We’re all experiencing the hype and excitement about what generative AI can do for our organizations, but the stories of early adoption are often stories of disappointment or disaster. When generative AI is implemented, it quickly reveals how dependent the results are on the quality and organization of the data it’s fed (GIGO) and addressing that can be a real challenge. Every day we see more articles about “cleaning up your data to prepare for AI” but they’re missing the irony – analytical AI should be used to prepare our organizations for generative AI.
What’s the difference? Analytical AI optimizes what already exists, streamlining processes, enhancing accuracy, and boosting productivity. Generative AI quickly creates content to help augment or enhance your existing processes. Without analytical AI, preparing our organizations for generative AI requires spinning up large, expensive projects and/or hiring external consultants which often becomes a budgetary non-starter. With analytical AI, we can quickly attack these data challenges at a low cost without impacting the business.
Let’s take a multinational corporation that has accumulated decades of data, often stored in siloed systems with varying levels of quality and accessibility. By leveraging analytical AI tools like IBM Watson or Microsoft Azure’s AI services, they can automate the complex and time-consuming tasks of data cleaning and consolidation. They can use analytical AI to identify duplicate records and missing data, correct inconsistencies, and streamline data transformation. AI-driven master data management (MDM) systems can standardize their customer data across multiple platforms, ensuring that all departments work from a unified source of truth.
The same applies to optimizing business processes. Analytical AI can map and refine workflows, identifying inefficiencies and suggesting improvements. Tools like Celonis allow organizations to use analytical AI to manage business processes and reveal bottlenecks, enabling them to reorganize workflows, eliminate redundancies, and maximize efficiency.
Utilizing analytical AI today helps us improve our current operations, reduce costs, enhance data accuracy, and enable better decision-making in the future. More importantly, it creates a strong foundation for future AI automation, allowing us to combine analytical and generative AI to deliver even greater value. Using analytical AI to clean and organize our data and processes is how we enable AI automation systems and minimize their risks, which is what will unlock the real value of AI across our organizations.
