As AI forecasts continue to explode in 2025, this ever-evolving technology brings unprecedented opportunities and complex challenges to organizations around the world. To help today’s organizations and professionals get the most value in 2025, I’ve shared my thoughts and anticipated AI trends this year.
Organizations must strategically plan the cost of AI
The world continues to be ecstatic about the potential of artificial intelligence. However, the cost of AI innovation is an indicator that an organization must plan. For example, AI requires GPUs, but many CSPs are deployed larger, and GPUs with N-1, N-2 or above are not built specifically for AI workloads. Additionally, as the project grows/scale (more expenses), the cost range of cloud GPUs is high and is easy for developers to turn on; in addition, purchase GPUs (if caused by scarcity if locally possible Use) can also be a very expensive proposition, then the cost of a single chip is good, reaching tens of thousands of dollars. As a result, server systems used to require AI workloads are becoming increasingly high or not possible for many people with departmental operating expenses (OPEX) budgets. In 2025, enterprise customers must redefine their AI costs and AI development budgets to synchronize. Now that so many siloed departments take the initiative and build their own AI tools, companies can inadvertently spend thousands of dollars a month on small or siloed uses of cloud-based GPUs, and their requirements for AI Compute instances ( Especially when the user leaves these instances run).
Open source model will promote the democratization of several AI use cases
In 2025, organizations will put huge pressure on the return on investment in AI projects and related budgets. As popular ISVs offer low code or no code tools for building AI applications, companies will continue to seek open source models that are easier to fine-tune, rather than training and building from scratch. Fine-tuning open source models more efficiently use available AI resources (people, budget and/or computing power), helps explain why currently only over 900k+ (and growth) models can be downloaded only on Hugging Face. However, when an enterprise launches an open source model, it is crucial to use open source software, frameworks, libraries, and tools throughout the organization. Lenovo’s recent agreement with Anaconda is a great example of this support, with Intel-powered Lenovo Workstation Portfolio and Anaconda Navigator helping simplify the data science workflow.
AI compliance becomes standard practice
Changes in AI strategies will bring AI computing closer to the source of company data, and more local (especially for the AI development phase of projects or workflows). As AI gets closer to the core of many businesses, it will shift from a separate parallel or special workflow to aligned with many core business functions. Ensuring AI is compliant and accountable is the real goal today, so as we enter 2025, it will be part of the basic building blocks of AI projects in the enterprise, which will become more standard practice. At Lenovo, we have a responsible AI committee composed of a group of employees who ensure solutions and products meet standards of security, ethics, privacy and transparency. The team reviews the use and implementation of AI based on risk and always applies security policies to conform to risk stance and regulatory compliance. The committee’s inclusive approach addresses all AI dimensions to ensure comprehensive compliance and overall risk reduction.
Workstations appear as effective AI tools to enter and exit the office
Using workstations as a stronger advantage and department-based AI appliances are already increasing. For example, Lenovo’s workstation portfolio (powered by AMD) helps media and entertainment professionals bridge the gap between expectations and the resources needed to deliver the highest visual content. Due to their smaller form factor and footprint, low acousticity, standard power requirements, and the use of client-based operating systems, they can be easily deployed as AI inference solutions that may not be suitable. Another use case is that AI-enhanced data analytics can bring real business value in standard industry workflows, which is very eye-catching for C Suite Execs trying to make a difference. Other use cases are smaller domain-specific AI tools that individuals create for their own use. These efficiency saving tools can become AI superpowers and can include everything from MS Copilot, private chatbots to personal AI assistants.
Maximize the potential of AI in 2025
Artificial intelligence is one of the fastest growing technological developments of our time, and regards every industry as a transformative technology that will increase efficiency for everyone – achieving faster and more valuable business outcomes.
AI, including machine and deep learning, as well as generative AI with LLM, requires enormous computing power to build and maintain the intelligence needed for seamless customer AI experiences. As a result, organizations should ensure they leverage high-performance and secure desktop and mobile computing solutions to revolutionize and enhance the workflow of AI professionals and data scientists.
