The Right Foundations: Starting your AI Journey

While artificial intelligence (AI) has been discussed and applied in various forms for decades, the pace and visibility of adoption have accelerated dramatically over the past year. For many organisations, it can feel as though the floodgates have suddenly opened, leaving leaders questioning whether they are prepared or already falling behind. If you are just starting your AI journey, you are not alone.

ADAPT's 2025 CDAO and CIO Edge Survey highlights a growing readiness gap: although data and AI now sit at the core of enterprise strategy, fewer than 24% of surveyed leaders believe their organisation's data is truly "AI‑ready".

As organisations grapple with where to begin and ask themselves, "Are we ready?", one lesson consistently emerges: successful AI is rarely about the model or the tool, it is about aligning people, data and decision-making with core business objectives and values. Getting the fundamentals right ultimately sets organisations up for sustainable AI success.

Larger organisations often already have pockets of AI‑ready data without realising it. Smaller organisations are more likely to need to create those foundations deliberately.

So how do you get (or find) your data to be truly AI-Ready?

Making your data AI-Ready

There are 3 key activities that are my short list for building the data foundations that enable AI projects to scale into business operations.

1. Refining Data Governance

Data governance isn't optional for AI-ready data, its foundational.

Effective modern data governance ensures:

• Clear definitions of what "good" data looks like for different use cases, with automated validation to align to those standards (Data Quality Standards).
• Proper controls around sensitive data, especially when AI models may expose or infer information about individuals (Security and Access Controls).
• Searchable metadata that helps teams find and understand available data. This accelerates AI development by reducing time spent hunting for information (Data Discoverability).
• Designated stewards accountable for data quality, documentation, and access policies within their domains (Ownership and Accountability).

Strong governance foundations enable AI to be applied consistently and safely, because quality, lineage and accountability are established upfront.

For a practical guide about reframing data governance and how to get started, read our article "Reframing data governance for non-data leaders".

2. Activating Metadata

While metadata is a core component of any strong data governance foundation, it deserves particular attention when preparing analytical data for AI. High-quality metadata is required for meaningful AI-generated insights.

AI-ready metadata is resident in five distinct categories:

• Descriptive and Schema Metadata
• Operational Metadata
• Trust and Governance Metadata
• Semantic and Contextual Metadata
• AI Specific Metadata

The first 3 categories form the pre-requisites for making data AI-ready. The last 2 categories improve the accuracy and precision of AI models and reduce the likelihood of hallucinations and erroneous predictions. Embedding the metadata into datasets destined for AI models requires robust data engineering and the activation of policies and culture that render the five essential characteristics of AI-ready data.

3. Strengthening Data Engineering

AI models work with the data they are given. So having strong data pipelines built on trusted, high-quality data allows the AI models to start from a point of trust. Data engineering bridges the gap between raw data sources and AI-ready data made for AI models and analytics platforms.

It involves:

• Creating reusable frameworks and patterns
• Building robust, scalable data pipelines
• Embedding necessary metadata into datasets and pipelines
• Implementing appropriate data modelling and transformation logic
• Establishing monitoring and alerting for data quality issues and trends
• Managing data processing costs efficiently

Effective data engineering practices can assure requisite metadata is provided to ML/AI models, improving their accuracy and precision. Without it, organisations face a painful cycle: building AI models that fail due to data issues, spending months fixing the data issues, and discovering new issues when deploying the next model.

Data engineering teams that adopt metadata-driven frameworks can gain a clear advantage over those that don't when it comes to making data AI-ready. These frameworks can embed essential metadata directly into the pipelines built with them. Because metadata is treated as input parameters in metadata-driven frameworks, the resulting data products retain their descriptive and schema metadata.

Next steps

The foundations for successful AI Projects are likely already within your organisation. If not, or you need to strengthen them to do more, the good news is that you don't need to solve everything before starting. A simple next step isn't a total overhaul but perhaps implementing a metadata-driven framework that automates the "heavy lifting" of data engineering and metadata management. Whether you build this internally or adopt an existing platform such as Altida from Altis, the goal is the same: create a scalable foundation that makes AI achievable, not aspirational.

Would you like to know how your organisation measures up? We can help you assess your data foundations to see how quickly you can move from being data-heavy to AI-ready in weeks, not months.

Get in touch for a quick assessment of your AI-readiness.

Learn more about Altida, our metadata-driven data engineering framework.