While AI promises unprecedented insights and efficiencies, many organizations are hesitant to fully embrace this emerging technology for analytics and reporting due to concerns around explainability, data integrity, and security. In this context, building trust in AI tools is crucial to achieving successful and sustainable AI adoption.

The Importance of Trust in AI

In Forrester Research’s recent report, Want AI Success? Start With High-Performance IT Execution, they highlight how trust is essential in deploying AI technology. Without it, AI applications can quickly fail, undermining the confidence of employees and customers. One of the primary reasons for this failure lies in AI’s opacity: many AI systems generate results that are difficult to explain or justify, leaving end users unsure of the reasoning behind the outputs.

“You will only get one chance to deploy a new AI tool for employees: If it fails to wow the majority, good luck trying to get the next one into the hands of enough employees to matter.”

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

The Role of a Semantic Layer in Trusted Data

MicroStrategy has long recognized that trust starts with trusted data. Our native Semantic Graph provides a robust data fabric that supports the convergence of AI and BI, ensuring that the data driving every business decision is reliable, secure, and explainable.

For Gen AI to generate valid and reliable outputs, it must be built on a foundation of trusted, high-quality data. This is where a semantic layer becomes indispensable. The MicroStrategy Semantic Graph serves as a single source of truth for your organization. By integrating all your business intelligence (BI) assets into a unified framework, our native data fabric ensures that AI models are fed accurate and consistent data. This eliminates the risks of fragmented data sources and helps to generate explainable outputs.

Translating Random Requests into Valid Outputs

By ensuring all data is curated and linked within your organization’s trusted data fabric, MicroStrategy allows AI models to work with secure, reliable datasets to produce outputs that can be easily traced back to the original source. This level of explainability is critical for building trust among users and stakeholders.

As Forrester’s report states, “You will only get one chance to deploy a new AI tool for employees: If it fails to wow the majority, good luck trying to get the next one into the hands of enough employees to matter.”

Explainable Models and AI Governance

An essential aspect of trusted AI is ensuring that models are not “black boxes” but are instead explainable and auditable. MicroStrategy AI, the platform’s inherent Gen AI capabilities, emphasizes the need for transparency in AI processes to help organizations meet stringent compliance regulations and internal governance standards.

With baked-in AI governance, MicroStrategy helps organizations manage risk effectively, ensuring that AI is deployed within a structured framework that monitors outputs, tracks decisions, and enables accountability. As Forrester’s report suggests, AI success relies on security measures and compliance frameworks to evolve in parallel with AI applications.

Make Your Data Model Trustworthy

For Gen AI to be trusted with data, organizations must have the right foundation in place—such as ONE unified platform that emphasizes data integrity, transparency, and security. MicroStrategy offers this assurance, serving as a critical layer to ensure explainable AI outputs from trusted data.  

Read the full report from Forrester Research to learn more: Want AI Success? Start With High-Performance IT Execution.