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On Premise Gen AI

In 2025, enterprises transitioning generative AI (GenAI) into production after years of experimentation are increasingly considering on-premises deployment as a cost-effective alternative to the cloud. Since OpenAI ignited the AI revolution in late 2022, organizations have tested large language models powering GenAI services on platforms like AWS, Microsoft Azure, and Google Cloud. These experiments demonstrated GenAI’s potential to enhance business operations while exposing the substantial costs of cloud usage. To avoid difficult conversations with CFOs about escalating cloud expenses, CIOs are exploring on-premises AI as a financially viable solution. Advances in software from startups and packaged infrastructure from vendors such as HPE and Dell are making private data centers an attractive option for managing costs. A survey conducted by Menlo Ventures in late 2024 found that 47% of U.S. enterprises with at least 50 employees were developing GenAI solutions in-house. Similarly, Informa TechTarget’s Enterprise Strategy Group reported a rise in enterprises considering on-premises and public cloud equally for new applications—from 37% in 2024 to 45% in 2025. This shift is reflected in hardware sales. HPE reported a 16% revenue increase in AI systems, reaching $1.5 billion in Q4 2024. During the same period, Dell recorded a record .6 billion in AI server orders, with its sales pipeline expanding by over 50% across various customer segments. “Customers are seeking diverse AI-capable server solutions,” noted David Schmidt, senior director of Dell’s PowerEdge server line. While heavily regulated industries have traditionally relied on on-premises systems to ensure data privacy and security, broader adoption is now driven by the need for cost control. Fortune 2000 companies are leading this trend, opting for private infrastructure over the cloud due to more predictable expenses. “It’s not unusual to see cloud bills exceeding 0,000 or even million per month,” said John Annand, an analyst at Info-Tech Research Group. Global manufacturing giant Jabil primarily uses AWS for GenAI development but emphasizes ongoing cost management. “Does moving to the cloud provide a cost advantage? Sometimes it doesn’t,” said CIO May Yap. Jabil employs a continuous cloud financial optimization process to maximize efficiency. On-Premises AI: Technology and Trends Enterprises now have alternatives to cloud infrastructure, including as-a-service solutions like Dell APEX and HPE GreenLake, which offer flexible pay-per-use pricing for AI servers, storage, and networking tailored for private data centers or colocation facilities. “The high cost of cloud drives organizations to seek more predictable expenses,” said Tiffany Osias, vice president of global colocation services at Equinix. Walmart exemplifies in-house AI development, creating tools like a document summarization app for its benefits help desk and an AI assistant for corporate employees. Startups are also enabling enterprises to build AI applications with turnkey solutions. “About 80% of GenAI requirements can now be addressed with push-button solutions from startups,” said Tim Tully, partner at Menlo Ventures. Companies like Ragie (RAG-as-a-service) and Lamatic.ai (GenAI platform-as-a-service) are driving this innovation. Others, like Squid AI, integrate custom AI agents with existing enterprise infrastructure. Open-source frameworks like LangChain further empower on-premises development, offering tools for creating chatbots, virtual assistants, and intelligent search systems. Its extension, LangGraph, adds functionality for building multi-agent workflows. As enterprises develop AI applications internally, consulting services will play a pivotal role. “Companies offering guidance on effective AI tool usage and aligning them with business outcomes will thrive,” Annand said. This evolution in AI deployment highlights the growing importance of balancing technological innovation with financial sustainability. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Generative AI Overview

Generative AI Overview

Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest on Salesforce Einstein The Rise of Generative AI: What It Means for Business and CRM Generative artificial intelligence (AI) made headlines in late 2022, sparking widespread curiosity and questions about its potential impact on various industries. What is Generative AI? Generative AI is a technology that creates new content—such as poetry, emails, images, or music—based on a set of input data. Unlike traditional AI, which focuses on classifying or predicting, generative AI can produce novel content with a human-like understanding of language, as noted by Salesforce Chief Scientist Silvio Savarese. However, successful generative AI depends on the quality of the input data. “AI is only as good as the data you give it, and you must ensure that datasets are representative,” emphasizes Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer. How Does Generative AI Work? Generative AI can be developed using several deep learning approaches, including: Other methods include Variational Autoencoders (VAEs) and Neural Radiance Fields (NeRFs), which generate new data or create 2D and 3D images based on sample data. Generative AI and Business Generative AI has captured the attention of global business leaders. A recent Salesforce survey found that 67% of IT leaders are focusing on generative AI in the next 18 months, with 33% considering it a top priority. Salesforce has long been exploring generative AI applications. For instance, CodeGen helps transform simple English prompts into executable code, and LAVIS makes language-vision AI accessible to researchers. More recently, Salesforce’s ProGen project demonstrated the creation of novel proteins using AI, potentially advancing medicine and treatment development. Ketan Karkhanis, Salesforce’s Executive VP and GM of Sales Cloud, highlights that generative AI benefits not just large enterprises but also small and medium-sized businesses (SMBs) by automating proposals, customer communications, and predictive sales modeling. Challenges and Ethical Considerations Despite its potential, generative AI poses risks, as noted by Paula Goldman and Kathy Baxter of Salesforce’s Ethical AI practice. They stress the importance of responsible innovation to ensure that generative AI is used safely and ethically. Accuracy in AI recommendations is crucial, and the authoritative tone of models like ChatGPT can sometimes lead to misleading results. Salesforce is committed to building trusted AI with embedded guardrails to prevent misuse. As generative AI evolves, it’s vital to balance its capabilities with ethical considerations, including its environmental impact. Generative AI can increase IT energy use, which 71% of IT leaders acknowledge. Generative AI at Salesforce Salesforce has integrated AI into its platform for years, with Einstein AI providing billions of daily predictions to enhance sales, service, and customer understanding. The recent launch of Einstein GPT, the world’s first generative AI for CRM, aims to transform how businesses interact with customers by automating content creation across various functions. Salesforce Ventures is also expanding its Generative AI Fund to $500 million, supporting AI startups and fostering responsible AI development. This expansion includes investments in companies like Anthropic and Cohere. As Salesforce continues to lead in AI innovation, the focus remains on creating technology that is inclusive, responsible, and sustainable, paving the way for the future of CRM and business. The Future of Business: AI-Powered Leadership and Decision-Making Tomorrow’s business landscape will be transformed by specialized, autonomous AI agents that will significantly change how companies are run. Future leaders will depend on these AI agents to support and enhance their teams, with AI chiefs of staff overseeing these agents and harnessing their capabilities. New AI-powered tools will bring businesses closer to their customers and enable faster, more informed decision-making. This shift is not just a trend—it’s backed by significant evidence. The Slack Workforce Index reveals a sevenfold increase in leaders seeking to integrate AI tools since September 2023. Additionally, Salesforce research shows that nearly 80% of global workers are open to an AI-driven future. While the pace of these changes may vary, it is clear that the future of work will look vastly different from today. According to the Slack Workforce Index, the number of leaders looking to integrate AI tools into their business has skyrocketed 7x since September 2023. Mick Costigan, VP, Salesforce Futures In the [still] early phases of a major technology shift, we tend to over-focus on the application of technology innovations to existing workflows. Such advances are important, but closing the imagination gap about the possible new shapes of work requires us to consider more than just technology. It requires us to think about people, both as the customers who react to new offerings and as the employees who are responsible for delivering them. Some will eagerly adopt new technology. Others will resist and drag their feet. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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The Growing Role of AI in Cloud Management

The Growing Role of AI in Cloud Management

AI technologies are redefining cloud management by automating IT systems, improving security, optimizing cloud costs, enhancing data management, and streamlining the provisioning of AI services across complex cloud ecosystems. With the surging demand for AI, its ability to address technological complexities makes a unified cloud management strategy indispensable for IT teams. Cloud and security platforms have steadily integrated AI and machine learning to support increasingly autonomous IT operations. The rapid rise of generative AI (GenAI) has further spotlighted these AI capabilities, prompting vendors to prioritize their development and implementation. Adnan Masood, Chief AI Architect at UST, highlights the transformative potential of AI-driven cloud management, emphasizing its ability to oversee vast data centers hosting millions of applications and services with minimal human input. “AI automates tasks such as provisioning, scaling, cost management, monitoring, and data migration,” Masood explains, showcasing its wide-ranging impact. From Reactive to Proactive Cloud Management Traditionally, CloudOps relied heavily on manual intervention and expertise. AI has shifted this paradigm, introducing automation, predictive analytics, and intelligent decision-making. This evolution enables enterprises to transition from reactive, manual management to proactive, self-optimizing cloud environments. Masood underscores that this shift allows cloud systems to self-manage and optimize with minimal human oversight. However, organizations must navigate challenges, including complex data integration, real-time processing limitations, and model accuracy concerns. Business hurdles like implementation costs, uncertain ROI, and maintaining the right balance between AI automation and human oversight also require careful evaluation. AI’s Transformation of Cloud Computing AI has reshaped cloud management into a more proactive and efficient process. Key applications include: “AI enhances efficiency, scalability, and flexibility for IT teams,” says Agustín Huerta, SVP of Digital Innovation at Globant. He views AI as a pivotal enabler of automation and optimization, helping businesses adapt to rapidly changing environments. AI also automates repetitive tasks such as provisioning, performance monitoring, and cost management. More importantly, it strengthens security across cloud infrastructure by detecting misconfigurations, vulnerabilities, and malicious activities. Nick Kramer of SSA & Company highlights how AI-powered natural language interfaces simplify cloud management, transforming it from a technical challenge to a logical one. With conversational AI, business users can manage cloud operations more efficiently, accelerating problem resolution. AI-Enabled Cloud Management Tools Ryan Mallory, COO at Flexential, categorizes AI-powered cloud tools into: The Rise of Self-Healing Cloud Systems AI enables cloud systems to detect, resolve, and optimize issues with minimal human intervention. For instance, AI can identify system failures and trigger automatic remediation, such as restarting services or reallocating resources. Over time, machine learning enhances these systems’ accuracy and reliability. Key Applications of AI in Cloud Management AI’s widespread applications in cloud computing include: Benefits of AI in Cloud Management AI transforms cloud management by enabling autonomous systems capable of 24/7 monitoring, self-healing, and optimization. This boosts system reliability, reduces downtime, and provides businesses with deeper analytical insights. Chris Vogel from S-RM emphasizes that AI’s analytical capabilities go beyond automation, driving strategic business decisions and delivering measurable value. Challenges of AI in Cloud Management Despite its advantages, AI adoption in cloud management presents challenges, including: AI’s Impact on IT Departments AI’s growing influence on cloud management introduces new responsibilities for IT teams, including managing unauthorized AI systems, ensuring data security, and maintaining high-quality data for AI applications. IT departments must provide enterprise-grade AI solutions that are private, governed, and efficient while balancing the costs and benefits of AI integration. Future Trends in AI-Driven Cloud Management Experts anticipate that AI will revolutionize cloud management, much like cloud computing reshaped IT a decade ago. Prasad Sankaran from Cognizant predicts that organizations investing in AI for cloud management will unlock opportunities for faster innovation, streamlined operations, and reduced technical debt. As AI continues to evolve, cloud environments will become increasingly autonomous, driving efficiency, scalability, and innovation across industries. Businesses embracing AI-driven cloud management will be well-positioned to adapt to the complexities of tomorrow’s IT landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. 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