For years, the default answer to “we need feature X” was “subscribe to a SaaS for that”. It’s still the right choice in many cases.
What’s changing is the cost of exploring the alternative. With modern AI tools, the jump from “this workflow creates recurring friction” to “we have a working prototype” is dramatically smaller than it used to be — often minutes or a few hours, not weeks. Not because AI ships production systems by itself, but because it compresses the slowest part of the process: turning intent into a runnable, integrated draft.
That shift matters most for simple, niche products: the kind of tool that is useful, but only if it fits your process. When the process doesn’t fit the default, you either pay for a higher tier, stitch together workarounds, or live with friction. AI opens a fourth option: build a small tailored solution and keep refining it as your needs evolve. Even if you still choose a SaaS, a fast prototype is a powerful way to clarify requirements and evaluate what you’re actually paying for.
A concrete example: calendar booking
Calendar booking is a great example because it looks simple from the outside: show availability, let someone pick a slot, send an invite. There are plenty of great services here (Calendly and many alternatives). Free tiers tend to be limited (customization, features, number of connected accounts), which is completely reasonable — it’s how pricing works — but it often clashes with the messy reality of how people actually manage calendars.
If you want a quick overview of options and tradeoffs, this roundup is useful: https://www.usemotion.com/blog/calendly-vs-tidycal
I wanted a booking flow on my own site that matched my constraints precisely: one simple call type, availability merged across multiple Google accounts and calendars into a single view of open slots, a few pragmatic scheduling rules (weekdays, working hours, slot length, buffer time, minimum notice, how far ahead to show), and a lightweight approval step: the visitor requests a time, I confirm it, and only then the calendar invite (with a Google Meet link) is sent.
In practice, that approval step matters. After a visitor selects a slot and submits the form, I receive an email with the request details and two links: confirm or decline. If I confirm, the system re-checks the slot, creates the event in Google Calendar, and sends an official calendar invitation email to the attendee (and updates my calendar). If I decline, the requester gets a polite “not available” email and can pick another time.
Here’s what the public booking UI looks like:
Building the first version was fast. The bigger win was what came after: because the feature lived inside my project, it was easy to keep tailoring it.
I added a small admin page to update availability settings without redeploying: weekdays, start/end time, base slot length, buffer time around busy events, minimum notice, maximum days ahead, and the “hold” time used to prevent double booking while a request is pending.
The scheduler also supports connecting and disconnecting multiple Google accounts. That’s the whole point for many people: your availability is often split across several calendars, sometimes across personal and work accounts, and you want one merged “free/busy” view.
And then there’s the workflow nuance that generic tools rarely optimize for: sometimes the best scheduling flow isn’t “pick any slot in the next 30 days”. For group planning, I prefer sending a single link that’s locked to a specific week and a specific duration (30/60/90/120 minutes), so everyone looks at the same window.
So I added an admin tool that generates a signed, shareable weekly link and previews the availability before sending it:
Here’s what the recipient sees: a weekly overview, limited to that window:
None of this is “magic AI”. The work is still real — OAuth, edge cases, time zones, validation, rate limiting, and the operational details that make a feature safe to run on a public website. AI simply changes the pace at which you can iterate through solution options and land on something that fits.
The biggest personal shift for me is what I’m willing to attempt. In the past, I would not have gone down the path of implementing a booking system from scratch, because I already knew how many hidden user flows and “small” edge cases appear only after days of debugging and testing in a real application. With AI assistance, many of those nuances were covered early — nearly perfectly, and fast enough that iterating toward a reliable solution felt practical.
The bigger point: reclaiming workflow ownership
This is why AI is unbundling parts of SaaS. Not because subscriptions vanish, but because the price and time demands of building small, tailored workflows has dropped enough that it becomes a sensible option far more often.
For SaaS founders, it’s a reminder that defensibility rarely comes from a single feature. Distribution, deep workflow integration, trust, compliance, and operational excellence matter more when feature gaps can be closed quickly.
For buyers and small businesses, it’s an invitation to be more ambitious. Pick one recurring workflow where the tool forces compromises. Describe how you wish it worked. Prototype the smallest useful version. If it proves valuable, treat it like a product: add guardrails, monitoring, and the integrations that make it part of your system instead of another disconnected tool.
The payoff isn’t only saving on a subscription. It’s moving from “renting a workflow” to owning one — and using AI to make the path from idea to working software fast enough that experimentation becomes practical. The highest leverage comes from pairing that speed with good judgment: scoping, UX, and the operational details that keep a small tool reliable.