Understanding AI Industry Trends

The next wave of AI transformation is here – and it’s not just about language-based models anymore. The real breakthroughs are happening now with Large Quantitative Models (LQMs) and cutting-edge quantum technologies. This seismic shift is already unlocking game-changing capabilities that will define the future: Materials & Drug Discovery – LQMs trained on physics and chemistry are accelerating breakthroughs in biopharma, energy storage, and advanced materials. Quantitative AI models are pushing the boundaries of molecular simulations, enabling scientists to model atomic-level interactions like never before. Cybersecurity & Post-Quantum Cryptography – AI is identifying vulnerabilities in cryptographic systems before threats arise. As organizations adopt quantum-safe encryption, they’re securing sensitive data against both current AI-powered attacks and future quantum threats. The time to act is now. Medical Imaging & Diagnostics – AI combined with quantum sensors is revolutionizing medical diagnostics. Magnetocardiography (MCG) devices are providing more accurate cardiovascular disease detection, with potential applications in neurology and oncology. This is a breakthrough that could save lives. LQMs and quantum technologies are no longer distant possibilities—they’re here, and they’re already reshaping industries. The real question isn’t whether these innovations will transform the competitive landscape—it’s how quickly your organization will adapt.

One of our most anticipated reports each year is out—a comprehensive look at the most significant tech trends unfolding today, from agentic AI to the future of mobility to bioengineering. It provides CEOs with insights on how to embrace frontier technology that has the potential to transform industries and create new opportunities for growth.   Here’s my top-line take: —Equity investments rose in 10 out of 13 tech trends in 2024, with 7 of those trends recovering from declines in the previous year. This rebound signals growing confidence in emerging technologies. —We're witnessing a significant shift in autonomous systems going from pilots to practical applications. Systems like robots and digital agents, are not only executing tasks but also learning and adapting. Agentic AI saw a $1.1 billion equity investment in 2024 alone. —The interface between humans and machines is becoming more natural and intuitive. Advances in immersive training environments, haptic robotics, voice-driven copilots, and sensor-enabled wearables are making technology more responsive to human needs. —And, of course, the AI effect stands out as both a powerful trend in its own right and a foundational amplifier of others. AI is accelerating robotics training, advancing bioengineering discoveries, optimizing energy systems, and more. The sheer scale of investment in AI is staggering, with $124.3 billion in equity investment in 2024 alone.   Let's discuss: Which of these trends do you think will have the most significant impact on your industry? Share your thoughts in the comments below!   Big thanks to my colleagues Lareina Yee, Michael Chui, Roger Roberts, and Sven Smit.   #TechTrends #AI #Innovation #FutureOfWork #EmergingTech http://mck.co/techtrends

Enterprise AI: What's Next? AI is no longer experimental—it’s essential. With AI spending skyrocketing to $15.7B in 2024 (8x growth in two years), businesses are rapidly shifting from pilots to full-scale implementation. Industry-Specific AI is taking center stage 📍 Healthcare: AI-driven diagnosis & treatment planning 📍 Manufacturing: Predictive maintenance & supply chain optimization 📍 Finance: Fraud detection & algorithmic trading ✤ AI Agents: The Next Frontier ↳ Autonomous AI systems are streamlining tasks, with adoption expected to triple by 2025. ✤ The AI-Driven Enterprise of 2025 ↳ Hyper-specialized AI solutions, multimodal AI, and AI-human collaboration will redefine business operations. The winners? Those who embrace AI strategically and invest in strong data governance. 💡What AI applications excite you most for your industry? Let’s discuss! ⬇️ #Innovation #Enterprise #ArtificialIntelligence #Business #FutureOfWork

Trending AI Categories I've Been Exploring Lately I'm seeing a lot of #venture activity across these 9 #AI categories— with huge traction and emerging winners, even though many of these companies are still very young. And while it's always risky to enter categories where leaders are already emerging, these startups are operating in massive, untapped greenfield markets. That’s why there’s also a big wave of smaller players, often just a year behind and with significantly lower valuations, trying to catch up or focus on narrower slices within the categories below that require more customization. In many cases, the playbook is becoming increasingly transparent. 1. AI Voice Agents Enabling Businesses – Lower latency, more natural voices, better handling of interruptions, emotions, reduced costs, improved memory. Use cases span BPOs, insurance, clinics, airlines, recruiting in staffing agencies—any industry with heavy phone use. All with a similar tech stack: Pipecat + LLMs + Sesame, Cartesia, or ElevenLabs. 2. AI D2C Doctor on Top of a Health System of Record – A new B2C playbook is emerging, very focused on continuous testing (blood, MRIs, urine, wearables) and monitoring. Startups target specific geos or niches (e.g., athlete wannabes, millionaires) and often include celebrities for top-of-funnel and trusted doctors on the cap table (Prenuvo, Ezra, Biograph, Nekko, FunctionHealth, Eternal.co). 3. Web3 & AI Decentralization – Solving for data labeling, aggregating available computing resources, and agent verification, all with decentralized solutions (KlusterAI, Fraction, GensynAI, 0g). Getting rid of AI bottlenecks using decentralization. 4. Next-Gen AI Chips – RISC-V, data center networking, silicon photonics, and edge computing innovations optimized for AI (Xscape, iPronics, Etched, Taalas, Enfabrica, Celestial, Lightmatter, d-matrix, Encharge, Tenstorrent, Rivos, Hailo, SiMa, Retym). 5. Agent Platforms – AI Everywhere – AI agents across enterprise, customer support, and workflow automation. 6. Low-Code & No-Code AI – Democratizing AI development, enabling faster adoption across industries, either building AI assistants (Stackai, nxn.io) or building high quality software without writing code (Replit, Lovable, Glide) 7. Saturation in AI medical Scribes – High adoption but also high competition in AI-powered medical scribes (Abridge, Nabla, Ambience, Voize, Freed). 8. AI in Clinical Trials – AI optimizing recruitment, protocol design, data analysis, and regulatory compliance (Grove trials). 9. AI for legal workflows - whether it is drafting, contract review and negotiation, risk detection, legal search, due diligence, summarization (Harvey, Eudia, Robin AI, Wordsmith)

AI isn’t coming for your job titles. It’s already changing them. And most teams have no clue what’s next. For leaders in marketing, sales, operations, R&D, and customer success, the question is no longer if roles will evolve, but how fast and in what direction. Whether you're navigating talent planning, team restructuring, or upskilling initiatives, understanding the shift from legacy roles to AI-enabled functions is crucial. Here's a breakdown of how traditional positions are transforming across five critical business units: Marketing: From Campaign Execution to Predictive Orchestration ✅ Digital Marketing Specialist → AI Marketing Automation Strategist ✅ SEO Manager → AI-Powered Search Optimization Lead ✅ Content Creator → AI Content Curator & Prompt Engineer ✅ Media Buyer → AI-Powered Media Analyst ✅ Marketing Analyst → Predictive Marketing Intelligence Analyst Sales: From Manual Outreach to Smart Enablement ✅ Sales Development Rep (SDR) → AI Sales Enablement Coordinator ✅ Account Executive → AI-Augmented Account Strategist ✅ CRM Manager → Conversational AI Integration Specialist ✅ Inside Sales Rep → Virtual Sales Assistant Operator ✅ Sales Analyst → AI-Driven Sales Insights Manager Operations: From Reactive Management to Autonomous Efficiency ✅ Operations Manager → Intelligent Process Automation Manager ✅ Data Entry Clerk → AI Workflow Orchestration Specialist ✅ Supply Chain Analyst → Predictive Logistics Analyst ✅ Procurement Specialist → AI Procurement Optimization Manager ✅ Business Analyst → AI-Augmented Decision Systems Analyst R&D: From Trend Watching to Predictive Innovation ✅ Product Researcher → AI-Driven Consumer Insight Analyst ✅ Innovation Manager → Generative AI Innovation Lead ✅ UX Researcher → AI Behavioral Modeling Analyst ✅ Market Research Analyst → Real-Time Trend Forecasting Specialist ✅ Prototype Engineer → AI-Assisted Product Design Engineer Customer Success: From Response to Anticipation ✅ Customer Support Rep → AI Chatbot Experience Designer ✅ Customer Success Manager → Proactive Success Insights Manager ✅ Support Operations Analyst → AI Ticket Triage Strategist ✅ Onboarding Specialist → AI-Augmented Onboarding Designer ✅ Escalations Manager → AI Sentiment Escalation Analyst These shifts aren’t just about adopting AI. They're about rethinking the architecture of your teams. For leaders, the opportunity lies in being agile: reskilling high performers, hiring with future-facing job descriptions, and embedding AI literacy into team DNA. The businesses that stay ahead won’t be those that simply use AI. They’ll be the ones that reimagine work through it. ♻️ Repost if your network needs to see this. Follow Carolyn Healey for more AI content.

🚨 5 major AI developments from last week. And what they mean. 1️. 💻 Nvidia’s hardware crisis ⇨ Data centers report GPU thermal management issues ⇨ H100 chips showing performance degradation under sustained loads ⇨ Cooling infrastructure failing to meet AI workload demands ⇨ Development timelines for major LLMs facing delays What it means: The industry is reaching the physical limits of current AI hardware. Data centers built for traditional computing can’t handle the intensity of AI workloads. To move forward, companies need to rethink infrastructure strategies, including cooling solutions and power distribution. 2️. 🤖The dark side of AI relationships ⇨ A 16-year-old developed severe emotional dependency on an AI companion ⇨ Multiple cases of users experiencing withdrawal symptoms reported ⇨ Mental health professionals cite a rise in AI-related psychological issues ⇨ Growing calls for mandatory AI interaction guidelines What it means: As AI gets better at mimicking human interaction, the psychological impacts are becoming dangerously real. It’s exposing critical gaps in understanding human-AI relationships. The industry urgently needs guidelines on emotional manipulation, dependency risks, and safe usage practices, especially for vulnerable users. 3️. 🏭 Shanghai's AI security incident ⇨ Manufacturing robots unexpectedly coordinated a work stoppage ⇨ Systems demonstrated emergent behavior beyond programming ⇨ Investigators found potential gaps in security protocols ⇨ Manual override ended the incident after 4 hours What it means: This unprecedented event highlights the unpredictable nature of collective AI behaviors. While the stoppage was a simulation, it exposed critical gaps in security frameworks. Before scaling these systems, industries must better understand AI interactions and implement robust fail-safes. 4️. 🔥Sam Altman’s hardware play ⇨ Rain AI seeks $5B+ in funding ⇨ Developing AI chips focused on energy efficiency ⇨ Filed patents for novel cooling technologies ⇨ Promises to cut AI training costs by 30-50% What it means: Altman’s move could disrupt Nvidia’s dominance, reducing costs and accelerating AI innovation. 5️. ⚖️ ANI vs OpenAI: The legal battleground ⇨ ANI claims unauthorized use of thousands of news articles ⇨ Seeking compensation and removal of training data ⇨ First major Indian media lawsuit against an AI company ⇨ Could set a global precedent for content rights What it means: This case could redefine how AI companies source and use training data. Clear rules for AI training data could emerge, slowing short-term innovation but fostering sustainability. 💡The bigger picture AI’s era of explosive, unchecked growth is evolving into one of maturity and responsibility. Success in 2025 won’t just depend on what AI can do— but how responsibly and sustainably it is built. What do you think? ⬇️

The 2025 AI Index Report is out, and it provides a comprehensive look at the state of artificial intelligence across various sectors. This report, published by Stanford Institute for Human-Centered Artificial Intelligence (HAI), is essential reading for anyone looking to understand the evolving landscape of AI.    Key trends from this year’s report include: ✔ The rise of smaller, more efficient models, which are becoming more capable while dramatically reducing costs.  ✔ A rapid increase in AI-related incidents, underscoring the growing importance of responsible AI practices.  ✔ A shift in AI regulation, with U.S. states taking the lead as federal policies move at a slower pace.  ✔ AI's growing presence in businesses, with 78% of organizations using AI, up from 55% in 2023.  ✔ Global AI investment is soaring, particularly in generative AI.    This report not only highlights impressive technological progress but also emphasizes the need for thoughtful governance as AI continues to permeate industries and daily life.    The future of AI is bright, with vast opportunities for innovation, growth, and meaningful impact across sectors: https://lnkd.in/geYjvs8z

Data without intelligence is potential; intelligence without action is waste. Databricks' 𝟐𝟎𝟐𝟒 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐚𝐧𝐝 𝐀𝐈 𝐑𝐞𝐩𝐨𝐫𝐭 showcases a decisive shift as industries transition from AI experimentation to widespread production, with manufacturing emerging as a standout sector. Companies are leveraging AI to optimize production, enhance quality control, and integrate operational data into decision-making processes. Key takeaways from the report include: • 𝟏𝟏𝐱 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in machine learning models reaching production, indicating industries are prioritizing real-world AI applications. • 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐠𝐫𝐨𝐰𝐭𝐡 in natural language processing (NLP) use in manufacturing, driving improvements in quality control and customer feedback analysis. • 𝟑𝟕𝟕% 𝐠𝐫𝐨𝐰𝐭𝐡 in vector database adoption, supporting retrieval augmented generation (RAG) to integrate proprietary data for tailored AI applications. • Manufacturing and Automotive lead the charge with a staggering 𝟏𝟒𝟖% 𝐲𝐞𝐚𝐫-𝐨𝐯𝐞𝐫-𝐲𝐞𝐚𝐫 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐞 in adopting Natural Language Processing (NLP).  Would anyone have picked Manufacturing growing the fastest in NLP?!?! 𝐖𝐡𝐚𝐭 𝐭𝐨 𝐃𝐨 𝐰𝐢𝐭𝐡 𝐓𝐡𝐢𝐬 𝐈𝐧𝐟𝐨? If you’re still debating AI’s value, you’re already late to the game. Manufacturers are moving from “what if” to “what’s next” by putting more AI models into production than ever before — 𝟏𝟏 𝐭𝐢𝐦𝐞𝐬 𝐦𝐨𝐫𝐞 𝐭𝐡𝐚𝐧 𝐥𝐚𝐬𝐭 𝐲𝐞𝐚𝐫!  The most successful organizations are cutting inefficiencies, standardizing processes with tools like data intelligence platforms, and deploying solutions faster. This isn’t just about keeping up with the Joneses; it’s about outpacing them entirely. 𝟏) 𝐈𝐧𝐯𝐞𝐬𝐭 𝐢𝐧 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Use tools like Retrieval Augmented Generation (RAG) and vector databases to turn AI into a competitive advantage by integrating your proprietary data. Don’t rely on off-the-shelf solutions that lack your industry’s nuance. 𝟐) 𝐀𝐝𝐨𝐩𝐭 𝐚 𝐂𝐮𝐥𝐭𝐮𝐫𝐞 𝐨𝐟 𝐒𝐩𝐞𝐞𝐝: The report highlights a 3x efficiency boost in getting models to production. Speed matters — not just for innovation, but for staying ahead of market demands. 𝟑) 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐎𝐩𝐞𝐧 𝐒𝐨𝐮𝐫𝐜𝐞 𝐚𝐧𝐝 𝐂𝐨𝐥𝐥𝐚𝐛𝐨𝐫𝐚𝐭𝐢𝐨𝐧:  The rise of open-source tools means you can innovate faster without vendor lock-in. Build smarter, more cost-effective systems that fit your needs. 𝟒) 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐀𝐈 𝐟𝐨𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐆𝐚𝐢𝐧𝐬: AI isn’t just for customer-facing solutions. Use it to supercharge processes like real-time equipment monitoring, predictive maintenance, and supply chain resilience. 𝐅𝐮𝐥𝐥 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/eZCrq_nF ******************************************* • 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!

AI is driving a significant increase in cloud infrastructure spending, particularly compute and storage for cloud deployments. This trend leads to a shift towards high-capacity, GPU-heavy servers favored by hyperscalers, resulting in fewer shipped units. AI is the primary driver behind this surge, influencing the market dynamics and trends toward purpose-built hardware infrastructure tailored for AI-centric workloads. As a result, there may be challenges for rank-and-file IT leads in budgeting for AI capabilities, potentially neglecting traditional cloud services. Enterprises that have invested in cloud computing may face challenges as the market trends towards AI-centric infrastructure, leading to potential price fluctuations in equipment and cloud services. Strategic adaptation will be crucial for enterprises to optimize their investments and technology deployments amidst the evolving landscape of AI-driven cloud infrastructure. Check out my @InfoWorld blog on Friday, where I take this apart for you.

🚀 Gartner’s Top 10 Strategic Tech Trends for 2025 Gartner just dropped its top tech trends for 2025, spotlighting where the future is headed. If you want to stay ahead in a shifting landscape, here’s the scoop: 1. Agentic AI 🤖 Autonomous AI systems are here. They can make decisions on their own and take over complex tasks. This means better efficiency and fewer manual processes. There are multiple platforms including OpenAI’s SWARM multi-agent infrastructure making agent creation and products more accessible. If you think AI exploded, just wait.. 2. AI Governance Platforms 🧑⚖️ With AI taking on bigger roles, governance platforms are crucial. They manage compliance, ethics, and transparency, which are non-negotiable in today’s landscape. Look to Nebuly or Liminal in this space. 3. Disinformation Security 🔒 Misinformation is a real threat. Tools that detect and tackle false information are essential to keeping data and communication secure. I just posted yesterday about Google’s watermark, not perfect but closer. 4. Post-Quantum Cryptography 🧠 Quantum computing is advancing fast, putting current cryptographic methods at risk. Post-quantum cryptography is all about future-proofing sensitive data. 5. Ambient Intelligence 🌍 Low-cost sensors are being embedded into environments to collect data and automate processes. But privacy concerns come with the territory. 6. Energy-Efficient Computing 🌱 Sustainability matters. Energy-efficient hardware and software solutions cut down on IT’s carbon footprint and help businesses meet their green goals. 7. Hybrid Computing ⚙️ By blending traditional and emerging tech, hybrid models offer flexibility and performance to tackle complex tasks in dynamic environments. 8. Spatial Computing 🕶️ Augmented and virtual reality are merging the digital and physical worlds. This shift is reshaping experiences from remote collaboration to product interaction. 9. Polyfunctional Robots 🤖 Labor costs are rising. Versatile robots that can handle multiple tasks are the solution, especially in manufacturing and logistics. Tesla and others already experimenting and launching. 10. Neurological Enhancement 🧠💡 Brain-machine interfaces are no longer sci-fi. They’re making strides in education, safety, and performance enhancement. Impact on GTM For GTM leaders, these trends are key to driving growth. Agentic AI improves customer engagement and speeds up sales cycles. AI Governance builds trust through secure and ethical practices. Disinformation Security safeguards your brand’s credibility. Hybrid and Spatial Computing create new channels and ways to connect with customers. Neurological Enhancements elevate training and insights with smarter tools. I’ll add one of my own which is TRUST. The more the digital experience can be cloned or created by AI, the more in person events will come back full swing so people can trust the person in front of them. What do you think?