One thing I’d add is that the handoff itself can make a big difference, if AI can pass the conversation history and what’s already been tried to the human agent, the customer doesn’t have to start from scratch, that small detail can make the whole AI to human experience feel much smoother.

I agree with that, especially because customers usually care more about getting their issue resolved than how quickly they get a reply, if AI can save time while actually solving the problem that’s a real improvement, if it just gives a quick answer that leads to another support request, the speed doesn’t mean much.

I’d say ease of maintenance is a huge factor too, a platform can look impressive at launch, but if it takes constant manual work to keep the AI accurate and useful, the benefits start to disappear, the best systems should improve without creating another full time job for the support team.

One thing I’d add is consistency, even if an AI gives the right answer most of the time, customers will lose trust if its responses change depending on how the question is phrased being predictable, honest about uncertainty, and following the same policies consistently is what makes it feel reliable over time.

The predictive help part is really interesting, catching a customer’s problem before they have to stop and ask for help could make the whole experience feel much smoother, especially when they’re already stuck or unsure what to do next.

The handoff preserving context is such an important point, if the customer has already explained everything to the AI, making them repeat the whole story to a human just adds another layer of frustration, AI should make the human agent’s job easier, not make the customer start over.

The real advantage of AI native software is probably less about being “better” and more about reducing the amount of work the user has to do, if the AI can understand the goal, handle the routine steps, and still give people control when needed, that’s a much bigger improvement than simply adding another AI button to existing SaaS.

The $300/month figure is definitely eye catching, but the bigger story is how quickly AI is changing the build-vs-buy decision, for a focused internal workflow, having software built exactly around the business can be a huge advantage, the long term maintenance and security side will be what determines whether these savings really hold up.

The hybrid approach probably makes the most sense, AI built software is great for those specific internal workflows where existing SaaS feels like overkill, while core systems still benefit from a reliable vendor handling security and maintenance, the interesting shift is that “build vs buy” is no longer as clear cut as it used to be.

The hidden ownership costs are definitely worth considering, building a custom tool might be cheaper upfront, but maintenance, security, updates, and having someone responsible for it can add up quickly, the interesting part will be seeing where the long term cost of AI built software actually beats SaaS, not just where the initial build is cheaper.

The customization angle is probably one of the biggest threats to traditional SaaS, if businesses can build something around their exact workflow instead of adapting their processes to a generic product, that changes the value equation, the real challenge will be maintaining, securing, and integrating those custom tools as the business grows.

The integration side will probably be the real test here, AI agents can execute work, but they still need to work smoothly with existing systems and messy legacy infrastructure, if HappyRobot can pull that off across very different industries, that would be a strong sign this is more than just another AI hype cycle.

The infrastructure point is really important here, a powerful AI agent is only as useful as the systems and data it can actually work with, that’s where the difference between enterprise platforms and more accessible tools becomes clear, for smaller teams, being able to set up useful, action based workflows without taking on a huge implementation project can be a much bigger advantage than having every enterprise feature available.

Honestly, I think the knowledge base part is easy to overlook, everyone talks about how smart the AI is, but if the company’s info is scattered or outdated, the AI is going to struggle too, sometimes getting the data organized is probably a bigger job than setting up the AI itself, that hidden work can really change how valuable an AI support tool ends up being.

I think the browser becoming the main place we work is probably one of the biggest changes SaaS brought with it, it’s easy to take for granted now, but being able to open your tools from almost any device and just get to work is a huge shift from traditional software, do you think SaaS would have grown this quickly without the rise of remote work?