Why Generative AI is Only as Smart as Your Data: Teaching Employees the Role of Enterprise Data
As generative AI becomes more common in the workplace, many organizations are rushing to find the best tools and models. But there’s a critical piece most teams overlook: the data.
When generative AI doesn’t deliver useful results, it’s often not because the model is wrong. It’s because the model doesn’t have the right context. And in the enterprise world, context lives in your data.
If your employees are using AI without understanding enterprise data, you’re setting them up to fail.
Why Data Is the Secret Ingredient for Successful AI
A recent article on from Oracle on the importance of data in generative AI sums it up best: “GenAI without context is just guesswork.” When employees use generative AI to write emails, summarize information, or generate insights, the quality of the output depends on the quality and relevance of the input.
That input isn’t just what the user types in a prompt. It’s the internal data the AI has access to—your customer records, financial systems, HR platforms, service logs, and supply chain metrics.
Without that enterprise data, generative AI becomes generic. At best, it produces boilerplate. At worst, it makes things up.
What Is Enterprise Data?
Enterprise data refers to the structured and unstructured information generated across departments in a business. This includes:
Customer Experience (CX) data: support tickets, sales interactions, feedback
Enterprise Resource Planning (ERP) data: budgets, invoices, inventory
Human Capital Management (HCM) data: roles, benefits, performance records
Supply Chain Management (SCM) data: supplier info, logistics, product tracking
When all of this data is siloed or disconnected, AI tools can't access the context they need to deliver relevant, responsible output.
Learning About Data Isn’t GenAI 101—It’s GenAI 102
Most AI upskilling programs start with the basics: how to write prompts, use tools, or experiment with chatbots. That’s GenAI 101.
But getting value from AI in a business context requires the next level. That’s GenAI 102: learning how enterprise data works, where it lives, and how to use it responsibly. These are intermediate skills that require deeper knowledge of your systems, workflows, and the signals that matter in your organization.
If you're serious about integrating generative AI into daily work, prompt engineering alone won't cut it. Your team needs to know how to work with data, not just work with AI.
Foundational Data Skills for Non-Technical Employees
Generative AI is not a magic wand. Employees need foundational skills to work with it effectively—especially when using it with enterprise data.
Here are three areas where non-technical employees need upskilling:
1. Understanding What Data Is and Where It Lives
Employees should know what types of data their organization collects, who owns it, and how it flows across systems. This helps them ask better questions and design better AI prompts.
2. Recognizing Contextual Signals
Not all data is equal. What makes AI output valuable is combining multiple pieces of context—for example, using past customer purchases and service history to generate tailored responses.
3. Working Responsibly with Data
Employees should understand privacy, permissions, and data sensitivity. Using enterprise data in AI tools without the right safeguards can lead to compliance risks and breaches of trust.
What You Can Do Next
If your organization is rolling out generative AI, pair that effort with data education. Include data literacy as part of your AI upskilling strategy. Help teams understand:
How data flows in your business
Which tools have access to which datasets
What safe and responsible data use looks like
How to craft AI prompts that include relevant context
Generative AI’s impact comes not just from its capabilities, but from the data that powers it. By teaching employees how to work with enterprise data, you move from guesswork to strategy—and unlock the real value of AI at work.
Need help building your team’s understanding of data in generative AI use? Check out our AI learning labs for custom sessions.