Intentional AI: Building a Strategy That’s By Design, Not Default

TL;DR

AI has become the $10,000 espresso machine of business: shiny, overhyped, and we've barely scratched the surface of what it can do.

In multifamily real estate, marketing and leasing get the AI spotlight, yet critical issues like operations, NOI, and staffing remain untouched. And all the while, 78% of employees are sneaking personal AI tools into their workday.

We must shift from shiny-object syndrome to intentional action. Win executive backing, seek and test tools that actually address your pain points, run strategic pilots that move the needle, then scale.

Read on to understand the major roadblocks we need to clear and walk away with a seven-step roadmap to creating an intentional AI strategy.

The Espresso Effect

Thanks to Angie Cramp for this clever analogy.

Picture this: your office buys a top-tier espresso maker, everyone cheers, then keeps filling their mugs with instant coffee because no one took ten minutes to read the manual. That is how many teams treat artificial intelligence. The hype sizzles, yet day-to-day habits barely budge. We aren’t taking the time to learn how to optimize our new AI sidekicks, and, arguably more importantly, we aren’t even using them where we need them the most.

There are far too many shiny AI objects out there right now.

It’s easy to get caught up in the excitement, I mean, there are AI toothbrushes to help you improve technique and even an AI toilet – what’s not to love? However, if we continue to adopt new technology simply because it seems “cool and innovative,” we risk creating more problems for ourselves, rather than solving ones we already have.

Hype Over Here, Headaches Over There

This winter, I delivered a talk for the Institute of Real Estate Management (IREM®) . The charts below are a perfect example of what I expect is happening across many industries.

We can see that multifamily real estate professionals reported operational efficiencies, improving net operating income (NOI), and HR functions, such as staffing, recruitment and training, as their company’s’ most significant challenges. This was consistent in the 2023 and 2024 surveys (we will have 2025 data soon).

So… if these are the top challenges, why is the number one use case for the multifamily industry marketing & leasing?!? That wasn’t even on the list of challenges! Conversely, staff training and engagement, one of the industry’s biggest needs, utilizes AI the least.

Shiny. Object. Syndrome.

Does this ring true for your industry? 

To find out, use ChatGPT’s Deep Research tool to locate research on your industry’s top uses and challenges, and compare them. The bad news is that if the graphs contradict each other, like what you see here, your industry is in the same boat. The good news is it’s going to take some strategic thinkers to turn that around, and that’s a big opportunity for you!

Seven Speed Bumps Slowing Your AI Roll

Marketing and leasing lead the multifamily AI pack, yet they score just 2.4 out of 5 on the usage scale, meaning we’re barely tapping half the horsepower in our so-called top use case.

Why??

Here’s why AI isn’t scaling across organizations as fast as it’s advancing:

1. Executive buy-in: If the C-suite treats AI like a futuristic nice-to-have, you’re cooked (as the kids like to say). Budgets stall and experiments flatline. Rally leadership with clear wins (time saved, dollars kept) so the green lights come faster.
2. Implementation Ownership: AI doesn’t automatically belong to IT. It’s new ground, open for anyone to claim. If no one is stepping up, raise your hand and say, “Put me in, coach.” Or appoint a captain who will run the pilots, set milestones, and track the wins. When everyone owns AI, no one owns AI.
3. Best Practices: Where do you turn for guidance? It’s like the early days of COVID all over again. There’s no playbook. AI is the same. These tools are built as general-purpose technologies. The labs won’t tailor them to your industry. That’s on us. We have the domain expertise, so we have to crack the code on how AI can actually work in our world.
4. Knowledge Sharing: Stop hoarding hacks. Host a monthly “Prompt Club,” drop tips in Slack, and tap industry groups for fresh ideas and wins. Community beats trial-and-error every time.
5. Accuracy: Hallucinations happen, but humans can recognize them when they pay attention and refrain from using AI as an easy button. The other extremely important issue is – at least in multifamily real estate – we have very… dirty… data. Run your data through the dishwasher until it shines like the top of the Chrysler Building. Then invite AI to dinner.
6. Privacy Concerns: Know exactly what data your models touch and where that data lives. Encrypt, anonymize, and restrict access where necessary.
7. Talent & Skills Gap: Great tools flop without people who can build, fine-tune, and scale them. Upskill in-house staff, hire specialized talent, or partner with experts.

Why Intentional Wins

A quote from Appfolio’s Property Management Industry Pulse sums this up perfectly:

“In the short term, generative AI is overrated. In the long term, it is underrated.” 

The gap between potential and practice is a planning problem, not a technology problem.

Don’t use AI for the sake of using AI. 

Don’t look at what your competitor down the street is using, and automatically adopt it in your life or at your company. Every individual and organization has different needs, structures, and business models. You wouldn’t just grab someone else’s prescription glasses and hit the highway. Your AI strategy is the same idea: a one-size-fits-all shortcut is guaranteed to blur your vision.

Your Intentional AI Roadmap

So we’ve covered every wrong turn on the road to AI greatness. Now you’re probably wondering, Okay, GPS, which way do I go? Let’s shift from caution tape to blueprint. Below is the seven-step roadmap I shared during my talk. Feel free to copy-paste and save it as a resource.

1. Adoption – Departmental, Organizational, or Individual: What is your company’s stance on AI? Do they want departments to use it independently, scale it across the organization, or are they opposed to its use altogether? You can’t pass go without this information. This dictates everything you will do next. Sidenote: if your company is against AI, don’t let that stop you from learning outside of work. You can’t risk not being ready for the next opportunity. AI literacy is quickly becoming a high-demand skill for employers.
2. Survey Current Use: The 2024 Annual Work Trend Index Report by Microsoft and LinkedIn found that 78% of employees are bringing their own AI tools to work. It’s probably even more in 2025. You can bet your butt your employees are already using AI, you might as well understand how, for two reasons.First, you want to know if they are using it in a way that could put you, your company, or your intellectual property at risk.They could be doing something really genius that you can build on or roll out across the organization to help everyone work smarter.
3. Policy & Literacy: Document your AI policies and ensure your workforce understands what is and is not permitted. Create AI learning journeys for your teams.
4. Identify Needs: What are your challenges? What stops you from being more efficient, profitable, happy, whatever is important to you?  Research solutions that solve for your pain points, rather than looking for a problem to validate your desire to test a cool new tool.
5. Research Solutions: I kind of spoiled this one at the end of #4. Look for tools inside and outside of your industry. Talk with peers. Chat with vendors. See if you can build your own solution with an existing AI model.
6. Test Honestly: Once you identify a product that could solve your challenge, test it… honestly. What do I mean by honestly? I mean, logically, with a disciplined, strategic approach. Document what you want to achieve, what success would look like, and the metrics that would prove success. Run a pilot, then, stand firm on those success metrics. If the results meet the expectations you set before launch, pass go and collect your $200. If not, it’s back to the drawing board. As an extra check, if you have the resources to do so, test multiple products side by side to see which works best for your needs.
7. Consider Your Customers: Last but certainly not least, what do your customers want? How will they feel about interacting with AI products or content? 82% of consumers want to be told when brands use generative AI. Improving your processes while keeping your customers on board is a delicate balance. Transparency is key.

Do you and your customers have conflicting priorities? Most companies want AI for efficiency and optimization, but consumers seek inspiration, discovery, and fun. How can you make this align?

Pilot Your First AI Win

This is a lot to take in. Here’s what you’re going to do.

1. Follow the intentional AI checklist.
2. Map your biggest headaches.
3. Match them to a bite-sized AI pilot.
4. Measure, iterate, share, repeat.

If you work in real estate, consider starting with AI-driven process mapping that identifies maintenance bottlenecks. In retail, your first win could be sentiment analysis of product reviews. Finance? Let a GPT clean variance reports so humans focus on strategy.

Whatever your field, the game is the same: challenge first, AI second.

Comment below: Which headache will you test AI on first, and why?

Content originally posted on reneemcintyre.com