Redefining AI-Ready Data for Production

Building Reliable Foundations for Data + AI Systems

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It’s no big revelation that data teams are being challenged to do more with AI. But while deploying an AI prototype has never been easier, operating those applications safely in production is harder than ever. 

Achieving high-quality data remains one of the most critical components for trustworthy AI. However, as the complexity of the data environment continues to expand, classical data quality approaches are failing to keep pace.

Delivering “AI-ready” data has become a zero-day priority for nearly every enterprise data strategy. But while every team might be pursuing it, very few teams I’ve spoken to have a working definition of what “AI-readiness” actually means. 

In this article, I’ll present a practical framework for building AI-ready data foundations based on hundreds of my own conversations with data and AI professionals shipping AI to production—and how you can get started with your own. 

The Centre Can Not Hold—The New Reality of Data + AI

Before we can talk about where we’re headed, we need to know where we’ve been. 

If consumers don’t trust a product, they won’t use it. That much has always been true. But when it comes to AI systems, the playbook for trust is still largely unwritten. 

In classical applications, software engineers could reasonably rely on the predictability of inputs, deterministic logic, and a well-defined testing strategy to deliver reliable results for their stakeholders.

But AI ≠ traditional software. 

When it comes to AI systems — particularly those built on large language models (LLMs) and retrieval-augmented generation (RAG) — none of the classical assumptions hold.

Structured and unstructured data model inputs change constantly. Outputs are probabilistic in nature — not deterministic. Pipelines can traverse a multitude of systems and teams with limited oversight. And even the smallest issues in data, embeddings, prompts, or models can lead to dramatic shifts in a system’s behavior.

Data and the AI applications it powers are now both deeply interdependent and independently complex—and if we want to make these systems reliable, we need to find an effective solution to manage them that way.

Redefining “AI-Readiness”

The hard truth: no AI system that performs perfectly in testing will behave perfectly at scale.

Getting AI-ready isn’t first and foremost about the work we do to the data or the model before production—it’s about developing a framework to continuously and programmatically respond to vulnerabilities in production. Some of the most common AI pitfalls include:

• Poor source data
• Embedding drift
• Confused context
• Output sensitivity to prompt/model changes
• Too many humans in the loop (or too few)

Of course, we want to be as confident in the data as possible before we deploy any AI application to our users—but because AI isn’t deterministic by nature, the ability to detect and respond to issues like these in real time is what defines the fitness of an AI system for production.

So, what does baseline readiness look like?

Baseline Readiness—Creating a Reliability Loop

“AI-ready” isn’t a static state that your data achieves; it’s an operational model that a team embraces. 

Starting from the assumption that every data + AI system will eventually fail, the foundational model for data + AI reliability is built on four basic principles:

• Detect: Monitor pipelines and data for anomalies. 
• Triage: Assess the severity, scope, ownership and required actions.
• Resolve: Rapidly address data, systems, code or model failures. 
• Measure: Track performance against operational and quality metrics. 

Each of these principles is enabled by a mix of both tooling and process. The right tooling will empower your team to scale your reliability loop effectively across your data + AI estate; the right process will help your team operationalize it.

Without this continuous baseline loop supporting your data in production, data + AI systems cannot be operated reliably — no matter how good the underlying model might be.

Of course, baseline readiness is just that—baseline. What it means to be ready will largely depend on the use case in question. Some of the most common use cases include:

• Conversational BI: Making data accessible through natural language interfaces – the long-promised goal of “data democratization”.
• Structuring Unstructured Data: Transforming documents, images or other unstructured inputs into valuable data.
• RAG Chatbots: Powering internal or external knowledge bases.

So, with our operational model in place, consider readiness for two of the most popular use cases; conversational BI and agents. 

Ready for Conversational BI

Structured data has long been the bedrock of data products, but when it’s exposed to something like a conversational BI layer or an agent (through resources like Cortex Analyst, Databricks AI/BI, etc), even structured data requires a new standard of fitness. 

There are essentially four hallmarks of data that’s ready for conversational BI:

• It’s high quality—data is being monitored end-to-end for quality dimensions. 
• It’s certified—datasets have been validated for quality standards.
• It has consistent metadata—standards have been established that dictate how the data will be used, like:
  • Metric definitions 
  • Established synonyms  (e.g. “sale” = “order” = “purchase”) 
  • Documented relationships
  • Registered sample queries to guide agent behavior
• It has documented context—transparent provenance and lineage is provided for agents to interpret and explain outputs.

Making metadata observable — and, when needed, auditable — is essential to accelerating adoption for high exposure use-cases like conversational BI where trust is critical to effective decision-making. 

Ready for Agents

In addition to the petabytes of structured data in production, the rise of RAG-based architectures like agents have brought unstructured data into the forefront as well — and introduced all sorts of new risks in the process.

When documents, web pages, or knowledge base content form the inputs, poor data can cause AI systems to hallucinate, miss key information, or generate inconsistent responses. To mitigate these risks, your unstructured data should meet the following readiness dimensions:

• Accuracy: Content is factually correct, and any extracted entities or references are validated.
• Completeness: The data provides comprehensive coverage of the topics, entities, and scenarios the AI is expected to handle, where gaps in coverage can lead to “I don’t know” responses or hallucinations.
• Consistency: File formats, metadata, and semantic meaning are uniform, reducing the chance of confusion downstream.
• Timeliness: Content is fresh and appropriately timestamped to avoid outdated or misleading information.
• Validity: Content follows expected structural and linguistic rules; corrupted or malformed data is excluded.
• Uniqueness: Redundant or near-duplicate documents are removed to improve retrieval efficiency and avoid answer repetition.
• Relevance: Content is directly applicable to the AI use case, filtering out noise that could confuse retrieval-augmented generation (RAG) models.

While many of these dimensions mirror classical data quality for structured datasets, semantic consistency (ensuring concepts and terms are used uniformly) and content relevance are especially critical when working with unstructured knowledge bases, where clear schemas and business rules rarely exist.

Unfortunately, manual data quality rules won’t cover these dimensions for unstructured data, so choosing modern data quality tooling becomes even more important here. 

You’ll need to determine storage patterns, formats, and access controls before you can assess the readiness of the data itself. But once you have the basics in place, you’ll want to leverage a programmatic tool like data + AI observability that’s capable of monitoring both your structured and unstructured data in production. 

Readiness Is An Iterative Process

Remember how I said “AI ready” isn’t a static state? Well, neither is your strategy. 

Truth is: data doesn’t become AI-ready until it’s in use.

The first deployment always reveals new issues — gaps in data coverage, biased embeddings, misunderstood metrics, broken lineage. AI-readiness must therefore be treated as an iterative, production-driven process, supported by continuous monitoring and human-in-the-loop feedback.

The right readiness strategy (and tooling) will enable a team to not just respond quickly to failures across the data + AI estate, but also empower teams to quickly iterate, enable, and measure that process over time.

Readiness Is the Model—Trust Is the Goal

AI-ready data is not a checklist — it is an operating model for continuously earning and maintaining trust in data + AI systems.

Unlike the shallow and inflexible data quality tactics of the last decade, an AI-readiness model takes a comprehensive look at the data + AI system, including:

• Monitoring and observability across the full data + AI stack
• Clear data ownership and stewardship
• Iterative feedback loops driven by production experience

Data teams are critical enablers of trustworthy AI. By embracing this new model of AI-readiness, they can help their organizations scale AI confidently — and responsibly.

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