Trends in Enterprise AI Development

🤔 As we are nearing end of 2024, it is that time when everyone looks for comparing “what really happened with enterprise AI adoption”. I read through this fascinating report from Menlo Ventures that validates many trends. The numbers are staggering - enterprise AI spending surged to $13.8B in 2024, a 6x jump from 2023! But what really caught my attention is a validation that how we have moved from experimentation to execution. Three trends particularly stand out to me: 1. The rise of AI agents is real - while most current implementations focus on augmenting human workflows, seeing early examples of autonomous AI systems managing complex end-to-end processes. - bottomline, this isn't just automation - it's transformation. 2. Technical departments still lead adoption (49% of spend), but what is exciting is seeing AI budgets flowing to every department - from Sales to HR to Legal. - this widespread adoption signals AI's transition from a tech tool to a fundamental business capability. 3. The multi-model approach is winning- organizations typically deploy 3+ foundation models in their AI stacks, choosing different models for different use cases. - interestingly, while OpenAI's share has decreased to 34%, Anthropic doubled its presence to 24% in the enterprise space. 4. RAG (retrieval-augmented generation) is dominating at 51% adoption, up from 31% last year. - but here's a surprise - only 9% of production models are fine-tuned. Real-world implementation looks different from the hype. 5. Implementation costs are the hidden gotcha- while only 1% worry about purchase price, implementation costs derailed 26% of failed pilots. 6. The incumbent advantage is cracking- while ~60% still prefer established vendors, 40% question if current solutions truly meet their needs. - that's a massive opportunity for innovative startups. 7. Vertical AI is having its moment- no surprise, this provides maximum value for highly regulated industries - healthcare is leading, followed by Financial Services. - I advocate for AI solutions tackling industry-specific workflows in regulated industries rather than just generic use cases. So, what fascinates me most? The pragmatism, really, - companies aren't fixated on price (only 1% cited it as a concern!) - they're focused on ROI and industry-specific customization. This is not just tech evolution, it is business-centric and high time for incumbents to hone in on domain strengths in solving for AI-powered transformation - get reading for 2025 🚀 And, well to me, that is a clear sign of a maturing market. 🔍 Source: "2024: The State of Generative AI in the Enterprise" by Menlo Ventures (November 2024) - https://lnkd.in/g6j-nPVp What trends are you seeing in enterprise AI adoption? Would love to hear your perspectives! #artificialintelligence #innovation #technology #reflectingonAIin2024

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

🚀 AI Agents: 4 Trends to Watch in 2025🌍💡 AI agents are revolutionizing industries, moving beyond copilots to autonomous digital workers 🤖. As we enter 2025, four key trends are shaping the AI agent landscape: 1️⃣ Big Tech & LLM Developers Dominate General-Purpose Agents 🔹 Tech giants (OpenAI, Anthropic, etc.) are driving AI advancements, making agents cheaper, more powerful, and widely available. 🔹 400M weekly users on ChatGPT showcase the massive distribution advantage. 🔹 Enterprise adoption is increasing, but big tech’s dominance pressures startups to specialize. 2️⃣ Private AI Agent Market Moves Toward Specialization 🔹 Horizontal AI applications (customer support, software development) are crowded – differentiation is key. 🔹 Industry-specific AI agents in healthcare, finance, compliance, and logistics are poised for growth. 🔹 Deeper workflow integrations & leveraging proprietary data will create competitive moats. 3️⃣ AI Agent Infrastructure Stack Crystallizes 🔹 The AI agent ecosystem is evolving into a structured stack with specialized solutions: ✅ Data curation (LlamaIndex, Unstructured) ✅ Web search & tool use (Browserbase) ✅ Evaluation & observability (Langfuse, Coval) ✅ Full-stack AI agent development platforms gaining traction 4️⃣ Enterprises Shift from Experimentation to Implementation 🔹 63% of enterprises place high importance on AI agents in 2025. 🔹 Challenges remain: Reliability & security (47%), Implementation (41%), Talent gaps (35%). 🔹 Solutions: Human-in-the-loop oversight, stronger data infrastructure, and enterprise-grade agent platforms. 🚀 2025 is a breakout year for AI agents – the shift from copilots to autonomous digital workers is happening now! 📈 #AIAgents

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

4 Game-Changing Trends in Data & AI for 2025 The next year will be a turning point for organizations leveraging AI. The difference between leaders and laggards will be defined by their ability to navigate these emerging realities: 1. GenAI will shift from broad applications to targeted impact. The days of “GenAI for everything” are numbered. The real value lies in contextual use cases—AI solutions designed to address specific business problems with precision. Broad, one-size-fits-all approaches may dazzle, but they rarely deliver sustained value. 2. The AI talent shortage will intensify. The market will continue to be flooded with resumes, but identifying individuals who can genuinely drive impact will be harder than ever. Organizations that succeed will prioritize strategic hiring frameworks that distinguish technical skill from real-world execution. 3. Organizational design will take center stage. Great data alone isn’t enough. Companies will likely begin to focus on restructuring workflows, eliminating silos, and fostering collaboration to unlock the full potential of AI initiatives. Alignment between teams will be as critical as alignment between datasets. 4. Businesses will invest in AI with greater precision. The exuberance surrounding AI isn’t fading, but it’s becoming more intentional. Leaders will evaluate initiatives based on their ability to generate measurable ROI. AI investments will shift from exploratory to outcome-driven. 💡 Organizations that embrace these trends will position themselves for sustainable growth. Those that don’t risk being left behind in an increasingly competitive landscape. Which of these trends resonates most with your current challenges? I’d love to hear your thoughts. #AI #DataAnalytics #Leadership #GenerativeAI #BusinessInnovation

The AI hype cycle is fading, but its prevalence is only set to grow.    As I shared with Rocio Fabbro at Quartz, AI is becoming less of a rising star and more of a behind-the-scenes operator that’ll quietly (but significantly) influence how organizations think about every process, product, and decision.    We’re at an inflection point where AI is poised to evolve much like electricity 💡: invisible in our daily lives but powering everything. It won’t be about if AI is being used -- but how it’s driving transformation across industries.    Here’s what else is ahead according to Deloitte’s 2025 Tech Trends (https://deloi.tt/3BYn523):    🤖 AI Everywhere: We’re moving from experimentation to operationalization, with AI embedded into other major innovations—spanning customer service, supply chains, product development, and beyond.    📊 Fusing Small and Large Language Models: It’s not a matter of “either/or” between large and small language models—it's both. Organizations are combining the right models to address business needs.    🖥️ Practical Applications for Quantum Computing: From post-quantum cryptography to solving problems beyond the limits of traditional computing, the horizon is expanding. The AI of 2025 will be smarter, more focused, and deeply integrated into everything we do – albeit more quietly. The hype may fade, but the impact is just beginning! 

Who will be the winners in the multi-trillion dollar Enterprise AI race: AI-first startups, or established SaaS leaders? Founders, corporate executives, investors and LPs are all wondering. Enterprise AI solutions are often far more complex than the initial wave of highly visible prosumer AI apps. A successful Enterprise AI offering may include: ➤ GenAI plumbing with AI model orchestration, fine-tuned models  ➤ Agentic frameworks ➤ Advanced ML/data science capabilities ➤ Robust data pipelines connecting to disparate data stores ➤ Seamless human-AI workflows & services layer ➤ Enterprise-grade security, access control, governance Getting all this to work well is complex, and that creates multiple paths to success. Enterprise software incumbents in each segment have formidable advantages: customer relationships, institutional knowledge, workflow entrenchment, and valuable datasets. Their challenge lies in deeply leveraging modern AI capabilities while maintaining product cohesion and avoiding the innovator's dilemma from cannibalizing existing revenues. Those who can move quickly while leveraging existing strengths will do well. Meanwhile, AI-first startups have their own compelling advantages. Free from legacy technical debt, they can iterate at lightning speed. Their ability to reimagine workflows entirely – rather than simply applying AI band-aid – can help them leapfrog. ➔ Success in enterprise AI will play out segment-wise, and will be determined by several factors. In segments where AI-native solutions are a step change with new business models and workflows, AI-first startups may have the edge. Conversely, segments where the AI advantage is incremental may favor existing market leaders. Data access and learning loops are a key battleground. While incumbents may start with valuable historical data, startups can win by unlocking entirely new data sources or building superior compound learning advantages. Success hinges not as much on the volume of data, as it does on *actionable* data streams - which may be a byproduct of the offering itself. Ecosystem effects can amplify advantages in either direction – incumbents can extend their lead through established partnerships and integrations, while AI-first startups can challenge the status quo by building vibrant communities or partnerships with aligned parties. The most critical factor is execution velocity. With AI moving at truly unprecedented speed, the winners – whether they're emerging players or established giants – will be those who can rapidly iterate, adapt to market feedback, and build sustainable moats through technology, data, and ecosystem effects. Startups are set up for velocity by design, while larger companies may need to navigate significant culture change here. Success will come from understanding segment-specific dynamics deeply and backing (or being) the teams that can execute with speed, dexterity and precision. Share your views 👇 #enterpriseAI #vc #startups

I'm extremely excited and proud to share my latest research, entitled "The Intelligent Path Forward: Generative AI in the Enterprise," looking at how companies across 10 different industries and two different sizes are deploying Generative AI within their organizations. Based on a web survey of over 1,000 US-based IT Decision Makers across 10 industries and both medium business and large enterprises, the report uncovers several surprising trends about how companies are approaching their GenAI initiatives. From what types of GenAI applications companies are running, their level of deployment, the foundation model and platforms used to create them, where they are running them, the benefits and challenges of GenAI they’re currently seeing, the percentage of employees they’re providing GenAI licenses to, the use of agents, the partners they’re working with, the use on-device GenAI for PCs and smartphones, edge-based AI efforts, and the funding sources for their GenAI efforts, the report offers a comprehensive view of the state of GenAI in today’s US businesses. #GenAI https://lnkd.in/gKJQGcTF

In the past two weeks, I’ve met with leadership at NiCE, Amazon Web Services (AWS), Zoom, and 8x8. There’s a pattern - here are some reflections on these conversations. AI is eating the enterprise communications playbook for breakfast. If you're not paying attention, you're already behind. Key assumptions that have guided the industry for decades are rapidly becoming obsolete in the age of AI. Here are SIX critical shifts occurring: ONE: Voice is the New UI. Remember “My voice is my passport” as a security phrase, now it’s my voice is my keyboard. APIs are old school. Enterprise-wide, applications and integrations will be voice-enabled from meetings (with AI scribes) to customer service. The future is frictionless, voice-first interactions and integrations, multilingual, and without code. TWO: Mind Your Data: AI without contextual data is like a kiss without a squeeze. Every data repository is a treasure trove, and new moats protect the repositories instead of business practices. Examples include Microsoft putting up CAPTCHA to access Teams meetings and Slack locking down its customers’ data. The new browser wars are unconcerned about eyeballs. THREE: Workflows are the New Apps. Forget simple automation. We're entering the era of AI-native automation, where AI handles complex workflows that require judgment. An AI that doesn't just listen to a customer call but understands the intent, updates the backend systems, and routes the follow-up autonomously. #GameChanger. FOUR: Bottlenecks Be Gone: The modern workplace has largely been throttled by human bottlenecks, and these bottlenecks will disappear. We see this first with code generation; developing new code is becoming the fastest part of a project. Other bottlenecks are various barriers to decisions, such as data collection and analysis. We are moving from concept to code to scale in days, not months or years. FIVE: Soon This Will Matter: As disruptive and consuming as AI has become, none of this matters, yet! That’s because AI isn’t that useful, yet. We are in the Scantron era again. Scantron bubbles revolutionized paper scoring. A good step, but digitization is what mattered. AI is automating existing workflows. The real stuff comes in the reimagination of work. The first glimpse of this is in agentic AI. Focus on outcomes, not processes. SIX: The Barriers to Entry are Changing: The comms sector has enjoyed numerous barriers to entry over the decades, and most of them are disappearing. AI is simultaneously commoditizing and enabling competitive advantage. Giants may or may not fall, but their businesses will radically change. The Giants Cometh. #AI #FutureOfWork #EnterpriseCommunications #VoiceAI #Automation #Tech  #UCaaS #CCaaS Tanya (Blackburn) Shuckhart John Sun Megan Donaldson Schevone Johnson

Top 6 AI Predictions for 2024 from IBM 𝟭. 𝗖𝘂𝘀𝘁𝗼𝗺𝗶𝘇𝗲𝗱 𝗘𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗔𝗜 Tailored generative AI applications are becoming vital for businesses, offering personalized solutions and enhancing customer interactions. 𝟮. 𝗢𝗽𝗲𝗻 𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗜 𝗠𝗼𝗱𝗲𝗹𝘀 The rise of open-source AI, like IBM's collaboration with NASA, democratizes AI technology, notably in climate research. 𝟯. 𝗔𝗣𝗜-𝗗𝗿𝗶𝘃𝗲𝗻 𝗔𝗜 𝗮𝗻𝗱 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 APIs are simplifying AI application development, boosting productivity in various sectors. 𝟰. 𝗔𝗜 𝗮𝘀 𝗮 𝗡𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘆 Countries are increasingly recognizing AI's strategic importance, leading to significant advancements and regulations like the EU AI Act. 𝟱. 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 The integration of text, speech, and images is offering contextually richer AI interactions. 𝟲. 𝗔𝗜 𝗦𝗮𝗳𝗲𝘁𝘆 𝗮𝗻𝗱 𝗘𝘁𝗵𝗶𝗰𝘀 The focus on AI ethics grows, with alliances like the one between IBM and Meta fostering responsible AI innovation. These trends are more than predictions; they're a roadmap. We are collaborating with customers to leverage AI's potential in creating a more efficient, innovative, and inclusive future.