Artificial intelligence is being used by businesses all over the world to help process data, speed up processes, and so much more. While many companies have been slow to adopt AI practices, those who take the time to structure their data and prepare their teams will be the ones left standing when these new technologies are fully adopted.
But knowing you should do it and then implementing it well are two different things. Deploying AI based technologies takes thoughtful planning and decisive action. And underneath the need to train teams on new processes and get everything up and running is the data that the AI will draw from. While you could send an AI agent to pull from everything everywhere in your systems, this approach can lead to even more problems. Here’s how to structure that data so that you get the most out of these agents and your business is prepared for an AI future.
The Need for Good Data With Agentic AI
An AI agent is simply a system that can look at data and make sense of it. Think of these like data analytics 2.0. Not only do they look at the information, they can also make decisions based on that data with little human input. An agentic AI system is capable of being deployed on every piece of data you have and then pulling it together into one cohesive picture of your company. But unlike old methods of data analytics that pulled data from the past, these AI agents can look at data in real time from multiple places and make quick decisions from what it sees.
Using AI in this way requires good clean data, the cleaner the better. This means that you’ll only give it access to what makes sense and ensure that the data is accurate. If you don’t want AI accessing personal data, then it won’t be deployed in that system.
Cleaning up the data means that you can create better separations between types of data to help facilitate this process, and remove old or outdated information. It also allows AI systems to be more accurate with its output and decision making. Plus, some of these programs offer the option to record and save its steps so that you can go back and review what the AI did.
Structured vs. Unstructured Data and AI
AI needs data in order to make decisions. There are two main types of data, one is structured. This kind of data is very well organized in a clear and contained manner. It’s also very easy to search and sort through. Unstructured is more like the wild wild west of data. Most enterprise level data is actually unstructured. This can include email inboxes, social media posts, and even call transcripts.
AI and machine learning models can access both types of data, however, they each have their pros and cons. Businesses who want to implement AI and machine learning systems need to understand the key benefits and downsides of each of them.
For instance, AI systems being used for analytics do really well with structured data. It’s easy for it to evaluate and make decisions with. Machine learning tools can handle unstructured data really well especially with tasks like generative AI and chatbot text analysis. Knowing the differences between the best uses of each of them can help companies prepare their data for the future of AI.
Removing Data Silos
When every department is disconnected from the other departments, this siloing can create barriers to an effective AI deployment. Data that’s too siloed can keep AI from being able to do its job effectively. When it needs to pull data from multiple different databases to get a picture of the overall health of the organization, it’s important for companies to make strategic connections.
Instead of keeping everything completely separate, tech teams should explore how to connect data in the right ways. Legacy systems that are hard to connect to and business function specific technologies are just two examples of places where data is often siloed. Companies that want to prepare better for the implementation of AI systems need to be aware of these data silos so they mitigate them early on. That way when the AI systems go online they can function optimally and deliver the best results
Also Read : AIEnhancer and the Future of AI room design
