AI-Ready Data Infrastructure: How to Tell if Your System Can Support Your AI Strategy and Goals

The product team is pushing hard for a new AI feature development. They point out competitors have it, and failing to launch one soon could result in losing the competitive edge.

However, you’re unsure whether or not your data infrastructure is ready to support AI models, especially since AI is only as good as the data it's trained on.

Also, most AI initiatives don’t fail because the model was flawed—they do it because the data wasn’t ready (1) Although you understand the business need for adding AI to your product, how do you know if your infrastructure is actually AI-ready?

In this article, you’ll learn common AI challenges, how to conduct a quick infrastructure health check, examples of failed AI initiatives, and tips. Let’s get started.

Table of contents

• Why 85% of AI projects never see the light of day
• Quick data infrastructure health check
• Signs that your business is ready to take on and launch AI
• Examples of failed AI projects across industries
• How to improve your data infrastructure and make it AI-ready

Why 85% of AI projects never see the light of day

According to the Forbes Technology Council, up to 85% of AI projects fail before launch. Usually, the issue lies in data latency, fragmentation, or lack of governance.

Smart features rely on real-time data that’s fresh, accessible, and clean; otherwise, AI becomes guesswork.. Troy Demmer, co-founder and CPO of Gecko Robotics, explains it this way: “Today, despite advances in data collection technologies (robots that can climb, fly, and swim…), we are still largely gathering data manually (...) This manually collected data is low quality and low quantity, giving us little insight into the health of our infrastructure.”

For instance, let’s say you’re building a feature to predict customer behavior, one of the multiple use cases of AI in customer experience. If you don’t use a customer tracking tool to automatically store all events the user performs within your app in real-time, maintain historical records of all completed actions, and set up normalization processes, the model will be useless. How can you accurately predict future user behavior if you don’t have or can’t read historical records?

What’s holding back AI initiatives?

Before greenlighting your AI roadmap, audit these core infrastructure elements, check if these parts of your stack are holding you back:

• Data quality: Good data quality refers to its accuracy, relevance, consistency, completeness, and integrity. If you’re failing to collect and process quality data, your AI model will give inaccurate outputs.
• Fragmented data sources: When customer data lives in six tools and none of them talk to each other, you end up feeding incomplete or outdated data into your models. So, before coding an AI algorithm, centralize your data in a warehouse, lake, or lakehouse to avoid feeding incomplete or scattered inputs into your models.
• Batch processing bottlenecks: If your pipeline runs once a day, you’re already setting the AI model up for failure. Most AI initiatives, especially in fintech, ecommerce, or mobility, need real-time data to drive live decisions. Check out our real-time data processing services and discover how we can help out.
• Lack of governance: With no data lineage, versioning, or access controls, your AI initiative becomes a liability. That’s because it could become biased, breach confidential information, or share inaccurate results.

Common AI model failures

AI could also fail due to poor training data and lead to:

• Overfitting: When AI models work great with training data but can’t adjust to new information. This is like memorizing all the answers to a test, but getting there and finding out the teacher changed the questions.
• Underfitting: When models are poorly designed or undertrained and can’t complete actions.
• Data drift: When AI algorithms can’t pick up data changes over time.
• Data bias: Failing to train the AI model with a complete data set, driving it to come up with results that could jeopardize certain groups.
• Edge-case neglect: AI models that fail to generalize or predict rare but important edge cases.
• Correlation dependency: When an AI algorithm uses wrongful assumptions based on superficial correlations.

Quick data infrastructure health check

“Powered by AI, data is becoming something akin to a new oil: the key element for driving new productivity. Data infrastructure, of course, carries data, so the strength of a country’s data infrastructure will ultimately determine its ability to move forward and stay ahead,” says Chen Guoliang, Academician of the Chinese Academy of Science. So, how can you tell if your data infrastructure is up for the challenge?

Ask yourself these questions to audit your infrastructure:

Data pipeline latency and throughput assessment

You need low latency and high throughput in your data pipelines to support AI models. To assess your current state, ask yourself these questions:

• Can your current ETL or ELT processes support sub-second data freshness?
• Are transformations optimized for real-time feature engineering at scale?
• How does your pipeline handle training vs. inference load? (e.g., burst workloads during retraining)
• Can you process streaming data (Kafka, Kinesis, Pulsar) with the same reliability as batch jobs?

💡 Expert tip: For AI systems to be effective, you need stream processing, not daily batch jobs. If you’re not there yet, determine next steps to improve your data pipelines and make them AI-ready.

Data quality and governance readiness

As mentioned above, data quality and governance can severely affect your model’s accuracy. Since you’re looking to build a reliable, bias-free algorithm, you need to determine the state of your data quality and governance readiness. Start by answering these questions:

• Do you have automated data quality checks for each pipeline? (e.g., nulls, outliers, or schema drift)
• Can you monitor concept drift or feature distribution shifts in real-time?
• Is your data lineage trackable across ingestion, transformation, and model consumption?
• Do you version datasets or features for model reproducibility?
• Do you have access control systems in place?

If most of your answers are “no,” you have work ahead of you before even thinking about AI.

Also, according to a 2024 Monte Carlo report, data engineers spend 30%-40% of their time firefighting data quality issues. In most cases, teams identified data issues only after they affected downstream ML performance. To avoid spending time and money putting out fires, invest in improving the data quality before applying any AI/ML models to it.

Infrastructure scalability and performance

You want your AI model to grow with you and support model retraining without bottlenecks or quality degradation. To get there, evaluate your data infrastructure against scalability and performance. Do so by answering these questions:

• Does your infrastructure auto-scale to meet variable model workloads?
• Can you separate compute and storage to optimize cost?
• Is your setup multi-tenant to support different use cases or clients securely?

If you answered 'no' to any of these, scaling AI in production will lead to bottlenecks, cost spikes, or outright failure. Consider adopting a data lakehouse architecture and using AWS services with auto-scaling functionality.

Signs your business is ready to take on and launch AI

We’ve covered a list of things to watch out for before investing in AI, but how can you tell if your infrastructure is ready to adopt AI?

AI readiness isn’t just about tech but also about people and processes. Keep an eye out for these signs that indicate if your business is ready to train and launch AI models:

Organizational readiness indicators

Your organization is AI-ready if:

• There’s DataOps maturity. Meaning you have automated testing, CI/CD pipelines for data jobs, and incident alerting in place.
• You have stakeholder alignment and buy-in. This assesses if you’ve shared all that AI can and can’t do for the organization and if business leaders understand and endorse it.
• Your team has been properly trained. Your engineers should be fluent in cloud cost, data modeling, and ML debugging before launch.

Technical infrastructure maturity

You’re ready to invest in AI/ML models if your infrastructure shows these maturity signs:

• It’s a cloud-native and scalable architecture that uses technology such as Redshift, Databricks, and Snowflake
• It has real-time data capabilities and uses Kafka, Flink, or Delta Live Tables
• It has strong data governance with RBAC, PII tagging, and audit logs
• It has cybersecurity readiness and is SOC2/ISO27001 compliant

What happens when AI is built on bad infrastructure: 3 real-world fails

Sometimes, the business opportunity is so big that companies rush development without validating the data pipeline. This affects the business’s reputation, reliability, and performance. In some extreme cases, developing AI models that were trained on incomplete data sets could also become a risk to users.

Here are examples of AI fails from Amazon, Tesla, and IBM.

Amazon’s resume checker

Amazon’s resume checker

Back in 2018, Amazon decided to shut down its AI recruiting tool after it showed biases against women. When Amazon launched this secret tool, it needed to simplify and accelerate the candidate screening process as the company receives millions of daily applications.

This failure was probably due to edge-case neglect and correlation dependency, as the discrimination happened mostly for technical roles. Since, at the time, most of Amazon employees and accepted applicants for these roles identified themselves as male, the AI algorithm assumed the company only wanted to hire male developers. This assumption caused women’s applications to be discarded, preventing them from advancing to the next stage.

This controversy made Amazon abandon this model for candidate selection. However, this could’ve been avoided if the company had used synthetic data or added weights to data sources during training to even out the difference between male and female records.

Tesla self-driving crashes

Tesla self-driving crashes

Tesla is one of the world’s biggest EV manufacturers with cars that come with an autopilot feature. However, if this AI technology doesn’t operate correctly, it can lead to very dangerous and life-threatening situations.

According to The Verge, since 2018, there have been over 900 Tesla crashes, which have accounted for 30+ deaths. This demonstrates that Tesla’s technology isn’t perfect and it can fail in certain contexts.

This case reinforces the importance of using good quality and large amounts of data during AI training to simulate different scenarios and improve the system’s response. To prevent this from happening in the future, Tesla should:

• Use lab-generated data to stress the system and improve its reliability
• Extend the trial and testing period using complex and rapidly changing contexts
• Continue to train users on the right way to use the autopilot feature

IBM Watson and its inaccurate cancer treatments

IBM Watson and its inaccurate cancer treatments

IBM was one of the first companies to launch a large language model (LLM). When Watson was first introduced, it promised to help doctors make patient diagnoses faster and more accurately.

However, when IBM started promoting it to hospitals, doctors shared that the system often suggested unsafe and incorrect cancer treatment recommendations. For example, it recommended bleeding medication to patients with severe bleeding. This happened because Watson was trained on hypothetical scenarios and limited real-world data, which made its predictions dangerous when applied to actual patients.

In 2022, this system was sold in parts to a private equity firm.

How to improve your data infrastructure and make it AI-ready

You’ve diagnosed the issues, confirmed your architecture is struggling, and realized AI can’t be layered on top of your current infrastructure.

But it’s not the end of the world. Here are two ways to get an AI-ready data infrastructure:

1. Do it in-house

If you’ve got the internal talent and time, building AI-ready infrastructure yourself is the way to go. You should start by:

• Using the content in this article to identify your infrastructure’s areas of opportunity
• Building a plan to close those gaps
• Re-architecting for real-time by replacing batch jobs with streaming ingestion
• Modernizing your storage layer with scalable lakehouses (e.g., Databricks, Redshift Spectrum)
• Introducing observability tools
• Automating and strengthening governance

That said, doing it in-house requires having people with the right skills, expertise, and bandwidth to perform their current tasks and take on this additional project. If you don’t have it, you’re better off looking for external providers.

2. Hire data engineering experts to work alongside your team

If you want the same level of customization and a team that feels like yours without having to hire new employees and go through long onboarding processes, this is the way to go. Bringing in external cloud-native data engineering partners like NaNLABS can fast-track your plan to become AI-ready.

At NaNLABS, we help companies make their infrastructure ready for production-grade AI by:

• Designing and implementing real-time pipelines using AWS-native services like Kinesis, Lambda, Redshift, and Glue
• Building scalable lakehouse architectures that decouple compute and storage for elastic, cost-efficient resource use
• Embedding observability and governance from day one, so you’re always on top of your architecture’s health
• Collaborating closely with your internal team to ensure knowledge transfer and long-term maintainability

Whether you’re aiming to launch an AI agent, build predictive models across departments, or use AI automation, we build the robust data backbone your AI initiatives depend on. We’ve improved data processes and developed automation for EV, fintech, ecommerce, cybersecurity businesses, and more.

If AI is on your roadmap, your infrastructure needs to lead the way. We help you build the cloud-native, real-time data backbone that AI depends on.

Sources:

1. Why 85% Of Your AI Models May Fail. (2024). Forbes. Found on: https://www.forbes.com/councils/forbestechcouncil/2024/11/15/why-85-of-your-ai-models-may-fail/