Shahar Goldboim
April 20, 2026
There’s a real surge of excitement right now around vibe coding in the STR industry. The pitch is simple, and it’s landing.
Connect something like Claude to your PMS. Build your own agents. Create workflows that actually match how your business runs.
No more waiting on product roadmaps. No more working around rigid traditional property management systems. Just plug in and go. For an industry that’s spent years adapting to its software, but wanting bespoke tools, that idea is powerful. But it also needs a reality check.
The narrative is ahead of the reality
Right now, the conversation is being driven by what is newly possible: open APIs, AI models, MCP servers, and the growing ability to connect them together. That creates a compelling story; one where operators can finally take control and build around their existing systems.
It focuses on access, connectivity, and outputs, without addressing what it actually takes for any of this to work reliably inside a real business. And that gap — between what looks possible and what is operationally dependable — is where the risk sits.
Connecting AI is not the hard part
It is now relatively straightforward to connect AI to a PMS, move data between systems, and trigger actions. That is what most of the current excitement is built on.
What is not straightforward is everything that follows. Because the moment these workflows move beyond testing and into day-to-day operations, they are expected to behave consistently in an environment that is anything but predictable. Data changes constantly. Processes overlap. Decisions have downstream consequences and connected systems still require consistent human oversight.
At that point, the standard shifts from “does it work?” to “is it right?”
And that is a much harder problem to solve.
This is where STR complexity catches up
Short-term rental businesses do not operate in clean, controlled systems. They run on moving parts — guest communication, cleaning, maintenance, availability, pricing — all interacting in real time, often with incomplete or changing information.
Layering AI on top of that does not simplify the problem. It pulls that complexity directly into whatever you are building. Which means any workflow or agent is only as reliable as its ability to handle that reality, not just when everything aligns, but when things don’t.
Experimentation is being mistaken for readiness
A lot of what is being built right now is experimental. That is expected, and in many ways, necessary. The issue is how quickly those experiments are being interpreted as something operationally dependable.
There is a meaningful difference between seeing something work in isolation and trusting it to support real decisions inside a business. Right now, there are no guarantees around accuracy, consistency, or stability over time. And without those guarantees, what you have is not a system you can rely on; it is something that requires constant validation.
MCP servers
The same pattern is playing out in the conversation around MCP servers. They are being positioned as a major unlock — a way to connect AI more deeply into PMS platforms and build more advanced capabilities.
But that expectation needs to be grounded. MCP servers do not provide full operational control. They expose access to certain data and actions within defined limits. In many cases, that access will remain constrained by design.
That makes them useful for specific use cases: integrations, data retrieval, limited automation. It does not make them a foundation for running operations. Treating them as if they are leads to a mismatch between expectation and reality.
The second wave is where problems emerge
A small number of technically capable teams will build things that work well. But that is not what defines the broader industry outcome.
What follows is a second wave: people attempting to replicate those setups without the same depth of understanding or control over the underlying systems. That is where instability appears.
Workflows that depend on ideal conditions. Logic that breaks when inputs change. Systems that require constant monitoring to prevent mistakes. They function, until they are exposed to real operational pressure.
This is where it becomes dangerous
Because what is being quietly introduced here is not just a new way of working. It is a new responsibility. Operators are being pushed into a position where they are expected to design, validate, and maintain systems that directly impact how their business runs.
They are expected to think like system architects, data engineers, and QA teams, on top of running day-to-day operations. That is not a small shift. It is a fundamental change in what the job becomes. And most operators do not have the time, the resources, or the mandate to take that on properly.
The burden doesn’t disappear — it multiplies
One of the biggest misconceptions in this space is that layering AI into workflows will reduce operational burden. In practice, it often does the opposite.
Because now, alongside running the business, someone needs to:
- monitor outputs
- validate decisions
- troubleshoot failures
- adjust logic as conditions change
The complexity hasn’t gone away. It has multiplied, and it now sits closer to the core of the operation.
Where Boom takes a different position
This is exactly where Boom takes a fundamentally different approach.
The goal is not to give operators more ways to build on top of their PMS, experiment with AI agents, or stitch together workflows themselves. That approach assumes that the operator should take on the responsibility of designing, validating, and maintaining systems that are complex by nature.
Boom is built on the opposite premise. Operators should not have to become system architects in order to run their business effectively.
Instead, Boom is designed as an operational system where automation, decision-making, and execution are structured and connected. The ability to customize how the business runs is there, but it does not come from building agents from scratch or managing layers of AI yourself. It comes from working within a system that already understands the operational complexities and can carry it through reliably.
In other words, the flexibility exists, but without transferring the burden of building and maintaining it onto the operator. Because the real opportunity with AI in this industry is not giving people more tools to assemble, but removing the need to assemble them at all. The shift is simple: instead of operators adjusting to the system, the system adjusts to how they run their business — with full flexibility, starting with BAM Studio.
A necessary reset
Vibe coding reflects a real frustration in the industry, and that frustration is valid. Operators want systems that match how their businesses actually operate. But right now, the narrative is getting ahead of what is operationally proven. And that matters.
Because in this industry, things do not fail in theory.
They fail in real time.
What’s becoming clear is that “vibe coding” is not the end state — it’s a transition. It moves the industry from adapting to software, to assembling it. But assembling systems is still work, and over time, that burden doesn’t scale.
The next phase is different. It’s not about better ways to build workflows, but about systems that already understand how your business operates and can carry execution for you. Where the operator defines intent, constraints, and priorities — and the system runs with it.
In that shift, flexibility doesn’t disappear; it becomes embedded. And the need to “build” your software starts to fade, not because it failed, but because it’s no longer necessary.
The real shift isn’t from rigid software to flexible software — it’s from software you operate, to systems that operate with you.
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