Artificial Intelligence

Yesterday I had one of those rare conversations that stays with you. I sat down with Dr. Rebecca Hinds, PhD from Glean to unpack her new research, The AI Transformation 100, and it completely reframed how I think about AI in organizations. In the article I just published, I share the insights that hit me hardest — because they match exactly what I see with executives and teams every day. Here’s what you’ll learn: 🔸80% of AI transformation is just… transformation. The same human issues, politics, and leadership gaps — simply amplified by AI. 🔸AI is a megaphone. Healthy cultures accelerate. Broken workflows break faster. Rebecca’s data makes this painfully clear. 🔸Leadership behavior is the biggest adoption driver. If leaders don’t use AI themselves, their teams won’t either. 🔸100 practical ideas from 100+ leaders. Not hype — real moves happening right now. This is one of the most grounded and useful reports I’ve come across. The AI Transformation 100 releases today and I strongly encourage every business leader to read it. Access the full report and join the conversation: https://lnkd.in/e7YYwBrt And if you want the deeper story, don’t miss my full interview with Rebecca — you’ll want to watch it to the end: https://lnkd.in/egsdhVaA #GleanAmbassador #AITransformation #Leadership #ArtificialIntelligence #DigitalTransformation #ChangeManagement #FutureOfWork #BusinessStrategy #Innovation

The anatomy of a sales call has changed dramatically. Last week, I shadowed some of HubSpot’s top reps and what struck me was how differently the best sellers work today. They’re using AI at every stage: before, during, and after the call. And the results are real. The brain: before the call. AI does the heavy research — scanning 10Ks, news, emails, and past calls to surface the insights that matter most. Tools like Breeze Assistant can prep a full company overview in seconds. According to our State of Sales Report, 74% of sellers say buyers are showing up to calls more informed than ever before. Salespeople need to be just as ready. The heart: during the call. AI notetakers capture everything: next steps, budget mentions, open questions, so reps can focus on listening, not typing or scribbling notes on the side.  Also, AI assistants surface the right case study or testimonial in real time, making every answer sharper and every example more relevant. That means as a sales rep you are more engaged and relevant. The muscle: after the call. AI follows through fast. It drafts personalized follow-up emails in your own voice, outlines next steps, and flags what needs attention. More time with customers and less time writing emails. The result: sellers who prepare better, connect deeper, and close faster. The anatomy of a great sales call used to be manual effort and hustle. Now, it’s human connection powered by intelligence.

Every cloud provider faces the same AI infrastructure challenge: chips need to be positioned close together to exchange data quickly, but they generate intense heat, creating unprecedented cooling demands. We needed a strategic solution that allowed us to use our existing air-cooled data centers to do liquid cooling without waiting for new construction. And it needed to be rapidly deployed so we could bring customers these powerful AI capabilities while we transition towards facility-level liquid cooling. Think of a home where only one sunny room needs AC, while the rest stays naturally cool – that’s what we wanted to achieve, allowing us to efficiently land both liquid and air-cooled racks in the same facilities with complete flexibility. The available options weren't great. Either we could wait to build specialized liquid-cooled facilities or adopt off-the-shelf solutions that didn't scale or meet our unique needs. Neither worked for our customers, so we did what we often do at Amazon… we invented our own solution. Our teams designed and delivered our In-Row Heat Exchanger (IRHX), which uses a direct-to-chip approach with a "cold plate" on the chips. The liquid runs through this sealed plate in a closed loop, continuously removing heat without increasing water use. This enables us to support traditional workloads and demanding AI applications in the same facilities. By 2026, our liquid-cooled capacity will grow to over 20% of our ML capacity, which is at multi-gigawatt scale today. While liquid cooling technology itself isn't unique, our approach was. Creating something this effective that could be deployed across our 120 Availability Zones in 38 Regions was significant. Because this solution didn't exist in the market, we developed a system that enables greater liquid cooling capacity with a smaller physical footprint, while maintaining flexibility and efficiency. Our IRHX can support a wide range of racks requiring liquid cooling, uses 9% less water than fully-air cooled sites, and offers a 20% improvement in power efficiency compared to off-the-shelf solutions. And because we invented it in-house, we can deploy it within months in any of our data centers, creating a flexible foundation to serve our customers for decades to come. Reimagining and innovating at scale has been something Amazon has done for a long time and one of the reasons we’ve been the leader in technology infrastructure and data center invention, sustainability, and resilience. We're not done… there's still so much more to invent for customers.

🔮 AI Interaction Design Patterns (https://www.shapeof.ai), a fantastic (!) living catalog of emerging design patterns, heuristics, anti-patterns and real-life examples that shape the experience of AI — from identifiers and wayfinding to prompts, tuners and trust indicators. Incredible project by incredible Emily Campbell. 👏🏼 👏🏽 👏🏾 AI experience can go way beyond a text box. One of the most underrated yet impactful patterns for AI interfaces is the ability to tune AI experiences. This could show itself as a style lenses or temperature knobs — little tools to help users generate a more personalized output easier. E.g. Risky ↔ Risk-averse, Sad ↔ Happy, Concrete ↔ Abstract, Creative ↔ Precise. Instead of expecting large and highly detailed text prompts, we could slow people down when they prompt — e.g. with prompt constructors, prompt strength meters, presets or templates. Perhaps by defining an expected format, structure, personas, roles as checkboxes or chips — both for user input and AI responses (priming). Another much-needed feature is scoping. Users should be able to quickly scope their inquiry to a particular domain, level of expertise, sources or even a set of videos or PDFs. We need pre-screening of sources, and proactive alignment with users. These are features that would make output much more specific without having to write a long prompt. And: the AI output shouldn’t be bulky nor static. Users should be able to granularly iterate or revise little bits of it — e.g. by asking for sources of specific statements, or diverging from one view to another, or manipulating small parts of an image or a video. These refinements should happen not via text prompts, but contextually — acting on the relevant parts of AI outcome. We can go way beyond a text prompt. Better results come from combining good old-fashioned design patterns such as search, filtering and sorting with AI — to first find relevant and trustworthy sources, and then generate insights from them. That’s a great way to boost accuracy and make AI more relevant to more people. 💎 Design Patterns For AI Interfaces Prompt UX Patterns, by Sharang Sharma https://lnkd.in/eCytfAe9 Where should AI sit in your UI?, by Sharang Sharma https://lnkd.in/dyyMKuU9 AI UX Patterns, by Luke Bennis https://lnkd.in/dF9AZeKZ Design Patterns For Building Trust, by If https://lnkd.in/eEJngtVv AI Design Patterns Catalogue, by Maggie Appleton https://lnkd.in/ebAp9Sb8 --- 🚀 Fantastic AI Examples: Elicit (research tables): https://elicit.com Consensus (confidence levels): https://consensus.app/ Scispace (search + AI): https://scispace.com v7 Labs (AI auto-fill): https://v7labs.com/ Exa (semantic grid): https://exa.ai DeepL (translation): https://deepl.com NotebookLM (scoping): https://notebooklm.google/ [continues in comments] #ux #ai

Three AI recruiters look at the same 109 CVs. They agree only 14% of the time. That’s not the start of a joke. And that's not efficiency. That’s what I call 'Rank Roulette'. When I tested ChatGPT, Gemini and Grok against the same job spec and anonymised CV set, here’s what happened: • 14% overlap in shortlists → Four times out of five, the models disagreed. • ±2.5 places volatility → Yesterday’s #2 became today’s #5. • 55% of CVs never surfaced → Candidates vanished with no audit trail. • 96% recycled rationales → Fluent, but shallow logic. We’re told by vendors and in-house 'tinkerers' that LLMs can “shortlist in seconds”. The truth: they behave more like over-confident interns - smooth on the surface, but shockingly inconsistent. And the worst part? It’s not even random. In a follow-up piece, I explored why this happens: a technical quirk called batch non-determinism. In plain English: your candidate’s fate changes depending on what else the server was processing at that moment. Until volatility is tamed, hands-off AI screening with LLMs is more than risky. It’s completely unexplainable, indefensible and a governance nightmare. Go to the comments for 👉 Full research 👉 Follow-up on why AI recruiters play favourites

Microsoft just released a 35-page report on medical AI - and it’s a reality check for healthcare. The paper, “The Illusion of Readiness”, tested six of the most popular models (OpenAI, Gemini, etc)… across six multimodal medical benchmarks. And the verdict? The models scored high on medical exams. But they’re not even close to being real-world ready. Here’s what the stress tests revealed: ▶ 1. Shortcut learning Models often answered correctly even when key information, like medical images, was removed. They weren’t reasoning - they were exploiting statistical shortcuts. That means benchmark wins may hide shallow understanding. ▶ 2. Fragile under small changes Making small tweaks caused big swings in predictions. This fragility shows how unreliable model reasoning becomes under stress. In visual substitution tests, accuracy dropped from 83% to 52% when images were swapped - exposing shallow visual–answer pairings. ▶ 3. Fabricated reasoning Models produced confident, step-by-step medical explanations - but many were medically unsound… or entirely fabricated. Convincing to the eye, dangerous in practice. And more importantly, healthcare isn’t a multiple-choice exam. It’s uncertainty, incomplete data, and high stakes. So Microsoft’s team calls for new standards: - Stress tests that expose fragility - Clinician-guided guidelines that profile benchmarks - Evaluation of robustness and trustworthiness - not just leaderboard scores The takeaway is simple: Medical AI may ace tests today. But until it proves reliable under stress, it’s not ready for the clinic. When do you think popular LLMs will be clinic-ready? #entrepreneurship #healthtech #AI

Something VERY cool just happened in California and… it could be the future of energy.   On July 29, just as the sun was setting, California’s electric grid was reaching peak demand.   However, instead of ramping up fossil fuel resources, the California Independent System Operator (CAISO) and local utilities decided to lean on a network of thousands of home batteries.   More than 100,000 residential battery systems (made up primarily by Sunrun and Tesla customers) delivered about 535 megawatts of power to California’s grid right as demand peaked, visibly reducing net load (as shown in the graphic).   Now, this may not seem like a lot but 535 megawatts is enough to power more than half of the city of San Francisco and that can make all the difference when a grid is under stress.   This is what’s called a Virtual Power Plant or VPP. It’s a network of distributed energy resources that grid operators can call on in an emergency to provide greater resilience to our energy systems. Homeowners are compensated for the dispatch, grid operators are given another tool for reliability, and ratepayers are saved from instability. It’s a win-win-win.   Now, this was just a test to prepare for other need-based dispatches during heat waves in August and September. But it’ historic.   As homeowners add more solar and storage resources, the impact of these dispatch events will become even more profound and even more necessary. This was the second time this summer that VPPs have been dispatched in California and I expect to see even more as this technology improves.   Shout out to Sunrun, Tesla, and all companies who participated. Keep up the great work.

Cities are getting bigger. Populations are getting older. Are you living in one of the top 15 cities? By 2030, over 60% of the world’s population will live in cities, and for the first time in history, the elderly will outnumber the young. This double shift is pushing our healthcare systems, infrastructure, and resources to the limit. How AI can help: + Predictive healthcare: Early detection of diseases and remote monitoring allow seniors to live independently while reducing strain on hospitals. + Smart mobility & infrastructure: AI optimizes traffic, public transport, and energy grids for growing, denser populations. + Resource management: From water to waste, AI ensures sustainable city operations. + Workforce support: Automation addresses labor shortages, while AI-driven upskilling helps older workers stay engaged. + Inclusive services: AI breaks barriers in accessibility, language, and personalized citizen support. AI isn’t a silver bullet, but it’s a powerful enabler for making cities healthier, more efficient, and more inclusive. #Innovation #AI #Technology #Population

AI is not failing because of bad ideas; it’s "failing" at enterprise scale because of two big gaps: 👉 Workforce Preparation 👉 Data Security for AI While I speak globally on both topics in depth, today I want to educate us on what it takes to secure data for AI—because 70–82% of AI projects pause or get cancelled at POC/MVP stage (source: #Gartner, #MIT). Why? One of the biggest reasons is a lack of readiness at the data layer. So let’s make it simple - there are 7 phases to securing data for AI—and each phase has direct business risk if ignored. 🔹 Phase 1: Data Sourcing Security - Validating the origin, ownership, and licensing rights of all ingested data. Why It Matters: You can’t build scalable AI with data you don’t own or can’t trace. 🔹 Phase 2: Data Infrastructure Security - Ensuring data warehouses, lakes, and pipelines that support your AI models are hardened and access-controlled. Why It Matters: Unsecured data environments are easy targets for bad actors making you exposed to data breaches, IP theft, and model poisoning. 🔹 Phase 3: Data In-Transit Security - Protecting data as it moves across internal or external systems, especially between cloud, APIs, and vendors. Why It Matters: Intercepted training data = compromised models. Think of it as shipping cash across town in an armored truck—or on a bicycle—your choice. 🔹 Phase 4: API Security for Foundational Models - Safeguarding the APIs you use to connect with LLMs and third-party GenAI platforms (OpenAI, Anthropic, etc.). Why It Matters: Unmonitored API calls can leak sensitive data into public models or expose internal IP. This isn’t just tech debt. It’s reputational and regulatory risk. 🔹 Phase 5: Foundational Model Protection - Defending your proprietary models and fine-tunes from external inference, theft, or malicious querying. Why It Matters: Prompt injection attacks are real. And your enterprise-trained model? It’s a business asset. You lock your office at night—do the same with your models. 🔹 Phase 6: Incident Response for AI Data Breaches - Having predefined protocols for breaches, hallucinations, or AI-generated harm—who’s notified, who investigates, how damage is mitigated. Why It Matters: AI-related incidents are happening. Legal needs response plans. Cyber needs escalation tiers. 🔹 Phase 7: CI/CD for Models (with Security Hooks) - Continuous integration and delivery pipelines for models, embedded with testing, governance, and version-control protocols. Why It Matter: Shipping models like software means risk comes faster—and so must detection. Governance must be baked into every deployment sprint. Want your AI strategy to succeed past MVP? Focus and lock down the data. #AI #DataSecurity #AILeadership #Cybersecurity #FutureOfWork #ResponsibleAI #SolRashidi #Data #Leadership

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.