Trustworthy AI, Powered by Trusted Data

Key takeaways:

• The success of your AI initiatives hinges on the integrity of your data. Ensure your data is accurate, consistent, and contextualized to enable trustworthy AI systems that avoid biases, improve accuracy and reliability, and boost contextual relevance and nuance.
• Adopt strategic practices in data integration, quality management, governance, spatial analytics, and data enrichment. A comprehensive approach to data handling enhances the effectiveness of AI applications by providing them with a complete, high-quality, and context-rich dataset.
• Leverage advanced solutions and partnerships to reinforce your AI infrastructure. These tools help achieve a scalable and robust AI ecosystem primed for success.

When you think about the potential of artificial intelligence (AI) for your business, what comes to mind? Chances are it’s not just one use case but many. Ask another ten people in your organization, and each individual likely has their own set of unique needs and answers to add to the list, things like:

• chatbots for efficient and personalized assistance and happier customers
• fast and powerful AI recommendations to deliver tailored content
• machine learning applications for faster and more accurate business processes

All of these and so many more exemplify the impact of AI on modern businesses; it’s not just a technological advancement but a strategic asset.

But AI enthusiasts beware: there are crucial steps you must take before jumping into any initiative – primarily, ensuring your data’s up to the task.

Let’s explore this topic more in-depth by looking at how you can use trusted data to maximize AI success and avoid pitfalls.

Why Does Data Integrity Matter for AI Success?

What does AI-ready data look like? It needs to have data integrity – that’s maximum accuracy, consistency, and context. Think of it this way: without trusted data, there’s no trusted AI. Trusted data needs data integrity.

Without accurate, consistent, and contextualized data, even the most advanced AI systems can falter, leading to biased outputs, untrustworthy results, lack of contextual relevance, and, ultimately, a loss of trust in your AI. Whatever your use cases, you can’t take these risks lightly. Your investments will only pay off if your AI systems are built on a foundation of trusted data.

It’s worth noting, though, that many organizations face data integrity challenges, like:

• struggling to integrate data fast enough
• understanding and governing responsible use of data
• observing and improving data quality
• enriching with third-party data and spatial insights for deeper context
• guaranteeing security and privacy

Do any (or all) of these challenges ring true for you? You’re not alone. At the 2023 Gartner IT Symposium Research Super Focus Group, only 4% say their data is AI-ready.

How do you begin to tackle these issues? Next, you’ll discover the three data integrity considerations essential to success.

Three Data Integrity Considerations to Solve AI Challenges

Suppose you face harmful bias, unreliable results, and a lack of contextual relevance. In that case, your path to fair and responsible AI development starts with strategic data integration, quality management, governance practices, spatial analysis, and data enrichment.

To achieve trustworthy AI outcomes, you need to ground your approach in three crucial considerations related to data’s completeness, trustworthiness, and context. Here’s a snapshot of how you can address each:

1. A more complete dataset helps you realize the full potential of your AI. You need to break down data silos and integrate critical data from all relevant sources into Amazon Web Services (AWS). This ensures your AI models can access a comprehensive dataset, minimizing bias and enhancing accuracy.
2. Fuel your AI applications with trusted data to power reliable results. Implement robust data quality measures to ensure your data is accurate, consistent, and standardized, as well as a governance framework to maintain its quality over time. Using trusted data to train and fine-tune your ML and GenAI models in Amazon SageMaker and Amazon Bedrock is essential for reliable AI predictions and decisions.
3. Add context to your data for more relevant and nuanced responses. Enrich your data with spatial insights and third-party information to ensure accuracy, depth, and relevance for your AI applications. You can achieve this by bringing third-party data and spatial insights from Precisely to your Amazon SageMaker and Amazon Bedrock environments.

As you start navigating these data integrity considerations for AI, it might feel overwhelming – but it’s important to remember that you don’t need to do it alone. Get AI-ready by leveraging the power of trusted data to fuel your AI applications with combined capabilities from Precisely and Amazon Web Services (AWS).

With a robust cloud infrastructure from AWS and cutting-edge data integrity solutions from Precisely, you gain a formidable toolkit to maximize your AI investments.

Moving Forward with Trustworthy AI

Up to this point, there’s been no shortage of AI mishaps making headlines: everything from business chatbots recommending a competitor’s product to AI-written briefs containing fake citations. These are real-world examples of the adverse impacts of poor data practices on your AI results, regardless of your use case or industry.

How can you avoid becoming another AI story gone wrong? As AI technologies evolve, ensuring their success means committing to data integrity from the outset. This needs to be a top priority in your AI strategy to avoid the pitfalls of “garbage in, garbage everywhere,” ensuring AI solutions that are trustworthy and powerful.

Let’s future-proof your AI initiatives by starting with trusted data.

For more, watch the on-demand webinar: AI You Can Trust. Our panel of experts from Precisely and AWS dive deeper into the organizational challenges in maintaining data integrity for AI applications, the three top data integrity considerations, and real-world use cases showcasing the transformative impact of high-integrity data on AI success.