When the boardroom conversation inevitably shifts to AI, leaders tend to gravitate toward questions like: How will this technology impact our productivity? How will this impact hiring? But according to a recent study by the National Bureau of Economic Research, 90% of surveyed businesses reported that AI had no meaningful effect on either.

That’s because many businesses are overlooking AI’s most important capital investment. It’s the people who are actually using AI, rather than the cutting-edge software or shiny new chatbots.

Early AI adopters have moved past the experimentation phase and begun implementing it across their operational workflows. But in doing so, a hidden risk emerges: When AI systems are missing critical context, how can humans trust them to help with their work?

Where Trust in AI Breaks Down

In theory, AI should help us to make better, faster decisions in our day-to-day work. But in practice, if an AI agent is trained on a dataset that misses critical business and operational context, it doesn’t add much to human workflows.

Consider a common scenario: a critical business service slows down. Your monitoring tool shows an overloaded component, so the obvious response is to add capacity. But the real cause is that traffic was quietly rerouted onto a longer path. The most obvious response is to add more capacity. While this may fix the overload, it does not address the delays caused by that data taking the slower path.

What’s missing here is context. In order to be effective, an AI agent needs not only to know what is happening in the network but also to diagnose why it’s happening. In this example, the component may be behaving exactly as designed for a different set of assumptions. Traffic may be taking an unexpected path due to a recent change elsewhere in the environment. If you don’t understandwhy traffic flows as it does, you’re treating surface-level symptoms without addressing the underlying problem.

As anyone who’s used a chatbot knows, AI doesn’t always know when the right response requires pausing, asking better questions, or admitting that the situation calls for deeper context. And without this crucial operational context, AI can act in ways that reinforce incorrect assumptions.

Humans Are the Key to Unlocking AI's Value

When organizations treat AI as a shortcut around human expertise, they often discover that they’ve merely added noise. Although AI excels in pattern recognition and language-based reasoning, it lacks institutional knowledge. It doesn’t know your company’s business model, risk tolerance, or customer relationships. This knowledge belongs to people. AI adoption only works when paired with human judgment.

This dynamic mirrors how organizations develop talent. New hires may arrive with strong credentials and broad knowledge, but effectiveness comes from guidance, feedback, and exposure to real-world constraints. AI is no different. It needs domain experts to determine how it’s trained, where it’s implemented, and how human employees can use it most effectively.

4 Steps to Maximize Your Investment in AI

The key to achieving lasting value from AI? Investing in human capabilities. When we observe the current leaders in AI, we can extract several best practices:

1. Establish an Actionable Source of Truth

As your business grows, it becomes increasingly important to maintain a view of the network that’s both stateful and structured. Stateful data incorporates the full context of how your network is behaving from end to end, while structured data is normalized and presented in a format that AI can actually understand and use to take action.

Humans and AI agents can both benefit from having a shared view of how critical services are meant to operate. When every leader and AI agent are informed by a reliable understanding of their network, it creates a culture of trust. Only when you have that trust can you begin to delegate tasks to AI systems.

2. Prioritize Context in Automated Workflows

Even the most robust dataset isn’t any good unless it’s relevant to the teams and tools that are using it. Humans and AI systems alike thrive on context. The more focused the context, the better AI systems are at making decisions that align with business intent.

If an AI agent is tasked with validating network changes but it lacks the necessary business context, then it might greenlight a change that could cascade into security or compliance issues. However, if that same agent were given end-to-end visibility into network behavior and dependencies, it would have the context to identify the downstream impact before the change is made. This ultimately protects the business from unnecessary risk.

3. Rethink Hiring Practices

AI is forcing us to consider where humans can add the most value. Look for people able to translate digital infrastructure decisions into business outcomes, as they can give AI the context it needs to be useful. We should also consider data interpretation and long-term strategy as key factors in the hiring process, alongside technical skills.

4. Treat AI Literacy as a Critical Skill

It is essential for all employees to develop AI literacy so that oversight can keep pace with automation. Understanding how AI behaves is just as important as financial or operational literacy.

Do your employees know what tasks AI should and shouldn’t be used for? Do they know what “good” AI outputs look like in any given scenario? Do they know when and how AI needs human guidance? Leaders should invest in training programs to teach their employees how to answer these questions, and to work most effectively with AI.

With every passing year, AI will become more deeply ingrained in enterprise operations. However, this won’t have the intended impact if we don’t focus on how people actually use AI. Ultimately, the leaders who integrate AI more thoughtfully will be the ones who pull ahead in the end.

This byline was originally published in Fast Company.

Want to learn how IP Fabric can help you maximize your investment in AI? Sign up for a demo today!