Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Shomron Jacob, Head of Applied Machine Learning and Platform at Iterate.ai.
Private LLMs and AI assistants will change how businesses operate in 2025, as organizations move beyond experimental AI adoption toward more mature (and more security-focused) AI strategies. This shift will fundamentally transform workplace productivity while addressing the mounting concerns over data privacy and GPU costs that currently plague many AI initiatives.
Expect these trends to emerge throughout the next year:
1. AI assistants become standard-issue work tools, supercharging employee output.
From enterprises to SMBs and across industries, organizations will increasingly incorporate AI assistant applications to enhance the ease and productivity with which employees can complete their daily tasks. By utilizing AI interfaces that offer intuitive conversational answers and generative solutions to everyday needs, workers will significantly revise their workflows, while rapidly arriving at new best practices and improving their experiences. Specifically, employees will leverage AI to quickly and securely summarize documents, create marketing assets, draft business contracts and invoices, propose efficient project timelines, and much more.
The ability of next year’s AI assistants to produce business reports and provide budget and trend analysis will also enable more robust and beneficial support when it comes to data-driven decision-making. Beyond simply expediting mundane work in 2025, those capabilities will elevate AI assistants into roles as trusted advisors as well.
2. Private LLMs become the new corporate advantage, keeping data secure and results unique.
Many of those AI assistants that businesses deploy next year will not make use of public LLMs, but will instead be supported by private LLMs that allow a business to retain secure control and harness the unique benefits of their own data.
Leveraging any of the several massive cloud-based public LLM options out there comes with issues. First, public LLMs tap into an organization’s own proprietary data along with data collected from wide ranging sources. That generalized information-sourcing leads to generalized generative AI results, which are often less incisive and on-point than a business or user would prefer. Second, employees submitting their organization’s proprietary data to a public LLM can easily lead to data privacy issues, putting sensitive information at risk.
In 2025, more businesses will wisely avoid these limitations by instead opting for private LLMs and localized infrastructure that keeps their data under their own control. In contrast with public LLM results, private LLMs and RAG approaches to harnessing data can enable AI applications to deliver differentiated results and insights that no other business can achieve. At the same time, this strategy will mean much better data privacy, security and simplified compliance. These secure practices will also position businesses to confidently integrate their private LLMs with data collection systems across their operations and supply chains, making their AI assistants and other AI applications that much more capable and valuable.
3. Smaller, private models slash GPU costs without sacrificing performance.
Given the current default enterprise AI strategy of “Spend all you can on increasingly expensive GPUs, ignore the high costs and energy use and environmental impacts for now, and hope it works out”…well, something has to give.
The coming year will provide an answer in the form of private LLMs and SLMs capable of delivering compelling results and experiences, without requiring anything like the stacks of GPUs used to train large-scale LLMs. Teamed with RAG practices, private LLMs and SLMs can operate locally on a user’s PC, while holding their own versus the far more costly and cumbersome alternatives. These practices will open powerful AI benefits to more businesses, while providing organizations of all sizes with sustainable options.
4. Private LLMs supercharge app development-creating smarter software, faster.
Developers will see 2025 as the year that private LLMs empowered them to accelerate application development, optimize performance and deliver winning experiences by harnessing AI and their own business data like never before. By taking advantage of LLM orchestration engines, development teams will enjoy AI support in exploring the full potential of their available technologies. They’ll capitalize on those opportunities-and the unique data insights that private LLMs enable-to provide users with generative AI capabilities and results more attuned to their needs, amounting to breakthroughs in application quality and benefits.
5. Low-code democratizes AI, making complex tech more simple to deploy.
Mature low-code and no-code platforms will continue to advance in the efficiency with which they enable developers to leverage the most complex and specialized emerging technologies with ease. From AI to big data to the IoT and beyond, developers with no expertise in those fields can simply assemble modules that compartmentalize the functionality of those technologies to create complete applications with groundbreaking features. Developers can also easily refine and perfect their applications, at a pace that laps traditional strategies. As AI and other technologies become both more complex and more essential, more development teams will tap into these approaches to accelerate innovation in the coming year.
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ABOUT THE AUTHOR
Shomron Jacob is the Head of Applied Machine Learning and Platform at Iterate.ai. Iterate.ai is at the forefront of empowering businesses with state-of-the-art AI solutions, like Generate and its AI low code platform, Interplay. With six patents granted (including “drag-and-drop AI”) and nearly a dozen more pending, Iterate.ai’s platform offers corporate innovators a low-risk, speedy, and systematic way to scale in-house, near-term digital innovation initiatives.
