Generative AI offers incredible potential to transform industries like healthcare, finance, and retail by reshaping workflows, driving innovation, and enhancing personalized experiences.
Generic models, however, can fall short on delivering on this promise.
This is because most off-the-shelf generative AI models still rely on static, pretrained knowledge that can be outdated or unable to meet the demands of a large-scale, on-demand production environment.
The immense power of generative AI lies in its ability to connect with live data.
Data is the fuel of AI applications. The magnitude and scale of enterprise data often make it too costly and time-consuming to use effectively.
The value of generative AI applications is rooted in their accuracy — and accuracy is rooted in data. So, enterprises continue to invest in improvements to how their business data is stored, indexed, and accessed.
A generative AI system that draws on real-time information rather than static historical data, and that can use all available data in a timely and cost-effective manner, is the key to providing accurate, context-rich outputs and delivering actionable insights tailored to individual needs. In the new year, we expect to see enterprises increasingly put their data to work to power AI.
The power of now for data retrieval across industries
Generative AI models that rely on static datasets, or that can only use a fraction of stored data, often fall short in dynamic, real-world scenarios. To be effective, AI models must quickly adapt to new data, such as rapidly shifting market trends, medical records that are updated by the hour, or live inventory changes.
AI retrieval in healthcare can massively advance patient care by integrating real-time data, like a patient’s medical history, lab results, and treatments, with the latest research and guidelines. For example, a physician could instantly access insights on drug interactions or emerging therapies tailored to a patient’s needs, leading to faster diagnoses and more personalized care. Telemedicine platforms also benefit from AI retrieval, which offers accurate, informed responses that can build trust in remote consultations.
Retail-focused AI retrieval applications create highly personalized shopping experiences by analyzing live inventory, customer preferences, and shopping trends. Virtual assistants can recommend in-stock items and suggest alternatives when products become unavailable. For instance, someone looking for an outfit could receive size- and budget-specific recommendations with trending options in their region. For sales teams, these tools can prioritize high-demand items, boosting satisfaction and conversion rates.
Real-time data retrieval enables AI to go beyond static outputs and deliver high precision, responsiveness, and enhanced user experiences across industries.
Building AI that adapts to the real world
Enabling AI to access live data helps ensure accurate, timely, and relevant information, which builds users’ trust in the technology, especially in high-stakes industries.
This approach also enables enterprises to control their AI's data sources — whether from proprietary knowledge bases, application programming interfaces, or secure systems — ensuring customization, scalability, and compliance with organizational goals and regulations.
Enterprises investing in retrieval technology should prioritize software with key qualities that ensure seamless integration, high performance under real-world conditions, and the ability to deliver precise, contextually relevant information for downstream AI applications. A few capabilities to consider when evaluating AI retrieval software include:
- Accuracy and relevance: The ability to fetch contextually relevant data impacts the quality of downstream AI tasks, ensuring results are accurate and tailored to queries while minimizing irrelevant outputs.
- Scalability and latency: Real-time or near-real-time performance is essential. A strong retriever efficiently handles petabyte-scale data and concurrent requests, without sacrificing speed.
- Integration and flexibility: A robust retriever integrates easily into AI pipelines, supports varied use cases, and allows customization to meet specific organizational needs.
Evaluating software within an AI pipeline is no small feat — the solution should ensure seamless integration, bolster security, and meet system demands and performance requirements while minimizing disruptions to existing workflows.
Creating AI with greater purpose
As generative AI becomes increasingly sophisticated, its ability to use live data is becoming critical for applications. By moving beyond static knowledge, in 2025, AI will address complex, evolving needs with more accurate and relevant insights.
This shift will allow AI to play a bigger role in decision-making by adapting to challenges rather than simply reacting to them. Such developments are making AI tools more practical and valuable for all, better streamlining workflows and solving more problems.
To harness generative AI’s full potential, users must tap into its evolving capabilities as a flexible, responsive tool that can pull live data and provide insights fit for the moment.
Nicola Sessions is senior product marketing manager for AI Software at Nvidia.