype. 4. Be brutally realistic about your organization's maturity - Do you have the data infrastructure, talent, and risk tolerance necessary for an AI implementation? Remember this fundamental truth: AI is not a silver bullet. Even seemingly simple AI projects require time, focus, alignment, and resilience to implement successfully. The companies winning with AI aren't the ones with the flashiest technology. They're the ones methodically solving pressing business challenges with the most appropriate tools—AI or otherwise. 𝗜’𝗱 𝗹𝗼𝘃𝗲 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗳𝗿𝗼𝗺 𝘆𝗼𝘂: What business problem are you trying to solve that might (or might not) actually need AI?

Closing the gap between quantum theory and sensing reality Quantum sensing is often framed as a race for better hardware: longer coherence times, cleaner materials, improved qubit designs. All of this matters. But it is not the full story. In our latest work, published in Nature Magazine, the team at Terra Quantum AG demonstrates that algorithmic innovation alone can unlock major gains in quantum magnetometry. By redesigning phase-estimation protocols for superconducting qubits, we show how to expand the dynamical range by orders of magnitude while improving precision, without relying on entanglement or new hardware. The core insight is simple: Quantum advantage does not depend on coherence alone, but on how efficiently phase information is transformed into knowledge. Smarter algorithms extract more information from the same physical system, even under realistic noise conditions. This work brings quantum sensing closer to practical deployment. It shows that progress toward Heisenberg-limit performance can be achieved today, through software–hardware co-design, rather than waiting for ideal devices tomorrow. Quantum technologies will not scale through hardware alone. Algorithms are where quantum physics becomes real-world impact. Read the full paper here 👉 https://lnkd.in/dFpxKc-T and below 👇 #QuantumIsNow #QuantumSensing #QuantumAlgorithms #DeepTech #QuantumMetrology #SuperconductingQubits

You've built your AI agent... but how do you know it's not failing silently in production? Building AI agents is only the beginning. If you’re thinking of shipping agents into production without a solid evaluation loop, you’re setting yourself up for silent failures, wasted compute, and eventully broken trust. Here’s how to make your AI agents production-ready with a clear, actionable evaluation framework: 𝟭. 𝗜𝗻𝘀𝘁𝗿𝘂𝗺𝗲𝗻𝘁 𝘁𝗵𝗲 𝗥𝗼𝘂𝘁𝗲𝗿 The router is your agent’s control center. Make sure you’re logging: - Function Selection: Which skill or tool did it choose? Was it the right one for the input? - Parameter Extraction: Did it extract the correct arguments? Were they formatted and passed correctly? ✅ Action: Add logs and traces to every routing decision. Measure correctness on real queries, not just happy paths. 𝟮. 𝗠𝗼𝗻𝗶𝘁𝗼𝗿 𝘁𝗵𝗲 𝗦𝗸𝗶𝗹𝗹𝘀 These are your execution blocks; API calls, RAG pipelines, code snippets, etc. You need to track: - Task Execution: Did the function run successfully? - Output Validity: Was the result accurate, complete, and usable? ✅ Action: Wrap skills with validation checks. Add fallback logic if a skill returns an invalid or incomplete response. 𝟯. 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗲 𝘁𝗵𝗲 𝗣𝗮𝘁𝗵 This is where most agents break down in production: taking too many steps or producing inconsistent outcomes. Track: - Step Count: How many hops did it take to get to a result? - Behavior Consistency: Does the agent respond the same way to similar inputs? ✅ Action: Set thresholds for max steps per query. Create dashboards to visualize behavior drift over time. 𝟰. 𝗗𝗲𝗳𝗶𝗻𝗲 𝗦𝘂𝗰𝗰𝗲𝘀𝘀 𝗠𝗲𝘁𝗿𝗶𝗰𝘀 𝗧𝗵𝗮𝘁 𝗠𝗮𝘁𝘁𝗲𝗿 Don’t just measure token count or latency. Tie success to outcomes. Examples: - Was the support ticket resolved? - Did the agent generate correct code? - Was the user satisfied? ✅ Action: Align evaluation metrics with real business KPIs. Share them with product and ops teams. Make it measurable. Make it observable. Make it reliable. That’s how enterprises scale AI agents. Easier said than done.

Advancing useful quantum computing starts with expanding access to systems where new ideas can be tested and refined. IBM Quantum Credits lower barriers by enabling merit-based use of quantum hardware. Recent results show what that approach makes possible: https://lnkd.in/ez7ch4_j What stands out is that each of these projects introduces new algorithms that extend the reach of current hardware. They demonstrate particle collision simulations beyond classical limits, large-scale quantum state reconstruction, and more scalable circuit and state preparation techniques—advances driven by iteration on real systems.  Progress toward real-world utility requires both continued hardware innovation and a clear model for access. Our approach pairs broad entry points with pathways for deeper experimentation, so researchers can develop and validate their ideas on high-performance quantum computers and scale the most promising approaches.  IBM is helping build an open, collaborative ecosystem—where access supports high-impact research, and progress is driven as much by the community as by the technology itself. Work is beginning to shape where and how quantum computing delivers real-world impact.