I keep hearing that we are in the 1993 phase of AI, and the comparison feels increasingly accurate. Everyone knows something important is happening. Very few people actually know what the final form looks like yet. But the pressure to appear fluent in it, implement it and monetize it has already arrived.
As a CEO, you feel that pressure constantly right now. Every conversation eventually circles back to the same questions:
- Where can we become more efficient?
- What can be automated?
- Where can costs be reduced?
- How much operational leverage is possible?
Beneath all of it is another reality that people are less comfortable saying out loud: Nobody wants to be the executive who underestimated AI.
What I find interesting is how quickly the conversation jumped to replacement before most companies even figured out implementation. The narrative became very binary very fast. AI will replace jobs. AI will eliminate departments. AI will dramatically compress operating costs.
Theatrical AI
At the same time, there is already an enormous amount of AI theater happening across industries. Companies are racing to position themselves as AI-native, AI-powered, AI-first. You see it in investor decks, recruiting language, conference panels, product announcements, and corporate messaging. In many cases, the performance of fluency has arrived faster than actual operational fluency itself.
Sometimes it feels less like a technological revolution and more like a production. Beautiful set design. Convincing dialogue. Very little discussion about what is actually happening backstage.
Eventually, perhaps some of the larger predictions will happen. But from where I sit today, I think many companies are still misunderstanding where the real operational opportunity actually is.
The biggest efficiencies are not necessarily coming from replacing entire groups of people overnight. They are coming from reducing the thousands of small frictions that quietly slow organizations down every day.
- Internal reporting
- Data synthesis
- Marketing production
- Administrative repetition
- Knowledge retrieval
- Workflow bottlenecks
- Information trapped across disconnected systems
That is where I think the conversation becomes far more practical and far more interesting.
At Platinum Forbes Global Properties, we have spent significant time building internal context systems optimized for language-based models.
Training materials, brand voice, recruiting philosophy, market intelligence, company history and operational frameworks are being organized to enable our teams to work more intelligently and efficiently. Not because we believe AI replaces human judgment, but because organized intelligence increases human leverage.
I have also been encouraging our department heads to use these tools in a very specific way. Not to become software engineers, but to solve micro inefficiencies inside their own workflows.
A recruiting leader may build a better synthesis system for candidate conversations. A marketing department may reduce hours of repetitive production work. Small operational gains, compounded consistently across an organization, become very significant over time.
Analysis vs. experimentation
Ironically, adoption has also been far more uneven than many people expected. Some of the most analytical and systems-oriented people I know have been surprisingly resistant to these tools, while others with far less technical backgrounds are experimenting aggressively and adapting quickly.
The difference often has less to do with technical ability and more to do with tolerance for temporary inefficiency because AI implementation is often inefficient before it becomes efficient.
In real estate marketing, virtual staging has become one of the clearest examples of both the promise and the friction of AI adoption. Brokers and marketing teams can now digitally furnish and style empty apartments using AI rather than physically bringing furniture into a space.
In theory, it sounds immediate and seamless: faster turnaround, lower costs, endless flexibility. But in practice, the output can still become strangely distorted. Furniture proportions feel subtly wrong. Architectural details appear that do not exist.
Correcting those issues initially takes additional time, which is exactly why some people abandon the process too early. The organizations gaining the most advantage right now are often the ones willing to slow down long enough to learn where these systems actually create leverage.
That distinction matters because I believe leadership itself is changing. Increasingly, strong leaders will also need to become systems thinkers capable of identifying operational friction, organizing information intelligently, experimenting quickly and improving workflows in real time.
That’s not because technology replaces leadership, but because leadership now requires a deeper understanding of how technology and human judgment interact.
Despite all the rhetoric around automation, I actually believe human judgment is becoming more valuable, not less. The more AI-generated content, analysis, imagery and communication flood the market, the more important discernment becomes. Taste. Context. Timing. Pattern recognition. Emotional intelligence. The ability to know what is technically possible versus what is actually right.
Prioritizing humanity
Real estate remains deeply human work. Clients are emotional. Markets are emotional. Timing matters. Trust matters. Nuance matters. The firms that succeed in the long term will not simply be the ones that automate the fastest. They will be the ones who integrate technology without losing judgment in the process.
I also recognize the privilege of navigating this moment as a private company. Public companies are under enormous pressure to optimize aggressively and demonstrate efficiency gains quickly. One of the advantages of remaining private is the ability to think longer term about implementation, culture, and people rather than reacting quarter to quarter.
That perspective has made me skeptical of overly simplistic conversations around AI. I do not think this era belongs exclusively to the largest companies. Historically, scale created advantages because large firms had greater access to engineering resources, infrastructure and proprietary systems. Today, many of those barriers are lowering quickly.
The advantage now may belong less to the companies with the largest technology budgets and more to the organizations willing to ask better operational questions. The real challenge for leaders is not simply adopting AI quickly. It is integrating it thoughtfully enough to create organizations that are more efficient, more adaptive, and still deeply human.
Dezireh Eyn is CEO of Platinum Forbes Global Properties and holds a Bachelor of Arts in Economics from New York University.
