We often hear that AI is where it was in 1993, but the comparison feels increasingly accurate. We all know that something important is happening. Few people yet actually know what the final form will look like. But the pressure to make it look fluent, implement, and monetize is already here.
As a CEO, you feel that pressure all the time now. Every conversation ultimately comes back to the same question.
Where can we be more efficient? What can we automate? Where can we reduce costs? How much operating leverage is possible?
Underlying this is another reality that people don’t feel comfortable saying out loud. No one wants to be the business owner who underestimates AI.
What I found interesting was that the conversation quickly turned to replacement before most companies even considered implementing it. The story became binary very quickly. AI will replace jobs. AI will eliminate departments. AI significantly reduces operational costs.
Theater AI
At the same time, we are already seeing a huge amount of AI theater happening across industries. Companies are racing to position themselves as AI-native, AI-powered, and AI-first. This can be seen in investor materials, recruiting literature, conference panels, product announcements, company messaging, and more. Fluency performance is often achieved faster than actual operational fluency itself.
At times, it feels more like a piece than a technological revolution. Beautiful set design. Persuasive dialogue. There is little discussion of what actually happens behind the scenes.
Eventually, perhaps some of the bigger predictions will come true. But from where I sit today, it seems to me that many companies still misunderstand where the real operational opportunities actually lie.
Maximum efficiency is not necessarily obtained by replacing entire groups of people overnight. They come from reducing the thousands of tiny frictions that silently slow down organizations every day.
Internal Reporting Data Integration Marketing Production Administrative Repetition Knowledge Search Workflow Bottlenecks Information Trapped Across Disconnected Systems
I think the conversation will be more practical and more interesting here.
At Platinum Forbes Global Properties, we have spent a lot of time building an internal context system that is optimized for language-based models.
Training materials, brand voice, hiring philosophy, market intelligence, company history, and operational frameworks are organized to help teams work more intelligently and efficiently. Not because we believe AI will replace human judgment, but because organized intelligence enhances human influence.
I have also encouraged department heads to use these tools in very specific ways. Not to become a software engineer, but to solve micro-inefficiencies within my workflow.
Recruiting leaders have the potential to create a better overall system for candidate conversations. Marketing departments can potentially save time on repetitive production tasks. Small operational benefits add up consistently across an organization and become very significant over time.
analysis and experiments
Ironically, the hiring landscape was also much more uneven than many expected. Some of the most analytical, systems-oriented people I know are surprisingly resistant to these tools, while others with much less technical backgrounds are willing to experiment and quickly adapt.
This difference often has less to do with technical ability and more to do with tolerance for temporary inefficiencies, as AI implementations often become inefficient before they become efficient.
In real estate marketing, virtual staging has become one of the clearest examples of both the promise and friction of AI implementation. Brokers and marketing teams can now use AI to digitally place and style furniture in empty apartments, rather than physically bringing furniture into the space.
In theory, it sounds like it could be done quickly and seamlessly. That means faster delivery times, lower costs, and unlimited flexibility. But in practice, the output can become strangely distorted. I feel like the proportions of the furniture are slightly wrong. Displays architectural details that do not exist.
Fixing these issues will take more time initially. That’s exactly why some people abandon the process too quickly. The organizations that are most advantaged today are often those that are willing to slow down long enough to learn what impact these systems are actually producing.
This distinction is important because I believe that leadership itself is changing. Strong leaders also increasingly need to be systems thinkers who can identify operational frictions, organize information intelligently, experiment quickly, and improve workflows in real time.
Not because technology will replace leadership, but because leadership requires a deeper understanding of how technology and human judgment interact.
Despite all the rhetoric about automation, I actually believe that human judgment is becoming more valuable, not less. The more AI-generated content, analytics, images, and communications flood the market, the more discerning will become important. taste. context. timing. pattern recognition. emotional intelligence. The ability to know what is technically possible and what is actually correct.
prioritize humanity
Real estate is deeply influenced by human work. Clients are emotional. Markets are emotional. Timing is critical. Trust is key. Nuance is important. The companies that succeed in the long run are not just the ones that automate the fastest. They will be the ones who integrate technology without losing their judgment in the process.
I also recognize the privilege of being able to navigate this moment as a private company. Publicly traded companies are under tremendous pressure to proactively optimize and quickly demonstrate efficiency gains. One of the benefits of being private is that we can think long-term about implementation, culture, and people, rather than reacting quarterly.
That perspective has made me skeptical of oversimplified conversations about AI. I think this era is not just for major companies. Historically, scale created advantages because large companies had easier access to engineering resources, infrastructure, and proprietary systems. Today, many of those barriers are rapidly lowering.
The advantage now may lie with organizations willing to ask better operational questions, rather than those with the largest technology budgets. The real challenge for leaders is not just to rapidly deploy AI. It’s thoughtfully integrated to create a more efficient, more adaptive, and more human organization.
Dezireh Eyn is CEO of Platinum Forbes Global Properties and holds a BA in Economics from New York University.
