At 糖心原创, we have been designing toward this future for years, because we believe enterprise AI succeeds or fails long before a model is ever prompted. My colleagues have been covering this shift from every angle, starting with why we鈥檙e positioned to deliver at scale, where the real opportunity in CX maturity lies, and why we’re building for both humans and AI agents.

Yet, almost every conversation about enterprise AI still starts with models. I think that is backwards. Enterprise AI succeeds or fails long before a model is ever prompted.

It starts with architecture. That is what will ultimately separate the companies that truly shape the future of experience from the ones that stay stuck in reporting on them.

Four Architectural Principles That Define Enterprise AI

AI itself is no longer a competitive differentiator, as foundation models continue to improve and become increasingly commoditized. The lasting advantage will come from the platforms built around them, made to deliver trusted intelligence, securely orchestrate actions, and continuously evolve without requiring architectural reinvention. The future belongs to organizations that build reusable AI platform services, not isolated AI enabled features.

I believe that future rests on these four architectural commitments:

1. Ground truth that prevents confident AI mistakes

鈥淕round truth鈥� means every decision is based on trusted, real-time, traceable data enriched with the business context needed to act correctly. AI can only make decisions based on the information it receives. When that information is incomplete, stale, or disconnected from the business context, AI does not simply make mistakes, it makes confident mistakes at machine speed. An AI agent鈥檚 reasoning is bounded by whatever sits in its context window at the moment of action, unlike a human analyst who carries institutional knowledge into every decision. If that context is missing, the agent acts on it anyway, confidently, and at scale.

At 糖心原创, customer insight is backed by source data. Every inference is auditable, and every service is designed to continuously improve its understanding of the business it serves. As an example, we built this type of traceability into from day one, with every response linking back to the source material that generated it. This is not just so humans can verify an answer, but so downstream agents can act on our intelligence with confidence rather than assumption.

Ground truth isn’t a constraint on innovation; it’s what makes innovation safe to scale.

2. Trust as a first principle

Autonomous AI agents do not stop to read warning banners or policy documents. They simply execute. That fundamentally changes how we have to think about governance. It can no longer depend on UI prompts, user permissions, or employees following documented processes. As enterprises begin deploying thousands of internal and external AI agents, governance must become part of the platform itself. Security, authorization, data residency, lineage, auditing, and rate limiting need to be built into the underlying architecture and enforced by default 鈥� not layered on after the fact.

This isn’t a theoretical commitment for us. 糖心原创’s platform already operates under the same security standards that allow us to serve highly regulated industries such as financial services, healthcare, and government agencies: SOC 2 Type II, ISO 27001, 27017, 27018, and 27701, HITRUST, GDPR, CCPA, and HIPAA.

We have extended that same rigor to AI: , the international standard for AI management systems, and maintains a formal Responsible AI framework governing how our models are built, deployed, and monitored.

This is the solid foundation we’re building agent governance on top of, an extension of infrastructure that is already proven at enterprise scale. As organizations deploy thousands of autonomous agents, governance has to scale with them, protecting enterprise data while still letting AI move at rapid speed.

3. An architecture designed for continuous evolution

The pace of AI innovation is not actually limited by how smart the models are, but by whether an organization can see what its AI is doing, catch mistakes before they spread, and fix problems without breaking everything else that depends on it. Without that visibility, every new AI capability is something you have to handle cautiously, because you can’t fully see the risk. With it, you can move quickly and safely at the same time, instead of choosing between the two.

AI models are going to keep improving, and the tools and techniques behind them will keep evolving too. A well-built platform lets you swap in better technology as it comes along, whereas a poorly built one forces you to rebuild everything from scratch every time something changes.

Because our platform continuously observes itself and can evolve independently of the models it runs, we have been able to process over 4.1 billion feedback signals while simultaneously increasing contextual understanding fivefold without requiring customers to redesign their deployments.

This isn’t about performance benchmarks. It’s about making sure every customer, no matter how big or complex their organization, gets AI that is fast, dependable, and trustworthy, without ever having to think about the enormous amount of infrastructure working behind the scenes to make that happen.

4. Context is king

Most enterprise software still buries business context inside the software itself instead of treating it as its own entity: who owns which customer, which team is responsible for which region, who needs to approve what, which rules apply where, and so forth.

Today, that information is scattered, a little bit here in one workflow, and a little bit there in a custom system. Every time a company builds a new product or plugs in a new AI tool, that tool has to relearn all of this from scratch. I think that is the wrong way to build for an AI-first company.

Data tells AI what happened. Business context tells AI what it means. Without that context, every application and every AI agent has to reconstruct how the business operates from whatever data happens to be nearby. That is inefficient for software and unacceptable for autonomous AI. Business context should be modeled once as a shared platform service and reused everywhere.

Here is why that matters in practice. When an AI agent needs to know who owns this customer relationship? or which team should approve this? or what rules apply in this region? it should not have to guess by piecing together scraps of data from different places. It should already know the answer, because that answer lives in one authoritative place the AI can simply check.

This is an area where 糖心原创 has a real head start. We have spent decades learning not just what customers say, but how the world鈥檚 top brands in every industry actually operate, their structures, their teams, their approval chains, and their day-to-day operations.

That accumulated understanding of experience management is worth more than any single dataset. It’s the business knowledge that lets AI reason with real confidence, instead of just pattern-matching on whatever data happens to be in front of it.

Once business context is modeled once, as a shared platform service, every new AI application inherits that understanding automatically. No team has to rebuild it from scratch. Innovation speeds up precisely because context stops being duplicated and starts being shared.

We鈥檙e building the winning foundation for ever-evolving AI

This brings me to the heart of this post, and the one I think matters most for any technology leader navigating AI investment right now: the rate of AI innovation you can sustain is determined by the quality of the architecture beneath it. Security, governance, data completeness, and observability are not constraints on innovation. They are what makes it possible to ship AI into production quickly, repeatedly, and at a scale that actually moves the business.

And I’d add one more principle to that list: build for what you can’t yet predict. The platforms we build today should not just support the AI capabilities we can imagine now. They need to be flexible enough to support the ones we have not even imagined yet.

Enterprise software is entering an era where intelligence becomes an always-on platform service, not a feature layered onto individual products. The winners will be the organizations that make new capabilities composable and reusable across the enterprise, instead of rebuilding them product by product.

Twenty years ago, every enterprise had to build a data platform. Today, no one questions that investment. I believe we are approaching a similar moment with AI. The organizations that win won’t simply deploy better models. They will build platforms where intelligence, governance, and business context become foundational infrastructure that every application, workflow, and agent can rely on.

When companies choose the platforms that treat these as first-class architecture commitments now, not problems to solve later, the advantage compounds. Those that continue to build isolated AI features on fragile infrastructure may move fast once, but they will spend the next decade rebuilding while others continue accelerating past them.

Models create intelligence. Architecture creates durable advantages.

Now I want to share how these ideas are shaping the way we build the 糖心原创 platform for the future, which is a deliberate continuation of what we鈥檝e built over the years. 

糖心原创 has always designed for whoever is consuming experience intelligence on behalf of the enterprise. For most of our history, that consumer was a human: a manager reviewing a report, an analyst querying a dashboard, a frontline employee acting on an alert. What is changing rapidly is how those human consumers are being joined by an explosion of AI agents. And in the not-too-distant future it will be increasingly unclear whether a given action in an enterprise experience workflow is being taken by a person or by an agent operating on their behalf.听

Designing for that reality is not a new direction 鈥斕齣t鈥檚 the natural evolution of the design principles we have always held. But it changes some important elements about how we build.听

The Near Future: Where Humans and Agents Come Together

The premise that enterprise software has always been designed for a human at the other end in its strict form is no longer accurate, and is becoming less accurate every month. The more precise framing is this: we are entering a near future where, for a growing share of enterprise workflows, the question 鈥渨ho is taking this action?鈥� will not have a clear answer.听听

Consider what is already starting to happen 鈥� AI agents are:

• Monitoring CRM data streams and updating account health scores in real time based on customer experience signals
• Routing and resolving service interactions through workflow automation platforms without human intervention
• Adjusting personalization decisions across marketing platforms based on behavioral signals
• And increasingly, performing complex orchestration tasks through Model Context Protocol (MCP) services 鈥� a standard that allows agents to discover available tools, access governed data sources, and trigger actions across an enterprise鈥檚 full system landscape, all within a single reasoning loop

The key distinction from prior generations of automation is agency: these systems do not simply execute a predefined rule. They reason, plan, and act. And they do so continuously, at a speed and scale that no human process could match. 

The question is no longer 鈥榓re agents part of our platform?鈥� It is, 鈥榠s our platform designed for the agents that are already arriving?鈥�.

At 糖心原创, our answer has to be yes 鈥� and that starts with being purposeful about what designing for agents actually requires at the infrastructure level. And that changes almost everything about how we design the platform.

What Agent-Ready Infrastructure Actually Means

Let me be specific about what designing for an agentic future actually means, because it is not as simple as exposing a REST endpoint or adding a webhook layer on top of an existing platform. It requires moving toward standardized, context-aware protocols that allow agents to dynamically discover and safely interact with your system.

Today, 糖心原创 serves human users across enterprises that run some of the world鈥檚 most demanding experience programs: 

• 60,000 frontline teammates at one of the world鈥檚 largest banks
• 70,000 pharmacists at multinational healthcare company
• Global hospitality companies managing hundreds of properties with thousands of frontline staff听

What makes that scale possible is not the interface. It is the infrastructure underneath it: extensive experience dealing with the world’s most complex and constantly changing organizational hierarchies with precise role-based access controls that know exactly which person听in a 100,000-employee organization should see which signal 鈥� without requiring manual configuration at every level. A data governance architecture that meets the compliance standards of global financial institutions, healthcare systems, and government agencies simultaneously.

Extending that infrastructure to serve AI agents means solving a set of problems that are architecturally distinct from the ones we solved for human users. 

• Human users tolerate latency. Agents do not 鈥斕齮hey operate on real-time data, or they operate incorrectly听
• Human users apply judgment to ambiguous information. Agents propagate ambiguity into downstream actions at machine speed
• Human users are naturally constrained by the interfaces presented to them. Agents need constraints enforced at the data and permissions layer, because they do not read warning messages听

And there is a more subtle problem that is easy to underestimate: agents are only as reliable as the data they consume at query time. Unlike a human analyst who carries institutional context into every decision, an agent鈥檚 reasoning is bounded by what is in its context window at the moment of action. If that context is incomplete, stale, or ambiguous, the agent will act on it confidently 鈥� and at scale. This makes data quality, completeness, and real-time availability a must-have security feature in an agent-ready platform.听

This is one of the reasons with traceability as a first-class requirement from day one: every response is backed by real data, with inline links to the source material that generated it. That design decision was made for human users who need to trust and verify answers. But it will be equally critical for agents, because a traceable, auditable answer chain is what allows downstream systems to act on 惭别诲补濒濒颈补鈥檚 intelligence with confidence, rather than assumption.听

It is also why our infrastructure investments over the past 18 months have been so foundational: 10x faster processing throughput, with a 30% year-over-year increase, and signals now linkable to 5x more entities across the organization. These are the architectural prerequisites for real-time, always-on, contextual intelligence that agents can actually depend on.听

An AI agent doesn’t need a beautiful dashboard. It needs governed, real-time, contextually complete intelligence it can act on. Those are different design problems.

The Importance of Guardrails: Power Requires Constraints

There is an aspect of agentic AI that does not get enough attention in product conversations: agents can go rogue. Not in a science fiction sense 鈥斕齣n a very mundane and consequential enterprise sense. An agent with access to customer engagement tooling and insufficient guardrails can send the wrong message to the wrong person at the wrong moment. An agent with access to operational systems and poorly defined boundaries can trigger a cascade of actions that are individually defensible and collectively disastrous.听

Guardrails are not a constraint on what agents can do. They are the condition that makes it safe to give agents meaningful capability in the first place.听

This is an area where 惭别诲补濒濒颈补鈥檚 existing enterprise architecture is genuinely difficult to replicate. The organizational hierarchy modeling, role-based access controls, and data governance frameworks we have built over the years to serve human users translate directly into the constraint layer that agents require.  The goal is that when an agent queries 惭别诲补濒濒颈补鈥檚 intelligence through an MCP service, it inherits the same permissions, the same data residency rules, and the same organizational boundaries that apply to the human user it is acting on behalf of. Those constraints can not be bolted on after the fact. They must be structural. 

In practice, this means that as we would expose 惭别诲补濒濒颈补鈥檚 intelligence to agent ecosystems 鈥� whether existing CRM, workflow automation, or customer鈥檚 own agentic infrastructure 鈥� the governance model travels with the data. The agent can do what the authorized user can do, and nothing more 鈥� by design.听

From Frontline-Ready AI to Agent-Ready Infrastructure

The platform work we are doing right now is happening in two simultaneous timeframes.

In the near term, Frontline-Ready AI is our current expression of AI that acts at the human layer.听听

• Intelligent Summaries that compress hours of analysis into seconds听
• Root Cause Assist that identifies systemic issues before they escalate听
• Smart Response that enables frontline teams to respond to customer feedback at a scale no manual process could match
• Smart Topic Builder that replaces days of manual text analytics configuration with minutes of AI-powered setup
  

These capabilities are live, they are in production, and they are already changing how the world’s most complex organizations operate.

Over 650 of the world’s leading brands are now using Frontline-Ready AI features 鈥� from zero to enterprise-wide deployment in under a year, with some exciting outcomes already. AI processing requests on the 糖心原创 platform tripled in that same period. The adoption curve is steep, and it is accelerating.

In parallel, we are building what the next generation of the platform requires. An always-on, real-time signal processing architecture that never sleeps. A data model designed for agent consumption, not just human navigation 鈥� one that surfaces the full context of a customer relationship in a single, governed retrieval, so that an agent has what it needs to reason correctly without requiring multiple round trips or additional enrichment. Integration architecture that exposes 糖心原创’s intelligence to the CRM workflows, workflow system processes, marketing platform engines, and custom agentic systems that thousands of our customers are building every day.听

Insights Assistant, our first conversational AI agent, deliberately sits at the intersection of both timeframes. It is designed to work as a natural language interface for human users 鈥� allowing anyone from an executive to a frontline GM to ask questions and get answers grounded in real data with full traceability. But it is also designed to be invokable programmatically by agents. That dual-use architecture is purposeful. It is the bridge between the human-facing intelligence layer we have built and the machine-facing infrastructure we are building. Every design decision we make for Insights Assistant has to work for both users simultaneously.听

Frontline-Ready AI is what we built for today. Agent-Ready Infrastructure is what we are building for tomorrow. Supporting both simultaneously is no small task 鈥� and far harder than a slide deck makes it look.

The Strategic Direction We Are Committed To

I am so excited about what this architectural commitment means and how game-changing I believe it will be for the industry. And I want to be clear about why we are making it now.

The conventional approach in enterprise software is to build for current users and extend from there. Optimize the dashboard. Improve the analytics. Add more features to the interface people already use. That is a reasonable strategy for a stable category. But this category is not stable. 

Within a relatively short timeframe, we expect the majority of value flowing through an enterprise experience platform to be consumed not by a human sitting at a screen, but by AI agents operating continuously, at machine speed, across every system that touches the customer relationship. The platform architected for that reality now will have a structural advantage that compounds for years.

The investment behind 糖心原创’s next chapter 鈥� backed by Blackstone, KKR, and Apollo, with $150 million in new capital and a $500 million commitment to AI innovation 鈥� is what funds this innovation approach at the scale it requires. Not because the capital creates the architecture. But because the architecture requires sustained investment, a听long-term commitment to build for a future that is not yet fully visible, and the kind of enterprise trust that only comes from years of delivering at the highest standard.

We already have the trust. Now we have the investment. The platform we are building is the result of both.

What This Means Right Now

If you are a 糖心原创 customer, here is the practical implication.

The AI capabilities you are using today 鈥� Root Cause Assist, Smart Response, Intelligent Summaries,听Smart Topic Builder, Insights Assistant 鈥� are , while simultaneously establishing the infrastructure foundation that your AI agents will depend on tomorrow. You are building both simultaneously.

The organizational intelligence that 糖心原创 has embedded in your deployment 鈥� the role hierarchies, the data governance, the signal routing logic, the permissions architecture 鈥� is the same infrastructure that your agentic systems will inherit through MCP services and our integration layer.

The work you have already done to embed 糖心原创 in your operational fabric is the foundation of your agentic future. And if you are not yet where you want to be on any of those dimensions, now is the time to build those 鈥� and our team can help. In my next post, I鈥檒l share more about the importance of having the context from across your organization to make the most of the agentic future.听

The question is not whether your organization will eventually operate with AI agents consuming and acting on experience intelligence. It will. The question is whether the platform infrastructure you are building on today is designed for that world. Ours is, and that鈥檚 an advantage 糖心原创 customers will uniquely enjoy in the years to come.

The customer experience discipline did not arrive at its current limitations by accident. It evolved through a series of distinct phases. Each one a genuine step forward at the time, each one eventually outpaced by the complexity of what enterprises actually needed. Most organizations today are running infrastructure built for an earlier phase. The world has moved on. Many CX programs have not.

Here is how I think about the arc 鈥� and where the real opportunity sits.

Phase One: The Survey Era

The first phase of enterprise experience management was defined by a single insight that was, at the time, genuinely revolutionary: you could ask customers how they felt, aggregate and track the responses, and use the data to improve the business. The Net Promoter Score, introduced in 2003, gave organizations a simple, benchmarkable metric that resonated with executives in a way that traditional market research never had.

For the better part of two decades, the survey was the experience program. You designed it, deployed it, measured the response rate, tracked the score, and reported the results. Organizations designed entire functions around this model. Careers were built on it.

And it worked鈥p to a point. The point was reached when organizations realized that scores, however carefully measured, did not reliably translate into the actions that would achieve key business outcomes. The survey told you something had gone wrong. It rarely told you what, where, why, or what to do about it unless you applied more context, and analyzed the language of experience, not just the scores. At the same time that survey programs were deploying at scale, customers started shifting their focus to increasingly diverse engagement platforms 鈥撯�� online forums, social media, location-based review sites 鈥撯�� which stole attention from survey platforms, and decreased survey response rates.听

“The survey told you something had gone wrong. It rarely told you what, where, why, or what to do about it.”

听

Phase Two: The Omnichannel or Multi-Signal Era

The second phase was marked by an attempt to solve the survey’s fundamental limitation: it only captured a fraction of the customer’s experience. The solution seemed obvious 鈥� listen to more channels. Add the contact center. Add digital feedback. Add social listening. Add in-app feedback mechanisms. More signal coverage equals a better picture.

This was real progress. But it introduced a new problem: the data collected in each channel stayed in that channel. The contact center team had their data. The digital team had theirs. The CX team had survey results. Each had a partial view. Nobody had a complete understanding of the full customer journey, and insights from one channel weren鈥檛 being shared to the business functions who could resolve the matter, reduce the cost and effort of bad experiences, and increase overall customer loyalty. For instance, what negative product or digital experiences need to be fixed to reduce calls to a contact center? What in-store experiences are likely to create online chat experiences? What call center experiences escalate to complaints and trigger financial risk to the company?听

Most large enterprises are still living in Phase Two. They have invested significantly in multi-signal listening. They have dashboards 鈥� often many dashboards. They have more data than they can act on. And the signal that a customer is about to leave is invisible to the organization because the evidence is distributed across three departments that do not share a common platform or a common conversation.

This is the phase where the volume of data outpaced the organizational capacity to do anything coherent with it. And where the gap between what CX teams knew and what the business actually did about it became a source of real frustration.

“Most large enterprises are still living in Phase Two. They have more data than they can act on 鈥� and the signal that a customer is about to leave is invisible to the organization.”

听

Phase Three: The Connected Intelligence Era

The third phase is the one we are in the middle of right now. It is defined by the recognition that the value of experience data is not in any individual signal 鈥� it is in the connections between signals, across the customer鈥檚 full journey, mapped to the organizational context needed to act on them.

This is the phase that requires platform investment that most organizations have underestimated. Connecting a contact center transcript to a digital session to a survey response to an in-store interaction 鈥� for the same customer, in sequence, in real time 鈥� is not a data engineering problem. It is an architectural commitment that requires rethinking how the entire experience infrastructure is built.

At 糖心原创, this is what we mean when we talk about the Total Experience (TX) Profile 鈥� a unified view of every signal a customer or employee generates across their entire journey, connected to the organizational roles and teams that have the authority and responsibility to act on it. We processed 4.1 billion feedback signals last year, a 30 percent increase year over year, and expanded signal entity linking by 5x. Those numbers matter not because of their scale but because of what they represent: the infrastructure to see the full picture, not just a channel slice.

Phase Three is also where AI enters the story in a meaningful way. Not as an analytics add-on, but as the engine that makes sense of the full signal ecosystem 鈥� identifying patterns across channels that no human analyst could detect at scale, surfacing root causes rather than symptoms, and routing intelligence to the right person at the right moment. Over 650 of the world鈥檚 leading brands are now using 惭别诲补濒濒颈补鈥檚 Frontline Ready AI capabilities. The shift from AI as a research tool to AI as an operational capability is well underway.

Phase Four: The Continuous Action Era

Most organizations have not yet reached Phase Four. The ones that have are redefining what experience management looks like for everyone else. In order to achieve full value in this phase, organizations should connect listening posts, apply AI to infer patterns and trends, identify root causes, and assign actions and actors to make practical, impactful changes to improve customer experiences and business outcomes.听听

Phase Four is defined by a single principle: experience intelligence that does not drive action is a cost center, not a competitive advantage. The shift is from programs that surface what is happening to systems that change what happens next 鈥� automatically, across the organization, at the speed of the business.

This requires three things working together. First, AI that moves beyond surfacing insights to recommending and triggering action 鈥� not just telling a manager what went wrong, but telling them what to do about it, and in some cases doing it. Second, an organizational model that connects experience intelligence to the operational systems and the people who own the metrics that actually change 鈥� the COO, not just the CXO. Third, a closed-loop architecture that measures the impact of the action taken, feeds that learning back into the system, and drives continuous improvement that compounds over time.

The organizations operating in Phase Four have done something that most CX programs never achieve: they have connected experience signals directly to financial outcomes. Not as a slide in a quarterly business review, but as a live, always-on system that identifies revenue at risk, operational cost drivers, and retention threats in real time 鈥� and coordinates the organizational response before the impact reaches the P&L.

惭别诲补濒濒颈补鈥檚 is an early expression of what Phase Four tooling looks like 鈥� a natural language AI agent that allows any user in the organization, from the frontline manager to the C-Suite, to query the full signal ecosystem conversationally and receive answers grounded in real data. Not a dashboard. Not a report. An answer, with a recommended next step. But the tools are only part of it. The organizations reaching Phase Four are also changing how they are structured 鈥� building cross-functional experience governance, embedding CX metrics into operational KPIs, and treating the CX practitioner not as a scorekeeper, but as a transformation driver and changemaker.

“The organizations reaching Phase Four have connected experience signals directly to financial outcomes 鈥� not in a quarterly review, but as a live system that identifies revenue at risk in real time.”

听

Phase Five: The Agentic Era

Phase Five is where the market is heading, faster than most organizations are planning for.

The defining characteristic of Phase Five is that the primary consumers of experience intelligence are no longer humans. They are AI agents 鈥� Salesforce workflows, ServiceNow processes, content personalization engines, and the custom agentic systems that enterprises are actively building to automate their own operations. These agents need to understand what is happening in the customer relationship and take action on it, without waiting for a human to read a report.

This shifts the role of the experience platform from a system that helps humans make decisions to infrastructure that enables machines to act. The implications are significant. The organizational hierarchy modeling, role-based access architecture, and signal routing logic that underpin today鈥檚 enterprise experience platforms need to be rebuilt for a world of machine consumers. The governance frameworks need to account for AI acting on behalf of the brand in real-time customer interactions. The trust framework 鈥� the permission that enterprises have earned to engage directly with their customers 鈥� becomes even more valuable as the stakes of that engagement increase.

At 糖心原创, this is the architecture we are actively building toward. We have spent 20+ years earning the trust of some of the world鈥檚 largest enterprises to manage their most consequential customer relationships. That trust, combined with an AI-native platform rebuilt for the demands of an agentic economy, is the combination that will define leadership in this market. The next generation of the platform 鈥� accelerated by new investment and a $500M commitment to AI innovation 鈥� is being designed for 70 million AI agents, not just 7 million human users.

So where are you?

The honest question for every enterprise leader reading this is: which phase are you in? And more specifically 鈥� is the phase you are in aligned to the phase your customers are living in?

Because customers do not experience your organization through the lens of your CX program structure. They experience it as a continuous journey 鈥� across every channel, in every interaction, in real time. And the gap between a Phase Two program and a Phase Four or Five experience is not invisible to them. It shows up in response times, in the feeling of being known or unknown, and in whether the organization acts like it remembers what just happened.

Most organizations know they need to evolve. The challenge is that each phase transition requires not just a technology upgrade but an organizational one. Different skills, different structures, different ways of defining success. The CX practitioner who excelled at Phase One was a survey designer. Phase Three requires a data architect. Phase Four requires a business transformation agent who sits at the intersection of experience intelligence and operational accountability. And Phase Five organizations are embracing agentic AI, workflow automation, and linking experience signals operationally to business processes and outcomes.听

The good news is that the transitions between phases are not taking as long as they used to be. The AI capabilities that took years to build are now available, at enterprise scale, as part of a platform that is already embedded in how the world鈥檚 most complex organizations run their businesses. The path from Phase Two to Phase Four is shorter than most organizations realize.

The question is not whether to make the journey. The question is whether you start now, or whether you start after your competitors already have.

I joined 糖心原创 18 months ago and brought in a new executive team to reinvent both our business and the experience management space, building on 惭别诲补濒濒颈补鈥檚 strong foundation. This team represents the deepest collective expertise in the industry. And over the last year and a half, we鈥檝e been executing a deliberate strategy to capture the market opportunity ahead of us. Today, we are officially ushering in our next chapter.

Here is what we are building 鈥� and why now is exactly the right moment.

What the world’s largest enterprises are demanding right now

Across every industry we serve, the conversations I am having with enterprise leaders have shifted significantly. The question is no longer whether AI belongs in their experience programs, it is which platform can deliver AI capabilities with the scale, security, and reliability their operations require. What they are asking for is specific. They want platforms that do not just surface what is happening across customer and employee journeys 鈥� they want platforms that act on it. Automatically. Across the organization. Connected to the operational systems their business runs on. They want continuous improvement programs driven by real-time intelligence, not quarterly review cycles. They want the ability to resolve a customer issue before it escalates, to identify an employee experience problem before it affects retention, to connect a signal from the contact center to a decision in product or operations without routing it through a manual process.

The demand is real and it is accelerating faster than most anticipated. We are built to meet it.

The platform we are building

For the past 18 months we have been reimagining the platform around exactly this demand. The work is already underway and today’s capital accelerates every dimension of it, with focus anchored in:

Conversational feedback experiences. AI-driven orchestration and automation that transforms how enterprises engage with and respond to customers  鈥� continuously, across every channel, in real time. Not periodic listening programs but interactive, always-on dialogue that captures feedback and provides contextualized responses automatically.

AI-driven action orchestration. The ability to connect experience data across every touchpoint in the customer and employee journey and coordinate enterprise-wide responses in real time. When a signal emerges in one part of the organization, the platform connects it to action across systems and functions 鈥� services, operations, product, HR 鈥� without requiring a human to broker the handoff. This is how large enterprises move from managing experience in silos to driving systemic, continuous improvement.

Agentic automation. A platform that does not just identify what is happening but autonomously determines root causes, triggers the appropriate response, and resolves issues without manual intervention at every step. This is the capability enterprise leaders are asking for most urgently: AI that can take action, not just generate a report. The result is a system that gets smarter and faster with every interaction.

We have new solutions in development in each of these areas 鈥� ahead of anything we have publicly shared. Today’s capital accelerates our innovation.

Why 糖心原创 is positioned to deliver this at enterprise scale

The enterprises that trust 糖心原创 are operating at a scale and complexity that sets a high bar. Global organizations manage hundreds of millions of customer interactions and millions of employee touchpoints, across dozens of markets, on infrastructure that has to continue to modernize. 

We don鈥檛 just serve large organizations, we integrate into their operational fabric. By anchoring enterprise-grade security, complex data integrations, and the operational resilience of global deployment into our architecture, we鈥檝e built structural stability that others simply cannot replicate.

What we are adding is an AI-native platform layer that is built from the ground up, not retrofitted onto existing products, for the demands of AI-first experience management. All while preserving and extending everything our customers rely on today.

The combination of proven enterprise infrastructure and a platform reimagined for AI is what will separate the leaders in this market. That is the conviction driving everything we are building. The capital from today’s transaction funds that commitment at the scale it requires, backed by investors who have spent decades building enterprise technology companies and who understand what it takes to win at this level.

The timing is deliberate. We have de-levered the business at a moment when financial flexibility is becoming a genuine competitive differentiator. The ability to invest aggressively, move quickly, and make long-term platform commitments without a debt overhang is not something every player in this market can claim right now. We can.

What this means for our customers

If you are a 糖心原创 customer, here is what today means: the investment behind your partnership is now better resourced than it has ever been.

What changes is the pace. The AI-driven orchestration tools, the conversational feedback experiences, the agentic automation capabilities that will change how your organization acts on experience data 鈥� all of it moves faster. We鈥檙e pairing that accelerated innovation with investment in professional services and modernization to help you maximize the value you get from 糖心原创.

We are in this business to drive outcomes. Business transformation. Measurable improvement in the metrics that matter most to your organization. That has always been true. What changes today is how fast we can deliver on it, and at what scale.

The trust our customers place in us is not lost on me. They run some of the world’s most consequential customer relationships through our platform. Billions of interactions, across millions of their customers, every day. That is a serious responsibility. We have built the enterprise-grade governance, security, and controls that trust demands. Today’s investment means we go further and faster on behalf of the organizations that trust us to get it right.

If you have questions or want to talk through what this means for your business, reach out to me directly at ceo@medallia.com.

To the 糖心原创 team

You have been executing a transformation plan that required you to trust the direction before the destination was fully visible. You rebuilt how we go to market, how we build products, and how we serve customers. Now, we are poised to invest more in this business.

We already have a world-class team that has been driving innovation and building this platform to where it is today 鈥� strong and ready to scale. Now, we intend to grow, recruiting even more engineers, product managers, services talent, sales talent, and more.

Today closes the open question on the capital structure and raises the bar on what we are expected to deliver. We have the foundation, the investment, and the backing to execute at a level this company has not had before. I expect us to meet that moment 鈥� and I have no doubt we will.

The standard for customer experience in financial services has quietly shifted. 

In our latest Financial Services Benchmark Report, we share that customer expectations have never been more unforgiving. Customers no longer measure their bank against competing banks 鈥� they measure it against every friction-free interaction they’ve had with any company.听

The patience you’d get from a loyal customer a decade ago has been spent. That pressure is made more precarious by how recently the industry earned back its credibility. According to the financial services only just crossed the threshold into “trusted” territory for the first time since the 2008 financial crisis, with a score of 62%.听

That’s not a position of strength. It’s a position of fragility. 

An industry that spent over a decade rebuilding consumer confidence cannot afford to squander it through poor experiences, disjointed interactions, or a frontline that isn’t equipped to deliver on the relationship it promises.

At Issue: The 鈥淩estart鈥� Problem

The clearest symptom of falling behind in customer experience is what might be called the restart 鈥� the moment a customer crosses from one channel to another and discovers that no information came with them. They’ve navigated a phone tree, finally reached a live person, and must now re-explain everything from the beginning. 

Meanwhile, the agent is aware of the customer’s frustration but not its cause, and spends the first several minutes of the conversation managing tone rather than solving a problem. It’s a failure mode that compounds itself, and it originates almost entirely from internal operational problems that customers should never have to feel.

A customer who has been made to feel like a stranger 鈥� again 鈥� doesn’t forget it.

Why Self-Service Alone Isn’t the Answer

This is why the needs a counterweight. 

Automation and self-service channels are genuinely valuable when they reduce effort and add convenience. But automated systems, with IVRs in particular, are consistently viewed as a source of friction rather than relief. Whether they succeed or fail, customers still want to talk to a person. And when that person is empathetic, knowledgeable, and actually empowered to act, the impact on brand perception is substantial 鈥� our research points to double-digit swings in net sentiment scores tied directly to frontline behavior.

The implication is that the human element isn’t a legacy cost to be engineered away. It’s instead a differentiator. The question is whether the people on the frontlines are equipped to perform that role effectively.

Investing in the (Human) Frontline

These days, equipping your frontline to be more empathetic requires two tactics most organizations underinvest in. The first is integrating tools that give agents a complete, real-time view of each customer’s history so that context travels with the customer in their time of need. The second is training that prioritizes empathy and resolution over efficiency metrics. Speed matters, but an agent who closes a call quickly without resolving the underlying issue has just moved the problem downstream. Worse, agents who default to jargon during sensitive conversations about rates or fees create confusion that erodes the very trust they’re meant to reinforce.

The urgency of getting this right is compounded by what’s coming. Over the next two decades, an $84 trillion generational wealth transfer will reshape the client landscape 鈥� and the retention risk is stark. Eighty percent of clients change advisors at the point of inheritance, at the moment the transfer actually happens.听

Institutions that want to retain those assets need to be building relationships with the next generation now, and that requires frontline teams who are empowered with AI-powered speech and text analytics to listen for the life events 鈥� a marriage, a health change, a transition to assisted living 鈥� that signal when proactive, personal outreach would matter most.

Modern CX for Financial Services

The future-proofed financial institutions are the ones treating these as strategic priorities, not operational checkboxes. They鈥檝e made a deliberate decision that the customer relationship is the product, and that every internal process either serves that relationship or needs to be rethought.

Eliminating the restart isn’t a product feature. It’s an expression of how seriously an organization takes the relationship it claims to value. Customers can feel the difference between a company that has genuinely built systems around their needs and one that has bolted on a chatbot and called it transformation. That felt difference, accumulated across dozens of small interactions over months and years, is what separates financial institutions with long-lasting loyal customers from those with merely retained ones, who are one better offer away from leaving. 

Stay ahead of rising expectations. Download our Financial Services Insights Benchmark Report to unlock key industry trends, insights, and advice.

Investing in efforts to improve your customer experience strategy is not only something all companies can benefit from, it鈥檚 something all businesses need to add to the top of their priorities list for 2026.听

Why?

To put it bluntly, brands are confident (overly confident) that they鈥檙e delivering better-than-expected experiences, but ONLY 17% of consumers say their customer experience has improved over the past year. That鈥檚 according to our new study: The 2026 State of Customer Experience Report, which is based on insights from 552 CX practitioners, 1,522 consumers, and benchmarks from over 600 anonymized enterprise CX programs.

This matters because customer loyalty is more fragile than ever. Less than one in four consumers (22%) say they鈥檙e 鈥渧ery loyal鈥� to a brand, a rate that dropped three points in just one year, according to the same study.

Companies that are managing to hold onto their loyal customers are doing things differently. They鈥檝e ditched the old (ineffective) ways of doing CX, in favor of the . They鈥檙e using the following five customer experience strategies that actually work.

What It Takes to Improve Your Customer Experience Strategy

Go Beyond Surveys

Our study found that customer feedback surveys are still the #1 source of CX insights, even as survey participation is declining, with response having dropped 11% since Q1 2024.

Perhaps unsurprisingly, most CX teams (78%) plan to use a new customer experience metric or approach to measure CX in 2026.

This aligns with previous 糖心原创 research published in our Conversational Intelligence Report that found that 59% of customer communications happen outside of feedback surveys and that most CX practitioners (75%) say surveys aren鈥檛 enough to get the complete picture of the customer experience.听

If you鈥檙e ready to improve your customer experience strategy by expanding to include new metrics and approaches, we鈥檝e got a guide that will help you evolve when good survey response rates are getting harder to come by.听

Gather Omnichannel Insights

Tapping a variety of data sources from across the omnichannel customer experience, from digital experience and social media touchpoints to phone calls and in-person interactions, is a strategic move that鈥檚 giving high-growth teams a considerable advantage.

Companies on the earnings fast track are 2x as likely as those with flat or declining revenues to prioritize using more CX signals and data sources, according to our 2026 study.

The proof is in the ROI: Teams using five or fewer data sources are less likely to be able to show the return on investment of their CX strategies compared to teams using 10 or more sources.

Companies that are still sampling recorded customer service calls to see what鈥檚 working and what鈥檚 not鈥攐r aren鈥檛 even listening to these conversations at all鈥攁re paying a price. We鈥檝e crunched the numbers in our guide: The Hidden Cost of Ignoring Your Customer Conversations, which breaks down all the ways brands can both save and uncover new revenue by adding customer conversations to their omnichannel insights mix.听

Use Conversational Intelligence to Accelerate Your CX Program鈥檚 Maturity Level and Success

Powered by AI, conversational intelligence (CI) helps companies turn unstructured data from conversations between customers and organizations across channels like voice, chat, email, and messaging into insights they can use to improve the customer experience.听

Conversational intelligence is a powerful engine that enables brands to take what they know about their customers to deliver more effective, personalized upselling and cross-selling in the moment. It can also instantly analyze the sentiment and intent of these customer interactions to coach agents in the moment, analyze conversations in the aggregate to instantly get to the root-cause of issues, and improve the overall quality of customer service interactions.

Our 2026 State of CX report finds that teams that use conversational intelligence data are 63% more likely to say they鈥檙e exceeding their goals than teams that do not, but despite this massive advantage, conversational data remains vastly underutilized, with only 30% of teams leveraging it frequently.

Similarly, our 2025 Conversational Intelligence Report found that CX leaders are 6x more likely to use CI in a meaningful way vs. laggards and that investing in CI helps brands improve first-call resolution (FCR), upsell rates, revenue per customer, 狈笔厂庐, overall satisfaction (OSAT), cost savings, compliance risks, and churn, but only almost half of CX teams aren鈥檛 using it at all.

As part of our 2025 CI study, we found that most CX practitioners (64%) plan to increase their use of CI, with fast-growing brands being even more likely to say so (73%).

Curious about giving conversational intelligence a try? Use our Conversational Intelligence ROI Calculator to see how much revenue you could recover by acting on the conversations you already have.听

Empower the frontline with AI

While 36% of CX teams report their company鈥檚 use of AI-based data analytics is advanced, a smaller share (29%) say they鈥檙e using AI for internal employees, according to our 2026 State of CX report. 

Companies are aware this needs to change, with the majority (83%) saying empowering the frontlines is a key part of achieving their goals this year and a similar amount (85%) saying they feel confident that equipping their employees with AI will help them serve their customers better.

This investment appears to be a differentiator between companies with leading CX programs and those at the bottom of the CX maturity curve. Leaders are more likely to feel confident about their plans to integrate AI into workflows vs. laggards. 

Want to join the CX teams at the head of the pack? The world鈥檚 leading brands are already using our Frontline-Ready AI capabilities to give employees access to AI that鈥檚 specifically designed for their needs and skillsets (no prompting expertise required) to help their customer-facing teams act faster, solve problems smarter, and deliver results in the moment.

Don鈥檛 Chase Scores鈥擳ake Action on Insights to Drive Business Outcomes

The most successful CX teams aren鈥檛 stopping at measuring metrics like 狈笔厂庐. They鈥檙e unlocking the full potential of customer experience to deliver meaningful business outcomes鈥攚hether that鈥檚 to empower their organizations to make more money, save more money, or lower their risk.

This was a central theme at , where CX leaders shared the ways their teams are translating CX measurements and strategies into quantifiable financial impact and going from being perceived as cost centers to earning recognition for being revenue generators.听

The Real Cost of Not Doing the Work to Improve Your Customer Experience Strategy

Inaction comes at a steep price. It leads to stagnation, stalled progress, missed revenue opportunities, and customer turnover. 

And it鈥檚 not just theoretical. This is what鈥檚 currently happening. 

According to our 2026 State of CX report, 30% of consumers say they experienced an issue during their most recent interaction with a company. When that happens, the likelihood of customers considering switching brands more than doubles. That鈥檚 something no business can afford to ignore. 

For more insights into what CX leaders are doing to prove value and earn priority with their C-suite leaders, check out the complete 2026 State of Customer Experience Report.听

Over the past year, 糖心原创 has been driving a fundamental shift in what experience management can do for the business.

The market changed. AI accelerated everything. Expectations intensified.

The question in the boardroom moved from 鈥淗ow are our CX metrics performing?鈥� to 鈥淲here are we losing money, and what are we doing about it?鈥�

That shift raised the stakes for every experience program. Measuring is no longer enough. Organizations need a system that translates insight into decisions and action across the business.

糖心原创 has been leading through that change, working with our customers to create engines of revenue growth, operational efficiency, and resilience.

We are proud to be redefining the industry.
Proud of the innovation we continue to deliver.

And now our brand reflects that evolution.

We are no longer a system of record.
We are a system that drives action.

A declaration, not decoration

Our evolved brand identity reflects who we are today and where we are taking the industry next.

It is bold because leadership should be unmistakable.
It is focused because attention is finite.
It is minimal because distraction is expensive.

This is not about aesthetics.

It is a signal to the market.

糖心原创 is not here to measure experience.

We are here to transform it.

Designed for the changemakers

This shift is bigger than just our brand, and the notion of being all in on action is one that stems from a group of people that inspires us every day 鈥� our customers.

The experience leaders working to connect the customer journeys they curate to true business impact and building relationships across the organization to eliminate systemic friction, resolve issues quickly, and prove that the work they do drives growth, retention, and brand loyalty.

These changemakers need a platform and a partner that matches their ambition 鈥� and this is our way of saying confidently we are doing just that.

Built for what comes next

Passive listening is over.

Experience is your competitive advantage.

Powered by enterprise scale.
Frontline-ready AI.
Relentless action tied to measurable business results.

This is more than a rebrand.

It is a commitment.

Built for the changemakers who refuse to stand still.

Experience 鈥�26 felt different.

Not because of the scale. Not because of the production. Not because of the announcements.

It felt different because the industry itself has crossed a line.

Across financial services, hospitality, retail, logistics, automotive and more, leaders are no longer asking how to measure experience better.

They are asking how to help transform their business as experiences are evolving at an ever increasing rate of change.

How to connect experience to revenue.
How to remove structural inefficiency.
How to reduce risk.
How to unify fragmented journeys.
How to adapt to a world of human, hybrid, AI-powered, and soon, agentic experiences.
How to embed intelligence into the operating model itself.

The shift is no longer incremental.

It is structural.

Here are my biggest takeaways.

Experience friction is a financial problem

For many years, CX was structured around measurement. Surveys were deployed. Scores were tracked. Dashboards were reviewed.

Measurement matters. But measurement alone does not change performance.

In many organizations, metrics were a proxy for progress because surveys were the only signal available. Movement in a score was interpreted as improvement. But improvement only happens when the underlying system changes. It doesn鈥檛 explain the why, or the how, or the root cause of experience failure or excellence.

Today, experience no longer lives in a single survey response.

It lives in calls, chats, digital sessions, behavioral signals, and operational data across the full journey.

For the first time, we can see the system in motion.
Where it breaks.
Where effort increases.
Where emotion shifts.
Where revenue is exposed.

That was the shift at Experience 鈥�26.

The conversation moved from observing sentiment to addressing structural friction.

Because friction is not abstract.

A failed redemption in an app.
A broken API.
A transfer from chatbot to IVR.
A repeat contact that should never have happened.
A customer who simply does not come back.

Individually, these appear operational. At scale, they are financial.

, Mark shared the story of a single high value customer whose journey broke across three departments. The digital failure was invisible to the contact center. The contact center failure was invisible to operations. The customer left.

One customer lost is unfortunate.

Twenty thousand customers lost for the same reason is a structural flaw.

At a lifetime value of $20,000 per customer, that is a $400 million problem.

Not a CX issue, a business problem.

That is the difference between score watching and transformation.

The role of modern experience leadership is not to observe sentiment. It is to identify structural friction, trace it to its origin, and remove it systemically.

As I said on stage:

鈥淲e are more than the survey people.鈥�

We have to be connectors across silos.
Conductors of the journey.
Architects of systems that did not exist five years ago.
Business leaders who map experience directly to outcomes.

That is what this moment requires.

AI is becoming operational infrastructure

Last year, many organizations were experimenting with generative AI.

This year, the focus has shifted to operational integration.

The question is no longer whether to use AI. It is how to embed it responsibly into daily workflows.

Capabilities like conversational analytics and automated topic discovery lower the barrier to insight. But insight alone does not create value.

Value is created when signals are unified across journeys and tied directly to outcomes:

Revenue growth.
Cost to serve efficiencies.
Risk mitigation.
Operational performance achievement.

When you can quantify the financial impact of friction and launch action within the same environment, experience becomes a management system.

The advantage in 2026 will not belong to the company with the most dashboards.

It will belong to the organization that can consistently close the loop between insight and action.

That requires governance.
Clear ownership.
Defined accountability.

Technology enables the shift.

Leadership operationalizes it.

Alignment is the new advantage

One of the most powerful aspects of Experience 鈥�26 was seeing how organizations are reshaping their internal models.

From the to the Expy Awards.
From the Customer Advisory Board sessions to MUG roundtables.
From the Partner Summit to the Executive Summit.
From masterclasses and workshops to a product hub that never stopped buzzing.

Everywhere you looked, .

Leaders from Shipt, Hyatt, Maersk, Verizon Business, Bank of America, Toyota Financial Services, CIBC, and so many more shared how they:

• Elevated experience metrics into company objectives
• Connected feedback directly to revenue and retention
• Embedded intelligence into frontline workflows
• Built executive sponsorship across finance, operations and technology

What stood out was not perfection.

It was alignment.

Experience is no longer being managed as a reporting function.

It is being positioned as a strategic capability.

And the organizations leaning into that shift are moving faster because of it.

Change is built between sessions

Transformation is strategic. It is also deeply human.

Yes, we spent our days debating models. Reviewing use cases. Walking the product hub and seeing new capabilities in action.

But we also created space to connect.

The dog park was one of my favorite stops.

There is something grounding about watching leaders who manage experiences at global organizations sit on the floor for a few minutes and reset.

The 糖心原创 Market brought a different kind of energy. Shopping brands like the Disney Store, Kate Spade, and Vuori. Testing reflexes in the McLaren simulator. Conversations that started in breakout sessions continued over dinner, music, and a lively dance floor.

These moments are not side notes.

They build relationships.
They build trust.
They build the informal networks that make real change possible.

Strategy scales faster when relationships are strong.

Looking ahead

Experience 鈥�26 was not defined by features.

It was defined by direction.

Experience is moving from measurement to management.
From dashboards to decisions.
From insight to institutional change.

The organizations that win in this next chapter will:

• Tie experience directly to revenue, efficiency and risk
• Embed AI into disciplined operating models
• Empower leaders across the enterprise to act on intelligence
• Build accountability into how work gets done

This is a defining moment for our industry.

The opportunity is significant.

And it belongs to the changemakers.

Experience ’26 marked a defining moment for the future of customer experience. The conversation has shifted decisively from reactive, survey-led programs to a proactive, agentic model where every signal 鈥� voice, digital, social, and employee feedback 鈥� is converted into measurable business impact.

Central to this transformation is the evolution of experience professionals from scorekeepers to changemakers. Rather than simply reporting on the past through static dashboards, these leaders are now acting as conductors of organizational change, using real-time insights to drive the growth that the C-suite actually cares about. (As 糖心原创 CEO Mark Bishof noted in his , 鈥淐EOs kind of care about scores and sentiment, but they really care about impact and financial outcomes.鈥�)

Leading the charge are the recipients of this year’s 糖心原创 Expy and Partner Awards. These organizations represent the forefront of what modern experience excellence looks like in practice, driving meaningful gains in revenue, retention, and cultural transformation 鈥� proving that a bold commitment to experience is, above all else, a competitive advantage.

These awards celebrate organizations and individuals who are leading the way in this transformation, turning insights into action, empowering employees, and delivering measurable impact. These changemakers set the standard for what鈥檚 possible when experiences are truly transformed. 

糖心原创 Expy Award Winners

CIBC: The Growth Accelerator

CIBC took home the Growth Accelerator Award by proving that CX belongs at the center of the business model. , who leads enterprise client experience strategy for CIBC, shared that their journey began a decade ago when they realized their brand felt fragmented and they were lagging behind peers.

The solution was a massive cultural and operational overhaul. “We built a really mature operating model so that we were listening, learning, and acting from our client insights in a meaningful way,” Leheta said. Crucially, they moved beyond NPS to a “Client Experience Index,” tracking 20 metrics tied to variable compensation for everyone from the frontline to the CEO.

Perhaps the most actionable takeaway for practitioners was CIBC鈥檚 governance model. “We actually built CX into our enterprise delivery framework,” Leheta explained. “Projects do not get funded unless they complete this client experience assessment. We get to be at the table, and we’re actually evaluating client risk.”

Vanguard: Insights to Impact

Vanguard was honored with the Insights to Impact Award for their ability to look beyond what customers say to what they actually do.

, who leads the engagement technology team, highlighted the power of digital behavioral signals. “Prior to us having digital insights, our CX experts really struggled to understand what clients were doing based on our experiences,” Civera noted.

By utilizing session replays and heatmaps, Vanguard could visualize friction points that surveys missed. This shift led to a massive bottom-line win: “We were able to pinpoint and address an issue with why we weren’t converting sales leads. Now we’re seeing double the amount of sales leads from our website.”

Verizon Business: The Power of Employee Empowerment

Verizon Business received the Employee Empowerment Award, proving that a world-class customer experience is impossible without an engaged frontline. , who leads CX and EX for Verizon Business, shared how they bridged the gap between internal jargon and customer reality.

 “Customers don’t speak Verizon,” Scott pointed out. To fix this, they brought the Voice of the Employee under the same umbrella as CX. They also launched the “Experience Hero” program to drive a cultural shift. “It was a required training for the entire organization, not just the sales and service teams. Everyone needed to know that the work you do on a daily basis eventually produces something that your customer will see.”

Santaluc铆a: Leading Experience Transformation

, CX Manager for Santaluc铆a in Spain, was named the Experience Transformation Leader of the Year. 

In the insurance industry, interactions are often high-emotion “moments of truth,” such as a home fire or the loss of a loved one. Paraja recognized that relying solely on NPS was insufficient due to a lack of context and timing. “We were just measuring dissatisfaction, but we were doing nothing to prevent it,” she said. By transitioning to an omnichannel model 鈥� listening to 100% of calls and over a million digital sessions 鈥� Santaluc铆a moved toward prevention. “We are avoiding 54% of churn risk that we detect over all these patterns,” Paraja shared, adding that “listening is not a project鈥t鈥檚 a discipline.”

糖心原创 Partner Awards: Celebrating Innovation and Growth, Together

During the Partner Summit, 糖心原创 also recognized the partners that make these large-scale transformations possible. As 糖心原创 CSO Sid Banerjee noted, these partners are “critical in helping drive value across the CX landscape.”

惭别诲补濒濒颈补鈥檚 Annual Partner Awards Program celebrates partners who go above and beyond to deliver customer value, drive innovation, and accelerate growth through collaboration. 

糖心原创 Partner Award Winners

: Services Partner Award, for the services partner with the highest sourced revenue for 糖心原创 in 2025.

: Technology Partner Award, for the technology partner with the highest 糖心原创 software revenue impact.

: Fastest Growing Partner Award, for the partner with the highest percentage growth for sourced revenue year over year for 糖心原创.

: New Partner Award, for a new partner with the highest sourced revenue for 糖心原创.

: Business Impact Award, for the partner that delivered the greatest overall business impact.

: Solution Innovation Award for the partner with most impactful solution innovation built on the 糖心原创 platform.

: EMEA Partner of the Year, for the partner with the highest sourced revenue for 糖心原创 in the EMEA region.

: LATAM Partner of the Year, for the partner with the highest sourced revenue for 糖心原创 in the LATAM region.

: APAC Partner of the Year, for the partner with the highest sourced revenue for 糖心原创 in the APAC region.

鈥淲inning together matters. These achievements reflect shared accountability, strong execution, and a deep focus on client outcomes 鈥� and we鈥檙e incredibly grateful for the partners who make that possible,鈥� said Eric Din, 糖心原创 SVP of Alliances.

Closing Advice from the Award Winners

For experience leaders looking to mirror the success of these winners, our spotlight panelists offered three final pieces of advice:

Prioritize Progress over Perfection: “Momentum matters,” said CIBC鈥檚 Stephanie Leheta. “Even if those first few steps aren’t perfect, you’ll actually build confidence.”

Normalize Failure: Verizon鈥檚 Samantha Scott urged leaders to “be bold and to normalize failure as a moment to learn.”

Change the Language: As Santaluc铆a鈥檚 Paloma Paraja emphasized, when you stop speaking “CX speak” and start speaking the language of business value, you earn your seat at the table.

The award winners of Experience 鈥�26 proved that CX is no longer a “survey shop.” It is a growth engine, a risk-mitigation tool, and the ultimate differentiator in an AI-driven world.