All industries agree that advances in AI have the potential to revolutionize every business process. While some organizations are starting to bring AI out of the lab and into production, most are just getting started and are still in the exploratory stage of adoption.
However, in the race to establish a competitive edge, many organizations are rushing to adopt AI. This means that there are some problems in the early stages. In some cases, AI output can be inaccurate because it lacks the necessary contextual data for an accurate response. There have also been cases where organizations effectively leaked valuable data through AI due to a lack of strong governance in place.
Organizations need to take a considered approach to AI adoption, weighing the risks and ensuring guardrails are in place to mitigate them. At the same time, it's important to establish the most valuable use cases. But with so many practical applications of AI emerging every day, from chatbots to risk modeling, it can be difficult to know where to start.
Somewhat ironically, AI can help here too.
Cloudera's EMEA Field CTO.
Prioritize AI
By conducting background research with LLM, organizations can understand exactly where AI can provide the most value in just minutes. LLM also helps organizations prioritize these use cases by answering questions such as:
- What are the top 10 use cases for AI in my industry? Here, organizations can quickly cut through the noise and gain insight into where AI can provide the most industry-specific value. Some are universal, such as content recommendations for marketing or chatbots for customer service. However, other use cases are more industry-specific, such as network optimization for telcos or credit risk assessment for banks.
- Can you rank these use cases by their financial impact on revenue? One of the objectives of enterprise AI is to increase revenue. Therefore, the next logical step is to understand which of these use cases have the greatest impact on revenue, allowing organizations to focus on the most impactful use cases first.
- Can these use cases be mapped to risk categories in EU AI law? Regulators around the world are seeking to govern the safe use of AI. However, the EU is one step ahead of most companies and recently passed the EU AI Act. This law applies to both companies based in the EU and companies operating there. Therefore, mapping use cases against EU AI legislation can help organizations understand the risks of AI adoption.
- My company operates under [insert locations]. Can you tell me which regulations these use cases may violate? Beyond AI-specific regulations, there is a mosaic of laws that organizations must comply with, especially those operating internationally. This is especially true if your business is in a highly regulated sector, such as finance. The LLM helps uncover the wide range of regulations that apply to AI use cases.
These questions provide an excellent starting point for your organization. However, knowledge is key to making informed decisions, so it is important not to take this information at face value and supplement it with further research. LLMs can provide references for their own research that provides direction for further reading of potential use cases to present to your business.
Armed with this knowledge, organizations can better understand where AI can be most useful. But having the ability to leverage AI is one thing, and having a successful implementation is another. It's about more than understanding use cases, risks, and regulations.
Data must be the foundation of AI
To derive real value from AI, organizations must ensure that data is the foundation for success. However, in today's hybrid, multicloud environments, data is often stored in silos and difficult to access. Implementing consistent controls and compliance across such a vastly distributed environment is also a challenge.
Therefore, it is important to have a unified data platform supported by a modern data architecture. This enables organizations to feed AI with data from any environment, whether in the cloud or on-premises. Strict governance can also be applied to ensure that data does not leak outside the organization and incur the wrath of regulators.
Get the most out of AI
As the use of AI in production becomes more commonplace, prioritizing use cases will be key to success. But instead of just following the crowd, organizations need to take the first steps to truly become ready for enterprise AI.
With a modern data architecture, your organization can lay a solid foundation for AI success. But this must be the first step, or organizations risk embarking on an AI project that is doomed to fail from the start.
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