How GenAI is changing the way businesses handle data

The launch of the Claude 3 series models in March 2024 and their successful performance in numerous benchmark tests is great news for businesses. It looks like enterprise customers will have access to more high-quality AI and GenAI tools from more vendors in order to evaluate and select the best solution.

While the quality and variety of public tools and services increase, it’s important to remember that it all starts with data. It’s not just the data used to train the underlying models that power AI and machine learning tools, there are tools for data analysis that uncover hidden patterns and insights.

As I explained before, there are some key preparations when it comes to getting your business ready to leverage AI, and you can’t have a successful AI strategy without a successful data strategy. The first step is to prepare your data to make it suitable for AI, which involves assessing, integrating, protecting and curating your decentralized digital gold so that it can be accessed by the growing variety of AI tools and services on the market.

In this post, I’ll focus on why it’s critical to have efficient pipelines between your data and cloud-based AI services, and what this might mean for your business.

Designing an AI Pipeline

Once you've assessed, consolidated, and secured your data in the cloud, you'll want to curate specific data sets relevant to different groups or use cases, and then build a pipeline that This selected data is transferred to the AI ​​tool of your choice. If your data resides in an Amazon Simple Storage Service (S3) bucket, you'll want to take advantage of the S3 APIs, which support a wide range of AI tools and services for comprehensive and fast access to your data.

Both tools should be a priority - you want these tools to run at high speeds, and you want to avoid locking yourself into a specific vendor or provider. The leading GenAI tool you choose today may not be the best fit for your needs three months from now, and you may want the flexibility to leverage data from different AI tools. This field is changing so quickly.

When you use hyperscale computing services, you usually need to avoid forcing customers into closed campuses to avoid affecting their experience. Therefore, to ensure that your data resides in AWS S3, you can use tools from Microsoft or Google. For example, if you want to use Google Vertex, you can use the S3 API to set up a pipeline between your S3 dataset and Google services.

So what’s next? Well, once your data is suitable for AI, and you’ve built or mapped out pipelines to connect your chosen services to your curated data, it’s time to see what these tools can actually do What have you done for your business? We started noticing a variety of interesting use cases from our customers.

How businesses are using AI today

If you run a manufacturing business, you likely have imaging and IoT devices that capture data throughout the automated manufacturing process. Today, at my company, we are working with customers who take this scanning and IoT data, build pipelines to cloud services, and then build machine learning (ML) models that end users can interact with to learn more about them What happens internally on a manufacturing, quality assurance, or assembly site. They are discovering more efficient ways to use workflows. They are discovering and fixing product defects faster.

If you have a marketing company, you may want to leverage a service like AWS Rekognition or AWS Kendra to analyze and search video and image content. One of our clients is an advertising giant with hundreds of studios around the world, each with its own rich history of creative work. Global businesses like this can leverage AI tools to help their creative teams easily find inspiration from past projects and use GenAI services to create new campaigns when pitching proposals to new clients.

However, the most common AI applications we see in enterprises today involve some variation of a chat interface. This tool can be used for customer support, marketing, and even internal research to promote the dissemination of institutional knowledge.

Implementing these services proved surprisingly easy. Google Vertex is a great option because it's easy to use, cost-effective, and leverages Google's LLM while ensuring private data is protected. The Amazon Bedrock is equally impressive.

Our customers have also been using Microsoft Copilot and Copilot Studio, a web application that helps you create chatbots that target specific needs and do so in a way that maintains data privacy and compliance. A technology company with a large knowledge base of documents could create a curated dataset consisting of these texts, train a custom Copilot, and then provide its customers or internal users with a tool that makes it easier for them to find and extract relevant information from that knowledge base. information.

Every industry and every business has its specific needs, but every business I’ve worked with in recent years has had one problem in common – ever-increasing amounts of data. Ultimately, these AI, GenAI, and ML tools can provide businesses with the opportunity to turn disparate data into assets that can help increase efficiency, accelerate business processes, and create significant competitive advantage.

We don’t know which AI tools and services will prevail, or which specific tools will be best for your business. One thing is clear, however: this technology will transform your industry, and tomorrow’s leading companies will be those that make data AI-friendly today and start building data pipelines for AI tools and services.