Building an AI-Ready Data Foundation: Turning Information into ROI

A version of this article first appeared on EideBailly.com. 

Gartner predicts that organizations will abandon 60% of AI projects this year because they lack AI-ready data, and over 40% of agentic AI projects will be canceled through next year due to escalating costs, unclear business value, or inadequate risk controls.  

Artificial intelligence is transforming business, but its success depends on the quality, accessibility, and governance of your data. CEOs and IT leaders rank AI as a top priority, yet many organizations struggle with fragmented systems, poor data hygiene, and unclear objectives. 

To harness AI’s full potential, you need a strategic approach to data — one that aligns technology, people, and processes for measurable impact. Poor data quality, inadequate security, and governance issues can hamper the most ambitious AI strategies. 

Start with Clear, Business-Driven AI Objectives 

It’s important to have a clear understanding of what you hope to gain from AI before any work can be done. 

To begin, assess your processes. Identify where AI can create the most impact, whether it’s automating repetitive tasks, uncovering insights from customer data, or improving forecasting. 

Five Practical Steps to Prepare Your Data for AI 

Once you’ve identified your AI objectives, it’s time to look at your data. In our recent survey, only 6% of business leaders reported having data that is well-structured and secure. 

Here’s how to optimize your data: 

1. Accumulate Raw Data 
   Gather historical and real-time data from across your organization—transactional systems, customer interactions, and connected devices. The more relevant data you collect, the smarter your AI will become. 

1. Practice Good Data Hygiene 
   Cleanse your data by fixing errors, removing duplicates, and resolving inconsistencies. Reliable data prevents AI from making inaccurate or biased decisions. 

1. Unify Your Data 
   Break down silos by consolidating information into a centralized warehouse. A single source of truth ensures your AI has full access to the data it needs to perform effectively. 

1. Govern Your Data 
   Implement regular quality checks, enforce privacy standards, and control access to sensitive information. Effective governance ensures your data is accurate, secure, and compliant. 

1. Annotate and Organize 
   Add clear, descriptive labels to your data assets so AI can learn effectively. Use plain language that reflects each asset’s content, purpose, and attributes. 

Using AI to Integrate Systems and Reduce Manual Work 

• Integrate Systems: 
  Use AI to automate the detection of anomalies in financial data, streamline workflows, and enable seamless data flow between platforms. Well-organized data allows AI to pull information from the right places, draw conclusions, and execute correctly. 

• Automate Manual Processes: 
  Automation removes repetitive work, freeing teams to focus on strategic, high-impact tasks. This boosts operational output and job satisfaction. 

• Focus on Security and Compliance: 
  In regulated industries, pay meticulous attention to data security, integration, and compliance. Vet data-handling processes and implement stringent security measures. 

Aligning AI Investments with Business Value 

AI is not one-size-fits-all. Assess your business processes to identify strategic applications for AI, such as generative AI for content creation or machine learning for predictive analytics. 

Next, invest in training so your employees can use AI effectively and ethically. This includes establishing a governance framework with guidelines around costs, roles, compliance and decision-making. 

IT and finance teams should work together to ensure technology investments drive both innovation and financial stability. 

Here are a few examples of strong data-driven AI in action: 

• Cybersecurity: AI can respond faster to a variety of cyberattacks and potential threats by constantly scanning data as it comes in. Use it to scan your emails and web traffic. If AI identifies an anomaly, it can identify and triage the potential threat with specific if-then actions. 

• Financial Data Analysis: Specific indicators often signal potential issues or anomalies within financial data. By integrating AI, you can automate the detection of these red flags in your financials, significantly accelerating the anomaly detection process. 

• Customer Support: AI-powered chatbots and virtual assistants can be integrated into the workflows of automation solutions to handle routine customer inquiries, providing instant support and freeing up human agents for more complex tasks. 

• Predictive Maintenance: Businesses that rely on machinery and equipment can use AI to analyze sensor data and predict when maintenance will be needed. Automation tools can then automate the scheduling of maintenance tasks, ensuring that issues are addressed before they lead to downtime. 

Moving Forward with Confidence 

Organizations that prioritize data quality, integration, and governance see measurable improvements in efficiency, forecasting, and customer experience. By consolidating data, automating processes, and investing in upskilling, you lay the groundwork for scalable, secure, and impactful AI. 

An AI Readiness Assessment can help you understand where your organization stands today — identifying gaps in data, technology, and governance so you can focus investments where they will deliver the greatest value.