From Data to Orchestration: How Contact Centers Unlock the Intelligence They Already Have
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FROM DATA TO ORCHESTRATION: HOW CONTACT CENTERS UNLOCK THE INTELLIGENCE THEY ALREADY HAVE
Featuring Insights by Andrew Traba VP of Product Marketing NiCE
Scott Russell Chief Executive Officer NiCE
Michele Carlson Director of Product Marketing NiCE
Introduction As high as 99% of organizations are maintaining or increasing their AI spend. To most, this would seem to be a positive sign for better customer experience. However, the results tell a different story. According to CCW Digital research, 56% of customers state that experiences are the same or worse than they were last year. While AI has all the promise to support better experiences, most organizations’ operating models are actively keeping them from realizing that potential. Most organizations investing in AI for customer experience bolt new capabilities onto their existing systems. Instead, to realize the positive impact of AI on customer experience, their investment must be part of a holistic strategy with AI as part of a unified system, rather than one element in a growing stack of technology. CCW Digital research demonstrates how widespread a problem tool-led tech stacks have become with 60% of organizations increasing their use of disparate contact center products and tools over the past three years. The result is a phenomenon NiCE’s Andy Traba, Vice President of Product Marketing, calls the Frankenstack, “A collection of disconnected AI tools, bots and point solutions stitched together over the years, where every piece does its own job and none of them learn from one another or operate as a system.” “AI has stopped being the differentiator,” Traba adds. Every vendor is training on much the same foundational models now, so the advantage shifts to whoever can put those models to work as one system. “You can’t compound anything without putting the pennies in the same piggy bank,” he says. It is an increasingly accurate description of where most businesses stand today; each tool works individually, but none of them are compounding intelligence, insights, or value together. Unifying that stack isn’t a metaphor; it’s an architectural claim. A single coordinating layer connects interactions, AI models, workflows, and business systems in real time, so a service issue, a system alert, or a complaint buried in a recorded call all move through that one layer rather than ricocheting between disconnected tools. This report highlights the root cause of this growing disparity and unpacks new insight into how contact centers can unlock the intelligence they already have. It will also showcase key challenges and opportunities that separate the organizations compounding true value from AI from the ones stuck navigating the growing complexities of point solutions. By securing a strategy of orchestrating intelligence, an operating model that coordinates people, AI, workflows, and systems across the operation in real time rather than sitting alongside it, organizations can deliver consistent experiences across the www.customercontactweekdigital.com
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FROM DATA TO ORCHESTRATION: HOW CONTACT CENTERS UNLOCK THE INTELLIGENCE THEY ALREADY HAVE
The Problem: AI Agent Sprawl Enterprise CX should behave like a system. Yet most organizations still manage it like a series of isolated moments, because until recently, it was the most effective way to achieve specific outcomes.
“Point solutions existed for a technical reason: the early language models had small context windows, so work had to be chopped into discrete tasks that would fit. Larger windows and genuine reasoning removed that constraint, which means the ceiling on orchestration is no longer the model but the operating model around it.” - Andy Traba, Vice President of Product Marketing, NiCE
Traba explains it like this: Point solutions were not necessarily a strategic choice, they were implemented as a workaround for contextual limitations. But, because today’s models can hold context across a full journey, organizations can now leave behind this one-off task focused strategy. “Pilots automate tasks. Operating models automate operations,” Traba states. This shift, however, will not happen overnight. Over half of organizations, 51%, added two to three new tools to their tech stack this year, with another 10% adding four or more. Complexity is at an all-time high and results are increasingly mixed: 46% of CX leaders state that over the past two to three years, their technology has been excelling in some areas while underperforming in others. Analysts have a name for where this leads: AI agent sprawl. Traba outlines the idea: one business unit stands up a chatbot, another builds a voicebot, and a third stands up an agent for quality management. Each is a real, defensible investment, working for its narrow job. But because none of them share data or reach each other, they become deeply isolated and ineffective as a system. In this landscape every new agent adds cost and operational risk instead of value. www.customercontactweekdigital.com
Why Orchestration Works (vs. Every Other AI Strategy) With the considerable increase in AI investment, leaders are prioritizing a more innovative approach to CX. But, without a proper strategy in place, this outcome can be difficult to achieve. A short-sighted approach to innovation hits a ceiling: insight, prediction, or the automation of a single task. This lack of strategy is increasingly common — as few as 22% of organizations say they have an end-to-end strategy for AI governance, measurement, and insightsharing, backed by an integrated technology framework. Nearly 80% are operating without the connective layer that would let an AI agent that understands a request carry it through to resolution. To break away from this cycle, organizations must look to orchestration. As a system, orchestration offers a necessary alternative that connects touchpoints and enhances contextuality. NiCE calls it “orchestrating intelligence,” where artificial, human, customer, and operational intelligence act together through one coordinating layer, rather than as separate capabilities.
“The future of customer experience belongs to those who orchestrate intelligence. Not just artificial intelligence, but also human intelligence, customer intelligence, operational intelligence, and market intelligence. All connected. All acting together. All improving with each and every interaction.” - Scott Russell, Chief Executive Officer, NiCE
Traba points out that, “CX AI has moved from assisting work to executing it, from responding to resolving, and that shift is what turns orchestration into an operating question rather than a feature question.”
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FROM DATA TO ORCHESTRATION: HOW CONTACT CENTERS UNLOCK THE INTELLIGENCE THEY ALREADY HAVE
Contrary to traditional approaches, orchestration ensures that leaders can proactively predict and prevent problems before they arise. It ensures context that continues throughout the customer journey, building toward consistent and personalized interactions at every touchpoint. “Orchestration takes on the harder job of carrying the customer’s intent and context forward and then coordinating the people, AI agents, workflows and systems needed to finish the work,” Traba shares. Orchestration is the difference between a system that notices and a system that executes. Which is also why orchestration is easy to get wrong. Traba notes that, “An AI agent might understand a request perfectly and lose the person anyway, simply because it cannot update the right system, invoke a policy, pull in a human at the right moment or hold context through a handoff. Effective orchestration lives in the gap between understanding and resolution.” An orchestrated approach empowers organizations to gain a deeper understanding of customers’ needs and then apply those signals in the right moments. “This changes the question leaders should be asking.” Traba states. “Not, ‘Where can we add AI,’ but, ‘How does intelligence move through our operation to produce a governed, measurable outcome?’”
What True Orchestration Requires Orchestration is not just something you can achieve by piling tools together. True orchestration, Michele Carlson, Director of Product Marketing at NiCE, shares, takes three things working consistently, and yet, most organizations have only built one or two:
“The first is unified data that every AI agent and employee can draw from. The second is governance that scales as fast as the AI does. The third is a shared context layer that keeps the memory of a customer relationship alive across channels and across time. Organizations must have all of these to ensure a truly orchestrated end-to-end experience. Skip any one of them and orchestration collapses back into automation with better branding.” - Michele Carlson, Director of Product Marketing, NiCE
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Unified data: The AI agent handling a return and the employee handling an escalation draw from the same understanding of the customer in real time, not two systems reconciled after the fact. That understanding stays constant across every channel and every touchpoint, so no conversation starts from a partial picture.
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Governance: Every autonomous action (issuing a refund, updating a record, rescheduling an appointment) operates inside boundaries set before the action happens, not discovered after it. There is always a way to intervene before a mistake reaches the customer.
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Context: A conversation that starts in voice and resumes in chat three days later continues, rather than restarts. The system carries intent and history forward, so the next interaction begins where the last one ended.
Carlson cautions that the third pillar carries a warning many teams often miss: more context is not automatically better context. She calls this “context poisoning” — dumping every available record in front of a model degrades the answer rather than improving it. Accurate, current, and relevant data produces better responses; volume alone does not.
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FROM DATA TO ORCHESTRATION: HOW CONTACT CENTERS UNLOCK THE INTELLIGENCE THEY ALREADY HAVE
An AI agent confidently quoting a policy from 2019 is arguably worse than no AI agent at all, Carlson argues, and most enterprises are carrying several knowledge bases that quietly contradict one another after years of acquisitions and reorganizations. Both failure modes point to the same underlying truth: trust in an AI system isn’t a feature that gets turned on. It has to be engineered into how data, context, and knowledge are governed from the start — which is exactly what the governance discussion later in this report takes up. AI can help expose those gaps, surfacing where an answer was missing, outdated, or in conflict. So where should organizations actually start? With a high-value journey: map where context gets lost, where handoffs create delay, and where human judgment stays most essential. One caution while mapping: leaders instinctively automate the first mile of the customer experience and the last mile of delivery, the two most context-rich moments in the journey. Automate those completely and the organization loses the signals that show what’s breaking everywhere else. From there, leaders can design the end-to-end path more intentionally.
A supervisor is no longer managing a team of people; they’re managing work that runs across people and AI at once, which means they need the same visibility into an AI agent’s performance that they’ve always had into an employee’s: containment, quality, sentiment, and the ability to step into a live interaction and coach or take it over.
Key Challenges to Avoid The industries and enterprises moving fastest with AI share one key commonality: a sturdy and secure foundation that precedes implementation. Disconnect, however, is not the only obstacle to overcome. To unlock intelligence and move toward an orchestration strategy, organizations must also adapt how they measure experiences. Traba points out a statistical disconnect that exposes how little the current scorecard is picking up: 96% of companies believe their customer service is good or excellent, while CCW Digital research finds 56% of customers say experiences are actually the same or worse than last year.
At NiCE World 2026, Fabletics showcased the discipline to begin with their most complex use cases. Rather than starting with simple, low-risk interactions, the company built AI agents to handle its hardest journeys first: refunds and account changes that had to execute identically across multiple markets and languages under strict policy. The payoff showed up as member loyalty, not just cost savings. Scale, in this case, came from attacking complexity head-on rather than working up to it.
Where Human Judgment Fits In Organizations with a unified CX AI strategy build what amounts to one workforce for their employees and AI agents, not two managed separately. AI absorbs the high-volume, repeatable, policy-driven work that has historically consumed frontline capacity. Human agents focus on the interactions that call for judgment, personalization, and expertise – the work no system can do. Displacement anxiety is real, but the more consequential shift is transformation: a front line doing less repetitive work becomes a front line doing more strategic work. “AI optimizes for accuracy. People optimize for meaning,” Carlson says. That changes what oversight looks like, too.
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FROM DATA TO ORCHESTRATION: HOW CONTACT CENTERS UNLOCK THE INTELLIGENCE THEY ALREADY HAVE
That gap, he argues, is the blind spot itself: a scorecard that tells leaders they’re winning while the people they serve say otherwise. “The correction is to measure resolution rather than deflection. Containment tells you a customer stopped asking. It does not tell you their problem was solved,” Traba states. Handle time deserves the same scrutiny, and it’s about to move in a direction that looks like a step backward for the right reason: once AI absorbs the simplest, fastest contacts, what reaches a person is harder by definition, so average handle time rises even as the operation improves. A more accurate measure is emerging: AI-assisted handle time, or how much faster a human resolves the work AI hands off to them. If organizations are optimizing for the wrong outcomes, they never understand the root cause of that challenge. An outdated AI strategy often leverages customer-service metrics that don’t tell the whole story. They may tell you how quickly a customer interaction ends, but they don’t touch whether the customer’s problem was solved or whether the experience improved. Beyond this, Traba warns of the barrier of fragmented accountability. “Technology owns deployment, operations owns the workflow, CX owns the experience, and risk turns up late. Nobody owns the complete outcome, so nobody can prove its value to a board that is losing patience. Leaders break the pattern by assigning end-to-end ownership of a priority journey, setting outcome-based measures before deployment, and changing what they measure before they change what they buy.” The solution is to give one leader or department ownership of the entire customer journey: decide how success will be measured before launching, and focus on business outcomes rather than simply buying new technology. Finally, change management is just as important as technology. AI adoption often fails because companies change how people work without preparing employees for that change. Everyone, from leadership and frontline employees to data, security, and operations teams, needs to be involved from the beginning.
Governance Considerations With an increasingly complex AI framework, companies have one question at the top of their mind today: “How do we let AI move faster without losing control?” This is becoming the question that ultimately holds many organizations back: the fear that AI is introducing more risk than they are equipped to handle.
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FROM DATA TO ORCHESTRATION: HOW CONTACT CENTERS UNLOCK THE INTELLIGENCE THEY ALREADY HAVE
Sovereignty has moved from a procurement footnote to a condition of purchase. Buyers now ask where data resides, which models touch it, who can audit a given decision, and whether the arrangement satisfies regional regulation, and they ask all of that before they ask about features. By 2030, fragmented AI regulation is expected to cover roughly 75% of the world’s economies, which makes this a business imperative rather than a best practice. Counterintuitively, the organizations moving fastest are often the most regulated ones: banks, insurers, and healthcare providers already had the audit trails, access controls, and compliance discipline that AI governance now demands, so they are extending existing muscle rather than building it from scratch. The standard is simple to state and hard to meet: AI agents must be held to the same standards as human agents. Trust, in other words, is not a feature added to a platform after it works; it is the prerequisite for the platform to work at all.
Building in Governance Is the Key to Scaling AI Trust Rather than treating governance as a ceiling on scaling AI, Citi’s approach showed that governance is the key to building trust in AI within one of the most heavily regulated industries there is. What mattered most about their approach was building governance into the system from the outset, rather than treating it as something to bolt on afterward. At NiCE World 2026, Mia Carraro, Director, Head of Service Excellence at Citi, said the system Citi built wasn’t off-the-shelf and wasn’t built from scratch either. It was recognizably NiCE at its core, but shaped around Citi’s own taxonomies, standards, and definition of what actually counts as a complaint. Standardizing that definition across the organization meant more consistent, accurate identification of complaints across every interaction, and, in turn, better follow-up and a stronger customer experience. What made this approach work, she explained, is the combination of NiCE’s infrastructure and Citi’s own requirements.
Citi’s best practice reflects a broader pattern Traba recognizes across the enterprises that are scaling AI responsibly: •
Governance needs to scale alongside AI
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Autonomous actions need to operate within clear boundaries
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There must be mechanisms to intervene before an error reaches the customer
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Governance crosses traditional organizational boundaries: CX, operations, IT, and risk
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Accountability cannot sit neatly within one function
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Governance should be seen as an enabler of speed, rather than an obstacle
That reliability rests on a few things working together as one system: AI agents that reason and act within clearly defined boundaries, grounded in an organization’s own data and policies; a persistent memory layer that keeps a customer relationship’s relevant history and intent alive so nothing restarts from zero; a supervisory layer of AI checking AI that tests every autonomous action against policy in real time; and an engagement layer that holds one continuous conversation intact no matter how many agents, human or AI, touch it. www.customercontactweekdigital.com
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FROM DATA TO ORCHESTRATION: HOW CONTACT CENTERS UNLOCK THE INTELLIGENCE THEY ALREADY HAVE
Underlying all of it is identity and consent: a customer should authenticate once and have that authentication travel with them for the rest of the journey. When something does go wrong, the same system needs a clear escalation path and a record of how the error was caught and corrected, and it needs to hold up when a customer’s own AI agent or a third party’s is the one on the other end of the interaction. Companies need both real-time protection: catch and correct problems as they happen, and after-the-fact analysis, looking at overall performance and figuring out what needs improvement. Human employees alone can’t monitor millions of AI decisions in real time. The key, Traba highlights, is that you should only give AI more freedom as quickly as you can increase your ability to supervise it.
“Autonomy expands only as fast as oversight does. Anything slower is theater.” - Andy Traba, Vice President of Product Marketing, NiCE
Conclusion With almost every organization increasing AI investment, it is essential to ensure a model that unlocks intelligence, enhances experiences and streamlines operations. Too many organizations are simply adding tools, contributing to the burgeoning Frankenstack rather than building a system that works. This distinction is top of mind at NiCE: ensuring that organizations are moving away from AI for CX toward the strategy of CX AI. Bolted-on tools add capability, but disconnected capability doesn’t deliver results. Only when CX AI is orchestrated as one operating model and not just layered onto the existing stack does that capability become a result. Point solutions alone are not the safer, more proven path to that result; they are the reason most organizations are still waiting for one. The architecture is what CX AI is built from, but governance is what empowers it to enhance interactions with real customers at scale. As leaders work to leverage an orchestrated approach, they will shift away from the mindset of, “Where should we add AI?” to a more comprehensive question of, “Does this investment strengthen the shared operating layer everything else draws from?” This question gives a deeper look at the intentions, goals and outcomes the organization is looking to achieve. By focusing on building a system that creates a seamless, context-rich experience, organizations move from asking where AI fits to asking whether the foundation underneath it can carry the weight of what gets built on top. The constraint on ambition is rarely the model; it is whether the data, the integrations, the memory, and the oversight are ready to hold it. Build that foundation properly, and the ambition can follow.
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