Does this sound familiar? “Digital transformation will streamline customer service tools and optimize manual processes.” If so, you’re in good company. McKinsey estimates that 90% of organizations are currently undergoing some form of digital transformation. But how has the promise of easier, more efficient work actually panned out?
For many, the reality couldn’t be further from expectation. Tools that were marketed to minimize employee effort and speed up time-consuming tasks have instead added new hoops and hurdles to already burdensome processes: more logins, more windows, more toggling between fragmented systems. And while each extra step may seem like a minor inconvenience, those small nuisances quickly add up—in the form of employee burnout, disengagement, and digital fatigue.
But that’s changing. Faced with ballooning tech stacks, customer service leaders are reevaluating their transformation strategies—including the role of artificial intelligence. With the rise of composable, zero-data AI architecture, decision-makers are shifting from a “one problem, one solution” approach to one that favors holistic, unified AI.
And it’s working.
Companies that have implemented a unified customer service AI program routinely outperform their patchwork peers in time-to-value and long-term value. (Need proof? Check out these case studies.) The reason: unlike fragmented point solutions, whose gains are offset by their integration limitations, unified AI integrates with new and legacy systems alike. The result: a fully unified customer service AI stack that transcends data siloes, software incompatibilities, and vendor-imposed restrictions to create an endless value pipeline.
Let’s take a closer look at how unified AI is fixing the fragmented customer service stack.
Today’s tech stack is a mixed bag of old and new tools.
Unless you’re a startup, odds are you’re already juggling several customer service tools and systems: contact center software from Company A; call analytics from Company B. Some are new, AI-driven investments, others are legacy solutions that have been around for years. Each exists in its own bubble with its own functionality, formatting, and data governance rules.
“The contact center tech stack is an eclectic mix of old, new, emerging, and tailored tools — all of which are being upgraded to be new and improved,” writes Vasupradha Srinivasan, Principal Analyst at Forrester, in a recent article. “AI tools often struggle to integrate with this complex ecosystem, leading to inconsistent data, broken workflows, and poor customer experiences.”
That’s a problem for customer service providers who are increasingly turning to AI to solve operational gaps and meet rising expectations for AI-driven experiences. And it’s one that requires a rethinking of AI’s place within the broader business stack.
The answer isn’t more tech. It’s unifying what you already have.
For years, business leaders have answered their customer service challenges the only way they knew how: with problem-specific solutions. Inconsistent call summaries? There’s a solution for that. Want to standardize QA compliance? Ditto.
While this approach may have worked for a handful of core use cases, for many it quickly snowballed into an ever-growing tech stack. Today, it’s not unusual for customer service agents to use more than 20 applications, toggling between apps as much as 10 times an hour, according to CMSWire. This “app fatigue”, as it’s been called, isn’t just a source of agent frustration. It’s also a primary reason why only 37% of customer support executives said they were satisfied with their current tech stack in a recent Forrester study. Clearly, the answer to tech overload isn’t more tech.
But what do you do with all those applications? Scrap them and start over? Accept the inefficiency as the cost of doing business? Or do you connect them under a single, unified platform? That’s exactly what today’s customer service leaders are doing.
Fixing the fragmented customer tech stack with AI
AI has the power to simplify complex processes and unify disparate data sources. It also has the potential to become just another point solution. The difference comes down to architecture and strategy.
In order to create a unified AI platform, AI must be able to access customer service data from anywhere, in any form. Uniphore’s Business AI Cloud solves this challenge with a composable architecture that enables organizations to harness their data exactly where it resides, without moving or transforming it. By making data composable, the platform breaks down siloes and connects fragmented tools across the customer service tech stack.
Using this unified platform, customer service leaders can then apply AI strategically, identifying and executing high-impact use cases and validating results and methodologies. What’s more, they can do this on their own or by using pre-built customer service solutions housed within the platform’s Business AI Suite. Designed specifically for today’s (and tomorrow’s) leading customer service use cases, these solutions include:
Real-time agent guidance
Also known as agent assist, real-time agent guidance optimizes live customer service interactions by providing agents with in-call recommendations, next-best action prompts, and workflow automation. Advanced solutions, like Uniphore’s Real-Time Guidance Agent, go even further with pre-call intelligence, knowledge assistance, and AI-powered conversational summarization. Businesses that deploy real-time agent guidance have reported double-digit improvements in average handle time (AHT), hold times, and other key metrics.
Intelligent self-service
Today, self-service (i.e. chatbots, intelligent virtual assistants, etc.) is a customer service mainstay, empowering customers to resolve their own issues while keeping low-value queries out of busy call queues. Intelligent solutions, like Uniphore's Self-Service Agent, combine conversational, generative, and emotion AI to create truly human-like interactions—complete with empathetic and contextual reasoning—that drive faster, fuller resolutions at scale.
Conversation insights
Conversation analytics (also called interaction analytics) is a popular use case for organizations seeking to better understand customer behavior, preferences, and motivators. Unlike traditional analytics tools, which require rigid keywords and manual sampling, Uniphore’s Conversation Insights Agent allows business users to uncover call drivers, analyze customer sentiment, and detect topic trends using simple, natural language queries.
Communication recording
Customer service organizations have been recording calls for compliance and quality assurance for years. However, most legacy recording platforms weren’t built with AI in mind. Uniphore’s Communication Recording Agent bridges the gap between yesterday’s tech and today’s AI-driven reality, enabling businesses to accurately capture and reformat 100% of customer communications for AI usage.
Unified AI is the future of customer service AI.
Remember that Forrester stat about 37% of executives being satisfied with their current tech stack? That’s not good. Customer service leaders today are tired of fragmented solutions that overpromise and underdeliver. And they’re starting to do something about it. Instead of molding their processes to accommodate a patchwork of disconnected programs and platforms, they’re rearchitecting the entire ecosystem to meet their operational needs.
And it’s working.
That’s the power of unified AI—it brings the chaos of the modern tech stack to order by overcoming its biggest AI integration barriers. Now, organizations can leverage AI across their entire customer service stack without limitations or restrictions. With a composable, AI-ready architecture, they can maximize the value of every application—old and new alike—and explore new use cases that were previously unimaginable.
Ready to unify your tech stack?
See how Uniphore’s unified Business AI Suite for Customer Service can help.
