In today’s fast-paced world, businesses are always looking for ways to improve their efficiency and productivity. One of the most promising technologies in this regard is Generative AI (GenAI). However, before implementing GenAI, it is crucial to establish trust in the technology. This trust involves the design, functionality, and utilization of GenAI’s outputs. In this comprehensive guide, we will discuss how to build trust in GenAI through data governance and security, and how to maximize ROI by crafting a scalable strategy.
Building Trust through Data Governance and Security
The first step towards building trust in GenAI is through governance. However, the focus should be on data governance and security. A robust strategy for trusted AI starts with securing the GenAI system itself. A well-secured GenAI system is typically private, operating within your network, and backed by a licensed large language model. It ensures the protection of data and intellectual property and is steered by responsible AI practices.
It is also essential to consider the potential risks from GenAI features integrated into enterprise applications like ERP and CRM. Enhancing security for GenAI goes beyond the system itself. It involves evaluating your network’s architecture, security policies, and data governance in light of the new challenges posed by generative AI. Addressing these challenges and fortifying the security environment is a process that, based on our experience, can be accomplished in approximately 60 days.
Crafting a Strategy for Generative AI to Maximize ROI
When strategizing for GenAI, it’s essential to think on a larger scale. The goal is to identify GenAI patterns that can be scaled across the organization to yield a significant return on investment (ROI). Instead of focusing on individual use cases, the strategy should revolve around patterns that can be applied universally.
For instance, while GenAI’s ability for deep retrieval from unstructured data might offer limited value in one domain, its application across various business lines can result in substantial ROI. This scalability distinguishes GenAI from traditional AI, which often requires a unique model for each task.
To further enhance ROI, businesses should concentrate on their primary processes. For instance, the most significant impact might not come from using GenAI for deep retrieval but from its application in software writing, proofreading, or tailoring customer experiences. An effective strategy should also evaluate the readiness of your data, processes, and workforce, and outline a roadmap to address any shortcomings.
Conclusion
In conclusion, GenAI is a promising technology that can help businesses improve their efficiency and productivity. However, it is crucial to establish trust in the technology through data governance and security. Crafting a scalable strategy that focuses on patterns, rather than individual use cases, can maximize ROI. By following these guidelines, businesses can harness the power of GenAI to achieve their goals.