Industry executives and experts share their predictions for 2025. Read them in this 17th annual VMblog.com series exclusive.
By Lenley Hensarling, Aerospike Technical Advisor
Generative AI (GenAI) has matured significantly over the past several years, supported by a growing workforce gaining a better understanding of the tools. This amounts to a maturation of the basic platform. However, the platform extends beyond well-known large language models (LLMs) like GPT-4, Claude, Cohere, Gemini, and Llama. While these models excel at understanding language and images and extrapolating insights, they are now complemented by small language models (SLMs) that enhance the platform’s capabilities.
The platform has also evolved with the adoption of techniques like Retrieval-Augmented Generation (RAG), which leverages data stored as vectors that can be submitted to the LLM for more precise responses. Additionally, the ability to deal with hallucinations or incorrect responses is improving reliability. RAG and knowledge graphs provide additional context and clarity by representing real-world relationships and guiding answers within relevant frameworks.
Today, GenAI powers more capable chatbots, leading to cost savings and better customer satisfaction. But is that the full extent of its potential? Enterprises and investors are putting an enormous amount of capital into expanding the capabilities of the base LLMs and training new SLMs on specific industry domains. But the ringing question from investors and the C-suite executives is: When will we see transformative efficiencies delivered at scale? Here are predictions for how GenAI platforms will evolve in 2025.
Retaining Extensive Data Sets Will Become Fundamental
GenAI is fueled by diverse information: structured, unstructured, inter- and intra-enterprise data. Its potential depends on a data ecosystem that supports training, fine-tuning, and RAG processes. For models specific to be industry- or company-specific, organizations must retain massive amounts of data over long time periods. The world is continuously changing, so determining relevant data becomes clear only in hindsight, uncovering new patterns that will help identify waste, anomalies, and opportunities.
For instance, consider this scenario: “If we had only kept that data from three years ago, we might have better adapted to today’s new geopolitical environment.” This highlights an important point: Retaining and managing extensive data sets isn’t an option-it’s fundamental. By integrating historical data with real-time insights, organizations can transform AI from an experimental tool into a driver of value at enterprise scale.
Over the next several years, we’ll see how the platform evolves to meet these challenges and deliver on its promise.
Filling Visibility Gaps Will Drive the Next Wave of GenAI Data Platform Deployment
While the technology for a GenAI data ecosystem exists, its deployment remains spotty. Real-time visibility into transactional, IoT, and customer behavior data is often siloed. In 2025, enterprises will focus on filling these gaps by enhancing their data platforms to support vector data, similarity searches, knowledge graphs, and raw data stores for many types of data.
Achieving this will require balancing the ability to control data with accessibility while understanding its deployment across various uses and models. This shift marks a departure from simple chatbot usage toward truly incorporating GenAI into core enterprise systems to gain new insights and controls.
As enterprises ramp up from proof of concepts to trials and full deployment, their existing systems will be taxed. While initial trials may have been easy, unlocking GenAI’s internet-scale advantages demands data platforms that can support massive parallel access while simultaneously providing high-volume ingestion capabilities.
Enterprises Will Augment GenAI with Real-Time Data
The real value of GenAI materializes when enterprises begin deploying the first sets of applications across core systems at scale. In the past several years, enterprises have cautiously experimented with these tools. However, 2025 will mark a turning point, as enterprises struggle to launch and scale GenAI across systems like customer support, manufacturing, supply chain, and finance. This will require tools to manage the deployment and tracking of data and GenAI models, ensuring visibility into how and where data is used.
One lesson learned over the last several years is that providing broad, generic answers is flawed. To make GenAI effective, it must be augmented with specific real-time data in the right forms of vectors and graphs. In 2025, we will start to see major vendors like Oracle, SAP, and Workday, as well as in-house solutions, release enterprise applications that leverage these advancements.
Positioning for Success
The need for robust data ecosystems becomes clear as enterprises move beyond experimentation and begin integrating GenAI into core systems. Over the next few years, we’ll see these platforms evolve to drive significant efficiencies and business value at scale. Organizations that successfully navigate the challenge of data management and platform integration will be positioned to lead in AI.
##
ABOUT THE AUTHOR
Lenley Hensarling is a technical advisor at Aerospike. He has over 30 years of experience in engineering, product, and operational management at startups and large, successful software companies. Lenley previously held executive positions at Novell, Enterworks, JD Edwards, EnterpriseDB, and Oracle. He has extensive experience delivering value to customers and shareholders in enterprise applications and infrastructure software. Lenley believes that business is now happening in real-time and that the right infrastructure for serving data to new real-time applications is a rapidly accelerating requirement for businesses to succeed.
