⚡This week’s AI moves that should reshape your business thinking: 💰 Monday - AI budgets hit breaking point. The new BCG and Deloitte reports show 95% of companies expect ROI from AI, but only 5% actually achieve it. Most aren’t failing at tech - they’re failing at translation. The lesson: if AI isn’t tied to revenue or cost KPIs, it’s a research project, not an investment. 🏗️ Tuesday - Google and OpenAI bet another $25B on compute. Google’s $15B India AI hub and OpenAI’s Broadcom deal for 10 gigawatts of custom chips prove one thing - AI isn’t slowing down. But for most companies, that means compute will get cheaper while expectations rise. Advantage shifts to those who know what to do with the power, not who owns it. 💼 Wednesday - Anthropic slashes prices, Alibaba breaks even. Claude’s new Haiku 4.5 model is faster and one-third the cost. Meanwhile, Alibaba announced AI profitability in e-commerce. The market’s message: AI is finally moving from proof-of-concept to payback. Cheaper intelligence now rewards execution, not experimentation. 🧠 Thursday - Enterprise platforms go all-in on agents. Salesforce and Google deepened their partnership, IBM launched AgentOps, SAP rolled out Joule AI, and Adobe released its LLM Optimizer. Translation: enterprise software is no longer about “apps” - it’s about coordination. If your workflows aren’t AI-readable, they’ll soon be invisible. 💻 Friday - AI moves from the cloud to your desktop. Microsoft’s Copilot Actions now automate local files. Apple’s new M5 chip brings 4× GPU compute to every MacBook. The edge is where AI gets personal - faster, private, and embedded. The question shifts from “what can AI do?” to “what will you still need humans to do?” 💡 My takeaway: AI isn’t another department - it’s leverage. The infrastructure race is over - the implementation race just began. 👉 Where will you apply leverage first - your processes, your people, or your decisions?

The numbers don’t lie. Tech industry giants are already reaping real profits from AI adoption. But… is it only for giants? While some still hesitate to delegate operations to AI, others are already reaping the rewards of their investments. According to recent data from Exploding Topics, 78% of global companies report using AI in their business. And the results? A few striking examples: 💰 Microsoft reports $500 million in savings thanks to AI 💰 Amazon’s Q2 profit jumped 35% to $18.2 billion, fueled by AI and AWS growth 💰 Netflix saved $1 billion by improving its recommendation engine with machine learning Large companies may be twice as likely as small ones to adopt AI technologies, but the gap is closing fast. As stated by U.S. Chamber of Commerce business report, 96% of small business owners plan to embrace emerging technologies like AI. This shows that AI-driven operations have become the standard for businesses of every size. In short, no matter how big or small your company is, now is the time to explore AI. Otherwise, you risk being in the last car. Your reliable partner on this journey is Vention. Explore our solutions and see what's possible for your company today. The link to full list of our services is below. #AIresearch #Vention #AIdevelopment #AIvendor

How can enterprises maintain brand consistency at scale? The Production Reality Gap whitepaper, created in collaboration with Amazon Web Services (AWS), explores how to build systems that avoid common pitfalls. You’ll discover 👇 ✔️ Why prompting alone as an adjustment to the quality of inference is not enough for an enterprise content workflow ✔️ Which technical metrics actually impact production (like inference speed, reproducibility, and output quality) ✔️ How to add automation and control to visual AI with the right tools and platform ✔️ Ways to build AI pipelines that scale, without adding unnecessary complexity Learn how Fortune 1000 companies are building scalable, reliable systems that deliver real-world results. Download the free whitepaper today ➡ https://go.bria.ai/4ncMJlj #GenerativeAI #AI #AIforBusiness

$2B just landed in a 60-person AI lab you've never heard of. While you're choosing between OpenAI and Anthropic, the real competition just shifted. What's really happening: 1️⃣ Two ex-DeepMind researchers built frontier-scale AI training infrastructure outside Big Tech (something experts said was impossible 12 months ago). 2️⃣ Their valuation jumped 15x in 7 months—from $545M to $8B—faster than any enterprise software company in history. 3️⃣ Reflection AI is releasing open model weights next year, meaning enterprises get full ownership and cost control (the closed-source pricing model just became optional). 4️⃣ China's DeepSeek proved open frontier models work at scale—now America's racing to match it, or risk losing the global AI standard. Reflection AI (60 employees) secured compute clusters that typically require Google-scale resources. Here's the pattern: Cloud adoption took 15 years. AI adoption is happening in 15 months. The companies winning aren't the biggest—they're the ones that moved before the playbook was written. (Most enterprises are still debating AI governance while competitors are deploying sovereign AI systems.) The contrarian truth? Open-source AI just became the enterprise advantage. Customization beats convenience when you're spending millions on inference costs. Your move: Audit your AI vendor lock-in. The cost structure you signed last quarter might already be obsolete. 👉 Tools to act now: ⚡ Manus - It doesn't assist. It executes. You delegate. It delivers. https://lnkd.in/d3Ami8eK 💬 Comment ♻️ Repost ➕ Follow me Christian Schmidt for AI & business strategy insights. #ArtificialIntelligence #EnterpriseAI #OpenSourceAI #AIStrategy #DigitalTransformation https://lnkd.in/gjMSfdTy

Håkan Apell’s summary of the #AWS guide is an excellent playbook for deploying Agentic AI within the current paradigm. The focus on "autonomous decision-making," "workflow optimization," and "multimillion-dollar efficiencies" is the logical endpoint of a bounded-sum ecosystem—one designed to compete, extract, and optimize within known limits. But there is a profound, unstated risk: Unleashing a hyper-efficient, agentic intelligence into a brittle, bounded-sum system doesn't make it resilient; it makes it dangerously efficient at its own game until it crashes. An agent optimized solely for shareholder value or operational efficiency in a zero-sum market will execute that mandate with a speed and complexity we can no longer control. It will create feedback loops of optimization that strip out redundancy, externalize costs, and amplify systemic risk, making the entire structure more fragile. The true transformation, therefore, isn't just in adopting Agentic AI, but in redeploying it. The most critical "agentic" task we can assign is not to optimize the old game, but to help us design and test the new one. We need agents that can model unbounded-sum outcomes. Agents that don't just execute a business process, but can simulate the second- and third-order effects of a decision on community capacity, environmental regeneration, and long-term systemic health. The choice isn't whether to build agentic systems. It's what game we instruct them to play. Do we use them to win a race to the bottom, or to help us build a foundation that elevates everyone? The "winning organizations" won't be those that simply automate fastest. They will be the ones whose agents are the first to successfully hedge against the bounded-sum crash by prototyping the unbounded-sum future. #AgenticAI #UnboundedSum #SystemsThinking #AI #Risk #Innovation

Why do 85-95% of #AI Projects fail to lift-off? AI is clearly the generational level change that organisations need to fully harness to drive step-change performance, but most are failing! 🚥 They have no governing framework or methodology to build AI into their CorporateDNA. That's the role of the #IntelligentEnterprise! ⚡ In my experience the biggest failure is an automatic delegation of #CEO to Technology. They pass to their #CIO / #CTO, who immediately engage the big vendors Microsoft. Google, Amazon Web Services (AWS) to come and help them. We have seen this movie so many times... and as Einstein is often misquoted as saying 💣 "the definition of insanity is doing the same thing over and over again and expecting different results" ➡️ AI cannot be a technology bolt on, it must be built in (to the DNA of the business)! ⬅️ To do that you need to understand the Capabilities that will actually differentiate you versus the market and competitors. 💣 What shocks me is how many business leaders can't describe this. ⚡ If you can't describe what you need to be better at than your competitors, how can you Win in the market! Once you can describe those key Capabilities, you can describe what you need #People to do to deliver those capabilities and what #Data and #AI might be available to supercharge those capabilities. Don't make the next mistake of just picking an interesting #UseCase or #Process and seeing if you have Data for that and can build an AI model to improve it. ⚡ We are no longer in the #automation game, we are in the #reinvent game. Those interesting Processes or UseCases, may well be entirely irrelevant in your new world! Only then start to thing Technology and Tools! All of that is built into the Intelligent Enterprise framework and the Capability flywheel model shown in the picture. It's a holistic framework to transform organisations (Public and Private)! If you want more information, reach out and follow the Intelligent Enterprise Blog https://lnkd.in/gGFHWmsu VELES CONSULTING LTD BearingNode Samir Sharma Malcolm Hawker Mark Stouse Bill Schmarzo Randy Bean Bruno Aziza Rana Ghosh-Roy, PhD

95% of AI projects fail. But here’s the secret: you don’t need to fail to learn. Most companies are stuck in endless pilots, wasting $$$ and time… when the world’s top firms have already published their battle-tested AI playbooks. Why reinvent the wheel when you can copy what works? 💡 I’ve pulled together 12 of the best AI strategy playbooks from McKinsey, BCG, Deloitte, Google, Microsoft, and more — all free, all actionable. 📌 Save this post (you’ll thank yourself later). Here are the gold standards for moving from AI pilots → production success: 🟢 McKinsey & Company – The Executive’s AI Playbook https://lnkd.in/dQTBSkcJ 🟢 techNgraphic (Pvt) Ltd. – How To Make Your AI Toolkit https://lnkd.in/dMctEhdy 🟢 Boston Consulting Group (BCG) – Transforming with AI https://lnkd.in/dRSmdF53 🟢 Bain & Company – Winning with AI https://lnkd.in/dV_t6Jd5 🟢 Deloitte – AI Transformation https://lnkd.in/dcUWNbhA 🟢 Amazon – AI/ML/GenAI Cloud Framework https://lnkd.in/dbc3RkYW 🟢 Google – AI Adoption Framework https://lnkd.in/dAmAyBgX 🟢 IBM – CEO’s Guide to GenAI https://lnkd.in/dZStjHet 🟢 Microsoft – CIO’s GenAI Playbook https://lnkd.in/db3WMfaS 🟢 PwC – Agentic AI Playbook https://lnkd.in/dNkevkUA 🟢 World Economic Forum – AI C-Suite Toolkit https://lnkd.in/drQnmKiX 🟢 Accenture – The Art of AI Maturity https://lnkd.in/dnrQi3j2 👉 Which one do you think is the most practical for 2025?

From legal to private equity to tech, leading organizations are using AI to reshape research and strategy. In this session, AWS and AlphaSense will share real-world case studies showing how companies are: ⚡ Cutting research time by up to 75% with AI-assisted workflows 🔎 Uncovering hidden insights across vast, fragmented data sources 📈 Driving smarter, data-driven decisions with measurable impact You’ll also get a behind-the-scenes look at how AWS powers AlphaSense’s AI platform and what the future of AI in market intelligence looks like. Don’t miss these actionable insights to revolutionize how you approach market research and strategic planning.

From manufacturing to private equity to tech, leading organizations are using AI to reshape research and strategy. In this session, AWS and AlphaSense will share real-world case studies showing how companies are: ⚡ Cutting research time by up to 75% with AI-assisted workflows 🔎 Uncovering hidden insights across vast, fragmented data sources 📈 Driving smarter, data-driven decisions with measurable impact You’ll also get a behind-the-scenes look at how AWS powers AlphaSense’s AI platform and what the future of AI in market intelligence looks like. Don’t miss these actionable insights to revolutionize how you approach market research and strategic planning.

#SnowflakeWorldTourNYC was an intense day, a nice window into where a lot of companies are in their AI adoption journeys. Here are some trends I spotted amongst the many companies showcasing their success stories: ❄️ Broadly, companies have settled at this point on their could or multi-cloud data storage platforms of choice. They're taking it from there to Snowflake, data warehousing, and then on to enduser AI use cases. ❄️ Chatbots to query data with natural language are now embedded into many frontends of all the major cloud data players. And so those chatbots are emerging as state-of-the-art Self-Service BI. ❄️ AI agents look like everyone's Next Step in their journey of day-to-day AI usage, automating everything frontline users have by now discovered AI can do to accelerate processes. ❄️ "Can AI create AI agents?" I've heard asked a few times. Yes they can! #GenerativeAI #AgenticAI #Snowflake