The AI Revolution: Bridging the Gap Between Potential and Readiness in the Data-Driven Era

AI talent is in high demand; AI reveals brain oscillations for memory and disease; AI may allow us to talk to whales; AI can help plan meals and other tasks; an AI platform for dentistry just raised $53 million.

That was the tip of the AI news iceberg—in one day.

Has technology ever garnered so much interest, so widely, so fast? Amid the hype lies the incalculable promise of AI—and the fear of getting left behind by competitors that figure it out first. It’s no wonder that organizations are rushing to implement AI, ready or not. The trouble is, many are not ready.

Harnessing the Power of AI

Leveraging machine learning (ML) and AI for applications such as smarter indexing and retrieval systems enable real-time insights and decision making. By integrating AI to optimize data flows, organizations can ensure their infrastructure is not only responsive but also anticipatory, ready to adapt to the demands of tomorrow’s data-driven challenges.

However, many organizations lack the foundational elements to make this happen. To meet the intensive demands of AI without compromise, organizations need platforms that can effortlessly scale and process unstructured data in real time, as well as perform fast, efficient data retrieval with support for complex queries. In fact, AI requires a new stack of technologies, including large language models (LLMs), vector databases, optimized data pipelines, and AI-focused observability components.

But it’s not just technology that’s needed to harness AI safely and effectively now and in the future; it’s also expertise and a culture in which continuous testing and iteration are the norm. For example, ensuring that AI application outputs align with expectations requires a deep understanding of AI model behavior, continuous monitoring for anomalies, and the ability to adapt quickly to new insights, thereby ensuring that applications remain accurate and relevant.

What’s Old Is Critically Important Again

While a variety of technologies and skill sets must effectively combine to fuel modern predictive and generative AI, the venerable database is at the heart of AI. However, the data that drives today’s (and tomorrow’s) AI applications is less about what fits neatly into tables and more about unstructured data. In fact, while traditional databases provide foundational support, the unique demands of AI require dedicated tools that integrate seamlessly with AI and ML frameworks to enable efficient data processing and insight generation in real time, at a large scale.

Vector databases are an especially key element when building an AI infrastructure stack. With a vector database, datapoints are stored in vectors with a fixed number of dimensions, depending on the complexity of the data. Vectors are clustered based on similarity, which enables fast, accurate queries. This design is ideal for storing and providing access to the types (and growing volume) of unstructured data that AI-based applications are built on.

AI Champions

Closing the gap from what’s in place now—especially when it comes to databases— to what’s needed to effect true AI innovation is a steep but conquerable challenge. The champions for making it happen—in a way that aligns with organizations’ business, security, privacy, and governance goals—will, in many cases, be data professionals who will be called on to do the following:

• Act as strategic innovators: Moving beyond traditional roles, data professionals must now act as innovators, educating and guiding their organizations through the integration of AI into their data strategies.
• Learn new architectures: Professionals in these roles must familiarize themselves with emerging concepts and architectures, such as retrieval augmented generation (RAG), to build and manage scalable, AI-ready systems.
• Ensure data compliance: Data administrators, managers, and engineers must navigate the dual challenges of making data available for AI applications and ensuring strict compliance with customer data privacy mandates. This balance will require a sophisticated approach to data governance, where secure, compliant data usage becomes integral to achieving accurate, AI-driven outcomes.

For many organizations, closing the gap between what they have and what they need to have to effectively enable AI-powered applications may seem close to impossible. It’s not, but the road to AI will require a focus on infrastructure expertise, a rethinking of the database, and increasing reliance on the evolving know-how of data professionals.