Questions to Ask AI… Before Implementing AI
There is consensus across industries that advances in AI have the potential to revolutionize a whole range of business processes. While some organizations are starting to take AI from the lab to production, most are just beginning to dip their toes in the water and are still in the exploratory phase of implementation.
However, in the race to gain a competitive advantage, many organizations have rushed into AI implementations, meaning there have already been some teething problems. In some cases, AI’s output was incorrect because it lacked the contextual data needed for accurate answers. In other cases, organizations have leaked valuable data through AI because they lacked strong governance.
Organizations must take a measured approach to deploying AI, weighing the risks and ensuring they have guardrails in place to mitigate them. At the same time, it is important to identify the most valuable use cases. But with so many practical applications of AI emerging every day, from chatbots to risk modeling, it’s hard to know where to start.
Somewhat ironically, AI can help here too.
EMEA Field CTO at Cloudera.
AI prioritises AI
By using an LLM to conduct background research, organizations can gain a better understanding of where exactly AI can provide the most value in just a few minutes. LLMs can also help 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 value, specific to their industry. Some will be universal – like content recommendations for marketing or chatbots for customer service. But other use cases will be more sector-specific, such as network optimization for telecom companies or credit risk assessment for banks.
- Can you rank these use cases by financial impact on revenue? One goal of enterprise AI is to increase revenue. So the next logical step is to understand which of these use cases will have the biggest impact on revenue, so organizations can focus on the most impactful first.
- Can you map these use cases into the risk categories of the EU AI Act? Regulators around the world want to regulate the safe use of AI. But the EU is one step ahead of most, having recently passed the EU AI Act, which applies to both companies based in the EU and those operating there. Mapping use cases based on the EU AI Act can therefore help organizations understand the risks of deploying AI.
- My company operates in (enter locations). Can you tell me what regulations these use cases might violate? Beyond AI-specific regulations, there is also a mosaic of legislation that organizations must adhere to, especially if they operate internationally and in highly regulated sectors such as finance. LLMs can help shed light on the wide range of regulations that will apply to AI use cases.
These questions offer organizations a good starting point. But it is important not to take this information at face value and supplement it with further research, as knowledge is the key to making informed decisions. LLMs can provide references for their own findings, which will provide some direction for further reading on possible use cases that can be presented to the company.
Armed with this knowledge, organizations will better understand where AI can help them most. But having the ability to use AI is one thing, successful implementation is another: it means more than understanding use cases, risks and regulations.
Data must form the basis of AI
To get real value from AI, organizations must ensure their data lays the foundation for success. However, in today’s hybrid, multi-cloud environments, data is often siloed, making it difficult to access. In such large, distributed environments, implementing consistent control and compliance is also a challenge.
That’s why it’s important to have a unified data platform, supported by a modern data architecture. It enables organizations to provide AI with data from any environment – both in the cloud and on premises. Strict governance can also be enforced to ensure that data does not leak outside an organization and incur the ire of regulators.
Get the most out of AI
As the use of AI in manufacturing becomes more common, prioritizing use cases will be the key to success. But organizations need to take the first steps to ensure they are truly ready for business AI, not just following the crowd.
With a modern data architecture, organizations can build a solid foundation for AI success. But this must be the first step, otherwise organizations risk embarking on AI projects that are doomed to failure from the start.
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