Productivity

💎 Accessibility For Designers Checklist (PDF: https://lnkd.in/e9Z2G2kF), a practical set of cards on WCAG accessibility guidelines, from accessible color, typography, animations, media, layout and development — to kick-off accessibility conversations early on. Kindly put together by Geri Reid. WCAG for Designers Checklist, by Geri Reid Article: https://lnkd.in/ef8-Yy9E PDF: https://lnkd.in/e9Z2G2kF WCAG 2.2 Guidelines: https://lnkd.in/eYmzrNh7 Accessibility isn’t about compliance. It’s not about ticking off checkboxes. And it’s not about plugging in accessibility overlays or AI engines either. It’s about *designing* with a wide range of people in mind — from the very start, independent of their skills and preferences. In my experience, the most impactful way to embed accessibility in your work is to bring a handful of people with different needs early into design process and usability testing. It’s making these test sessions accessible to the entire team, and showing real impact of design and code on real people using a real product. Teams usually don’t get time to work on features which don’t have a clear business case. But no manager really wants to be seen publicly ignoring their prospect customers. Visualize accessibility to everyone on the team and try to make an argument about potential reach and potential income. Don’t ask for big commitments: embed accessibility in your work by default. Account for accessibility needs in your estimates. Create accessibility tickets and flag accessibility issues. Don’t mistake smiling and nodding for support — establish timelines, roles, specifics, objectives. And most importantly: measure the impact of your work by repeatedly conducting accessibility testing with real people. Build a strong before/after case to show the change that the team has enabled and contributed to, and celebrate small and big accessibility wins. It might not sound like much, but it can start changing the culture faster than you think. Useful resources: Giving A Damn About Accessibility, by Sheri Byrne-Haber (disabled) https://lnkd.in/eCeFutuJ Accessibility For Designers: Where Do I Start?, by Stéphanie Walter https://lnkd.in/ecG5qASY Web Accessibility In Plain Language (Free Book), by Charlie Triplett https://lnkd.in/e2AMAwyt Building Accessibility Research Practices, by Maya Alvarado https://lnkd.in/eq_3zSPJ How To Build A Strong Case For Accessibility, ↳ https://lnkd.in/ehGivAdY, by 🦞 Todd Libby ↳ https://lnkd.in/eC4jehMX, by Yichan Wang #ux #accessibility

It’s simple math 🧐 I use to think that motivation was the key to monumental success. Long story short, it’s not. It’s about the little things you do every day that will take you from reasonable to slightly unreasonable to completely unreasonable progress. Your future is not defined by how motivated you are, but by your daily routines and systems. I believe in this so much that we named our company Butterfly 3ffect to reflect the value of incremental gains. we believe that that’s how the best people and brands grow. Here’s how you grow the small way: 1. Start by setting achievable goals, like reading one chapter of a book each day or going for a short walk 2. Practice gratitude by writing down three things you're thankful for every night before bed 3. Engage in daily self-reflection, even if it's just for a few minutes, to assess your thoughts and actions 4. Incorporate small acts of kindness into your daily routine, like holding the door for someone or offering a genuine compliment 5. Learn something new every day, whether it's a fun fact, a new word, or a new skill 6. Prioritise self-care by getting enough sleep, staying hydrated, and taking breaks when needed 7. Surround yourself with positive influences, whether it's uplifting books, supportive friends, or inspiring podcasts 8. Embrace failure as a learning opportunity and a stepping stone to growth 9. Stay consistent and patient, knowing that small progress over time adds up to significant improvement 10. Celebrate your achievements, no matter how small, to stay motivated and encouraged along the way.

42 years ago, in his 1981 interview below, Steve Jobs shared an incredible analogy to understand the impact of Artificial Intelligence on human beings. In simple terms, human beings are tool makers. We create tools that amplify our abilities and free us up for more creative work. AI is such a tool. Here are 5 simple ways I've used ChatGPT in the the last month to amplify my creativity (it’s like I’ve added 5 amazing people to the team overnight!): 1) Startup Advisor I’m currently building my next tech company. I used ChatGPT to help identify the most critical success factors and the biggest vulnerabilities in the plan. I’m now focusing my creativity on the 20% of issues that will drive 80% of the success. 2) Growth Marketer I recently surveyed tens of thousands of my newsletter subscribers. ChatGPT quickly processed the unstructured text responses, revealing the topics that most interest my readers. I can now apply my creativity strategically and craft the content they desire. 3) Recruitment Consultant I'm soon to hire a pivotal team member who'll function as an executive assistant and project manager. ChatGPT assisted in swiftly crafting an enticing job ad, allowing me to channel my creativity into the selection and interview process. 4) AI Business Tutor Eager to sharpen my ability to leverage AI in business, I asked ChatGPT to test my understanding within the area. ChatGPT asked me questions, pointed out knowledge gaps, and provided improved answers to fill those gaps. Once again, I amplified my creative effort. 5) Time Management Coach Two weeks ago, I was pressed for time with only 90 minutes to finish prep for a workshop. I explained the situation to ChatGPT. It helped me break down the 90 minutes, better structure my thinking, and maximize my output. The workshop was a huge success, thanks to this AI-powered productivity boost. ________ Steve Jobs foresaw the incredible power of AI 42 years ago. Are you using AI to amplify your creativity? If you like content like this, follow me Eric Partaker, for more.

Right now, every CEO is wondering the same thing: “How can artificial intelligence help maximize our impact?”   Delivering on the promise of AI isn’t just good business, it has the potential to help us address some of society’s most pressing challenges. So today, I wanted to offer a closer look at how AI is helping us discover new medicines at Novartis.   The process of identifying a new drug, running patient clinical trials, and bringing it to market takes over a decade. Each new medicine costs on average $2 billion to develop, and we know nearly 9 in 10 of the treatments we work on will fail before they ever reach patients.   A major early step in that process is identifying individual targets in the body that we want to design a drug for. Once we identify that target, which most commonly is a protein, we look for molecules that might address the target’s underlying issue – ultimately those molecule structures form the basis for every successful treatment.   Unlocking the right protein and molecular structures is complex stuff – each step often takes years to get right and our scientists consider billions of potential chemical structures that might lead to effective and safe drug candidates.   AI offers us the chance to accelerate that process. Working with partners at Isomorphic Labs – including members of the Google DeepMind team that were awarded the Nobel Prize this year – we’re now able to do things like model how a protein folds and interacts with the molecules we design. AI models also make it possible for us to analyze different chemical structures simultaneously. It has the potential to add up to significant time savings for our drug development scientists and their work to predict what molecules might treat specific diseases better and faster.   We’re just at the beginning of what this technology can do. As we incorporate AI throughout Novartis’ work, I’m excited to see all the ways it helps us unlock the mysteries of human biology, so we can deliver better medicines that improve and extend patients’ lives.

I struggled with work/life balance throughout my career. This is because the world has set a clever, two-part trap for us. I will explain the trap and how to escape it. Part One – Our own goals and ambitions. I wanted to be successful, to get more pay, and to be a part of bigger decisions. If you follow me here, I bet you are the same. You want to “be the best” and have a great career. Part Two – Corporate pressure. Companies have a simple goal of making profits for shareholders. This is most easily done by getting more work from the same people. The Trap: The two parts converge to destroy work/life balance because our healthy desire to do good work, earn a living, and find meaning is easily manipulated by corporate systems designed to maximize profits. Here is how they do it: 1) Most companies give bigger raises to “better” performers. What is better? Usually, doing more work. Sometimes you can be “better” by being smarter or more efficient, but over time even the best of us usually work harder 2) Competition. Since raises and promotions are limited in number, there will always be someone else willing to put in very long hours to come out ahead of you. Some of you will recognize this as “the prisoner’s dilemma” – if only one person works harder, they will get a lot of advantages for only a little extra work. But, when we all strive to be first it becomes a maximum effort race with no winners. Ways to Escape the Trap: 1) Set limits. Recognize the trap and decide what you will and will not give to your work. This may mean accepting some career tradeoffs, but unless you set the limits your body will do it for you over time. It is better to make the choices yourself. 2) Seek work only you can do. We are all gifted at some things, and you get two benefits from focusing on your gifts. First, you can stay ahead of others with less effort. Second, it is more fun to do things that come easily. 3) Choose companies and bosses wisely. Some leaders push you into the trap, some leaders try to keep you out of it. Seek those that keep you out. 4) Work for yourself. If you can be your own boss you can escape the corporate side of profit maximization, or at least have it under your control. 5) Redefine success. There is nothing wrong with wanting pay, promotions, influence, etc. But if the cost gets too high, remember that plenty of people are happy without corporate success. My own path was to climb the ladder, make the money, and then step off. I sacrificed many good years to work and high stress in order to get a set of years without it. A good trade? Time will tell. Readers, what are some other ways to escape the trap?

Few Lessons from Deploying and Using LLMs in Production Deploying LLMs can feel like hiring a hyperactive genius intern—they dazzle users while potentially draining your API budget. Here are some insights I’ve gathered: 1. “Cheap” is a Lie You Tell Yourself: Cloud costs per call may seem low, but the overall expense of an LLM-based system can skyrocket. Fixes: - Cache repetitive queries: Users ask the same thing at least 100x/day - Gatekeep: Use cheap classifiers (BERT) to filter “easy” requests. Let LLMs handle only the complex 10% and your current systems handle the remaining 90%. - Quantize your models: Shrink LLMs to run on cheaper hardware without massive accuracy drops - Asynchronously build your caches — Pre-generate common responses before they’re requested or gracefully fail the first time a query comes and cache for the next time. 2. Guard Against Model Hallucinations: Sometimes, models express answers with such confidence that distinguishing fact from fiction becomes challenging, even for human reviewers. Fixes: - Use RAG - Just a fancy way of saying to provide your model the knowledge it requires in the prompt itself by querying some database based on semantic matches with the query. - Guardrails: Validate outputs using regex or cross-encoders to establish a clear decision boundary between the query and the LLM’s response. 3. The best LLM is often a discriminative model: You don’t always need a full LLM. Consider knowledge distillation: use a large LLM to label your data and then train a smaller, discriminative model that performs similarly at a much lower cost. 4. It's not about the model, it is about the data on which it is trained: A smaller LLM might struggle with specialized domain data—that’s normal. Fine-tune your model on your specific data set by starting with parameter-efficient methods (like LoRA or Adapters) and using synthetic data generation to bootstrap training. 5. Prompts are the new Features: Prompts are the new features in your system. Version them, run A/B tests, and continuously refine using online experiments. Consider bandit algorithms to automatically promote the best-performing variants. What do you think? Have I missed anything? I’d love to hear your “I survived LLM prod” stories in the comments!

MCP is an MVP if you are exploring ways to supercharge your AI workflows. I am very impressed by the MCP (Model Context Protocol) architecture and proud of the way we have embraced it at Visa to accelerate our GAI work. MCP is an open standard, introduced by Anthropic. It acts like a universal connector, seamlessly linking AI applications to external tools, data, and services. Think of it as Bluetooth for AI – enabling plug and play integrations without multiple, messy connections and custom code.   For companies embracing the power of GAI, MCP is a dream come true. It eliminates the headache of building bespoke API integration for every tool, letting AI agents access resources like file systems, wikis, shared drives, databases etc in real time. This means your AI can pull custom data, automate tasks or analyze reports instantly. As an early adopter, we are already using MCP to streamline workflows and with 1000s of community built MCP servers, the eco system is exploding.   My advice to those beginning their MCP journey – start small. Identify a repetitive task (like updating CRM records or generating analysis). Setup an MCP server for your tool or service (many are prebuilt), connect it to your AI client and watch the magic happen. Experiment, scale, and explore the open-source MCP community for inspiration. Once you start using MCP, you will see a step function increase in your innovation velocity.

Innovation isn’t just about upgrading your tools—it’s about reinventing how you create, deliver, and capture value. Digital business models are reshaping industries by creating value in ways unimaginable a decade ago. These aren't your grandparent’s business models with a digital veneer—they're transformative, leveraging tech to disrupt markets, engage customers, and redefine competition. This revolution is captured brilliantly in the book: 𝐷𝑖𝑔𝑖𝑡𝑎𝑙 𝐵𝑢𝑠𝑖𝑛𝑒𝑠𝑠 𝑀𝑜𝑑𝑒𝑙𝑠 𝑓𝑜𝑟 𝐼𝑛𝑑𝑢𝑠𝑡𝑟𝑦 4.0: 𝐻𝑜𝑤 𝐼𝑛𝑛𝑜𝑣𝑎𝑡𝑖𝑜𝑛 𝑎𝑛𝑑 𝑇𝑒𝑐ℎ𝑛𝑜𝑙𝑜𝑔𝑦 𝑆ℎ𝑎𝑝𝑒 𝑡ℎ𝑒 𝐹𝑢𝑡𝑢𝑟𝑒 𝑜𝑓 𝐶𝑜𝑚𝑝𝑎𝑛𝑖𝑒𝑠. 𝐅𝐨𝐮𝐫 𝐏𝐢𝐥𝐥𝐚𝐫𝐬 𝐨𝐟 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐌𝐨𝐝𝐞𝐥𝐬: • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥𝐥𝐲 𝐄𝐧𝐚𝐛𝐥𝐞𝐝 𝐕𝐚𝐥𝐮𝐞 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧: Value driven by tech, not just supported by it. Think smart thermostats optimizing energy, not just controlling it. • 𝐌𝐚𝐫𝐤𝐞𝐭 𝐍𝐨𝐯𝐞𝐥𝐭𝐲: New offerings or ways of doing business—like predictive maintenance or on-demand manufacturing. • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐓𝐨𝐮𝐜𝐡𝐩𝐨𝐢𝐧𝐭𝐬: Customer relationships built through apps, IoT, and connected services. • 𝐃𝐢𝐠𝐢𝐭𝐚𝐥𝐥𝐲 𝐃𝐞𝐫𝐢𝐯𝐞𝐝 𝐔𝐒𝐏: Unique selling points rooted in data and digital capabilities. But how do we map the revenue streams emerging from these shifting dynamics? I’ve come to see it through three essential components: • 𝐂𝐨𝐫𝐞 𝐕𝐚𝐥𝐮𝐞 𝐏𝐫𝐨𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧 (What is being offered?) • 𝐕𝐚𝐥𝐮𝐞 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 𝐌𝐞𝐜𝐡𝐚𝐧𝐢𝐬𝐦𝐬 (How is value created?) • 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐒𝐭𝐫𝐞𝐚𝐦𝐬 (How is value captured?) 𝐑𝐞𝐚𝐝 𝐟𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/ewhRUM28 ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

My biggest takeaways from Ethan Smith on how to win at AEO (i.e. get ChatGPT to recommend your product): 1. Being mentioned most often beats ranking first. In Google, the #1 blue link wins. In ChatGPT, the answer summarizes multiple sources—so appearing in five citations beats ranking #1 in one. Ethan’s strategy: get mentioned on Reddit, YouTube, blogs, and affiliates. Volume of mentions matters more than any single placement. 2. LLM traffic converts 6x better than Google search traffic. Webflow saw this dramatic difference because users who come through AI assistants have built up much more intent through conversation and follow-up questions, making them highly qualified leads. 3. Early-stage startups can win at AEO immediately, unlike with SEO. Traditional SEO requires years of domain authority. But a brand-new Y Combinator company mentioned in a Reddit thread today can show up in ChatGPT tomorrow. The playing field is finally level. 4. The long tail of AEO is 4x bigger than SEO. People ask ChatGPT questions with 25 or more words (vs. 6 in Google). Ethan found gold in queries like “Which meeting transcription tool integrates with Looker via Zapier to BigQuery?”—questions that never existed in search but are perfect for AI. Own these micro-niches. 5. Reddit is proving to be the kingmaker for AI visibility. ChatGPT trusts Reddit because the community polices spam better than any algorithm. Ethan’s exact playbook: make one real account, say who you are and where you work, give genuinely helpful answers. Five good comments can transform your visibility. No automation, no fake accounts—just be helpful. 6. YouTube videos for “boring” B2B terms are a gold mine for AEO. Nobody makes videos about “AI-powered payment processing APIs”—which is exactly why you should. While everyone fights over “best CRM software,” the high-value, zero-competition long tail is wide open in video. 7. Your help center is now a growth channel. All those “Does your product do X?” questions flooding ChatGPT can be answered by help-center pages. Move them from subdomain to subdirectory, cross-link aggressively, and cover every feature question. Ethan calls this the most underutilized opportunity in AEO. 8. January 2025 was the inflection point in AEO growth. That’s when ChatGPT made answers more clickable (maps, shopping cards, citations) and adoption exploded. Webflow went from near zero to 8% of signups from AI. This channel is accelerating faster than any Ethan’s seen in 18 years. 9. The AEO playbook: (1) Find questions from competitor paid search data, (2) set up answer tracking, (3) see who’s showing up as citations, (4) create landing pages answering all follow-up questions, (5) get mentioned offsite via Reddit/YouTube/affiliates, (6) run controlled experiments, (7) build a dedicated team. This exact process is driving real results at scale.

McKinsey & Company shows how Danone turns operations into a growth engine. A sharp interview by Pierre de la Boulaye and Søren Fritzen with Vikram Agarwal highlights a structural shift across the FMCG industry. For decades, operations were treated as a cost center. That paradigm is changing. Leading companies now position operations as a driver of growth and competitiveness. The transformation at Danone shows how AI, digital manufacturing and advanced supply chains are reshaping the sector. Several insights stand out. 1) AI turns factories predictive Operators increasingly monitor production lines via tablets instead of control rooms. AI systems detect potential equipment failures before they occur, for example overheating motors in packaging lines. Maintenance shifts from reactive repair to predictive intervention, improving uptime and efficiency. 2) Capacity planning becomes strategic Danone distinguishes three ways to build manufacturing capacity: • Release capacity from existing assets • Transform capacity by converting underperforming lines • Create capacity through new production investments Transforming existing lines enables growth with much lower capital intensity than building new factories. 3) AI reshapes supply chains Danone uses AI models to forecast ingredient costs and supply chain dynamics across global agricultural markets. Instead of analyzing thousands of variables, systems process millions of data points. For a company managing roughly €13.7B in COGS, forecasting accuracy becomes a competitive advantage. 4) Digital manufacturing at scale Danone’s Digital Manufacturing Acceleration program already covers 80+ factories, with 40 more joining soon, across 140+ production sites globally. The ambition goes beyond Industry 4.0 toward Industry 5.0, combining machines, AI and human expertise. 5) People remain central Danone employs 47,000+ people in operations, about half of its workforce. Through its Industry 5.0 Academy, the company has already trained around 20,000 employees in digital manufacturing capabilities. Why this matters The global FMCG industry generates over $4 trillion in annual sales and operates on tight margins. Even small improvements in forecasting, manufacturing efficiency or capacity utilization can translate into billions in value creation. As demand shifts toward health, high-protein and plant-based products, supply chains must become faster and more flexible. AI-driven operations are becoming a strategic advantage. The signal for FMCG leaders is clear: Competitive advantage is increasingly built beyond brands and marketing — in operations. #operations #manufacturing #ai #digitaltransformation #foodindustry #foodtech #retailtech #innovation #procurement #datadriven #danone #france #europe #startup #investors #marketing #sales #technology #logistics