Stop speed-dating Agents
Breaking up is hard to do
In 2025, our customers plugged 34 Agents other than Fin into Intercom. Thirty-four vendors, all promising to be the answer, all competitors to Fin. Most of them are gone.
Companies are speed-dating Agents, falling in and out of love like college students. They’re making decisions in weeks that used to take quarters. They sign a contract, go live, and then a few months later, do it all over again with someone else.
Not because the technology failed, and not entirely because the market shifted. But really, because the first decision was never really a decision, it was an experiment that resulted in a contract. They are flirting with Agents, not getting serious.
Welcome to the era of provisional AI buying. And if you’re an executive with an AI strategy, you should be deeply uncomfortable with what that means, because breaking up is way harder to do than you might think.
Commitment phobia
The Agent market now has over 100 vendors with real customers. These are not just paper launches and demo videos; these are actual production deployments handling real support volume, and this figure doesn’t even count the myriad build-your-own attempts in the market.
You could argue this is healthy competition – there are more options, there’s more innovation, better outcomes for buyers. In reality, what’s happening is fragmentation. Vendors are finding footholds by being narrowly easier: faster to set up (maybe); cheaper per ticket (at first); simpler to manage (at first); more comfortable for teams that aren’t ready to fully commit to AI.
Some win by specializing in a single vertical. Some win by offering pricing that removes the risk of commitment. Some win by showing up in the room before anyone else – through investor networks, founder connections, or sheer speed – and capturing attention or creating FOMO before a proper evaluation happens.
These are real competitive advantages in the moment, but they erode over time. Speed-to-deploy doesn’t matter at month six, low commitment pricing stops being an advantage when you need the product to do more, vertical specialization narrows what’s possible, and showing up first only works until someone starts asking harder questions.
This is a market where decisions get made fast and unmade almost as fast. We see this firsthand because in addition to Fin, we also have a helpdesk platform in Intercom, where customers can install competitor AI tools into their workspace. Our Intercom data shows us how long those competitors stay plugged in.
There’s a not-uncommon pattern we see: exploration peaks a few months after deployment but the honeymoon period is short. Vendors who sold in based on a speed or simplicity advantage get quietly dumped because they’re not robust enough for the operational reality.
The buying is provisional, and provisional buying is expensive in ways most people haven’t calculated. It has a unique switching cost.
The switching costs nobody’s pricing in
Software companies spent decades building switching costs into their products strategically. Switching is destructive, it’s a pain point for the buyer, and in SaaS it means a mess of data migration and retraining. It’s painful, but there are limits to the pain – just enough to make it hard to leave.
But with Agents it’s different. When you run an Agent in production, your organization adapts around it. Your knowledge base gets structured for how that system retrieves information. Your escalation workflows assume specific handoff patterns. Your team evolves based on how the agent behaves – when it handles things well, when it needs help, etc. Over time, your support operation and the Agent become interdependent.
This is a good thing. It’s actually the whole point. With a good Agent, the product and the team learn together, and collectively, their performance compounds through use. But it means that every month of operational learning you’ve accumulated and all the collaboration that was poured into the Agent gets zeroed out when you switch.
Companies that treat Agents like interchangeable utilities – swap one in, swap one out – are discovering that the accumulated operational intelligence matters more than the sticker features.
Why the fast wins don’t last
In any market with high uncertainty and a lot of vendors, there’s a playbook for winning early: get in the room before the competition. Lower the barriers to saying yes (often by slashing prices at the start), make the first deployment feel effortless, worry about depth later.
It’s a rational playbook. And right now, it’s working – for a certain definition of “working.”
Some vendors are winning by positioning as the zero-effort option: no knowledge base cleanup needed, no workflow design, no complex configuration. Just turn it on.
These wins look convincing at the point of sale. But the pattern that follows is remarkably consistent. Month one looks great. The Agent is live, it’s handling volume, the team feels relief.
Month three, operational reality starts to surface. Escalations aren’t clean, edge cases pile up, the things that were easy to set up turn out to be hard to refine, and security or compliance gaps that weren’t visible in the initial deployment start to matter.
Month four or five, someone starts looking at alternatives. Not always with urgency, more like a slow loss of confidence. A quiet exploration.
And then the cycle repeats.
The vendors winning on speed aren’t building durable customer relationships. They’re building a pipeline of future re-evaluations.
The fragmentation trap
The market isn’t consolidating around a few strong players. It’s splintering.
Some companies are now running three AI tools simultaneously: one for B2B customers where quality matters, another for B2C where cost matters, and a third for internal operations. Three knowledge bases, three sets of analytics, three systems that don’t learn from each other.
This looks like maturity, but it’s actually a compounding tax on complexity. The operational overhead of managing multiple tools grows while the performance of each individual tool stagnates, because none of them have the full picture.
The fragmentation isn’t evidence that the market needs dozens of specialized Agents. It’s evidence that most buyers haven’t yet committed to a platform that’s deep and broad enough to replace the patchwork.
What separates the Agents that stick
If this is the era of provisional buying – and the evidence strongly suggests that it is – then the question for every executive evaluating AI is straightforward: what kind of product survives provisional buying?
Not the fastest to deploy. Speed wins the first contract, not the renewal.
Not the cheapest per interaction. Low price wins in segments where support is a pure cost line, but it puts a ceiling on what the product can do, and eventually, companies outgrow that ceiling.
Not the most specialized. Vertical focus wins a niche, but it also limits what’s possible as your needs expand.
The products that survive provisional buying are the ones that compound.
Performance that improves the longer you use them, not just because the models get better, but because the product learns from your specific operation.
Resolution rates that climb month over month as the system absorbs more of your knowledge, more of your edge cases, more of your customers’ patterns.
Self-management that reduces your team’s burden over time, rather than adding to it. The best Agent goes beyond just resolving conversations, it reduces the operational overhead of running AI support. It gets easier to manage at scale, not harder.
And a platform broad enough that you don’t need three tools for three problems. One system that handles chat, email, voice, and the internal workflows around them. One knowledge base, one set of analytics, and one place where every interaction makes every future interaction smarter.
Robb Clarke, Head of AI at RB2B, had the same hesitation a lot of leaders have going in:
“Initially, I was hesitant about using Fin. I’ve experienced the last decade or so of terrible chatbots and didn’t want our users to have a poor experience of getting stuck and frustrated. But now I know that Fin is completely different. It allows our users to solve problems anytime, and if I could go back, I would have integrated it into our process much sooner. What really made me realize that it was working was suddenly my days weren’t consumed with solely answering support tickets.”
That’s what it feels like from the inside: a gradual shift in what running support actually looks like.
This is the compounding test. At month six, is your Agent better than it was at month one – because you’ve been using it? Or is it exactly the same, and you’re doing all the improving yourself?
The bet you’re actually making
Every company deploying AI in customer support right now is making a bet, whether they realize it or not. Not about which vendor has the best feature list. Feature lists converge – they always do. The bet is about trajectory.
There are products that reward commitment. Resolution rates that climb month over month. Operational overhead that decreases as the Agent handles more.
And there are products that feel good on day one and plateau at day ninety. Where switching feels painless because, truthfully, not that much would be lost.
The companies still cycling through Agent trials when this market consolidates won’t just be behind. They’ll have spent a year accumulating nothing.
If you haven’t asked that question yet, you’re probably on your second or third Agent already. And you’re about to be on your fourth. The problem travels with you. Switching vendors doesn’t fix a readiness gap, it just resets the clock.
 | A guest post by
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