There is no good quality Agentic AI without good quality consolidated data 

22. 08. 2025.

Imagine having a team of top chefs, but your fridge is packed with spoiled ingredients. No matter how talented they are, the dish won’t turn out right. 

That’s exactly how it works with Agentic AI. You can have the most advanced algorithms, but if you feed them poor, scattered data, all you’ll get are faster mistakes. 

Today, more and more companies are adopting AI agents, expecting miracles. But before artificial intelligence can make smart decisions, it first has to understand the world it operates in. And without high-quality, consolidated data, AI is like a pilot flying blindfolded. 

Do you know how ready your data is for AI agents? 

Maybe it’s time to take a closer look at the foundation of your AI strategy. 

What exactly is Agentic AI and why does it need a “clean slate”? 

Agentic AI is a set of systems that make decisions on their own, adapt to new information, and learn from context. It’s not just a “fancy chatbot,” but a digital assistant that can manage supply chains, optimize customer support, or even coordinate human teams. 

Here’s the catch, AI knows nothing upfront. It learns from data. If you feed it with messy, fragmented data, you’re teaching it to make mistakes. For more on the basics, functions, and the role of analytics in AI agent performance, check out our guide AI Agents Analytics, which explains how data becomes the “fuel” behind smart AI systems. 

Bad data = Bad decisions 

Here’s a real story from banking. A major European bank rolled out an AI agent to speed up credit risk assessments. 

The problem? Client data was coming in from four disconnected systems, many records were incomplete or outdated. The result? The AI rejected reliable customers while greenlighting high-risk ones. 

After the chaos, the project was put on hold, and the data team spent six months consolidating and cleaning up the databases. 

How much do bad data cost? 

When it comes to deploying AI agents, the stakes are higher than most businesses realize. It’s not just about automation also it’s about trust, precision, and real-world consequences. 

MIT Sloan Management Review writes: 

“Bad data costs companies an average of 15–25% of revenue every year.” 

It’s not just a financial cost. You’ve lost time, your reputation has taken a hit, and customers are slipping through your fingers.  

Take healthcare, for example. An AI agent assisting doctors can be a lifesaving tool. But if the patient’s record contains incorrect allergy information or outdated treatments, the consequences can be fatal. No algorithm can fix a fundamentally flawed dataset. 

In e-commerce, AI agents recommending products can lift sales by 20–30%. But that kind of impact only happens when the system genuinely understands customer preferences, behaviors, and context. Without clean, consistent data, recommendations become noise instead of value. 

So it’s no surprise that Gartner reports: 

“By 2027, over 60% of failed AI projects will be directly attributable to poor data management.” 

In the end, it’s simple: smart AI needs smart data. Everything else is just automation with a blindfold. 

Amazon, Walmart and what we can learn from them 

Amazon’s AI agents track inventory, delivery times, and shopping behavior in real time. Their success isn’t magic, it’s the result of rigorous data consolidation from thousands of sources, updated continuously and structured with precision. That’s how they predict what you’ll need next before you even search for it. 

Walmart achieved a 25% reduction in waste in its fresh goods section by connecting the dots, linking data from warehouses, POS systems, and even weather forecasts to adjust supply in real time. 

Or take Uber. Its AI agents don’t just handle ride pricing and driver-passenger matching. They rely on a steady stream of real-time traffic data, user behavior, fuel trends, and more to optimize routes and keep wait times low. 

Another example? Airlines like Delta use AI agents to minimize delays and improve customer experience. By merging data from aircraft sensors, maintenance logs, crew schedules, and airport operations, they’re able to predict disruptions before they happen and re-route resources accordingly. Without that consolidated, high-quality data, AI in aviation would be little more than guesswork. 

If you’re wondering whether Agentic AI is right for your business, we highly recommend reading our article How Do I Know if Agentic AI Is Appropriate for My Business, which offers practical tips on assessing your readiness. 

AI can help clean up the mess 

It might sound counterintuitive, but AI doesn’t just need clean data. It can actually help create it. Even before you fully deploy advanced AI agents, there are powerful AI-driven tools designed to clean and organize your existing data. These tools can identify duplicate entries, correct formatting errors, fill in missing values, and even recommend consistent naming conventions across systems. 

Think of a company preparing to implement a customer service chatbot. If their CRM is cluttered with outdated or duplicate customer profiles, the bot’s performance will suffer. But with AI-based data cleaning, that same company can streamline its customer records, ensuring that the bot has access to accurate, relevant information from day one. 

As Harvard Business Review notes: 

“Organizations using AI to improve data quality report a 40% reduction in manual work and a 30–50% increase in accuracy.” 

In short, you don’t have to wait until everything is perfect. You can start using AI today to prepare your messy, fragmented data for tomorrow’s intelligent systems. It’s like training the soil before planting a smart garden AI, helping to clear the path for itself. 

How to get started? 

The first step might be the hardest. You need to admit that your company’s data isn’t in great shape. In most companies, data lives in chaos: Excel sheets on someone’s desktop, CRM databases that don’t sync with ERP, email lists with no organization. Start by mapping out where your data currently lives. 

Who enters it? 

Who uses it? 

Who, if anyone, cleans it? 

Next, go for small wins. You don’t need to clean your entire database going back ten years. Focus on the part of the business where you want to introduce AI say, customer support or demand prediction and fix just that data. That means removing duplicates, standardizing formats, filling in missing fields, and ensuring compliance with data protection laws. 

Once you build momentum, think about bringing in tools that can integrate data from different sources. Instead of manually reconciling data across five systems, use software that does it in real time. And perhaps most important, establish processes and responsibilities. Who will be your “data steward”? Who will monitor quality and check it regularly? 

Preparing your data isn’t glamorous, but if you skip this phase, your AI projects will have no solid ground to stand on. For more tips on how to train AI agents to make useful decisions, check out Training Your AI Agents, where we dive into the details of training systems on good, clean data. 

What’s next? 

If you’ve read this far and are thinking, “Okay, this sounds powerful, but we have no idea where to start,” this is where things get simple. 

Things Solver offers end-to-end solutions for implementing and training AI agents, from assessing your data readiness, to consolidating it, to developing and training agents that do the work for you. 

Our expertise covers the entire cycle, meaning you’re not left alone with complex challenges. Whether you need help with analytics, agent training, or evaluating business value, the Things Solver team is here to help turn AI into your ally, not another headache. 

If you want to learn more, reach out to us because great AI starts with the right partner.