AI Strategy Isn't AI Shopping.
By
John Fischer
·
3 minute read
Why buying more AI tools won't make your brokerage more innovative
Spend ten minutes on LinkedIn and you'll come away with the impression that every brokerage is falling behind.
A new AI platform launches.
Another promises to automate lead follow-up.
One claims it can recruit agents more effectively.
Another can summarize meetings, answer emails, write listing descriptions, create marketing campaigns, analyze contracts, build websites, or generate social media content.
Every week seems to bring another announcement that sounds impossible to ignore.
For brokerage leaders, it's exhausting.
Not because the technology isn't impressive.
Because every demonstration ends with the same unspoken question:
"Are we supposed to be using this too?"
I've started noticing a pattern in the conversations I have with brokerage owners (and other business owners).
Very few are asking how AI works.
Most are asking where to begin.
They're trying to separate signal from noise.
They aren't looking for another app.
They're looking for confidence.
Because AI strategy has almost nothing to do with collecting AI tools.
It has everything to do with deciding where artificial intelligence belongs inside your business—and where it doesn't.
Imagine walking into a professional kitchen.
There are dozens of knives.
Each serves a different purpose.
A chef doesn't buy every knife because it exists.
They choose the ones that improve how the kitchen operates.
The goal isn't to own more equipment.
The goal is to prepare better meals.
Technology works the same way.
Over the past 6 months, I've watched many organizations unintentionally create what I think of as AI clutter.
One department starts using ChatGPT.
Marketing experiments with another platform.
Sales adopts something different.
Operations discovers an AI note-taking tool.
Recruiting finds its own assistant.
Soon everyone is using artificial intelligence.
No one is using the same artificial intelligence.
Leadership has no visibility into where company information is being entered, how consistent the outputs are, whether anyone is paying for duplicate capabilities, or whether any of the tools actually solve meaningful business problems.
From the outside, it looks like innovation.
Inside the organization, it often feels like fragmentation.
This is one of the reasons I encourage leaders to resist making AI their strategy.
Artificial intelligence is simply another capability.
The strategy is deciding what your business needs to become.
Those are two very different conversations.
Before evaluating a single AI platform, I think every leadership team should answer a handful of questions.
Where does work consistently slow down?
Which tasks consume the most time without creating much value?
Where do employees repeat the same work every day?
What information is difficult to find?
Which decisions depend on one person because no one else knows the process?
Those questions existed before AI.
They'll still matter after today's tools are replaced by tomorrow's.
One of the biggest misconceptions about AI is that it's primarily about automation.
Sometimes it is.
More often, it's about removing friction.
Helping someone find an answer in seconds instead of fifteen minutes.
Making information easier to access.
Reducing unnecessary handoffs.
Improving consistency.
Giving people better context before they make decisions.
Those improvements rarely make headlines.
They quietly make organizations better.
This is also why copying another brokerage's AI roadmap rarely works.
On the surface, two firms may look nearly identical.
Similar size.
Similar markets.
Similar agent counts.
Behind the scenes, they may operate completely differently.
One struggles with recruiting.
Another with compliance.
Another with onboarding.
Another with transaction management.
Another with leadership capacity.
The technology that creates meaningful value for one organization may create very little value for another.
Context matters.
I've become convinced that AI projects should begin the same way many product decisions begin.
Not with software.
With observation.
Watch how work moves through the organization.
Pay attention to where people create spreadsheets because existing systems don't help them.
Notice which questions are asked repeatedly.
Identify where employees spend their mornings copying information from one platform into another.
Those moments tell you far more about AI opportunities than any software demonstration.
One exercise I sometimes recommend is surprisingly simple.
For one week, ask your leadership team to write down every repetitive task they perform.
Not the important work.
The repetitive work.
Approving the same requests.
Answering the same questions.
Searching for the same information.
Moving the same files.
Checking the same documents.
By Friday, patterns begin to emerge.
That's where the AI conversation should start.
Not because every task should be automated.
Because every repeated task deserves to be questioned.
There's another reason to think this way.
The AI tools you're evaluating today won't be the tools you're using five years from now.
Some companies will disappear.
Others will be acquired.
New capabilities will emerge that make today's software look surprisingly limited.
If your strategy depends on one specific platform, you'll eventually have to rebuild it.
If your strategy depends on understanding your business, adapting becomes much easier.
When people ask whether they should adopt AI, I think they're asking the wrong question.
The better question is this:
What kind of organization are we trying to build?
Faster?
More consistent?
More responsive?
More scalable?
Better informed?
Once those answers become clear, artificial intelligence becomes much easier to evaluate.
Not because every tool suddenly looks valuable.
Because most of them don't.
The brokerages that gain the greatest advantage from AI won't necessarily be the first to adopt every new capability.
They'll be the ones disciplined enough to ignore most of them.
They'll understand that innovation isn't measured by the number of AI subscriptions on the company credit card.
It's measured by whether the organization actually works better.
Technology can absolutely help make that happen.
But only after leadership decides what "better" looks like.
At Pickup Studios, we don't begin AI conversations by comparing software. We begin by understanding how your organization operates, where work slows down, and what problems are actually worth solving. Technology comes later.