Why Your Contact Center Stack Wasn't Built for AI

The contact center industry is in the middle of a reckoning. For years, organizations have been layering tools on top of tools. Telephony here, CCast there, CRM somewhere else, and AI bolted on top of all of that. And most leaders are only now realizing the architecture they built wasn't designed for the AI era. I'm Marcus Law for UC today. And today I'm joined by Arpana Bajage, partner group product manager for Dynamics 365 contact center at Microsoft. We're talking about what's really keeping IT and contact center leaders up at night, the hidden cost of fragmented stacks, and why you can't simply bolt AI onto a broken foundation. Welcome, Afana. It's great to have you with us today. Thank you so much, Marcus. Super excited to be part of this conversation. Yeah, we'll get right into it if it's okay with you. when when you're speaking with IT and contact center leaders today, what are they struggling with right now as AI becomes central to their strategies? That's a wonderful question to begin with. Marcus over the last many decades companies have been accumulating their tech stacks be it telephony vendors platforms collaboration tools CRM systems and very recently AI bots and all of them have been accumulated over the time of decades in a very different siloed manner and most leaders are realizing that accidentally they have built an architecture that is optimized for humans stitching pieces together as opposed to AI and this surfaces a very critical problem wherein fragmented systems are creating fragmented intelligence. A customer calls the contact center. AI doesn't even know what happened before that. The contact center rep doesn't know the workflow history, doesn't have the context. And the expert who is in the back office sitting inside teams isn't connected to the interaction at all. And suddenly what should have been a two-minute resolution turns into a 20-minut apology tour. That is what is keeping leaders up at night. Not just the c of cost of text stack, not the complexity of it. It's the entire architecture chaos that has been created. And this fear that their architecture wasn't built for the AI era and AI is going to expose every crack in the fragmented architecture is something that's uh that the contact center IT folks are finding it to be very challenging. It's it's a really, you know, difficult environment to be in, of course. and and you've touched on it really well there Al partner. So you know I suppose the next question to you is is why do you think organizations are suddenly rethinking this separation between UKASS and CCAs? That is because Marcus traditionally um UKASS and CCAs have lived in a different world. UKASS was employee communications and on the other hand CCASP was all about customer communications different teams different budgets and different vendors but customers don't want their experience to be driven by the experience of the organization's chart. A customer issue today often requires a contact center rep, a product expert, a field technician and maybe even a finance expert who's sitting inside teams all collaborating in real time and hence the old model breaks and AI accelerates this convergence because AI works best when communication and collaboration are connected. The industry is realizing something very simple but profound that the best customer experience usually happens when the entire company can participate in the service moment with consistent context. The future contact center is not sitting inside silo departments. It is the entire enterprise connected in real time to serve the needs of your customer. So I suppose Alana if we if we look at it as the market waking up to this there's a the question of what are the prices of staying where we are now. So my next question to you is what do you see as the real costs of ma of maintaining fragmented communication and contact center platforms? You know what you know that that risk of being held back. That is indeed a very uh correct question for us to be thinking about because if you take a look at the end toend journey of contact center life cycle of course there is a visible cost visible cost in terms of licensing cost to implement cost to maintain and there is cost of operations right but there is invisible cost which is more critical and that is the organization ational drag impacting the moment of of the transformation. Every disconnected platform creates duplicate administration, duplicate policies, duplicate reporting, duplicate integrations. But the bigger problem is the speed because every CXO that I talked to today has a mandate from their board to transform their customer engagement with AI and transform at a speed that AI is evolving. So every time you want to launch a new AI workflow, automate a process or improve routing, you're coordinating across five different vendors and 12 different integration layers. That's not innovation. That's archaeology. And in the meanwhile, customers feel the fragmentation immediately because when they hear, "Please repeat your customer account number. please uh let me transfer to a representative or a finance advisor sitting inside teams who doesn't have full context. The team can't see what happened. So customers may not know your architecture but they absolutely can experience it because of the fragmented exposure that you're giving across the end to end journey. Fragmented stacks, they don't just increase the cost, they slow down the company's innovation and that is the real cost of this fragmented communication and platform. You highlighted it right there at the answer. You know, the the real cost of these fragmented environments and when we look when organizations are looking at deploying AI, you know, there's there's clearly huge issues there. But I'd love you to sort of talk us through a little bit, you know, just why is it that organizations can't simply bolt AI onto these fragmented environments that you've been talking about? It's actually very simple and you may find it funny. As much as we would like it uh to be the case that AI is the magic wand, but it is not. AI is only as smart as the systems connected beneath it and the data that is fed to it. For years we have been saying garbage in garbage out. Right? So if the customer context lives in one system, telephone is in another system, collaboration is happening somewhere else, workflow is in yet another platform, AI becomes narrow and isolated. You end up with something that is perhaps a demo grade AI, a chatbot here, maybe a summarization there. Things sound interesting, but they're not transformational. Real AI transformation happens when AI can reason across the entire end-to-end journey in real time. And that requires connected systems, connected data, and connected workflows. A customer was telling me this the other day that Alpena I have AI in like five different systems in their entire contact center and customer management ecosystem. But when I ask a question about my customer to each of these systems, none of them agree with each other because the data is so fragmented. Right? It proves that the disconnected system systems only create disconnected AI which is not going to be transformational for your ecosystem. Before we wrap up, u where should people go to learn more or take a first uh sensible first step? Could you just tell you know the floor is floor is yours for a few seconds? Uh please feel free to connect with me on my LinkedIn profile. I post very often about the innovations we are doing in the customer engagement space, contact center space and the AI innovations we are infusing at every step of the customer journey. So looking forward to hearing uh from all of you there. Thanks for watching and if this video has got you thinking about your own contact center architecture, we've got a lot more on this topic at UC today. And if you want to hear our partner's take on what a connected platform actually delivers in practice, that's in our next video. Links are below and we'll see you there.