Why AI business outputs can’t be intelligent if your data isn’t

In both these sectors, leaders are increasingly expected to deliver more with fewer resources, making performance and efficiency top priorities. To many, AI seems like the game-changing tool that leaders have been looking for to fast-track performance decisions and find new efficiencies and opportunities. But outputting precise, action-ready decisions from AI tools starts with aligning the technology with business processes and required user experiences. It demands the highest data quality, practices and governance, and that means embracing best-in-class processes that require investment and effort to implement.

If you’re one of these sector leaders facing pressure from leadership to deliver better results and from the board for faster adoption of AI tools, here are the factors you need to know to make your data, and the decisions it informs, more intelligent.

Adopting a new technology mindset for AI integration

Instead of looking at AI as an off-the-shelf solution to be bought and switched on, leaders are increasingly recognizing the need to evaluate their technology infrastructure and data practices before implementing AI. This starts with prioritizing both the integrity of the data and accessibility to it to build best-in-class foundations that will help AI enhance organizational performance and navigate complexities and change more effectively.

This process includes a few key steps:

What your organization can expect from integrated data

• Product-centric mindset: Embedded data in products that support business objectives, use cases and user experiences that benefit from AI
• Unified data management: By bringing together valuable enterprise data from various sources, organizations can confirm that their data is ready for AI applications, driving better business outcomes
• Seamless data sharing: Using open formats allows for efficient data sharing without complex data movement, reducing silos and associated costs
• Cost efficiency: Organizations can minimize or eliminate the need for costly data engineering, making advanced data solutions accessible to more businesses
• Scalable AI and governance: A flexible data management solution accommodates growing data volumes and complexity, allowing organizations to start small and scale as needed
• Simplified integration: Native integration capabilities streamline processes, enabling efficient execution without multiple technologies
• Enhanced AI and machine learning (ML) implementation: Access to broader datasets improves insights and accelerates AI and machine learning implementation
• Alignment to business ROI: Most important, align technical implementation to business use cases and expected ROI with clear alignment to both business and technical stakeholders