AI is a big deal for your enterprise. Here's why.

For decades, organizations have invested in various technologies to harness data for transformative business insights. These include master data management, Big Data, and data warehouses, all with mixed success due to common challenges like data silos, poor data quality, and manual processes.

Today, cloud-scale architectures and AI have revolutionized data handling. AI, in particular, excels at complex tasks like pattern recognition and product classification, reducing manual effort and enhancing personalized experiences such as retargeting ads and product recommendations.

So why is AI such a big deal for enterprises like yours right now? Let’s start with a simple example for how you might answer a business question about a historical legal case with your existing workflows:

1. Look for a folder: An employee seeks files related to a specific legal case. After searching through many folders, they pull one related to a particular testimony.
2. Read documents: The employee reads the documents found in the folder. They also note any relevant cross-referenced materials from other folders. They repeat steps 1 and 2 until they think they have all relevant documents to generate a comprehensive answer.
3. Generate an answer: The employee spends time noting key takeaways from each document and synthesizing all notes into a concise response to the question. The response cites relevant documents. This process can take days or even weeks, depending on the scale.

Now what if you could have an algorithm do all that for you? AI technology has recently advanced to the point where it can generate language and images that mimic human cognitive abilities, making it easier for people to interact with machines and data.

Now, you can get information from business data using natural language questions instead of complex queries. With the right tools, AI can provide context-rich responses by searching, analyzing, and combining different datasets, delivering human-readable answers. A prime example of this is the experience introduced by ChatGPT and OpenAI.

Growing interest in AI

Organizations want to use AI to gain business insights, but concerns around data consolidation, data security, data quality, and lack of expertise create barriers to adoption.

While more and more organizations are ready to embrace AI, it’s important to note that generative AI and LLMs are only as good as the data that underpins them. Data silos across organizations continue to make it difficult to leverage enterprise data at scale, hindering the ability for AI outputs to be both complete and accurate.

What’s more, running LLMs can be extremely compute-intensive and cost prohibitive. When data is not properly deduplicated, or doesn’t have metadata that can improve data retrieval and response generation, response quality goes down and costs continue to go up. This phenomenon has long been described as “garbage in, garbage out.”

An underappreciated prerequisite for AI in the enterprises, then, is high quality backup data that’s accessible on a modern platform and can be integrated with LLMs.

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