Operating at Scale: How Five Enterprises Are Turning Agentic AI Into Infrastructure

2026 ANALYST REPORT SERIES

Operating at Scale: How Five Enterprises Are Turning Agentic AI Into Infrastructure WRITTEN BY: BROOKE LYNCH

SPONSORED BY:

ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

Featuring Insights From

Sebastian Glock Director of Product Marketing NiCE Cognigy

Michael Rice

Senior Digital Product Owner Bosch Automotive NA

Peter-Pascal Meik

Manager of Innovation & Projects Toyota

Elisha Wright

Global Director of Learning Design and Delivery Hyatt

Introduction: The Age of Agentic AI in the Contact Center Agentic AI has now moved past the pilot stage. Across industries, organizations are no longer asking whether AI agents will fit into the experience — they’re asking how to integrate agentic AI reliably at scale; across thousands of interactions, and legacy systems that were not necessarily built for autonomous decision-making.

This is where a divide is emerging: organizations that have figured out how to operate agentic AI at scale and those working to operationalize it across the enterprise.

The difference lies in how organizations align agentic AI with architecture, governance, and operating models.

This report examines five organizations that have done exactly that: Henkel, Bosch, Toyota, Salzburg AG, and Hyatt.

Together, they offer insight into complex, global enterprises that have moved agentic AI from isolated use case to true operating capability. Each leveraging a different deployment context, but together revealing the pattern of what it actually takes to scale agentic AI successfully.

Taken together, these five cases build the argument that the organizations pulling ahead with agentic AI are the ones treating it as infrastructure to be engineered and refined, not a one-off feature. This report unpacks how CCW Digital and NiCE see that infrastructure taking shape — and what it reveals about the future of AI-powered support.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

1. Always On, Always Informed: AI That Knows the Customer Before They Ask The Shift from Availability to Intelligence We are now in the age of 24/7 availability; customers expect instant support, on their timelines. In the past, this level of support used to mean staffing. Contact centers worked to cover every time zone, every shift, every spike in demand. Brands measured success in their ability to manage headcount and staff coverage.

But availability alone is no longer a differentiator today. With AI-powered self-service and instant responses, customers are simply not impressed by sheer accessibility. They want to feel known — to reach out to a brand and not have to explain their problem repeatedly. To be met with meaningful, proactive support.

The most forward-looking organizations are not just asking how to be available at all hours, but how to be intelligent at all hours; deploying AI that doesn’t just respond, but recognizes a customer’s intention and engages accordingly. That distinction is reshaping what customer engagement means at scale.

The Personalization Gap Is (Somehow) Increasing Despite increasing investments in CX technology, the gap between what customers expect and what they actually experience is becoming too big to ignore.

In fact, only 18% of customers say they receive experiences that are genuinely personalized to their needs and expectations, according to CCW Digital.

Despite unprecedented access to customer data and increasingly sophisticated AI, customers continue to experience generic interactions.

Digging into what their typical experience is like, 40% of customers share that they have to repeat information across interactions, reask questions and reauthenticate. Sebastian Glock, Director of Product Marketing at NiCE Cognigy, shares, “The number one

Sebastian Glock, Director of Product Marketing at NiCE Cognigy, shares,

“The number one thing

we hear from customers is that they’re tired of starting over every time they reach out.

New channel, same problem, no memory of the last conversation.

AI that carries context across every touchpoint doesn’t just improve the experience. It changes how customers feel about the brand.

”

The problem isn’t the data itself, however, it’s the ability to orchestrate and act on it. In the past, getting that insight to the agent at the right moment has required layers of integration work and manual intervention in the past. Today, AI is in the position to change that.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

From Always-On to Always-Informed Henkel, the global consumer goods company behind Persil, offers a compelling use case demonstrating how AI can move beyond availability into genuine intelligence.

Like most organizations today, Henkel Consumer Brands recognized that traditional service channels were not sufficient in the era of always-on support. To address the needs of increasingly digital customers, Henkel looked to implement a solution that was both scalable and intelligent. Shifting from an always-on to an always-informed and intelligent approach, Henkel introduced one of its first AI Agents, the Persil Stain Bot. The AI agent guides users through the stain removal process, tailored to fabric types and stain categories.

With this use case, customers are able to receive instant product support, personalized solutions and a deeper understanding of the product itself. Henkel’s strategy demonstrates the dedication to proactivity; creating an outlet for customers, whenever they need it, before requiring live supportwithout having to reach out.

Henkel also implemented the Schwarzkopf chat, supporting consumers on hair care routines and product selection.

In this case, the consumer brand company is giving real-time support with detailed beauty expertise, working to enhance consumer confidence and improve product satisfaction.

For organizations like Henkel, consumer relationships are built over years, across product lines, retail touchpoints, digital platforms, and consistent interactions. The ability to maintain continuity across that journey is no easy feat.

Agentic AI, however, can support a dynamic experience that feels consistent during every brand moment.

Christian Hohmann, Head of New Technologies (AI) & IT-Management at Henkel, describes the impact directly, “AI Agents are a real success for us. First: we help our beloved consumers. Second: we build a direct relationship with them. Third: we see a positive brand experience.”

While AI can often be positioned as an efficiency lever, it has the ability to create unique and memorable brand experiences. Hohmann describes a mechanism for building relationships, offering real-time support and creating consistency at every turn. Every interaction handled intelligently builds confidence in the tool and supports a better journey.

The Key: AI Memory AI memory that can carry context signals a deeper connection with the customer. For example, persistent customer memory enables AI agents to retain relevant context across channels, products, and prior interactions eliminating repeated authentication and creating continuity throughout the customer journey. For many businesses, that signal, delivered consistently across every touchpoint, becomes a true differentiator.

The mechanics of the always-informed experience come down to one capability that most organizations struggle to deliver: persistent, cross-channel customer memory.

Building the AI infrastructure to deliver cross-channel memory requires more than a capable model. It requires intentional orchestration; a platform that unifies interaction history across channels and surfaces relevant context at every touchpoint. When this works effectively, the experience stops feeling transactional and becomes personal.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

2. From Copilot to Colleague: When AI Agents Own the Resolution In 2026, it is not a question of whether AI can handle customer interactions, it’s how to design a system that enhances the value and qualities of both humans and their AI counterparts. While we know that AI can take on simple tasks, and increasingly more complex ones as well, leaders must find a balance that empowers humans to take on a more distinct role.

For years, AI played a supporting role, surfacing realtime insights, flagging sentiment, prompting nextbest actions. But, the agent still owned the resolution; they were leading the charge and providing the customer with a solution.

That model is now shifting as we see AI agents that go beyond assistance and actually take action, resolving high-volume, repetitive interactions, autonomously. Now, human agents are empowered to engage, providing empathy, judgement and care where it is actually needed.

The Humanity Question One of the most common sticking points in the AI transformation is the idea of humanity. With more AI in the contact center, will experience become less human? More robotic? Void of a human touch?

Interestingly, CCW Digital research confirms that this is likely not the case. Thus far, 50% of leaders say that investing in AI has led to more humanity in agent-led interactions. This offers a striking reversal of the narrative that automation diminishes the human touch in AI-powered support.

However, when we look at the facts, it makes more sense. The logic is fairly straightforward: when agents are not burdened by repetitive, low-value tasks, they have more cognitive and emotional bandwidth for complex interactions. Escalations, complaints, highly emotional customers; these interactions ultimately benefit from an agent who is present and focused.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

Navigating the Role of the Human + AI Agent There is no question that a more focused agent is a more productive agent. The problem, however, is understanding just how they will be most productive moving forward.

Many organizations have yet to define their future plans. According to CCW Digital research, 64% of contact center leaders acknowledge they understand how AI will change their agents’ core day-to-day tasks, but have not yet mapped out the new responsibilities and functions those agents will take on.

While it is clear that AI will play a role in how work shifts in the contact center, leaders continually miss the mark in defining these changes and assigning new tasks. The risk, then, is that organizations focus too much on innovation and leave behind redefinition, leaving contact center agents without a clear picture of where their value is most needed.

Bosch, one of the world’s largest manufacturing and technology enterprises, has been focused on getting this right. Bosch recognizes the potential of an AI Orchestration Platform and the need to invest in AI Agents for streamlining daily tasks for their agents.

With the goal of implementing a solution that seamlessly integrated into their existing tech stack, Bosch leaned into a centralized, scalable enterprise solution with NiCE Cognigy. The company formed a team of AI agents that operates across worldwide time zones.

‘The Bosch Chatbot Suite’ engages internal customers by providing training and access to the platform. The suite also encompasses intelligent AI Agents for day-to-day operations.

Bosch Power Tools, for example, listens in on calls, retrieves relevant knowledge and surfaces next-best-action recommendations. This enhanced support gives agents the ability to add value where it matters most: when connecting with customers.

Michael Rice, Senior Digital Product Owner at Bosch Automotive NA shares, “Focus on the outcomes first. Take care of your customers.

If you keep that in mind, the rest falls into place. The savings and efficiency gains will inevitably follow.”

The best balance of humans and AI agent orchestration is actually almost imperceptible to customers: a resolution that feels seamless regardless of who delivered it. AI should be handling the interactions it was built for, with speed and consistency at scale. The human agent steps in during moments where context and empathy becomes essential. In a perfectly orchestrated interaction, the customer should not even feel the handoff.

Sebastian Glock shares, “What surprises people is how quickly the dynamic between AI and human agents clicks when it’s set up well. AI handles what it’s built for, the agent steps in when it matters, and the customer never feels the seam between the two. That’s the standard enterprises should be aiming for.”

That standard of invisible orchestration, and a productive balance between AI and human support, is what separates companies that are genuinely transforming their contact centers from those that have implemented AI onto inefficient processes.

By defining the new scope of human roles, enhancing consistency in AI-human handoffs, and committing to a strategy that deploys every resource, human and AI, at its highest potential, organizations will consistently succeed.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

3. Proactive by Design: How Agentic AI Gets Ahead of the Customer For decades, the contact center operated around a reactive model of support: wait for the customer to reach out, then respond as quickly and effectively as possible. Speed became the metric of success, and the entire industry optimized for efficiency over outcomes. But, the standard is now much higher. By the time a customer picks up the phone, frustration is already present.

experience is built through iterative change and intelligent technology.

Agentic AI has the ability to shift this model. Unlike traditional automation, which executes predefined workflows, agentic AI can reason across data, anticipate customer needs, and initiate outreach independent of human intervention. This process begins before an issue even escalates.

Toyota’s deployment of AI agents illustrates exactly what proactive, AI-driven CX looks like in practice.

Rather than waiting for vehicle owners to notice a dashboard warning light and remember to schedule service, Toyota deployed an outbound E-Care AI agent that initiates contact automatically when a warning light appears. The AI agent contacts the vehicle owner, books a service appointment end-toend, and simultaneously notifies the dealership with all relevant technical and warning data so both the

Today, this shift from reactive to proactive is not just a strategic initiative, processes must be implemented and architecture must change. The proactive

This is a key priority for the majority of leaders today — when asked which trends were most impacting their technology investment strategy, 58% shared that an emphasis on personalized, predictive, and proactive CX is driving their decisions.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

dealer and the customer arrive prepared. No human handoff required. Running across 25+ AI agents on chat and voice, the results reflect the impact of reaching customers at the right moment: 95% of vehicle owners in the E-Care flow book their service appointment directly with the AI agent.

As Peter-Pascal Meik, Manager of Innovation & Projects at Toyota, explains, “With NiCE Cognigy AI, we’re able to provide our customers with real added value and deliver even better services and products.”

Ultimately, customers remember the brands that anticipate, conversations where the company flagged the issue before they had to reach out or made them feel recognized and understood rather than deferred. Proactive engagement should not just resolve problems, it builds trust.

Winning brands don’t simply respond faster, they anticipate customer needs before customers ask.

But proactive CX is more than just availability. It is about delivering on timing and context. When an organization understands when and why they are reaching out, they are able to connect at the right moment with the right message.

Agentic AI’s capacity for contextual reasoning becomes essential during these moments.

Recognizing signals across customer data, transaction history, and behavioral cues, agentic AI can identify friction points and initiate outreach that feels less like automation and more like predictive intelligence.

Sebastian Glock states, “When an enterprise understands the full context and engages at exactly the right moment, AI agents can turn outreach into an invitation for a real conversation, one that feels natural, helpful, and human. They guide the customer toward a solution before an issue becomes a problem, and that is the kind of experience customers remember.”

Ultimately, customers remember the brands that anticipate, conversations where the company flagged the issue before they had to reach out or

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

4. How Agentic AI Closes the Loop at Scale Closing the loop remains one of the hardest challenges in customer experience. Understanding what a customer needs, navigating requests that don’t fit a template or script, and knowing when to bring a human agent into the conversation, is where so many leaders struggle to execute with traditional AI. To move past these challenges, organizations are shifting away from individual AI tools to unified systems. When AI agents, humans and customer data connect in a single orchestrated platform, this level of connection becomes innate. With greater orchestration abilities, every interaction carries the full context of what came before it, and organizations can close the loop effectively.

This shift is already in motion. According to CCW Digital, 40% of leaders share that they have moved away from disparate, point-solution tooling toward consolidated contact center platforms.

Leaders increasingly recognize that fragmented technology stacks cannot deliver seamless customer experiences.

“A lot of organizations have invested heavily in AI and still feel like they’re managing a collection of disconnected tools. The shift happens when you stop thinking about individual solutions and start thinking about orchestration, how AI agents, human agents, customer data, and operations all connect into something that actually works as a system,” shares Sebastian Glock.

From Automation to Orchestration Salzburg AG, one of Austria’s leading regional utilities and public services providers, has firsthand experience making this transition a reality. The company partnered with NiCE Cognigy not simply to automate volume, but to ensure that effective orchestration preserved the quality of every customer touchpoint.

Assistant, a multimodal, self-service AI agent now handling over 160,000 customer interactions annually. The AI agent is capable of managing endto-end transactions like order processing and public transport ticket purchases directly within the chat interface.

The more telling measure of orchestration, though, is what happens when the AI agent reaches its limit.

If an inquiry exceeds what LEA can resolve, or calls for human judgment, the handoff to a live agent preserves context, it carries the customer’s full intent and history into the conversation. The human agent steps in already prepared, which eliminates the need for customers to re-explain themselves. That continuity is what determines whether the experience feels seamless or fragmented.

This orchestration also reshapes how human agents spend their time. Sarah Haider, Digital Manager for Conversational AI at Salzburg AG, states, “Our customer service team truly appreciates the collaboration with the Digital Chat AI Agent. It efficiently handles routine inquiries and precisely manages orders, saving us around 2,500 calls each month. This reduction in call volume allows our agents to dedicate more time to engaging with customers who have complex requests.” The value comes from orchestration that redirects human attention toward the interactions that actually need it.

To actually close the loop, AI agents must consistently deepen and improve integrations, enhance response quality, and optimize outcomes. Agentic AI requires a commitment to ongoing orchestration and refinement — ultimately becoming a system built to get better at seamless handing off, not just handling, every interaction.

Organizations that build systems designed to learn, connect, and improve, where every interaction contributes to future success, will be in the position to continuously meet customer expectations.

Salzburg AG introduced LEA, Learning Electronic

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

5. The Intelligence Layer: Turning Every Interaction into Institutional Knowledge Every customer interaction offers an opportunity to learn; recurring questions may highlight a sticking point in selfservice channels, an increase in handle time might surface a knowledge gap. Individually, these interactions are a brief moment in time, but together, they offer a deeper look at meaningful patterns and behaviors at scale. When looked at across thousands of interactions, these moments become incredibly valuable.

When organizations are in the position to capture this intelligence, they can enhance knowledge and consistently power more intelligent interactions.

The Challenge of Customer Intelligence and Analytics According to CMP Research, customer intelligence and analytics has emerged as the number one executive priority heading into 2026–2027. While the contact center was often thought of as a cost center to be managed, it now acts as one of the most integral sources of real-time intelligence.

Surprisingly, so many organizations are still unable to act on this insight. Sebastian Glock states, “Every enterprise we work with is sitting on an enormous amount of customer intelligence that they’re barely using. The interactions are happening, the data is there, but without AI that’s built specifically to surface it and act on it, most of it just disappears. That’s the gap we’re focused on closing.”

The challenge isn’t collecting customer data; it’s organizing, interpreting, and activating it consistently across every interaction. Without an intelligence layer to interpret, analyze and access data, there is no ability to act on it.

Inconsistent and inaccessible data leads to not just inefficiencies but mistakes.

Beyond this, organizations now must be in the position to compound intelligence and continuously improve.

Without consistent optimization, intelligence stalls and interactions fall flat.

At a high level, this continuous loop leads to smarter AI systems, powering more effective decision making, coaching and real-time guidance. By compounding intelligence and continuously improving, organizations will see real results.

Securing Confidence at Scale For a global hospitality enterprise like Hyatt, operating across hundreds of properties, agents are handling an exceptionally diverse set of interactions. The challenge for the Hyatt team was not volume, but consistency.

Hyatt’s support teams were engaging with an outdated knowledge platform. Information was scattered across documents, portals, and departments and agents were unable to answer urgent guest questions. They were spending unnecessary time searching, leading to inefficient and potentially inaccurate communication. On top of this, new hires were struggling to source insights and had to circumvent tech to ask colleagues for information.

The leadership team understood the need for a single central source of truth that was searchable, scalable, and built for action.

A guest calling about a reservation issue, a loyalty program question, or a special accommodation should receive a

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

consistent and accurate message, delivered with confidence, regardless of which agent responds.

When organizations are in the position to capture this intelligence, they can enhance knowledge and consistently power more intelligent interactions.

NiCE Cognigy’s Copilot capabilities were deployed to secure this level of consistency. The team has been able to integrate an intelligence layer that equips agents in real time. By surfacing the right information at the right moment, providing real-time guidance and ensuring that institutional knowledge is accessible, Hyatt has enhanced the agent and customer experience.

Elisha Wright, Global Director of Learning Design and Delivery at Hyatt Hotels Corporation, shares, “Our top priority is helping our teams find and deliver the right information fast — and NiCE makes that possible. With Expert and Copilot, our agents are confident, consistent, and supported every step of the way.”

This confidence is critical. As agents take on more complex, emotional work, it is essential that they enter into an interaction with the insight and knowledge needed to respond.

Beyond consistency, this dedication to effective knowledge management powers long-term improvement. As they look ahead, the team is working to provide a unified approach to content, training, and AI that makes work easier. In optimizing their intelligence layer organizations feed a continuous loop, making interactions more seamless and intentional.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

From Signals to Strategy This intelligence layer does not just benefit the agent during calls, supervisors also gain visibility into coaching opportunities. With AI-surfaced patterns, organizations can easily flag development opportunities and pinpoint systemic knowledge gaps. The entire organization benefits from enhanced institutional knowledge and more accessible insights.

With purpose-built AI, businesses can finally act on data systematically, at scale, and in real time.

The compounding effect is key. When interactions are making AI more sophisticated and streamlined, it continues to improve. With an effective intelligence layer, organizations are able to go beyond day-to-day improvements to continually reshape the future of experiences.

Conclusion: What Enterprise-Grade AI Actually Looks Like in Practice Enterprise AI is no longer defined by successful pilots. Competitive advantage now belongs to organizations that can operationalize agentic AI consistently across the enterprise. Leading brands are now executing agentic AI at enterprise scale: Henkel, Bosch, Toyota, Salzburg AG, and Hyatt all showcase the power agentic AI has when leveraged intentionally. By securing the right infrastructure, building deliberately and refining continuously, these organizations have been in the position to optimize operations and improve experiences at scale.

None of these organizations succeeded by deploying AI agents in isolation; they built the architecture, governance, and operational discipline to empower AI agents, humans and data to function as one connected system.

Enterprise-grade AI is not a collection of AI agents. It is an orchestrated operating model where AI, people, knowledge, and business systems continuously learn from one another and improve with every interaction.

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ANALYST REPORT: OPERATING AT SCALE: HOW FIVE ENTERPRISES ARE TURNING AGENTIC AI INTO INFRASTRUCTURE

About the Author Brooke Lynch is the Divisional Director and a Principal Analyst for CCW Digital. With a background in television news and production, she’s worked across industries covering B2B marketing, procurement, and finance events.

Brooke Lynch Divisional Director, Principal Analyst CCW Digital

Her current work highlights challenges and opportunities for customer experience and contact center leaders, with a recent focus on e-commerce, retail, and technology.

Brooke studied journalism at the University of WisconsinMadison and held positions at Fox Business in Development and as a college associate on Countdown to the Closing Bell.

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