AI Is Only As Good As The Data Behind It—And What You Can Do About It

Eilon Reshef, cofounder and CPO of Gong, is a seasoned entrepreneur, executive and investor in the internet and software spaces.

As organizations embrace AI to fuel everything from individual productivity to better-informed decisions, the key to ensuring success lies in one simple truth: Your AI is only as good as the data behind it. Enterprise-grade AI solutions must deliver accurate, precise, actionable insights, but the value they provide depends entirely on having the right data feeding into them. Without a strategic approach to collecting and contextualizing data, even the most advanced AI platform will fall short.

The Impact Of Getting Data Right

If your AI applications are built on a foundation of limited data, that becomes an issue when you start asking questions about the application, and it returns incorrect answers. But when your AI strategy is built with the right data foundation, you can move beyond surface-level metrics and toward deeper insights relevant to your business. For example, business leaders can gain insight into customer behavior, competitive threats or campaign performance.

This clarity empowers teams to drive the right insights. Once you connect those insights to actions, you transform workflows across your organization. As your team takes action, that, in turn, creates more data, which feeds this virtuous cycle.

When data, systems and people are working in concert, you get a growth flywheel that allows for flexibility and rapid evolution. AI becomes more than a reporting tool or auto email composer; it becomes a predictive engine that alerts you to risks, identifies growth opportunities and equips your teams with the insights they need to succeed.

The primary value lies not in the data itself but in how effectively AI can turn raw signals into meaningful business outcomes.

The Three Key Elements Of Good Data

There are three critical pillars to getting your data foundations right: quantity, quality and context. Each of these ensures that the data feeding your AI platform is robust enough to generate trustworthy insights that fit your business needs.

Let’s go through the three elements to understand how they each support an AI strategy that drives results:

1. Completeness: Do You Have Complete Data?

Many data stores, like CRMs, primarily rely on manually entered data, which is often incomplete and subject to human error. Other data stores may have some operational data (e.g., transactions) but not other types of data (e.g., unstructured data), which was not as useful before the generative AI (GenAI) era.

For example, a financial services organization rolling out a new product and working to generate sales using only CRM data might overlook early signs of buyer hesitation. However, an AI platform that automatically captures signals from website engagement, direct conversations and text—wherever buyers engage—can generate insights into the new product‘s market traction and competitive pressures to help influence its rollout.

Actions You Can Take: To address this gap, organizations should adopt tools and processes enabling automated data capture—including structured and unstructured data—across multiple touchpoints. This includes integrating signals from customer interactions or buying behaviors. A holistic data ecosystem ensures your AI isn’t limited by what employees remember to input but instead is continuously fueled by real-time signals from diverse sources.

2. Quality: Can You Trust The Data?

Harnessing a large quantity of data simply isn’t enough—it must also be objective and trustworthy. This is where the potential for human error or unintentional bias is most apparent. If the underlying dataset is incomplete, biased or outdated, the AI output will be flawed, potentially leading to poor decision making.

Actions You Can Take: A key challenge is ensuring your AI platform works with data that reflects reality rather than assumptions, so organizations should avoid manual inputs—the source of any data bias. Instead, ensure that you’re automatically capturing substance, not just activity. A simple example: Capture what was said in the meeting, in addition to the fact that the meeting happened.

3. Context: Does The Data Map To Your Business?

Even with the right quantity and quality, AI only becomes truly powerful when it understands your specific business context.

To illustrate this, let’s say a representative at a logistics services company gets a call that a client wants to churn because they don’t feel they’re getting the best service. As long as that representative is using an AI model without the company’s specific business context, they’ll get a meaningless response in return. This wouldn't help address the client’s issues.

If the AI model did, in fact, have the business context, the response would take into account who the client is, their previous interactions and the products and services the client is currently buying. To help solve the issue, the AI with context will know what offers are available and what features have been requested and will already understand the customer’s pain points.

Actions You Can Take: Ensuring AI-created insights are relevant to your business requires mapping discrete data to the business context and, most likely, integrating the AI elements within your business processes.

Data Is The Foundation Of AI Success

AI promises to revolutionize business operations, but there’s no shortcut to success. The effectiveness of any AI solution depends on the data that fuels it. To unlock the full potential of enterprise AI, companies must focus on implementing a robust data strategy based on quantity, quality and context.

This means finding a solution that automates data collection, prioritizes trustworthy sources and ensures that insights are tailored to the specific nuances of your business. Successful companies leverage these insights to deliver the actionable intelligence necessary to make smart, forward-looking decisions.

The right data foundation isn’t just a technical requirement—it’s a competitive advantage. Organizations that get it right will optimize their operations and position themselves to win in the age of AI.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?