Future Trends in Intelligent Automation

Over the past two years, the cost of running GPT-4 has plummeted by 240x, fundamentally altering the landscape for enterprise AI. This isn't just a reduction in expense—it's a gateway to a new era of AI innovation. For C-level leaders and developers, it’s time to stop thinking about replacing today's features and start thinking about unlocking capabilities once considered superhuman. As AI becomes more affordable, many will first look at automating existing processes. But that’s short-term thinking. The real transformation comes when you ask: What can AI do that was previously too expensive or complex? With the cost of knowledge work approaching the cost of air, second- and third-level processes that were once out of reach are now achievable. Imagine: Real-Time Dynamic Strategy: AI continuously processes global trends, competitor data, and internal metrics, allowing real-time strategy shifts based on constantly evolving insights. Predictive Supply Chain Optimization: AI systems that foresee supply chain disruptions, shifting production and distribution before issues even arise. Supercharged R&D: AI scanning and synthesizing worldwide research to suggest novel discoveries in fields like pharmaceuticals, engineering, and beyond. The future is about much more than simply making existing tasks faster or cheaper—it’s about doing what was once unthinkable. Driving this change even further is the dramatic decline in hardware costs, alongside rapid improvements in AI-specific infrastructure. Amazon's new Inferentia instances, for example, deliver up to 2.3x higher throughput and up to 70% lower cost per inference than comparable EC2 instances. But that’s just the start. With the release of Intel Gaudi 3, AMD MI325X, and Nvidia's B-series, we’re on the cusp of another massive drop in AI costs. These hardware advancements, combined with increasingly sophisticated software, are about to unlock capabilities we haven’t even imagined yet. The cost of AI is dropping fast, and those who innovate beyond today's features will redefine their industries. The future isn’t just about automating—it’s about unlocking new, superhuman possibilities. KamiwazaAI #1trillionInferencesDay #5IR #EnterpriseAI #EnterpriseTakeoff

AI is no longer just an experimentation tool. It’s reshaping the entire optimization landscape. With this shift comes many untapped opportunities. Working with Andrius Jonaitis ⚙️, we've put together a growing list of 40+ AI-driven experimentation tools ( https://lnkd.in/gHm2CbDi) Combing through this list, here are the emerging market trends and opportunities you should know: 1️⃣ SELF-LEARNING, AUTO-OPTIMIZING EXPERIMENTS 💡 Opportunity: AI is creating self-adjusting experiments that optimize in real-time. 🛠️ Tools: Amplitude, Evolv Technology, and Dynamic Yield by Mastercard are pioneering always-on experimentation, where AI adjusts experiences dynamically based on live behavior. 🔮 How to leverage it: Focus on learning and developing tools that shift from static A/B testing to AI-powered, dynamically updating experiments. 2️⃣ AI-GENERATED VARIANTS 💡 Opportunity: AI can help you develop hypotheses and testing strategies. 🛠️ Tools: Ditto and ChatGPT (through custom GPTs) can help you generate robust testing strategies. 🔮 How to leverage it: Use custom GPTs to generate test ideas at scale. Automate hypothesis development, ideation, and test planning. 3️⃣ SMARTER EXPERIMENTATION WITH LESS TRAFFIC 💡 Opportunity: AI-driven traffic-efficient testing that gets results without massive sample sizes. 🛠️ Tools: Intelligems, CustomFit AI, and CRO Benchmark are pioneering AI-driven uplift modeling, finding winners faster -- with less traffic waste. 🔮 How to leverage it: Don't get stuck in a mentality that testing is only for enterprise organizations with tons of traffic. Try tools that let you test more and faster through real-time adaptive insights. 4️⃣ AI-POWERED PERSONALIZATION 💡 Opportunity: AI is creating a whole new set of experiences where every visitor will see the best-performing variant for them. 🛠️ Tools: Lift AI, Bind AI, and Coveo are some of the leaders using real-time behavioral signals to personalize experiences dynamically. 🔮 How to leverage it: Experiment with tools that match users with high-converting content. These tools are likely to develop and get even more powerful moving forward. 5️⃣ AI EXPERIMENTATION AGENTS 💡 Opportunity: AI-driven autonomous agents that can run, monitor, and optimize experiments without human intervention. 🛠️ Tools: Conversion AgentAI and BotDojo are early signals of AI taking over manual experimentation execution. Julius AI and Jurnii LTD AI are moving toward full AI-driven decision-making. 🔮 How to leverage it: Be open-minded about your role in the experimentation process. It's changing! Start experimenting with tools that enable AI-powered execution. 💸 In the future, the biggest winners won’t be the experimenters running the most tests, they’ll be the ones versed enough to let AI do the testing for them. How do you see AI changing your role as en experimenter? Share below: ⬇️

🚀 McKinsey & Company Tech Trends 2025: What Business Leaders Must Know Now. The future is arriving faster than expected—and AI is at the core of it. McKinsey’s Technology Trends Outlook 2025 is a must-read for executives, founders, and technologists looking to stay ahead. The report evaluates 15 breakthrough technologies based on adoption, investment, talent availability, and real-world momentum. Here are the key insights and strategic takeaways 👇 🔮 1. #AI is the Central Force AI is not just one of many trends—it’s a foundational technology driving others. From developer productivity to robotics, AI is now integrated across industries and functions. Use cases have matured beyond experimentation into real-world value creation. 🧠 2. Generative & Agentic AI: From Tools to Teammates Generative AI continues to surge, but Agentic AI —tools that can reason and take action autonomously—is emerging as the next frontier. These systems will move from responding to prompts to completing tasks, triggering a shift in business automation. ⚙️ 3. Next-Gen Software Development AI-assisted development environments are accelerating time-to-code and shifting how engineering teams function. Companies investing here are cutting product cycles by up to 30%, according to McKinsey insights. 📡 4. Advanced Connectivity Fuels Edge Innovation With maturing 5G, low-Earth-orbit satellites, and edge computing, advanced connectivity is unlocking real-time applications across manufacturing, logistics, and smart infrastructure. This isn't future-talk—deployment is accelerating now. 🔬 5. Applied AI in Real Operations AI-powered vision systems, robotics, and simulation tools are already optimizing everything from warehousing to agriculture. What’s new? These tools are being used at scale, not just in pilot programs. 📊 6. Trust Architecture & Responsible AI As AI grows more autonomous, McKinsey emphasizes trust architecture—governance, risk controls, and ethical design must evolve in tandem. Regulation is coming fast. Companies that prepare early will lead with confidence. 🌱 7. Sustainable Tech: From Buzzword to Bottom Line Tech is finally aligning with sustainability goals. Energy-efficient compute, circular hardware design, and green cloud are becoming investment priorities, not side projects. 💡 Leadership Takeaways ✅ Embed AI as a horizontal strategy, not a vertical investment ✅ Invest in next-gen developer tools to stay agile ✅ Build or upskill talent to lead agentic workflows ✅ Establish clear AI governance frameworks early ✅ Use advanced connectivity to optimize operations ✅ Don’t overlook trust, ethics, and sustainability—they are competitive differentiators. #McKinsey #AI #TechTrends2025 #AgenticAI #DigitalTransformation #FutureOfWork #TrustInTech #GenerativeAI #Sustainability #AILeadership #TechStrategy #BusinessInnovation 🤖📈🌐💼

I always share a post each year talking about my predictions in technology. Here are my general technology trends for 2025. 🔺 Wider Adoption of Generative AI 🔹 Domain-specific models: We’ll see more specialized generators trained on targeted data (e.g., legal, medical, scientific) that can produce highly accurate and context-specific content. 🔹 Hybrid approaches: Enterprises will use generative AI alongside rule-based or traditional ML methods to achieve more reliable outcomes, minimizing hallucinations and biases. 🔺 Rise of Multimodal Systems 🔹 Unified AI experiences: Instead of siloed text, image, audio, and video models, we’ll see integrated systems that seamlessly handle multiple data types. This leads to richer applications, from next-gen customer support to advanced robotics. 🔹 Context-aware processing: AI will better understand real-world context, combining visual, audio, and textual cues to offer smarter responses and predictions. 🔺 Advances in Explainability and Trust 🔹 Regulatory frameworks: With stricter AI regulations on the horizon, model explainability and audibility will become core requirements, especially in finance, healthcare, and government. 🔹 AI “nutrition labels”: Standardized ways of conveying model biases, training datasets, and reliability will help build user trust and improve transparency. 🔺 Edge and On-Device AI 🔹 Lower latency, better privacy: More powerful AI models will run directly on phones, wearables, and IoT devices, reducing dependence on the cloud for tasks like speech recognition, image processing, and anomaly detection. 🔹 Specialized hardware: Continued investment in AI accelerators, TPUs, and neuromorphic chips will enable high-performance AI at the edge. 🔺 Human-AI Teaming and Augmented Decision-Making 🔹 Decision intelligence platforms: AI will shift from purely providing recommendations to working interactively with humans to explore complex problems—reducing cognitive load, but keeping humans in the loop. 🔹 Collaborative coding and content creation: AI co-pilots will expand from code generation and text drafting to more sophisticated collaboration, shaping design, research, and strategic planning. 🔺 Rapid Growth of AI as a Service (AIaaS) 🔹 “No-code” and “low-code” tools: Tools that allow non-technical users to deploy custom AI solutions will proliferate, lowering barriers to entry and accelerating adoption across industries. 🔺 Emphasis on Ethical and Responsible AI 🔹 Bias mitigation: Tools and techniques to detect and reduce bias will grow more advanced, spurred by public scrutiny and regulatory demands. 🔹 Standards for accountability: Organizations will create ethics boards and formal guidelines to ensure AI alignment with corporate values and social responsibility. 🔺 Quantum Computing Experiments 🔹 Hybrid quantum-classical models: Though still early-stage, breakthroughs in quantum hardware could lead to specialized quantum-assisted AI algorithms.