Your AI tools are like someone who’s read every book in the world but has never left their room. They know everything and nothing at the same time. Let me explain why this matters.

Working with AI is like having a brilliant but sheltered assistant who has absorbed all of human knowledge through books but lacks real-world experience. They can quote any fact, analyze complex data, and write eloquently about any topic, but they’ve never experienced a Monday morning meeting, haven’t learned your team’s communication style, and don’t know which coffee mug belongs to whom. This combination of vast knowledge and limited context creates unique challenges when working with AI tools.

Let’s explore this through a project management example. Imagine asking an AI to help plan your next major project. The AI knows every project management methodology ever written about – from Agile to Waterfall to Scrumban. It can recite the PMBOK guide chapter and verse, list every best practice, and generate perfect Gantt charts. But without crucial context, it’s missing the elements that often make or break a project’s success.

For instance, when you ask the AI to create a project timeline, it doesn’t know that Sarah from the design team does her best work in the morning, or that the development team has an unspoken rule about no meetings on Fridays. It can’t account for the fact that Team A and Team B had a rocky collaboration last quarter and might need extra buffer time. These human factors – the unofficial workflows, personal preferences, and team dynamics – are invisible to the AI but vital for realistic project planning.

Think about estimating task durations. The AI can provide industry-standard time estimates for various tasks, but it doesn’t know that your senior developer John can complete certain tasks in half the standard time, while new team members might need extra support. It can’t factor in that your organization typically sees slower progress during the summer months when team members rotate through their vacations.

So how do we make the most of AI’s capabilities while accounting for these limitations?

Start by providing clear context about your team and organization. Instead of asking “How should we structure this project?” try “How should we structure this 3-month website redesign project for a team of 6, including 2 new hires, working hybrid schedules across 2 time zones?” The more specific context you provide, the more relevant the AI’s suggestions will be.

Consider hidden assumptions too. Your team might have developed certain practices over time – like building in extra QA time before client presentations or knowing that certain stakeholders need more detailed documentation. The AI won’t know these unless you explicitly state them. It’s like bringing a new project manager up to speed – you need to share both the written and unwritten rules.

Keep updating the context as things change. If your team adopts a new tool mid-project, or if key personnel changes occur, you’ll need to inform the AI about these changes. Remember, it can’t observe these shifts on its own or remember them from previous conversations.

This might seem like extra work, and sometimes it is. But understanding these limitations helps us use AI more effectively. When we provide rich context, we get much better results. Plus, the process of explaining our needs often helps us clarify our own thinking and spot potential issues we hadn’t considered.

The key takeaway? AI is an incredibly powerful tool for project management and other business tasks, but it needs our help to understand the specific context of each situation. By being mindful of this limitation and proactive about providing context, we can better leverage AI’s capabilities while avoiding potential misunderstandings and mistakes.

Want to explore more about working effectively with AI tools? Join The Hybrid Advantage community, where we share practical tips and real experiences about implementing AI in business settings. Together, we can learn to make the most of what AI knows while acknowledging what it doesn’t.