Before You Add AI, Fix Your Foundations: How to Prepare Your Data for Intelligent Tools

Everyone wants AI. Few are ready for it. 

The question isn’t “When do we start?” but “Are we prepared to get it right?” 

Because switching on Copilots without fixing your foundations doesn’t accelerate you. it amplifies chaos. 

This article will cover how to fix your foundations for AI so that the AI tools you deploy are accurate and reliable. 

Challenges of deploying AI Directly 

Some of the common challenges of directly deploying AI on top of your business applications are –  

1. AI tools give inaccurate answers as they don’t really give an error – they confidently output incomplete information without context. 

2. Hallucinates by misinterpreting Prompts 

3. AI tools slowing system performance. 

4. AI responses themselves are slow and unreliable. 

And these issues just render the AI implementation as a failure immediately dismissing trust in using AI at all. 

But these challenges can be overcome once the foundations of AI are in place which we’ll discuss in the next section. 

Foundation of AI 

At CloudFronts, we call this the 3 Pillars of AI Readiness: 

1. Modern Systems – Cloud-first, API-ready, and future-proof. 

2. Connected Data – Integrated, consistent, and trustworthy. 

3. Governed Access – Secure, role-based, and AI-safe. 

Here’s how I sum up the foundation of the systems for AI – 

1. Application Modernization 
   Given that not all your systems you want to leverage for AI might be ready today. Hence, it’s critical to get these systems modernized by moving them to the cloud by trusted providers and making them available for AI. 
    
   When we say modernizing systems, it means upgrading systems to the cloud such that they can be future ready in terms of technical support, new features and support for modern APIs. This makes the systems be in better control in terms of data governance and accessibility. 
    

For example, when CloudFronts helped Tinius Olsen modernize their systems, the focus wasn’t just technical uplift. It was about ensuring every business process was cloud-ready so AI models could actually trust the data. 

 Upgrading from legacy systems

1. Improved Integration by replacing third-party integration platform

2. Connecting Systems 
   Each of your systems might not be connected with each other and hence, the information residing in these systems is likely to not be cohesive. Hence, connecting the systems are essential to ensure data integrity. 
    
   Connecting systems is all about making sure the transaction and master data are available in connected systems and depending on the type of the record, the system of record is chosen, and integration handles this. This makes the accountable system provide a vocabulary in the platform for AI i.e. Unity Catalog so that metadata has integrity and AI and Analytics can identify the data correctly for accurate answers and faster processing. 
    
   Similarly, at Buchi, connecting fragmented systems created a single data vocabulary, critical for AI to provide accurate, contextual insights instead of hallucinations. Microsoft has published a CloudFronts case study where we helped our customer Buchi connect systems and making systems ready for AI  

3. Data Governance
   One of the things organizations are skeptical to use AI is lack of clarity on the security aspect of AI. Often, it is believed that AI will give information that the end user is not authorized to access. Hence, Databricks’ Unity Catalog can be used to leverage in built-data governance capabilities which makes your data and systems ready for AI and Analytics. 
   Another stigma around governance is that stakeholders feel that AI will expose information which team members are not supposed to access and if the information is secure residing in these systems. Hence, Unity Catalog comes with Data Governance capabilities so that you define the right privileges in Unity Catalog so that right governance can be established.
    

And this is the foundation that needs to be had before AI can be implemented at your organization. 

Data & AI Maturity Curve by Databricks 

Given the above foundations in place for your AI Adoption strategy and choosing the right framework for your implementation, the Data & AI Maturity Curve shown below can be referenced to see where your organization is on the curve and where do you want to get to –  

On a high level, the foundation will get you to look back at the data and see what has happened in the past and AI tools can help you get this information accurately. 

Further, once trust is established, actions like making the AI predict the future state of operations, prescribe steps and even take decisions on our behalf can be achieved – provided you really want that to happen. It might be too soon just yet. 

To conclude,

AI success = Foundations × Trust. 

Without modern systems, connected data, and governed access, AI is just noise. 

But with these in place, every AI tool you deploy whether predictive analytics or Copilots becomes an accelerator for decision-making, not a distraction. 

Before you deploy AI, fix your foundations. If you’re serious about making AI a trusted accelerator not a costly experiment start with modernization, connection, and governance. At CloudFronts, we help enterprises build these foundations with confidence. Let’s connect over our email: Transfrom@cloudfronts.com