6 Pillars of Data Readiness for AI: A Roadmap to Trusted Data

Gen AI implementation and adoption are top priorities for most organizations. According to Accenture, one in two firms lacks the data readiness for AI needed to turn pilot projects into production systems. The same research shows that 75% of executives view data as the single most important factor for scaling AI.

On paper, the path for CIOs is clear. Close the data readiness gap, ensure that your AI models get timely, quality data, and watch pilot projects bring the ROI the board wants to see.

Yet when it is time to prepare data for AI, things can quickly get messy. Data silos across departments, critical data stored in unshared spreadsheets, tools that don’t talk to each other.

In this article, we break that chaos into manageable steps so you can clean up the data, prove results in pilots, and win full-scale support from both users and the board.

What you will learn

• How to check if your data is ready for AI and spot the biggest gaps
• The six pillars of data readiness for AI that every CIO should master
• A clear roadmap to move from pilots to production with proven ROI

Strategic Pillars of AI Data Readiness

AI pilots succeed when their data estate is in order, and that order rests on six core pillars. Once use cases are set, shift your attention to these pillars; the next section walks through each one, the risk of neglecting it, and the steps to fortify it.

Proprietary Data Fuels AI Engines

No one knows your customers, operations, and products better than you do. Real-time flow of proprietary data gives decision-makers instant intelligence, vastly improves efficiency across departments, and creates new monetization models.

However, many organizations struggle to gather and structure all the data they accumulate. Information is often mistreated, scattered across sources, and rarely reused.

This is confirmed by the fact that only 82% of companies use stale data for decision-making.

Investing in audits of key flaws and managing how data is stored and shared across departments are crucial first moves in AI readiness company data programmes.

The key to maximizing the value of your data is to foster a data-product mindset across the organization.

Most teams treat data as a single-use project asset, a.k.a. something that helps them accomplish their immediate goals. Once the task is done, all the data used gets forgotten and is never used anymore. This leads to duplicated work, because even for recurring tasks like quarterly reports, the data needs to be gathered all over again, wasting time doing redundant work.

Product mindset offers higher operational ROI because one asset supports multiple outcomes over time and accommodates new use cases.

How to adopt and reinforce the data product approach:

Catalogue key data flows

• Hold a short mapping workshop with domain leads.
• List every dataset that feeds customer-facing or revenue-critical processes.
• Record source system, refresh rate, sensitivity level, and downstream users in a simple inventory sheet.

Set up clear ownership

• Assign a data owner for each high-value dataset.
• Define a RACI table so everyone knows who captures the data, who checks quality, and who approves access.
• Review ownership quarterly to keep it current.

Track the value delivered

• Choose two or three metrics that tie the dataset to business outcomes, such as added revenue, cost savings, or lift in model accuracy.
• Add these metrics to the CIO’s dashboard and update them each quarter.
• Retire or archive data assets that show no measurable value after two review cycles.

Capture Contextual Information from Unstructured Data

Proprietary data comes in different formats. Data from analytics tools, CRMs, and productivity trackers is usually structured. For example, Google Analytics exports come with a predefined view, with specific table headers, rows, etc.

What about unstructured data? This includes call recordings, chat messages, emails, video, etc. These assets are rich with contextual information and provide additional, unfiltered insights into a company's operations.

Around 80% of organizational data is unstructured, and most companies struggled to tap into its potential before the wide availability of AI. Today, capturing and structuring this data became much easier. Workspace platforms transcribe and summarise calls automatically, corporate chats come with built-in AI, and raw PDFs or text files can be processed and mapped to a chosen schema by LLMs.

Unlocking this layer is a key step in any data readiness for AI programmes and forms part of a broader AI data readiness roadmap that shows teams how to prepare data for AI at scale.

How to unlock the value of unstructured data:

Set up a shared storage for every data type

• Choose object storage (e.g., S3, Azure Blob, GCS) that can hold raw audio, video, PDFs, and images alongside CSV files.
• Bolt on search layers that index three views of each asset:
  • Full-text search for documents and transcripts.
  • Vector search for semantic queries and retrieval-augmented generation (RAG).
  • Image metadata search for screenshots, scanned forms, and photos.
• Encrypt data at rest and apply lifecycle rules so large media files age out or move to colder tiers.

Automate contextual data capture and enrichment

• Enable call transcription in meeting platforms, auto-export chat logs, and schedule mailbox ingests.
• Run every new asset through an enrichment pipeline that:
  • Generates a transcript (speech-to-text).
  • Extracts key entities, dates, and sentiment with an LLM.
  • Stores the text embedding in your vector index so GenAI can find it later.

Label the business domains

• Identify the top three use-case areas—e.g., Customer Support, Sales Coaching, Compliance.
• Tag each record with its primary domain plus sensitivity level (public, confidential, restricted).
• Keep the taxonomy tight; fewer than 20 domain labels keeps retrieval accurate and governance simple.

Wire structured and unstructured data together

• Use shared identifiers (ticket ID, customer ID, order number) so chat transcripts or images can join back to CRM tables.
• Create a unified schema or semantic layer that lets dashboards and language models pull both data types in one query.

Measure impact and iterate

• Track adoption metrics: number of unstructured assets ingested, percentage linked to structured records, average retrieval latency.
• Pick one GenAI pilot, record the time saved or accuracy gain, then use these figures to secure the next budget slice.

By storing, labeling, and linking assets that were once “dark”, you can improve the AI output tenfold.

Use Simulated Data to Patch Gaps in your Data Estate

Synthetic data opens doors where real data is scarce, biased, or legally off-limits. Development teams can spin up rich, realistic test sets on demand, avoiding the slow, rules-based scripts that often miss edge cases.

For training, synthetic records let engineers recreate rare events and inject new domain patterns, raising model accuracy while sidestepping the bias baked into many live datasets.

Because every record is artificially generated, it is by definition privacy-safe, making it the preferred choice when regulations such as HIPAA, GDPR, or the CCPA limit access to sensitive information. Healthcare is a domain that sees immense value in synthetic data: researchers can experiment freely, refining algorithms for diagnosis or drug discovery without exposing any actual patient details.

Select the right synthetic-data framework

Start by matching the data type to the generator. Tabular records with relational keys suit GAN- or Bayesian-network tools (e.g., SDV, Gretel). Free-text needs large language-model–based synth engines, while images use diffusion models.

Validate output quality before production use

Train one baseline model on real data, and another on synthetic; check that accuracy, recall, and lift stay within an agreed threshold (often ±3 %). Add privacy checks such as re-identification risk. If data is sensitive, train only on locally hosted models in a safe environment.

Keep an audit trail

Store generation parameters, model version, seed, and date in your catalogue. Tag each synthetic table with a pointer to its original source and the business domain it supports. This will help anyone working with this data trace it back to the real dataset, simplify future data updates, and verify the credibility of synthetic data.

Integrating tools like Scale into your pipeline can help generate synthetic data to fine-tune LLMs for use cases where data is scarce or under restrictions.

These steps show how to prepare data for AI even when the source material is limited, ensuring data is complete and compliant, supporting data readiness efforts for generative AI.

Build a Connected Data Fabric

Generating insights with AI isn’t only about feeding it with data from different platforms. The quality of insights depends on guidance from domain experts across the organization.

However, most companies can't contextualize that expertise as much of it sits in silos across different departments, projects, and domains of expertise. Making data accessible, counting in the inputs of subject matter experts is essential to data readiness for AI and to discovering smarter ways of generating insights with it.

This work goes well beyond pulling data from multiple sources in a data warehouse. Creating a semantic layer bridges the gap between raw tables and usable insight by defining what every column means and how columns relate. The result is a “company-wide data language” that lets AI translate information consistently and produce reliable answers.

For example, marketing teams and tools might refer to “leads” in different ways: “prospects”, “contacts”, or “opportunities”. A semantic layer with precise metrics removes that confusion for both people and models, further improving your AI data readiness.

Steps to build a connected data fabric:

Map your core business domains

Sit with domain experts across target departments. List the entities they use daily, and pin down each definition. Capture synonyms (“lead,” “prospect,” “opportunity”) and key metrics (“qualified lead,” “closed deal value”). Turn this into a short glossary or diagram that everyone can see. This shared language becomes the foundation of your semantic layer.

Deploy a unifying storage and query layer

Choose the architecture that fits your size and resources:

• Data virtualisation lets you query data where it lives, ideal if most sources are already in relational stores.
• Data lakehouse can store every format in inexpensive object storage and lets you query it all through one SQL layer.

Whichever route you pick, link glossary terms to physical tables so meaning always stays connected to the underlying data.

Expose data through simple, shared APIs

Wrap the semantic layer in REST or GraphQL endpoints. Each call returns data in business terms rather than table names, so both analysts and AI agents pull the right fields without guessing. Add role-based access and version tags so teams can adopt new metrics without breaking older reports. When marketing calls /api/leads/qualified, it gets the same definition that the sales model uses. Consistent, governed access turns siloed data into fuel for reliable AI insights.

Adopt Data Governance Policies to Mitigate Risks

AI combined with poorly managed data can create significant legal and reputational risks. Compromised data quality, AI hallucinations, and intellectual property infringement all surface when models run on shaky inputs. Upcoming rules such as the EU AI Act raise the stakes for compliant data pipelines.

Strong governance is therefore central to data readiness for AI.

Data-related threats appear from many angles. Exposing data to AI makes it more accessible, yet without safeguards and automated checks the quality of insight falls and even source data can be jeopardised. Human error is also at play, especially when models rely on manually populated files.

Robust governance and automated guardrails keep source data accurate, secure, and compliant. This includes things:

• Automatically checking that no sensitive data is exposed.
• Verifying that source data makes sense (e.g. no letters in phone numbers).
• Enforcing consistent naming  (e.g.  “phone”, “number”, and “mobile” values all resolve to one field).
• Checking that source data contains exact entries that you specify within governance rules.

How to protect data integrity and control risks:

Raise awareness about AI-related risks

Invite leadership, data owners, legal, product leaders, and security to establish a single risk register. Assign responsible members for approving training data and reviewing model changes.

Conduct a preliminary risk assessment

Gather the team to list potential issues in each domain, then map them to the policies and checks needed.

Embed automated quality and compliance checks in every pipeline

Scan incoming records for sensitive or protected information; redact or block as needed. Validate formats, ranges, and relationships, enforce metadata standards so systems align cleanly, and log every result for audit and improvement.

Track full lineage and versioning

Record where each dataset comes from, which transformations touched it, and which models consumed it. If an error surfaces, you can trace the impact in minutes instead of days.

Keep humans in the loop until AI output is stable

Never rely blindly on pilot results. It’s mandatory to have a person (or a group) that will review outputs before they reach end-users. The main purpose of the human-in-the-loop approach is to flag errors and feed corrections back to the model until you observe trustworthy, stable outputs over a long period of time. This allows to balance speed with safety and ensures trust before full autonomy.

Use AI to accelerate data readiness efforts

AI can substantially improve the readiness of your data supply chain.

AI agents can document schemas, generate test data and migrate workloads, cutting months from modernisation projects.

AI’s rapid adoption will force companies to accumulate more and more data that needs to be organized and managed before feeding it to AI models. To ease the data management process, organizations need to start using metadata (“data about the data”).

One thing where AI can help is automating data labelling. By teaching AI models to sort data by different indicators, teams can label massive data sets much faster than by doing it manually and much cheaper than if they were to hire external data labellers.

Companies like Scale are already offering AI models for automated data labelling. However, the quality of output varies because it heavily depends on the quality and diversity of the training data they receive.

Another advantage of using AI for data labelling is that you can iterate extensively on your labeling instructions, which is very hard to do at scale with human labelling.

Data pipeline documentation is another process where AI takes the heavy lifting. It can automatically map sources, transformations, and destinations as data moves through your stack. While this is a minor benefit for the pilot project, production-ready systems are often heavy with documentation activities and AI can significantly cut data engineering time.

Here are several applications of AI agents that can accelerate your data readiness:

Pilot automated data labelling with active feedback loops

Start with one unstructured domain (e.g., chat transcripts or support emails), and feed a foundation model your labelling rules (“intent,” “sentiment,” “priority”, etc.). Review a sample of the model’s output each week, correct mistakes, and fine-tune the prompts. As precision stabilises, widen coverage to new data sets and let the model propose label improvements you might have missed.

Create a metadata monitoring agent

Set up an agent that scans each data source and lists every table, column, and data type it finds. The agent runs every day, updates that list automatically, and flags the team if something new appears or a field changes. The catalogue becomes the system of record for every pipeline, so engineers no longer have to track the details by hand.

Your AI Data Readiness Roadmap

The infographic below breaks data readiness for AI into clear, timed steps, each mapped to the pillars and actions we covered. Use it as a guide to plan, track, and measure your own journey from proof of concept to production-grade AI systems.

Close the AI Data Readiness Gap with Vodworks

If messy data or AI talent shortage are holding your AI programs back, Vodworks can help you close those gaps, assess your data infrastructure, and build a tailored roadmap to prepare your organization for further AI efforts.

Our team starts with a focused workshop where we identify AI use cases with quick payback periods, then runs a structured AI readiness assessment that inspects data quality, infrastructure fitness, and current state of data governance.

You receive a maturity scorecard, a gap-by-gap action plan, and realistic cost and timeline estimates, everything needed for the board to green-light next steps.

If you choose, the same specialists can stay on to implement the fixes, from data architecture and cleansing to ML Ops and compliance, so your first production model ships on schedule and under control.

Book a 30-minute discovery session with our AI solution architect. During the session, we’ll discuss the current state of your data estate, AI use cases you plan to implement, and define the next steps based on your data maturity.