5 practical ways to use AI in workplace planning (with prompts)

Managing workplace space today is fundamentally different than it was even a few years ago. Teams grow and reorganize more frequently; employees come into the office with different goals on different days; the spaces we design have to keep up with that reality.

In our recent webinar, Where experience meets design, presented by OfficeSpace and IFMA, we showed how AI can help workplace, facilities, and real estate teams navigate this complexity in very practical ways, with prompts for workplace planning. Not as a replacement for human judgment, but as a way to generate ideas faster, test assumptions, and move forward with more confidence.

This post is a recap of what we walked through, including step-by-step examples you can try today using tools like ChatGPT, and a look at what becomes possible when AI is embedded directly into your workplace operations platform.

Why data has become the foundation of workplace decisions

One of the points Andres Avalos, our Chief Product Officer, emphasized early in the session is that hybrid work today is about more than attendance. It includes flexible seating, frequent space changes, contractors and visitors, and evolving team structures. That makes static planning approaches harder to rely on.

What teams need instead is objective, real-time data about how space is actually being used. Badge swipes, reservations, meetings, work orders, and collaboration patterns all generate signals. When that data is accessible and usable, it becomes the foundation for applying AI in meaningful ways.

AI doesn’t work in a vacuum. It works best when it’s grounded in real workplace behavior.

If you’re already measuring something, you’re already partway there

One theme I keep coming back to is this: if you’re already measuring something in your day-to-day work, you already have an entry point into AI.

Presence, utilization, occupancy, and work orders are all datasets facilities teams work with constantly. Whether that data lives in an IWMS or a spreadsheet, it can be used to ask better questions and move beyond guesswork.

We focused on five examples in the webinar. The first three are things you can do today using large language models. The last two require a purpose-built AI workplace platform. Here are our 5 AI prompts for workplace planning:

1. Benchmark workplace strategy using AI as a research assistant

What this helps with
Understanding what peer organizations are doing without reading an entire analyst report.

What you need

• A workplace or market trends report (CBRE, JLL etc.)
• ChatGPT or another large language model

How it works

1. Upload the PDF of a market trends report into ChatGPT.
2. Start with a role-based prompt, like:
   “I’m a facilities leader at a tech company. Based on this report, what are peers in the tech industry doing with their workplace strategies?”
3. Review the themes it surfaces. In the webinar, this included shifts away from one-to-one seating, increased collaboration space, and a stronger focus on utilization.
4. Ask a follow-up to help guide internal conversations:
   “How do I align internal stakeholders around moving away from one-to-one seating based on these trends?”

I like this approach because it reframes internal discussions using third-party data rather than opinion. You’re not arguing for change. You’re contextualizing it.

2. Use AI to explore a floor plan and generate ideas

What this helps with
Generating ideas for improving collaboration and space mix before committing to redesigns or capital spend.

What you need

• A screenshot or image of a floor plan
• A large language model that supports images

How it works

1. Take a screenshot of your floor plan.
2. Paste the image into ChatGPT.
3. Ask a prompt tied to your goals:
   “This is a typical floor plan for my headquarters. Evaluate the mix of collaborative shared space versus assigned desks. How does it compare to peers described in this CBRE report? What are some capital-light strategies to improve the layout?”
4. Review the feedback. In the demo, the AI pointed out where collaboration space was fragmented and where desk-heavy areas limited flexibility.
5. Go deeper if needed:
   “What changes could we pilot without increasing budget?”

This doesn’t replace design expertise. It helps teams move from intuition to structured ideation, which is often the hardest part.

3. Analyze work orders to understand experience issues

What this helps with
Understanding how operational issues affect collaboration and employee experience.

What you need

• A CSV export of work orders
• ChatGPT or a similar model

How it works

1. Export work orders from your system as a CSV.
2. Upload the file into ChatGPT.
3. Ask a focused prompt:
   “I’m a facilities manager at a tech company. This is a table of facilities work orders from the past two years. Give me the top three takeaways regarding impact on collaboration, and create a summary table.”
4. Review the results. In the webinar, the AI surfaced issues with shared furniture and long resolution times in collaboration spaces.
5. Ask follow-ups:
   “Which issues should we prioritize to improve meeting reliability?”

Instead of spending hours in Excel, you can surface patterns in minutes and validate whether what you’re hearing anecdotally is actually supported by data.

One important note we called out live: always work with your legal and IT teams to ensure AI tools are used securely and in line with your organization’s data governance policies.

When chat-based tools aren’t enough

Some workflows, especially those involving geometry, adjacencies, and large-scale planning, simply can’t be handled in a chat interface.

That’s where purpose-built AI systems come in.

4. Generate stack plans and scenarios instantly

During the webinar, I demonstrated AI-driven stack planning inside OfficeSpace.

Starting with a stack view and real-world constraints like headcount growth and adjacency requirements, the system automatically rebalanced floors, placed related teams together, and optimized utilization in seconds.

Instead of manually restacking teams and recalculating capacity, planners can iterate quickly, compare scenarios, and adjust as business needs change.

This kind of workflow requires geometry-aware AI and structured workplace data. It’s not something a general-purpose chat tool is designed to handle.

5. Inform adjacencies using collaboration data

The final example showed how collaboration analytics can inform adjacency decisions.

Traditionally, adjacencies are driven by interviews, assumptions, or politics. By incorporating calendar and meeting data, teams can see which groups actually interact most frequently.

That shifts conversations from subjective debates to data-backed scenarios. Facilities teams still make the final call, but they do so with clearer evidence and more productive discussions.

AI works best as a partner

A point I strongly believe in is that AI works best as a creative partner, not a replacement.

AI can generate ideas, surface insights, and automate analysis, but it doesn’t replace context. Facilities and workplace leaders understand stakeholder dynamics, cultural nuances, and constraints that no model fully captures.

At the end of the day, it’s still your plan, your recommendation, and your responsibility to align people around it.

Looking ahead

As we discussed toward the end of the webinar, AI is moving toward more proactive systems. Instead of waiting for prompts, platforms will increasingly notify teams when patterns change, such as unexpected dips in utilization or emerging space constraints.

At the same time, employees are already using AI in their daily lives. Expectations for intelligent, personalized workplace experiences will continue to rise.

For workplace teams, the opportunity is to start with the data you already have, test ideas before committing, and use AI to create space for more strategic work. When used thoughtfully, AI doesn’t just make planning faster. It helps teams design workplaces that better support how people actually work.