Want to Become Data- and AI-Driven? Avoid These 6 Pitfalls

Becoming data- and AI-driven is a strategic priority for nearly all modern businesses. In fact, 98% of senior data and AI leaders report that their organizations are increasing investments in data and AI, up from 82% in 2024.

But as the saying goes, when you know better, you do better. That’s why we wrote and published the ebook: 6 Pitfalls to Avoid on the Road to Becoming Data- and AI-Driven. In it, we outline the six most common barriers that stand in your way to becoming data- and AI-driven, along with strategies to overcome them. 

The Road Looks Smooth… Until It's Not

Every company’s journey to become data- and AI-driven has its bumps in the road. But recognizing them early—and taking quick action to course-correct—can make all the difference.

6 Common Pitfalls 

• Your culture hasn’t changed
• You’re still relying on rules
• Nobody sees the value
• You rely on overnight syncs, not real-time updates 
• You’ve burned out your data team
• Your internal and external data are disconnected

Below, we take a look at three of these pitfalls. Download the ungated ebook to explore all six pitfalls—and our advice for avoiding these traps. 

Your Culture Hasn’t Changed

According to the Data & AI Leadership Exchange’s 2025 AI & Data Leadership Executive Benchmark Survey, over 84% of organizations have appointed a chief data officer (CDO). However, few organizations have given these leaders the budget and authority they need to influence meaningful change. Instead of being part of the leadership agenda, CDOs are often relegated to custodial roles, responsible for cleaning up the mess caused by dirty, siloed data rather than driving enterprise-wide transformation.

Without empowered leaders guiding the change, collaboration across the business falters—leaving stakeholders unsure what to prioritize, who is accountable for the change, and how the culture shift supports the organization’s broader strategy and goals. As a result, employees dismiss data- and AI-related initiatives as another passing fad.

To overcome this challenge, organizations must:

• Elevate data to a board-level discussion
• Shift mindsets early in the process
• Cultivate strong partnerships across the business

You’re Still Relying on Rules

When it comes to mastering your data, there is a simple rule of thumb: the more data sets you’re integrating, the more rules you need. And in many organizations, the number of rules they need to manage has reached the breaking point. 

Traditional master data management (MDM) solutions can’t keep up. But new, “AI-enhanced” ones are not much better. While these AI-enhanced solutions may reduce manual effort, they often introduce unnecessary complexity and limit access to real-time insights which leads to terrifying results.

Overcoming this hurdle requires organizations to invest in AI-native MDM. With AI embedded in the foundation of an MDM solution, humans can spend their time guiding the machine and building trust through bottom-up feedback that takes into account the context of how their organization is consuming the data. Using the subject matter expertise provided by humans, the AI models will learn and improve over time—in days or weeks, not months or years.

You Rely on Overnight Syncs, Not Real-Time Updates

Many solutions have architectures that do not allow updates in real time. Beware of these solutions! They will make it impossible to write back to the source system with updated information which will cause operational headaches down the line.

Instead, look for solutions that allow real-time connectivity—it’s essential for a modern MDM solution. Real-time connectivity ensures that every record across your systems is accurate, consistent, and connected. And with the ability to read and write while the data is still in motion, you can prevent bad data from entering your systems in the first place. 

The Road to Success

Every organization striving to become data- and AI-driven will encounter obstacles. But if you’re still using traditional, rules-based MDM solutions, the odds of hitting potholes are even greater. 

‍AI-native MDM is a critical part of your MDM strategy to deliver the accurate, accessible, trustworthy insights needed to be data- and AI-driven. By integrating data across fragmented systems and silos, AI-native MDM ensures that everyone across the business has access to accurate and consistent golden records—in real time. And with automation and governance built into the platform, AI-native MDM will enable your organization to streamline processes and reduce manual effort so you can deliver accurate, reliable data at scale.