There is No AI Without Alignment: How Stakeholder Collaboration Ensures AI Success

The Role of Stakeholder Alignment in AI Projects

AI adoption is not simply an IT initiative—it requires the buy-in and involvement of multiple departments.

As emphasized in Forrester’s report, strong alignment among IT, business leaders, and operations teams is essential for AI to deliver on its potential. From defining objectives and expectations to ensuring the right data inputs based on a trusted data fabric, stakeholder collaboration is foundational to achieving the desired outcomes of AI initiatives.

• Aligning IT: Building AI-infused applications depends on the symbiotic relationship between knowledge graphs and foundation models for language. Your proprietary knowledge, captured in knowledge graphs and stored in vector databases, is essential to generating meaningful AI outputs. Tools like the Semantic Graph native to the MicroStrategy platform are a powerful knowledge asset, ensuring your AI models have access to accurate, trusted data. This foundation allows AI models to work seamlessly, providing reliable insights that can scale across various business applications.

• Aligning Employees and Customers: For AI to truly succeed, it must empower both employees and customers. High-performance IT teams focus on delivering AI solutions that enhance the user experience. Forrester’s report explains that by providing employees with the right tools and ensuring customers receive timely, personalized interactions, businesses can maximize AI’s potential. A key factor in this is improving your workforce’s AI Quotient (AIQ)—the ability to effectively use AI in workflows.

• Aligning by Design: Ensuring AI systems meet business goals while adhering to company values is crucial. AI can go astray if not carefully designed with safeguards and guidelines in place. By building cross-functional planning and execution teams, businesses can avoid common pitfalls in AI development, such as biases in training data or untested outputs. A structured, high-performance IT approach enables consistent monitoring and improvement of AI systems, keeping them aligned with business objectives.

Collaborate Cross-Functionally

The need for collaboration is evident when considering how AI touches multiple aspects of a business. For instance, when AI models are used for customer service, they impact the customer experience, sales, and marketing.

Similarly, when used in operations, AI can affect supply chain management, logistics, and inventory tracking. All these areas must be on the same page to ensure that the AI system performs effectively and aligns with overall business goals.

Forrester’s report highlights the need for cross-functional steering committees to bring together key stakeholders at every stage of AI deployment. These committees help ensure that AI projects are not only technically sound and built on trusted information, but also aligned with the strategic objectives of the business.

Map Business Needs to a Unified Data Fabric for Data Integrity

At MicroStrategy, we have witnessed how organizations with strong cross-functional collaboration achieve more sustainable AI success. Our approach to AI integration—particularly the merging of business intelligence (BI) with generative AI in ONE comprehensive platform—ensures that AI systems are embedded within the organization’s existing workflows.

Single Source of Truth

A critical aspect of alignment is ensuring that everyone across the organization is working with trusted, accurate data: which is where the MicroStrategy Semantic Graph becomes indispensable. The Semantic Graph acts as a single source of truth for unifying the data fabric of the entire organization, ensuring that the information feeding your AI models is accurate, consistent, and trustworthy.

Reliable Gen AI outputs

By supporting business needs with native data integrity protections, the Semantic Graph enables organizations to safely use Gen AI across all departments. With a solid foundation of trusted data, AI models can provide explainable and reliable outputs, reducing the risk of errors and ensuring that AI initiatives support business goals effectively. ONE’s fully integrated MicroStrategy AI features all leverage the platform’s foundational Semantic Graph to eliminate these barriers. This not only improves the quality of AI outputs, but also makes it easier to scale AI across various departments.

Bridging IT and Business

The Semantic Graph is not just a data integration tool—it also serves as a bridge between IT and business units, allowing both sides to work from the same trusted data sets. This alignment between IT’s focus on data integrity and business leaders’ need for actionable insights ensures that AI models deliver relevant, accurate, and timely results.

Ensuring comprehensive AI + BI collaboration

The success of AI projects hinges not just on the technology itself but on the ability of an organization to align its stakeholders around a common goal. From IT and business leaders to operations and end users, all parties must work together to ensure that AI initiatives deliver value.

We believe that AI + BI is the future, and MicroStrategy is committed to helping organizations achieve success by fostering strong collaboration across all stakeholders.

For more insights on how to align with IT to ensure AI project success, explore Forrester Research’s full report: Want AI Success? Start With High-Performance IT Execution.