How is regulation changing the way enterprises think about autonomous customer experience?

Again, a really, I think, very important point in today's day and age, and I would say there's regulation that is getting even more stringent every day, right? So for example, if you look at the EU AI Act, it talks about AI being utilized, but also not being able to do a lot of things.

So for example, we spoke about understanding the emotional level of your customers.

So you can understand the emotion of your customers on an overall sentiment basis, but you cannot actually check the voice tone.

Also, the agents, you cannot actually do any kind of emotional tracking of your agents.

So it's really important that whenever you choose the right solution, you're looking at, is it truly compliant with what exactly you want to offer? Does it have the bells and whistles? Because even today, when we talk to our customers, a lot of the software that they buy is very US-centric.

Now, if you take a US-centric software, they've never looked at all these aspects around EU AI Act, around what exactly can you track, what can you not track? What are the boundaries of what exactly can be enabled and not enabled? And taking that further, We see a lot of concepts of AI agents, and you've basically got a lot of CCaaS, contact center players also talking about how they have these amazing AI agents, which is like a nice wrapper around ChatGPT, which you can basically throw in a question and gives you a nice response.

How can sovereign AI and flexible deployment models help enterprises maintain greater control over their data and AI systems?

What are your thoughts on that?

Yeah, I think if we talk to a customer, the parts that we try to educate them that are really important that they should look for are that firstly, can it scale with your organization? So that means that does it work for multiple languages? Does it work for multiple channels? Will you have to keep recreating the AI for every different piece or can you truly scale? Because that's going to be the difference of hey, this is an amazing solution and we will grow with this and this will create great value.

Or, hey, this solution needs eight to 10 FTEs, we don't have it, so we'll just let it run and this will die, right? And that's why I think that part is really important.

And the second piece is around data privacy.

Now, again, most companies think of AI as, okay, cool, I can choose the LLM.

I can just basically get the API and we can make it work.

But when you actually go a bit deeper into what truly is required to deploy these solutions, you require auditability, you require data segregation and data hosting.

So I'd love to hear your thoughts on that.