Key Takeaway
Data is crucial for agentic AI and business ROI, serving as the foundation for effective operations. High-quality data can significantly impact customer satisfaction and operational efficiency, especially in critical sectors like healthcare. To optimize agentic AI, businesses must unify and consolidate data from various sources into a scalable infrastructure. This involves breaking down silos and ensuring reliable, fast access to data, supported by high-throughput storage systems. By leveraging cloud-native technologies and hybrid deployments, organizations can enhance the accuracy of AI applications, ultimately improving their data management strategies and fostering successful AI adoption.
How significant is data for agentic AI and ROI in businesses?
Data is not just vital for agentic AI; it is essential for modern organizations.
With fewer human checkpoints, the quality of an organization’s data now determines whether a customer is delighted or a process is derailed.
For instance, one incorrect data point in travel could lead you to the wrong airport. You might wonder, but Kirsty, isn’t that a possibility regardless?
Now, consider healthcare, where lives are on the line; inaccurate data could jeopardize someone’s treatment. This highlights why secure, clean, and accessible data is foundational for successful AI adoption.
How should businesses handle their data?
To ensure agentic AI operates effectively, businesses require data that is unified, reliable, and fast. This entails dismantling siloes and consolidating data from all edge devices, on-premises systems, and cloud environments into a single, cohesive infrastructure.
This infrastructure should be scalable as the business expands. Companies should also recognize that AI workloads depend on high-throughput, low-latency storage systems.
Incorporating cloud-native technologies and hybrid deployments provides organizations with a strategy for scalability, security, and compliance.
Our solutions integrate data from a wide array of sources for this purpose, facilitating the development of customized AI applications that enhance the relevance and accuracy of AI-generated responses.
By adopting these strategies, businesses can effectively manage their data and excel in their agentic AI initiatives.
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