Build a Strong Data Foundation

At the heart of any successful AI initiative is a solid data foundation. As Forrester’s report rightly points out, organizations must “establish the foundations of data, access, and governance” to build successful applications with AI “anchored in a trustworthy foundation.” There are clear risks if you don’t: “garbage data means garbage AI responses."

AI models are only as effective as the data they’re fed, so the importance of data integrity cannot be overstated. If your data infrastructure is weak, you’ll struggle to produce reliable AI outputs—no matter how advanced your models are.

“Garbage data means garbage AI responses.”

Forrester Research: Want AI Success? Start with High-Performance IT Execution, September 12, 2024

The MicroStrategy Semantic Graph provides a powerful solution to this challenge. By creating a single source of truth for your organization's data, the platform’s native data fabric acts as the intelligence foundation for your business. Further, it manages how your model is trained by passing through necessary information with governance rules in place—ensuring that your AI model both leverages and returns accurate, trustworthy data insights.

Organizations typically face a multitude of challenges deploying AI, ranging from data management to user adoption. The platform’s fully integrated MicroStrategy AI features all leverage the platform’s foundational Semantic Graph to eliminate these barriers, making it easier than ever to integrate intelligent capabilities into existing workflows. This not only improves the quality of AI outputs, but also makes it easier to scale AI across various departments.

In a rapidly evolving AI environment, this symbiotic relationship between knowledge graphs and AI models is essential for maintaining data consistency and integrity. Establishing strong governance is equally important. With AI, the risk of data leaks or improper use of sensitive information is always present. A well-defined governance framework ensures that data is properly controlled, access is monitored, and security protocols are in place to mitigate risks. MicroStrategy’s focus on data governance through the platform’s best-in-class security posture give businesses the security and transparency needed to explore AI with confidence.

Anchor Applications in Core AI Models

In the rush to implement AI, it’s tempting to try every new model and technology that hits the market—or even to build your own. AI vendors are constantly releasing new versions of language models and tools, each promising to revolutionize your operations. But as Forrester’s report suggests, chasing after every new release is not sustainable. Instead, organizations should anchor their applications with a core set of trusted models and resist the urge to constantly switch.

Selecting a few proven models that you deploy over a longer period allows for refinement and optimization. This approach is far more effective than integrating every new release, which can lead to chaos in your AI stack. For instance, whether you’re using Salesforce Einstein, ChatGPT Enterprise, or a custom language model, it’s important to invest time in understanding the model’s strengths and limitations before expanding your portfolio.

Create and Iterate AI Solutions with Agility

Adaptability is crucial when it comes to AI application development, especially as businesses face an increasing number of requests for AI-powered solutions. However, not every request can or should be fulfilled immediately. To thrive in a fast-paced AI environment, IT teams need to prioritize projects with the greatest potential impact, while maintaining agility to respond to business needs.

An agile approach to application development ensures that AI projects can be launched quickly, tested in real-time, and refined as new insights emerge. By embedding flexibility into the development process, teams can experiment with different models and applications without getting bogged down by lengthy approval processes or rigid project plans.

For MicroStrategy customers, this often means starting by solving a business problem and introducing enhancements to refine and expand AI use cases as new features are introduced each quarter. The MicroStrategy Semantic Graph plays a pivotal role here as well. By providing simplified access to trusted data, it enables agile development practices where AI models can be quickly integrated into applications without sacrificing accuracy or security.

Choosing the Right AI Strategy

Adapting to the rapidly evolving AI landscape is no small task, but with the right strategies, organizations can stay ahead of the curve.

A strong data foundation ensures that AI models are fed trustworthy data—especially when it can do the hard work of training the models for you—based on your trusted data foundation in the MicroStrategy ONE platform.

Building applications using this core data fabric prevents chaos in the AI stack, while adopting an agile approach allows teams to innovate quickly and respond to changing business needs. As the AI landscape continues to evolve, these practices will become even more critical for businesses looking to succeed. By focusing on data integrity, strategic model selection, and agile development, organizations can navigate the complexities of AI with confidence.

To explore how to build a strong AI foundation, read the full Forrester Research report: Want AI Success? Start With High-Performance IT Execution.