Your AI strategy is really a data strategy
Many companies are not having the right conversation about AI. They focus on models, vendors, and pilots. However, their data infrastructure ensures that these pilots will never be able to grow. To leverage advanced AI, you first need to build a solid foundation for your models.
Start with a hard audit of your data quality
Generative models are not able to process messy inputs easily. If you feed a large language model with inconsistent, duplicated, or outdated data, the results will be incorrect output. Technically this is known as hallucination, but in business terms, it means “your AI provided a customer with incorrect information and your team failed to notice it”.
Before you make any adjustments to the model, perform a thorough audit of all your data sources. Identify and remove duplicates, highlight fields with high null rates, and track the original sources of your data. Unstructured data like PDFs, emails, call transcripts, and support tickets require special attention. While this data is the lifeblood of modern AI applications, it rarely goes through the same data quality checks as structured tables managed by your BI team.
The main reason for failure in this area are data silos. If customer data is stored in one place, product data in another, and support history in a third location with no common cross-referencing key, no model will be able to see the complete view of your organization. Invest the time in breaking down those silos before you begin training the model, it will be much easier to tackle these issues in the beginning than to debug them later.
Move toward an architecture built for AI workloads
Traditional relational databases weren’t built to handle the demands of generative AI. They excel at structured queries, not at storing the vector embeddings necessary for a language model to access relevant context during inference.
A data lakehouse architecture – the hybrid solution that serves structured BI workloads and unstructured AI training data – is now the minimum viable architecture for any team working on AI in earnest. A successful generative ai implementation requires vector database functionality as well, giving you the retrieval layer your models will rely on to access context without reprocessing everything each time a query arrives.
You’ll also need to re-architect your ETL and ELT pipelines. Monthly exports that are fixed in time won’t suffice if your AI is meant to provide users with up-to-the-minute business context. Real-time or near-real-time data processing ensures your model is making decisions based on the current truth, not the last quarter’s truth. The former can be absolutely catastrophic for anything customer-facing or operationally mission-critical.
Build data versioning and governance in from the beginning
Imagine your shiny new NLP model suddenly starts spewing garbage on certain types of external request; you can’t debug such a thing if you don’t have a record of the exact dataset used to train or fine-tune that version.
Data versioning solves this. The easiest way to think about it is the training dataset used; but that’s actually only the simplest version. Data versioning is to your training process what Git is to your code: a complete record of what went in, when, and under what processing conditions. When you need to reproduce an older model, or retrain from a certain point with new data, or fix a specific point in time to ensure a fair retraining comparison, this is what makes it possible.
Feature stores, by the way, also help a lot here. In feature store, you have a centralized, documented repository of features that the ML team has already validated as valuable in previous projects. This means you can also be sure that the same variables are being calculated consistently across iterative versions of the latest world-beating model.
Prepare your compute layer for production demands
Testing the solution on a small dataset of a few thousand records and rolling it out to inferencing for thousands of users every day are two entirely different problems in terms of infrastructure. And, GPUs and TPUs are very costly to use constantly, most teams aren’t prepared for the compute demand they’ll see in a production environment when it comes to generative workloads.
You have to plan for the solution’s scalability; you can’t just bolt it on afterward. It’s cloud architecture that can scale compute up and down, it’s storage that can move old, unused training data to cheaper storage without breaking the pipeline, and it’s low response times of the solution you design – it’s not something you can just tack on after the fact.
Bias mitigation needs to be incorporated into the solution as well. You need to constantly monitor your training data and fine-tuning datasets for historical biases. As new data comes in, these same models need to reevaluate for bias. This needs to be systematic as part of the solution, not a bolt-on.
The readiness question is the right question
The companies that will truly benefit from AI in the next couple of years are not those who have the most ambitious feature planning, but those who saw data infrastructure as a necessary foundation, rather than a nice-to-have add-on. The groundwork isn’t a fun demo, but it’s the demo that leads to a product.