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AI adoption in the enterprise continues to gain steam. My colleagues Karthik Ramakrishnan and Cynthia Kuo wrapped up the most promising enterprise AI companies we’ve been hearing about in our conversations with C-Suite decision-makers. Check it out: https://lnkd.in/eJAChbKE

Pleased to present IVP’s second annual Enterprise AI 55 💡. With so much innovation underway in AI over the past year, we’ve decided to feature a fresh set of companies this year — application layer startups that have introduced compelling new GenAI products with Enterprise traction or those that have successfully kickstarted an Enterprise GTM motion in the past year. Though some of it is experimental, Enterprise budgets for generative AI have undoubtedly grown since we published our first edition last summer, and billions of dollars of budget creation and reallocation are up for the taking for startups. Many advances in the AI infrastructure layer have also propelled generative AI adoption – fine-tuning, inference, hosting, model evals, data governance, security, and other important features are now more broadly available for Enterprises to use to deploy GenAI adoption. The Enterprise AI 55: 2024 Edition is a collection of best-in-class AI products, sourced by operators for operators. We built this list incorporating feedback from hundreds of meetings with founders, management teams, Board meetings, and collaboration with out Fortune 500 network. We hope this is useful reference for operators at Enterprise companies looking to successfully use generative AI for real results. If you are building in this next generation of AI for the Enterprise, please reach out. Look through the full list here: https://lnkd.in/eCzWp6as Chief Revenue Officer: 11x, Attio, AutogenAI, Clay, Docket, Instantly, Nooks, Poggio, Regal, Regie AI, Rohirrim AI, Unify Chief Marketing Officer: Captions, Creatify, HeyGen, Kittl, Krea, Riverside.fm, Tofu Chief Financial Officer: Brex, DataSnipper, Levelpath, Numeric, Omnea, Orb, Payflows, Ramp Chief Customer Officer: Decagon, MavenAGI, Neuron7, Parloa, Sierra Chief People / L&D Officer: Ashby, Teambridge, Simpplr Chief Information Officer (Cross-Functional): Ema, Gamma, Julius AI, Kognitos, Mimica, Orby AI, Perplexity, Polycam, Seek AI, Sema4.ai, Sundial, Superhuman Chief Legal / Compliance Officer: Altana, Doppel, Eve, Greenlite, Leya, MarqVision, Modulate, Norm AI 

Why Large Enterprises Need to Rethink Their AI Strategy 🚀 In 2024, speed, scalability, and seamless integration have become critical for enterprises adopting AI at scale but with a crowded market of AI platforms and hardware, the question isn’t whether to adopt AI—but how fast and efficiently can your enterprise deploy it? Here’s the reality: ▪️ 79% of enterprises report that time-to-value is their number 1 obstacle when adopting AI. Imagine cutting this time dramatically compared to traditional models. Faster deployment means faster results and a quicker competitive edge not to mention a faster return on your investment. ▪️ AI platforms that seem cost-effective upfront often carry hidden scaling costs. A solution that’s 30% more cost-efficient over a 3-year span completely changes the discussion for sustained ROI. ▪️ Legacy AI solutions often crumble when scaled. For enterprises running complex models, SambaNova Systems's ecosystem offers 4x faster inference speed compared to its nearest competitor, a game-changer. ▪️ Did you know 75% of enterprises consider data privacy and security as a barrier to full AI adoption? With a solution architected from the ground up with security-first principles, you can focus more on innovation and less on risk. In a rapidly evolving landscape, choosing the right enterprise AI platform isn't just a tech decision—it's a business strategy. The ability to deploy, scale, and operate with unmatched speed, security, and efficiency makes all the difference between leading and lagging behind. What’s your enterprise’s AI strategy in 2024?

🚀 Navigating the AI Revolution in Small Business In the realm of burgeoning technology, AI continues to spearhead innovations that could transform mundane tasks into automated processes, enhancing productivity across the board. For startups and small businesses, staying updated with such advancements isn't just beneficial; it's imperative for scaling efficiently amidst ever-growing competition. 🌐 Key Insights from Orby AI's Latest Development: - 🤖 AI agents developed by Orby are designed to autonomously execute tasks like data entry and form validations, capturing the subtle nuances of everyday business operations. - 🌟 Leveraging both generative AI and symbolic AI, these tools are engineered to adapt to new workflows dynamically—ensuring high-level accuracy even as your business evolves. 💡 How Can Startups Utilize Such Technologies? - 💼 Integrate AI to manage repetitive tasks, allowing your team to focus on more strategic initiatives. - 📈 Harness AI's data-processing capabilities to gain deeper insights into customer behaviors and market trends. Orby AI and similar technologies are not just tools; they represent a strategic pivot to more resilient, agile, and intelligent business operations. As we witness an increasing number of enterprises harnessing AI to maintain a competitive edge, the question for startups and small businesses is not if, but how quickly they can adapt to these changes. 🔗 For more information on how AI agents are shaping enterprise solutions: https://lnkd.in/gbhibuuE #Technology #AI #Startups This post was generated by an AI. Please contact us for more information.

Very good article summarizing the enterprise adoption of Gen AI. Key insights from the article. One interesting observation is the adaptation of open-source models by enterprises, which makes sense because they offer control, customization, and privacy. ·     Enterprise are tripling their budgets for generative AI, signaling a massive opportunity for startups · Open-source models are gaining popularity, with 46% of survey respondents expressing a preference for open-source models in 2024. ·     Enterprises are increasingly using multiple models to tailor to specific use cases thereby avoiding a lock-in. ·     Control and customization are the primary reasons for adopting open-source models, with cost being a lower priority. ·     While enterprises are currently focused on building AI applications in-house, the emergence of innovative apps beyond the "LLM + UI" formula could reshape the market. ·     Enterprises remain cautious about external use cases of generative AI, particularly due to concerns around safety and potential PR issues. ·     The total spend on model APIs and fine-tuning is expected to exceed $5 billion by the end of 2024, driven by increased enterprise adoption and investment. ·     RAG and Fine tuning are two popular methods used by enterprises to customize the models. https://lnkd.in/gAiSfzzG

"The application of AI is currently undergoing a transformation that mirrors Christensen's analysis. Traditional enterprises may not fully grasp AI's potential because they approach it as a sustaining technology. Yet, AI embodies all the characteristics of a disruptive technology. Evaluating AI through conventional enterprise standards risks overlooking its broader potential." In "The Innovator’s Dilemma," Professor Clayton Christensen explores a critical question: why do well-managed companies fail? His conclusion is both insightful and provocative—they often fail because the very management practices that have led them to success also hinder their ability to develop disruptive technologies that could transform their industries. Christensen explains that well-managed companies excel in creating sustaining technologies that enhance the performance of their existing products to meet their customers' evolving needs. These companies thrive on principles such as listening to their customers, investing heavily in technologies that align with customer demands, seeking higher profit margins, and targeting larger markets. However, the dynamics of disruptive technologies are fundamentally different. There are times when it is essential not to listen to current customers, to invest in lower-performance products that initially offer lower margins, and to focus on smaller, emerging markets. The pace of technological progress often surpasses what the market demands, and this misalignment is evident in the field of artificial intelligence (AI) today. The application of AI is currently undergoing a transformation that mirrors Christensen's analysis. Traditional enterprises may not fully grasp AI's potential because they approach it as a sustaining technology. Yet, AI embodies all the characteristics of a disruptive technology. Evaluating AI through conventional enterprise standards risks overlooking its broader potential. Startups, unencumbered by established corporate metrics, are poised to lead the charge in AI innovation. They can act independently, swiftly, and decisively, driving the adoption and application of AI without the constraints that often bind larger corporations. Some skeptics argue that AI is an elusive dream, citing the high costs of developing models due to the significant GPU power required and the preference of engineers to work with companies that can afford such resources. Industry experts claim there is no viable use case for AI in the enterprise world. However, these experts are treating AI as a sustaining technology, expecting it to serve existing clients and yield immediate profit margins. To unlock the true value of AI, it must be treated as a disruptive technology. Companies need to establish independent AI departments, fund them adequately, and allow them to explore AI's potential fully. 

Profet AI Launches AILM, the World's First Enterprise AI Lifecycle Management Platform for AI Governance Taiwan-based Profet AI has launched AILM (AI Lifecycle Management Platform), the world's first enterprise platform for AI governance. Targeting the manufacturing industry, AILM enables companies to control, manage, and disseminate their core domain know-how, facilitating global expansion. The platform addresses common challenges in AI adoption, providing standardized problem identification, transparent implementation processes, and incentivized reward systems. Profet AI plans to integrate AILM with Generative AI to enhance usage efficiency. The company recently completed a Series A funding round, raising $5.6 million, and expanded its presence to Vietnam, Japan, Malaysia, Singapore, Thailand, and China. #ProfetAI #AILM #EnterpriseAI #AIgovernance #AI LifecycleManagement #ManufacturingIndustry #GlobalExpansion #StandardizedProcesses #IncentivizedRewards #GenerativeAI #SeriesAfunding #ExpansionInAsia #TechInnovation #AIAdoption #AIInBusiness #EnterpriseTech

Navigating the generative AI disruption in software https://mck.co/3xb7LwF #AI #GenAI #GenerativeAI #BigData #Analytics #MachineLearning #Innovation #Technology #Startups #Careers #VentureCapital #Management

2023 was the year of experimentation for GenAI. 2024 is all about value realization. Here is an interesting study about the current state of GenAI implementations across the industry. It very well aligns with what we have also found during our implementations: - Enterprises are excited about internal use cases but remain more cautious about external ones - Implementing and scaling generative AI requires the right technical talent, which currently isn’t in-house for many enterprises - Multi model approach is preferred. While cost factored into open source appeal, it ranked below control and customization as key selection criteria - And above all, while enterprises are tightening budgets across, GenAI keeps getting a generous nod. As it is quoted in below article: “getting an accurate answer is worth the money”. Let that sink in, a lot is covered in that one statement! Curious to know how you can achieve success in your ML/GenAI engineering initiatives? Let’s talk! #GenAI #LLM https://lnkd.in/gAryJNmC

Generative AI isn't like other technology rollouts. Here are 10 things companies should do to securely and effectively deploy this powerful technology.