Many businesses are trying to implement a data and AI strategy that could give them a leg up on their competition. They want to move fast and get everything in place right away. But it’s usually not so simple. Organizations and their leaders often find out that implementation is far easier said than done.
Creating a comprehensive, next-level data and AI approach can be hard and frustrating, especially if you can’t put your finger on what’s causing your biggest challenges. Most likely, the problem is your data architecture—but AI fabric could help.
What Is AI Fabric?
AI fabric is an evolution of the data fabric that combines the best of data warehouses and data lakes, traditional data storage, and querying frameworks. AI fabrics go beyond data fabrics by harmonizing them with AI development and operationalization tools. This places a semantic (natural language) layer atop an organization’s data estate. This natural language layer enhances models’ context and reasoning, making interactions with data more precise, eliminating ambiguity, and providing real-world context. This improved context, grounding, and reasoning help unlock the full potential of generative AI applications.
Most organizations’ AI development and deployment problems are just data problems. Everyone has a lot of data, but data isn’t worth much if it isn’t organized. By bringing data and AI under one roof, organizations get a single source of truth and instant insight. Woven together within the AI fabric, all users—not just data experts, but also business users and executives—can take advantage of centralized, governed AI capabilities.
Knowledge Graphs: Powering Contextual, Precise AI
Though everyone is talking about data and AI, the technology pulling all the strings in the background is knowledge graph technology. These knowledge graphs are able to power the natural language layers used by models, providing a powerful way to model and represent data’s meaning. Knowledge graphs also connect disparate data sources, help LLMs better understand context, and provide everything with a single source of truth—simplifying and streamlining data estates. They ensure both humans and gen AI models alike can understand and use data and its context in its entirety—including information from previously siloed repositories.
Powered by knowledge graphs, AI fabrics could pave the path to the AI functionalities your organization wants: automation, AI agents, chatbots, interfaces, dashboards, low- and no-code tools, and more.
Activating the Entire Organization
When one thinks of the future of business—the future of data and AI—the goal is an organization where anyone can harness the power of any data estate. Ideally, you want this power without having to rip out and replace your entire data infrastructure. Luckily, that’s another benefit of AI fabric: Its modularity and ability to fit seamlessly beside and atop existing data investments—no overhaul needed.
AI fabric implementation works with what you have, goes at your pace, and evolves as you evolve. This minimizes disruption and facilitates AI adoption across users and teams with varying levels of tech integration and expertise. AI fabric implementation often starts at the department level. From there, teams demonstrate their progress to others, who see what it can do and want in on the action. The AI fabric then expands and begins spanning the entire organization, without needing to be an all-or-nothing initiative.
AI fabrics can help solve thorny data estate issues, seamlessly inject AI into all operations, are modular and flexible, and account for the investments you’ve previously made in your data systems and structures.
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