Your business is ready for AI, but is your data? A recent MIT Technology Review Insights report shows that only 53% of businesses have data foundations that are “somewhat ready” for AI. Somewhat ready isn’t good enough. As organizations rush to adopt AI solutions, one foundational truth often gets overlooked: AI is only as smart as the data behind it. Whether it’s large language models powering chatbots or autonomous AI agents acting on behalf of users, the effectiveness of these tools depends on high-quality, well-governed, and accessible data. Getting ready for AI means more than experimenting with models. It starts by preparing your data to fuel agents that can reason, adapt, and deliver real business outcomes.
What is AI-Ready Data?
Businesses historically have never lacked data. What they struggle with is bringing that data together to satisfy reporting needs, operational workflows, and now, their AI ambitions. Therein lies the challenge and the opportunity to invest in the foundations that ensure your data is AI-ready.
AI-ready data, according to Gartner, is data that is “governed, secure, unbiased, enriched, and accurate.” Any successful AI implementation relies on an organization’s ability to provide the right data sources and ensure that the data is complete, of high quality, and in the right format. Data management, process transformation, and digital strategy are all critical to its success.
Without structured workflows and strategic direction, AI only amplifies inefficiencies instead of solving them.
How Are Data Leaders Preparing Their Organizations?
82% of C-suite leaders say scaling AI is a top priority. But eagerness isn’t the same as readiness. And AI readiness isn’t only about deciding to purchase an AI system to use. It starts with examining what tools and technologies you already have and what new AI tools can best fit your needs. More importantly, do you have the data foundation to support this transition?
In this blog, we’ll discuss the three data essentials you need to successfully integrate AI into your operations successfully:
3 Data Essentials to Get Started
1. Assess Your Data
Start with the data you already have. Is your data accessible? Is it aligned? Do your data assets line up with the use cases you want to solve for? Evaluate whether your data systems support both model training and scoring.
Do you have an abundance of one type of data but not enough of the other? Is it skewed towards certain demands? Even having too much data can be a problem in itself. Enough data should be collected to create data sets that AI tools can analyze. For example, databases with customer behavior and sales data when combined create useful data sets.
Is my data FAIR?
The FAIR Data Principles—Findable, Accessible, Interoperable, and Reusable—provide guidelines to improve data usability for both humans and bots.
- Findable: Data should be easy to find with the help of unique identifiers and rich metadata
- Accessible: Data and metadata should be accessible with the help of standard protocols
- Interoperable: Data should use standardized formats and vocabularies allowing for easy integration with other data sets and systems
- Reusable: Data should be well-described, licensed for reuse, and meet domain-relevant community standards
By adhering to these principles, you ensure your data stays valuable and usable over time.
Data quality issues in structured data are usually more clearly defined, and so, more easily resolved. Unstructured data, on the other hand, is a different beast. Some of the most common issues we encounter are:
- Diverse document formats, like .pdf, .ppt, and .xls, contain elements that are hard to parse, such as images or complex tables
- A lack of metadata tags across documents makes search and retrieval difficult
- Siloed data storage across different business units and departments
- Conflicting information in multiple versions or outdated documents
- Irrelevant documents or documents containing boilerplate content
- Multiple languages in the document repository that aren’t suitable for the LLM
- Sensitive information, such as names and addresses, ingested without proper filtering or access control.
The silver lining? Generative AI can now make sense of all of this unstructured data, which previously companies had but could not access.
2. Develop a Data Transformation Strategy
Data without a well-defined business strategy is like a library without a catalog—lots and lots of information, but impossible to use. A solid data transformation strategy is the starting point. Below are some key components of a sound strategy:
Support Business Goals
Supporting key business goals—whether it’s boosting productivity, improving the customer experience, or revenue growth—helps you prioritize use cases that are feasible and more likely to have a greater impact.
Determine DataOps Best Practices
A DataOps approach improves collaboration between data producers and consumers while ensuring data quality and consistency across the organization.
Scale Your Data Architecture
The right data and infrastructure are prerequisites for moving AI from proof-of-concept to production. A modular data architecture that leverages cloud infrastructure is more flexible and can scale easily as data volumes grow. And an AI-optimized infrastructure in the cloud—cloud-based services, co-location for low-latency performance, or on-premises setups—supports large workloads, scalability, and cost-efficiency.
Embed Microsoft Fabric into Your Strategy
Microsoft Fabric combines some powerful tools—Data Factory, Synapse Analytics, Data Explorer, and Power BI—into a unified, cloud-based platform to simplify your data workflows. Together these tools enable you to innovate with AI safely and securely by managing your data in a single user-friendly platform.
These key findings emerged from a Forrester Total Economic Impact (TEI) study that measures the impact of Microsoft Fabric on businesses:
- 25% increase in data engineering productivity and faster time-to-value through tools like Copilot and automated data pipelines.
- 379% ROI from adopting Fabric, as it streamlines data architecture and reduces costs
- Up to 40% cost savings through options like reserved cloud instances.
Read the blog: Here’s Why I Would Choose Microsoft Fabric as my Data Management Tool
3. Determine a Framework for Data Governance and Security
The more policies and controls you have around data, the better off you’ll be. Though Generative AI holds tremendous potential, it makes companies vulnerable to data-related risks—legal, reputational or both—and regulatory issues around data privacy, data quality, bias, discrimination, and intellectual property, among others.
Data governance for AI is complex for several reasons:
- Hidden security risks: When a system is trained on petabytes of data, sensitive information may slowly dribble in, which becomes embedded in the model and potentially accessible to anyone.
- Unstructured user interfaces: With natural language prompting, sometimes confidential information slips in inadvertently and raises privacy concerns.
- Lack of explainability: Since AI models learn from patterns and don’t follow explicit instructions, they may appear difficult to audit or trust. Efforts towards explainability help build trust in these AI systems.
- Expensive testing: Flexible inputs result in chaotic AI system outputs. Imagine a scenario where the chatbot you trained gives incorrect answers. The costs of testing for these errors are prohibitive. But if AI systems are to be trusted, they would require consistent monitoring and auditing.
- Compliance standards: Regulatory compliance such as GDPR and the forthcoming EU AI Act adds another layer of complexity. Considerations like data sensitivity, residency, scalability, and governance directly impact infrastructure decisions, whether you deploy on-premises, in the cloud, hybrid, or both.
The Road to Data Readiness
Knowing where to start can be challenging. We recommend you begin by assessing the value of your data and investing in long-term data capabilities for AI. Reinvent your data architecture and governance models for new opportunities and risks.
Generative AI is changing data as we know it; elevating its strategic value, reshaping ecosystems, and creating new requirements that companies must address. How will you move forward and prepare your data for what’s next?
With over 25 years of experience in data and analytics, we can help you take your first steps towards becoming a data- and AI-ready organization. Contact us today.
References:
1. go-fair.org
2. MIT Technology Review Insights Report
3. OZ Data and AI team
