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Choosing Your First Generative AI Use Cases
To get started with generative AI, first focus on areas that can improve human experiences with information.
Organizations can realize the true value of generative AI by integrating it into enterprise applications at scale
As generative AI becomes more familiar as a work tool, the basic platform is starting to mature. What we are seeing is the emergence of small language models (SLMs) that, alongside the better-known large language models (LLMs) with their ability to understand language and images and extrapolate insights, can boost the capability of the platform.
Providing further enhancements while helping with reliability are techniques like retrieval-augmented generation (RAG). RAG leverages data stored as vectors and submits it to the LLM, helping to reduce hallucinations and improve the precision of responses. Combined with knowledge graphs, RAG delivers context and clarity because together these two technologies can represent real-world relationships and provide solutions to queries in relevant frameworks.
Apart from the ability of generative AI to power more capable chatbots, the question for enterprises and investors is when they can expect to see more transformative efficiencies delivered at scale. They are, after all, investing massive sums to expand the capabilities of base LLMs and training new SLMs on specific industry domains. This is what we can expect in 2025.
Generative AI depends on a wide range of structured, unstructured, internal and external data. Its potential relies on a strong data ecosystem that supports training, fine-tuning and RAG. For industry-specific models, organizations must retain large volumes of data over time. As the world changes, relevant data becomes apparent only in hindsight, revealing inefficiencies and opportunities. By retaining historical data and integrating it with real-time insights, businesses will be able to turn AI from an experimental tool into a strategic asset, driving tangible value across the business.
Although the technology for generative AI’s data ecosystem exists, deployment remains inconsistent. In 2025, enterprises will focus on filling visibility gaps by enhancing their platforms to support vector data, similarity search, knowledge graphs and raw data stores. This will require balancing data control with accessibility while integrating generative AI into core systems for better insights and control. As enterprises scale from trials to full deployment, their systems will face new challenges. To unlock generative AI’s full potential, platforms must handle massive data ingestion and provide parallelized access to support larger, more complex operations.
The true value of generative AI is realized when integrated into enterprise applications at scale. While enterprises have been cautious with trial deployments, 2025 will be a turning point as they begin to scale generative AI across critical systems like customer support, supply chain, manufacturing and finance. This will require tools to manage data and track generative AI models, ensuring visibility into data usage. Generative AI must be supplemented with specific real-time data, such as vectors and graphs, to maximize effectiveness. In 2025, leading vendors will begin rolling out applications that leverage these advancements.
The need for robust data ecosystems will become apparent as companies finish experimenting and start integrating generative AI into core systems. This year, these platforms will evolve to drive significant efficiencies and business value at scale. Navigating this challenge will be essential for organizations that want to lead in AI.
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