Trends in Enterprise AI Development

AI agents and physical AI are shifting industrial automation from equipment supply to autonomous, self-optimizing systems. The most mature vendors are moving from pilots to production, with robots navigating complex environments and digital twins optimizing the value chain. This CB Insights brief gives a good view of where the top 20 industrial automation companies stand on AI maturity. Three key trends. 1. Leaders like Siemens Industry and ABB are linking AI systems across design, logistics, manufacturing, and maintenance creating compounding benefits. 2. Optimization dominates near-term priorities, while digital twins are emerging as the backbone for connecting hardware and software. 3. Partnerships with tech companies like Microsoft, Google, and Nvidia are essential, but they create new dependencies that must be managed. Siemens at the top of the ranking, combining copilots, edge platforms, and digital twins. Its work with Microsoft and Nvidia expands capabilities but increases reliance on external tech. Honeywell takes a more focused approach, embedding AI into devices and workflows. Its Qualcomm partnership highlights product-level integration over broad system building. ABB advances through its OmniCore platform and acquisitions such as Sevensense and SensorFact, blending robotics, software, and energy management. Schneider Electric pushes AI in energy management, using digital twins and partnerships with Nvidia, Microsoft, and Itron to extend from factory optimization into grid intelligence. The path forward in industrial AI is moving beyond pilots or isolated tools. It will depend on how well vendors embed AI into their platforms, link technologies across domains, and balance the benefits of external partners with the need for strategic independence. Those that will get it right will turn AI from experimentation into durable advantage. Just as critical is how their customers adopt these technologies. Industrial firms must shift from isolated use cases to embedding AI in design, production, energy, and logistics. Success requires not only advanced tools, but also the data, skills, and processes to make AI scale in complex operations.

Anthropic’s latest report highlights a shift many enterprises still underestimate: AI agents have moved from experimentation into core operations and adoption is happening faster and wider than expected. The report tracks deployments across 15+ business domains. What stands out isn’t just scale, it’s how deeply agents are embedded into real workflows across the enterprise. This isn’t just a tech trend. It’s an operational shift. The companies pulling ahead aren’t spending more - they’re deploying smarter, where agents remove friction and accelerate outcomes. Here’s where enterprises are actually putting agents to work: • Software Engineering - Writing, reviewing, testing, and debugging code while accelerating delivery cycles. • Back-Office Automation - Eliminating manual workflows like data entry, processing, and internal operations. • Marketing & Copywriting - Scaling content creation, personalization, and campaign execution end-to-end. • Sales & CRM - Automating prospecting, follow-ups, and CRM workflows so teams focus on closing. • Finance & Accounting - Reconciliation, anomaly detection, reporting, and audit-ready operations. • Data Analysis & BI - Turning raw data into insights, dashboards, and narratives instantly. • Customer Service - Resolving tickets autonomously while escalating only complex edge cases. • Academic Research - Synthesizing literature, accelerating reviews, and mapping knowledge faster. • Cybersecurity - Monitoring threats continuously and triggering real-time automated responses. • Document & Presentation Creation - Structuring reports, slides, and outputs directly from inputs. • Education & Tutoring - Personalized learning, real-time assistance, and adaptive assessments. • Medicine & Healthcare - Clinical documentation, workflows, and patient communication support. • Legal - Contract review, due diligence acceleration, and compliance workflows at scale. • E-commerce Operations - Managing listings, pricing, inventory signals, and customer support. • Gaming & Interactive Media - Driving adaptive narratives and real-time interactive experiences. • Travel & Logistics - Route optimization, bookings automation, and supply chain coordination. The pattern is clear: Agents aren’t a single-use tool - they’re becoming infrastructure across every function and industry. The question isn’t whether your business will use agents. It’s whether you’ll move fast enough to stay competitive. Which domain are you building in?

The End of Cloud-Only AI: New Survey Shows 79% of Enterprises Shifting to Hybrid and On-Premises I’ve been reviewing the results of a recent survey of 432 enterprise executives on where they plan to run their AI systems. The findings are clear and point to a significant shift in enterprise thinking. Only 11% of organizations prefer a cloud-first approach. In contrast, 48% favor hybrid-first and 31% favor on-premises-first — meaning 79% are moving away from cloud-only as their primary strategy. The reasons are straightforward. When asked what drives their deployment decisions, executives ranked the following as most important: security and privacy, regulatory compliance, cost predictability, and data residency. Factors like speed to deploy and access to the newest cloud AI services ranked much lower. This suggests enterprises are prioritizing control, governance, and predictable costs over convenience. The data also shows that cloud does not lead in any major workload category. Hybrid is preferred for training, fine-tuning, customer-facing AI, and internal copilots, while on-premises leads for sensitive data and highly regulated workloads. When forced to choose, executives consistently selected control over speed, keeping sensitive data in-house over accessing the latest capabilities, and workload-specific deployment over a single model. This points to a clear pivot: beyond-prime resources are gaining priority over pure cloud deployments, driven largely by cost and security concerns. Over the next five years, I expect hybrid to become the dominant model for enterprise AI, with on-premises remaining essential for sensitive and regulated workloads and cloud playing a supporting, rather than leading, role. I’m not sure all technology providers are fully recognizing this shift yet. What are you seeing in your organizations?

Small experiment: AI is at a tipping point. After analyzing 20,000+ NEURIPS research papers and tracking 950+ AI startups, we’re seeing clear signals about where innovation-and business opportunity-are headed next. 🔎 Mainstream Trends: Enterprise AI Infrastructure: Despite 2,400+ research papers and a market set to hit $60–82B in 2025, only a fraction of companies have fully adopted enterprise AI. Huge room for growth in deployment automation, LLM optimization, and workflow tools. AI Safety & Governance: Nearly 2,000 papers focus here. As regulations tighten, demand is surging for compliance, bias detection, and privacy-preserving solutions. Generative AI 2.0: With 1,500+ recent papers and a $22B+ market forecast, the future is in industry-specific, controlled, and multi-modal generative AI. 🌱 Fastest-Growing Niches: Neuro-symbolic AI: 600% research growth, high commercial gap-think explainable, reasoning-driven AI. Few-shot & Privacy-Preserving Learning: Rapid research growth but little market presence-prime for new ventures. 📊 Market Gaps = Startup Goldmines Unsupervised, self-supervised, and few-shot learning. 🔮 What’s Next (2025-2027)? Highest Potential: Enterprise AI infrastructure, AI safety/governance, and specialized industry solutions. Strong Potential: Healthcare AI, multimodal systems, edge AI. Emerging: Specialized LLMs, autonomous systems, next-gen generative AI. ⏳ Insight: There’s typically a 1–2 year lag between research peaks and real-world products. Where do you see the biggest opportunity for AI innovation? Are you building in one of these spaces, or have a perspective to share? https://lnkd.in/gy3yVmWM #AI #ArtificialIntelligence #Innovation #Startups #ResearchToMarket #FutureOfAI

According to recent insights from BofA Global Research, we're witnessing a significant shift in #AI investments, with #enterprises increasingly channeling funds toward AI Agents. This pivot is a strong signal of optimism, driving a positive surge in overall AI spend. 📈 The data reveals that 90% of enterprises are planning to boost their AI budgets, with a notable focus on AI Agents—tools designed to automate tasks and enhance digital workforce capabilities. What’s particularly intriguing is the breakdown of adoption stages: 72% of enterprises are already leveraging AI to enhance productivity, primarily in the "Assist" phase, with a gradual move toward "Augment" and "Transform" stages. This evolution promises to reshape how we work, leveraging generative AI to mimic human-like decision-making. One key takeaway? AI Agents demand much higher inference compute, which not only scales with test-time compute but also opens new opportunities for tech infrastructure providers. While current AI Agent technologies aren’t yet at full commercial readiness, the trajectory is clear—they’re poised to improve significantly over time. This incremental progress could redefine enterprise efficiency, with 52% of workloads expected to be handled by AI Agents in the near future. The rise of AI Agents also brings challenges and opportunities. Businesses are investing heavily in #data readiness (57% of AI spend), recognizing that robust data foundations—whether enterprise-wide or function-specific—are critical. Yet, competition from software companies remains a key risk, with 50% of enterprises viewing it as a watch area. On the flip side, this shift could evolve commercial IT models into a digital labor framework, potentially boosting IP-led revenues. What does this mean for specialists and vendors? Enterprises are keen to expand their AI vendor ecosystem (40% net expansion), while also consolidating partners in mature categories like CRM and ERP. For those in the industry, it’s a call to adapt and innovate. EmpowerEdge Ventures

Wondering how enterprises are 𝘢𝘤𝘵𝘶𝘢𝘭𝘭𝘺 using AI in 2025? Andreessen Horowitz asked 100+ CIOs across 15 industries — and what they shared might surprise you 👇 𝟭/ 𝗔𝗜 𝗯𝘂𝗱𝗴𝗲𝘁𝘀 𝗮𝗿𝗲 𝗲𝘅𝗽𝗹𝗼𝗱𝗶𝗻𝗴 - Enterprise AI budgets are already bigger than expected; predicted to grow ~75% in the next year. - Spend has moved from experimental “innovation budgets” to core operational IT line items. 𝟮/ 𝗠𝘂𝗹𝘁𝗶-𝗺𝗼𝗱𝗲𝗹 𝗶𝘀 𝘁𝗵𝗲 𝗻𝗲𝘄 𝗻𝗼𝗿𝗺 - Enterprises are using 5+ models in production use cases - A key driver for this is to optimize cost/performance - Model selections are also based on use case. E.g. for writing tasks: OpenAI is the choice for complex Q&A, Anthropic for brainstorming. 𝟯/ 𝗧𝗵𝗲 𝗠𝗼𝗱𝗲𝗹 𝗹𝗮𝗻𝗱𝘀𝗰𝗮𝗽𝗲 𝗶𝘀 𝗰𝗿𝗼𝘄𝗱𝗲𝗱, 𝗯𝘂𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗮𝗿𝗲 𝗲𝗺𝗲𝗿𝗴𝗶𝗻𝗴 - OpenAI, Google, and Anthropic lead in enterprise adoption. - Many larger orgs prefer open source, with Meta and Mistral leading. - Newer players like xAI, DeepSeek are seeing traction right out of the gate. 𝟰/ 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗹𝗲𝘀𝘀 𝗰𝗿𝗶𝘁𝗶𝗰𝗮𝗹 - Newer models are more intelligent with longer context windows. - Using just prompt engineering, teams can now get similar/better results. - This reduces the need for fine-tuning for strong model performance. - It also avoids model lock-in, allowing portability across models. 𝟱/ 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲𝘀 𝗮𝗿𝗲 𝘀𝗵𝗶𝗳𝘁𝗶𝗻𝗴 𝗳𝗿𝗼𝗺 “𝗯𝘂𝗶𝗹𝗱” 𝘁𝗼 “𝗯𝘂𝘆”  - The AI app ecosystem has been maturing. Fast.  - Off the shelf AI-native apps can outperform internal builds. - Companies are also finding internally developed tools difficult to maintain.  - Purpose-built apps allow companies to innovate faster, leading to better outcomes + happier users = better ROI 𝗕𝗼𝘁𝘁𝗼𝗺 𝗹𝗶𝗻𝗲? Enterprise AI is moving fast. There’s real budgets. Real traction. Real focus on value. Real tools. And opportunity to create real impact! 🔗 Link to the full a16z report in comments. I'd rate it as a "must read". Save it. Study it. Share it. 🤔 Which of this surprised you the most? Or feels most urgent to act on? #EnterpriseAI #ArtificialIntelligence #AIforBusiness #GenAI

Future of AI in Enterprise I see a future where AI isn't just a tool but an integral part of the organization, influencing everything from strategic decisions to day-to-day operations. I believe we'll see a hybrid model emerging. Companies will combine proprietary, custom-built AI solutions with external AI services and open-source models. This allows them to leverage the latest AI advancements while developing specialized capabilities tailored to their unique needs. Another trend I'm watching is the democratization of AI within organizations. With low-code and no-code AI platforms, we'll see non-technical employees developing and deploying AI models. This could lead to an explosion of AI applications across all levels of the company. But here's the kicker: as AI becomes more pervasive, the ethical implications will become increasingly important. Companies will need robust governance frameworks to ensure responsible AI use. We might even see new roles like "AI ethicist" or "algorithmic risk manager" becoming common. Looking further ahead, I can imagine "enterprise digital twins"—comprehensive AI models of entire organizations used for simulation and strategic planning. AI will fundamentally reshape the nature of enterprise in the coming decades.

This week, Camilla Matias and I had the joy of hosting a dinner themed "Agents in the Wild" on how AI agents are actually being deployed inside fast-scaling companies—beyond the hype, decks, and demos. And fittingly, the stories around the table were just as wild as the tech: a bear stand-off, jumping off a burning Caltrain, buying a government-auction lighthouse. Between bites and laughs, we surfaced 10 real trends that reflect how AI agents are evolving inside organizations: 🔒 1. On-prem is back. 80–85% of enterprise deployments are now in VPC. Security, compliance, and the need for tighter control over data pipelines are pushing companies toward self-hosted models. 🤝 2. High-touch enterprise sales are (still) king. AI is bringing back multi-stakeholder, in-person, consultative enterprise sales. Companies are buying trust, not only tools. 🧩 3. Data orchestration challenge persists. AI agents are only as smart as the data they can see. Integrating siloed SaaS data is still a massive blocker, fueling interest in data ownership, self-hosted solutions, and clean integration layers. 👨💻 4. FDEs are a double-edged sword. FDEs have become essential to navigating deployment complexity. But overreliance on FDEs may slow down productization. 🧯 5. Real-time controls matter. As agents gain autonomy, so must their oversight. Enterprises demand real-time observability, rollback options, and mitigation controls. 🌱 6. Land & Expand > boil the ocean. Successful deployments often start with one team, not the whole org. From there, companies expand agent adoption across departments. 📈 7. Full autonomy = real ROI. Back-office automation shows clearest returns, especially when tied to measurable outcomes like time saved, leads closed, or ticket resolution. 🧠 8. Multi-agent systems are emerging. Complex workflows call for aggregation agents that manage parallel or sequential actions and cross-vendor agent interoperability. 🛠️ 9. Build AND Buy. Building agents for proprietary workflows, while buying vendor solutions for standardized use cases. Tech-forward companies lean toward the former while traditional industries prefer the latter. 📊 10. Adoption varies by function. Eng and marketing show the highest adoption due to clear ROI. Finance most resistant, largely due to compliance, risks and accuracy. Sales are mixed: great for PLG, trickier for complex enterprise sales cycles. Hearty thanks to everyone who shared this wonderful evening with us. Andrew Ferguson from Databricks, Cristina Cordova from Linear, Marc Wendling from Glean, Mary M. Liu from Runway, Kent Walters from Retool, João (Joe) Moura from CrewAI, Yash Sheth from Galileo, Yash Patil from Applied Compute, Zack Reneau-Wedeen from Sierra. If you’re deploying agents inside your org, building for enterprise use, or interested in attending the next dinner, please leave a comment. Always happy to compare notes. cc F-Prime, David Jegen, Brex, Ambika Kumar, Rachel Feely-Kohl, Lucia DG

The most significant shift in enterprise AI strategy I’ve seen yet just showed up in our newest CIO data. In my latest Futurum 1H Global Decision Maker Survey, the traditional AI narrative has clearly broken. CIO priorities are pivoting from productivity → innovation. The early “smart chatbot” era of enterprise AI is already being surpassed as organizations begin focusing on what AI enables them to strategically create, not just what it can accelerate. Another massive signal in the data: The pilot era is ending. Pilot-stage AI adoption has dropped to just 31%, while 74% of CIOs now report having well-formed AI implementation plans. At the same time, AI has more than doubled as a top IT spending category in just one year. Enterprise AI is no longer primarily an experiment—it has moved decisively into execution. Where AI is being applied inside organizations is shifting as well. AI adoption in R&D nearly tripled, rising from 9.9% to 27.9%, while sales-focused AI declined by 18 points. CIOs are increasingly directing AI into product development, engineering, and core intellectual property creation. AI is becoming a builder of products and capabilities, not just a tool for marketing and sales enablement. Budget signals reinforce this shift: Spending is shifting quickly toward systems of action, with workflow and orchestration platforms gaining share while infrastructure-centric providers lose momentum. The architecture of enterprise AI is evolving from a focus on compute toward a focus on capability. The takeaway for vendors is increasingly clear. The generic productivity pitch for AI is no longer sufficient. CIOs are now prioritizing AI that creates new products, modernizes legacy estates, and enables entirely new business models. Yes, efficiency gains still do matter, but they are no longer the strategic focus for enterprise AI in 2026. Details: https://lnkd.in/ddcV2rDA Antonio Vieira Santos Jay Ferro David Terrar Joe McKendrick Martin Davis (CIO) Deb Gildersleeve Louis C. Hans Brechbuhl Maryfran Johnson Yves Mulkers Brian Solis Ed Featherston Tamara McCleary Kirk Borne, Ph.D.

I've recently had 20+ conversations with Global 500 teams about their AI infrastructure strategies, and a few clear trends have surfaced: First, it's still extremely early in the evolution of enterprise AI stacks. Most organizations are just beginning to move past one-off prototypes toward defining standardized reference stacks and architectures. A few particularly interesting observations: 1. Many enterprises are consciously avoiding popular off-the-shelf AI frameworks such as LangChain, instead favoring custom-built agents tailored specifically to their internal requirements. 2. There's a clear preference for assembling a "best-of-breed" stack over adopting end-to-end agent platforms such as CrewAI or Letta. Enterprises are systematically evaluating individual components—selecting specific LLMs, memory solutions, observability tools, and more—rather than committing to integrated solutions. 3. Almost without exception, large enterprises want BYOC (Bring Your Own Cloud) deployment options, both on public and private clouds. This contrasts significantly with growth-stage companies, which usually approach AI infrastructure decisions with specific projects and immediate use-cases in mind. Enterprises, on the other hand, are looking for broader, scalable reference architectures deployable to their global orgs. The strong enterprise demand for BYOC presents a substantial challenge to many AI infrastructure vendors. Many are < 2 years old, and scaling to meet the needs of an enterprise-grade deployment is going to be a rude awakening. Overall, it's encouraging to see enterprises preparing for wide-scale AI rollouts—but it's clear we're still at the very early stages of defining what the mature enterprise AI stack will ultimately look like. What are you seeing?