As AI adoption accelerates, many organizations may feel pressure to invest in the newest models, the fastest GPUs, or the largest available cloud environments. However, purchasing powerful technology does not necessarily produce a strong business result. For manufacturers, life sciences companies, and other complex enterprises, AI infrastructure should be planned as a flexible portfolio rather than a one-time hardware purchase. To get started, the organization must consider the complete environ [...]

Compared to an Internet-accessible service such as ChatGPT, local LLMs can appear much more secure. Disconnected from the general public, this technology serves as a digital moat for confidential information and processes.However, most businesses looking into a local LLM are not proposing to train a foundation model from scratch. That approach would require substantial data, specialized talent, computing power, time, and money.Instead, the organization selects an existing open-weight model, ho [...]

For organizations, selecting an AI platform is not as simple as it may first appear. Each platform is built around a different combination of applications, data, and business processes. Guru’s overview of business AI platforms includes a wide array of solutions that can appear daunting at first glance: General purpose assistants such as ChatGPT, Cloud development environments such as Microsoft Azure, Knowledge management products such as Guru, Machine learning platforms such as Vertex AI, and O [...]

Many organizations begin their AI adoption in a familiar way. Employees experiment with ChatGPT, Copilot, Gemini, Claude, or another platform. Different departments identify possible applications, and leadership authorizes a few pilots to see what the technology can accomplish. These experiments can create valuable momentum. However, as organizations move forward, they will likely encounter more difficult questions: Which platforms should the organization support? How should different departmen [...]

Today, much of the AI conversation centers on agents and rapid software development. These tools may complete repetitive tasks, prepare documentation, track progress, generate test scripts, or help organizations deliver software faster and at a lower cost. Those applications are valuable, but they may represent only an early stage of the AI industrial shift. Organizations have spent decades looking for ways to reduce technology costs and accelerate delivery. They have adopted new development me [...]

As AI continues to advance, many organizations may feel pressure to introduce the technology as quickly as possible. Leadership may purchase an AI system, provide employees with a short demonstration, and expect them to begin identifying ways to use it. For a few low-risk applications, this approach may work. However, what happens when employees need to use AI for sensitive information, detailed analysis, customer recommendations, software development, or other consequential work? Proper AI use [...]