Exploring AI: Data as the New Black Gold

A few years ago, I was tasked by leadership to extract value from the data our company had accumulated—long before the recent breakthroughs in generative AI and the current scale of artificial intelligence.

We launched several initiatives: establishing a solid foundation with Master Data Management (MDM), optimizing profitability through discount analytics, and enhancing revenue recognition by improving point-of-sale (POS) data quality. We also leveraged data to implement a long-tail service renewal strategy that delivered significant value.

Despite these successes, our vision of gaining deep insights to drive growth and transform customer engagement was hindered by data limitations. Achieving a complete, high-fidelity 360-degree view of our business remained elusive, as it still is for many organizations today. Now, the stakes are higher than ever. In the coming years, if AI is the race car that will propel organizations forward, then data is the fuel that will determine how fast and far we can go.

Key steps organizations need to take to harness data and thrive in the age of AI:

1. Invest in a Business Architect: A business architect can envision opportunities and define a clear roadmap for AI integration. As AI capabilities become ubiquitous, organizations risk adopting tools without gaining a competitive advantage. A business architect who understands your business ensures that AI investments are strategic and differentiated, allowing you to capitalize on opportunities others might miss.
2. Create a Competitive Edge: AI is now a baseline requirement—simply implementing it won’t differentiate you. Most organizations will achieve personalized messaging, knowledge surfacing, and next-best-action guidance, but when everyone has these capabilities, standing out becomes more challenging. Not having AI will leave you behind, but having it alone won't make you great. To gain a competitive edge, think beyond standard use cases. With a business architect, push the boundaries of what AI can do and find unique ways to add value.
3. Ensure Data Completeness: Managing structured data is well understood, but handling unstructured data—both within the organization and across social channels—will determine the effectiveness of your data strategy. MDM and Customer Data Platforms (CDPs) are good starting points, but to predict customer churn and enable AI-driven actions, you need a complete data view. This means integrating data from sales, orders, customer support, product adoption, social media, chat transcripts, events, and calls. Achieving this requires a platform that automates these processes, with AI agents identifying and leveraging key attributes, constantly monitoring, and adapting.
4. Prioritize Data Quality: Data quality is foundational but can be a moving target. Addressing it is essential for successful AI adoption. Define acceptable data quality standards based on the intended application. You may not be able to fully clean your customer master data, but you can still establish meaningful relationships and derive value.

Conclusion: Sponsoring data projects used to be seen as risky—results were slow, and challenges around quality and integration were significant. Today, AI has changed the game, enabling fast, impactful outcomes. With imagination, flexibility, and strategic focus, organizations can harness data as a powerful asset to drive competitive advantage, accelerate growth, and create lasting impact.

Data is no longer a passive byproduct of business operations; it’s a critical enabler of future growth, innovation, and differentiation. The right capabilities and mindset will turn data into the high-octane fuel that powers AI-driven success.

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