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.