This article includes:

• 20+ CX conferences located in person all over the world, as well as events that offer virtual streaming
• Smaller, more intimate gatherings capped at 65 guests, as well as large-scale summits that attract thousands of participants
• Popular annual customer experience conferences from industry leaders like 糖心原创, Forrester, Adobe, Salesforce, and Customer Contact Week

Every year, some of the world鈥檚 most ambitious, curious, and innovative customer experience (CX) professionals come together to network, learn from each other, share best practices, gain insights about effective CX strategies, get access to industry benchmarks, and discover the latest CX technologies at customer experience conferences all over the globe.听

Whether you鈥檙e searching for your first CX event or looking to add new gatherings to your calendar, we鈥檝e put together a list of the best customer experience conferences to attend in 2027.

The Best Customer Experience Conferences in 2027 (by Date)

January 2027

Dates: January 25鈥�27, 2027

Location: Orlando, Florida

In 2027, 700+ attendees from 200+ global brands are expected to take part in the three-day Customer Contact Week Orlando, featuring 50 powerhouse speakers from category leaders like Mercedes-Benz USA and IHG Hotels & Resorts, curated networking sessions, the Customer Contact Week (CCW) Expo Hall, innovation summits, CCWomen Summits, and opportunities for the customer contact community to get strategies designed to elevate the customer experience.听

February 2027

糖心原创 Experience 鈥�27听

Dates: February 9鈥�11, 2027

Location: Las Vegas, Nevada

This year鈥檚 Experience 鈥�27, 惭别诲补濒濒颈补鈥檚 flagship annual conference, features training labs, sessions, workshops, award-winning playbooks, and masterclasses designed to future-proof your CX strategy, with themes exploring:

• Multi-signal intelligence听
• The economics of experience听
• Evolving from a cost center to a profit driver
• Becoming a changemaker
• Building a connected experience ecosystem
• Deploying AI that delivers
• Leading in the agentic era

Past speakers and attendees have included CX experts from brands such as Mayo Clinic Labs, Pfizer, Dick鈥檚 Sporting Goods, IHG Hotels & Resorts, and Capital One. The event offers inspiration, practical solutions, and mentoring from industry icons sharing game-changing strategies for experience management.听

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Dates: February 24鈥�25, 2027

Location: Houston, Texas

The 2027 CX USA Exchange is an invite-only event for a select number (65) of senior customer experience leaders from the country鈥檚 largest customer-facing organizations that features exclusive access to industry case studies as well as collaborative cross-industry benchmarking sessions. Now in the conference鈥檚 seventh year, this year鈥檚 theme is all about aligning people, processes, and platforms in the age of AI to turn business strategy into real value for customers.听

Past events have featured top executives from companies like IBM, DraftKings, Walgreens, Spotify, and Citi.

March 2027

Dates: March 16-鈥�17, 2027

Location: New York, New York

Part of a global series of CX events that spans New York, San Francisco, Boston, and London, from the network for customer success and support professionals, Customer Success Collective, the New York event attracts a community of 100+ senior customer support decision-makers from the world鈥檚 most influential companies. This year鈥檚 list of attendees includes professionals from Cisco, Meta, Google, Zoom, PayPal, Slack, Microsoft, and more.听

The 2027 agenda for the New York conference will cover:

• Mastering AI and human-blended experiences, without sacrificing empathy or trust
• Leveraging support insights to accelerate growth across the business
• Delivering exceptional customer experiences at scale using AI-augmented knowledge bases

The dates for the rest of the global series have not yet been released.

Date: March 17, 2027

Location: New York, New York

Another sought-after event hosted by Customer Success Collective, this one-day CX conference brings together leading chief customer officers to explore shared challenges, dive into industry insights, cut through the AI hype, and network. This year鈥檚 confirmed attendees for this invite-only event include professionals from Shopify, Adobe, Stripe, Square, Intuit, Google, and more.听

The 2027 Chief Customer Officer Summit agenda includes a range of topics that are top of mind for executives, including:

• Creating AI-first operating models using agentic systems and predictive workflows to scale precision, speed, and customer value
• Crushing customer churn by designing onboarding and lifecycle programs that surface value fast, enforce long-term engagement, and protect recurring revenue
• Driving influence across the organization at the highest levels听

Dates: March 17鈥�18, 2027

Location: Frankfurt, Germany

Created for decision-makers from corporate market research and CX/customer management, Succeet is an annual gathering designed to help attendees meet influential leaders who matter and shape what鈥檚 coming next in the areas of CX and market research. The event offers live presentations, keynotes, panel discussions, and networking events. In 2026, Succeet featured 2,900 registered participants, 185 presentations and workshops, and 150 exhibitors.听

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Dates: March 22鈥�23, 2027

Location: Sydney, Australia

Customer Contact Week Digital serves as an online hub for insights on customer service and contact center operations and also hosts major annual customer experience management conferences where top industry experts share their expertise. This flagship Customer Contact Week event for Australia and New Zealand connects contact center, CX, and digital transformation leaders in the region and has featured speakers from companies like Allianz Australia and P&N Bank.听

The theme of CCW ANZ 2027? Performance, precision, and customer impact鈥攊t鈥檚 all about redefining what great looks like and learning how to deliver it at scale, using AI, automation, and advanced workforce models, channel strategies, and more.听

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Dates: March 22鈥�25, 2027

Location: Las Vegas, Nevada and online

You鈥檙e invited to join leaders defining the future of CX at Adobe Summit, one of Adobe鈥檚 premier annual events. In 2027, attendees will learn how Adobe customers are using agentic AI to boost brand visibility, scale content creation, and deliver engaging experiences.听

Adobe Summit 2026 featured the company鈥檚 Chair and Chief Executive Officer Shantanu Narayen and President, Customer Experience Orchestration Business Anil Chakravarthy as well as executives from Procter & Gamble, DICK鈥橲 Sporting Goods, and Comcast/Xfinity.

To get a sense of what this CX conference has to offer, check out the, which are now available to watch on demand and explore timely topics such as:

• Moving from AI experimentation to customer experience impact
• Orchestrating discoverability for the agentic web
• The agentic future of customer engagement

April 2027

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Dates: April 26-鈥�28, 2027

Location: Toronto, Ontario

Presented by the global nonprofit dedicated to advancing the CX profession, Customer Experience Professionals Association (CXPA), CX Leaders Advance is a premier global conference where attendees get the insights they need to grow their CX leadership and drive results for their organizations with practitioner-led sessions focused on CX maturity in the GenAI era, proving business impact, and established and emerging CX practices.听

Event organizers promise to offer:

• Practical frameworks
• Peer-tested tools
• Networking and collaboration opportunities听
• 鈥nd more

May 2027

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Dates: May 4鈥�5, 2027

Location: San Diego, California

Offering an intimate experience with guests capped at 250 attendees, Customer Service & Experience West 2027 features 30 speakers and 15 interactive sessions and case studies.听

Billed as the meeting place for senior CX, service, digital, and operations leaders, this year鈥檚 conference will be focused on serving customers in an AI-enabled world, covering topics like:听

• Embedding AI into service delivery without losing trust
• Breaking down silos across service
• Building data foundations that connect insight to action

Over the years, this annual gathering has featured senior leaders from American Airlines, AT&T, E.L.F., CVS Health, Dairy Queen, and The Hershey Company.听

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Dates: May 12鈥�13, 2027

Location: Toronto, Ontario and online

Over the years, this dynamic summit, available both virtually and in-person, has offered attendees valuable insights on topics ranging from using CX as a proactive revenue-driving powerhouse to transforming your AI-powered personalization strategies with cutting-edge methodologies. Participants have had the chance to get best practices from companies like Hubspot, Yelp, TIME, Paramount, HP, NBA, McDonald鈥檚, TikTok, and others on boosting customer lifetime value, fostering empathetic communication, and driving business growth through improved CX strategies.

In 2027, the 16th annual Customer Experience Strategies Summit promises to offer:

• Interactive sessions and roundtable discussions
• Curated breakout opportunities
• Face-to-face networking
• Virtual streaming
• A breakfast, lunch, and evening drinks reception

Plus, there鈥檚 a reason this conference has been around for over a decade: According to event organizers, these CX events have earned an impressive 93% overall attendee satisfaction rating.听

Dates: May 24-鈥�25, 2027

Location: San Francisco, California

Forrester鈥檚 CX summits attract CX professionals, digital leaders, and marketers looking to raise the bar on customer experience and digital experience. In 2027, the theme for the two-day CX Forum West is 鈥淚deas into Action: The Future of Customer Experience Starts Here鈥� and will feature peer networking, hands-on programs, and strategic frameworks and models. The goal? To help participants confront their biggest experience challenges, create more value for their customers and organizations, and scale AI-powered CX without losing the human touch.听

Past speakers at CX Summits in North America have included practitioners from brands like Hilton, AT&T, and American Express.

June 2027

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Dates: June 7鈥�8, 2027

Location: Amsterdam, Netherlands

Every year, Forrester hosts a global series of CX forums offering actionable strategies and insights grounded in Forrester analyst research. These events equip customer experience, marketing, and digital leaders with the tools to stay ahead of the curve and drive impactful change. In 2026, the event included a welcome reception, an invite-only executive leader outing, a women鈥檚 leadership program, a CX certification program, and more.听

Previous featured speakers and attendees at CX Summit EMEA have included practitioners from Lufthansa, Vodafone, and Philips.

Dates: June 14鈥�15, 2027

Location: New York, New York

Following CX Summit West and CX Summit EMEA comes Forrester鈥檚 CX Summit East, for CX, B2C marketing, and digital leaders looking for insights on scaling AI-powered experience while increasing customer trust and loyalty. Forrester鈥檚 unique differentiator is that their summits are based on their research-backed insights, built for activation, and packed with strategies you can use as soon as you鈥檙e back at work to drive outcomes for your organization.听

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Dates: June 14鈥�17, 2027

Location: Las Vegas, Nevada

For over 25 years, Customer Contact Week鈥檚 flagship annual conference has brought together CX leaders for interactive workshops, think-tank discussions, case-study sessions, networking opportunities, and speaker meet-and-greets.听

This CX event draws 6,000 annual attendees from Fortune 100 and Fortune 500 companies, representing brands across healthcare, financial services, consumer goods, retail, and tech. Past participants have included leaders from The Home Depot, PetSmart, and Uber.听

In 2027, attendees will get the chance to learn the latest customer contact strategies, connect with customer contact experts, and network with peers and leaders.

July 2027

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Dates: July 12-鈥�14, 2027

Location: TBD

Organized by the business consulting and research firm Frost & Sullivan and designed to help companies reduce churn and increase revenue, the annual CX conference Customer Experience has featured headliners from companies like Pfizer, Mercedes-Benz USA, Teladoc Health, Visa, Tennis Channel, and UCLA Health. Billed as an 鈥渆xclusive and purposely atypical experience,鈥� the 2027 gathering will focus on two tracks, designing connected customer journeys and turning CX measurement into impact.听

August 2027

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Date: August 24, 2027

Location: Sydney, Australia

In 2026, Forrester hosted technology, security, and customer experience leaders at a gathering in Sydney that explored how organizations can get ahead in an AI-powered world by scaling AI adoption, strengthening resilience, and delivering experiences that customers value. In 2027, the event promises to help leaders navigate the next phase of AI adoption and business transformation, identifying where AI can help companies evolve beyond the competition, get more practical insights, and improve experiences.听

To Be Announced Dates in 2027

Salesforce鈥檚 Connections is an annual event held both in-person and online, drawing 10,000 customer-focused professionals from various industries, featuring visionary keynotes from world-class leaders like Martha Stewart. The event is designed to help brands build lasting customer relationships, with past sessions covering topics like always-on agentic experiences, data-driven insights, orchestrating seamless two-way engagement across channels, and more.

糖心原创 World Tours and City Tours

Stay tuned for the 2027 schedule of 糖心原创 events, which will offer international CX professionals the chance to gain valuable insights, engage in meaningful discussions, and leave with actionable takeaways to enhance your customer experience strategies.听

The east-coast counterpart to Customer Service & Experience West, this event from Reuters is intentionally capped at 250 attendees. In 2026, the theme was 鈥淎 new CX reality demands a new operating model,鈥� with 70% of attendees holding director level titles and above. Over the years, this customer experience conference鈥檚 featured speakers have included leaders from Nespresso, Papa Johns, Mastercard, IHG Hotels & Resorts, CVS, Subaru, and CBRE.听

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Another popular gathering from Customer Contact Week Digital, the annual Chief Experience Officer Exchange unites CXOs and senior-level CX, UX, and EX executives from across the country to tackle their most pressing business challenges. This exclusive, invitation-only event allows interested leaders to request an invitation online. Past speakers and participants have included executives from Cond茅 Nast, Maven Clinic, Kraft Heinz, and more.听

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This Australian customer experience conference connects key players across the CX ecosystem鈥攆rom CX and contact center teams to marketing, UX, and digital leaders鈥攁ll focused on driving business growth. Past speakers have included leaders from GM Australia, Milwaukee Tool Australia and NZ, Bayer, and Vanguard. In 2026, the event featured roundtables, VIP experiences, speed networking, an immersive exhibition floor, and dedicated streams across customer experience transformation, customer insights, and contact center transformation.听

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Customer Contact Week鈥檚 annual gathering in Europe brings together hundreds of contact center, CX, and digital leaders from the region. In the past, the multi-day summit has tackled timely topics such as automation, AI, innovation, operational excellence, and digital adoption, and has featured speakers from major brands that are setting the standard for CX in Europe. In 2026, CCW Europe showcased 250 speakers and offered opportunities to connect with 850+ attendees.听听

Customer Experience Conferences FAQs

Why Should I Attend a Customer Experience Conference?听

Attending a customer experience conference offers professionals the chance to network with CX leaders and practitioners, learn the latest best practices, and find out about the latest advances in CX technology, all of which can help individuals advance their career and deliver greater outcomes for their employer.听

How Can I Build a Business Case for Attending a Customer Experience Conference?

Attending a conference may require you to take a break from your day-to-day work duties, but by going you can learn new strategies that are to the advantage of your manager, your team, and your broader company. That鈥檚 how to make the case for getting permission to attend a conference, by shedding light on the benefits of attending a customer experience conference for your organization. For instance, you can mention what new skills you might gain, insights you expect to uncover, advanced technology solutions you can become familiar with, and the networking opportunities available at the conference.听

You can even use our handy justification letter template to make the case for attending 糖心原创 Experience 鈥�27.听

How Can I Prepare for Attending a Customer Experience Conference?

Before heading off to the conference, you can prepare by finding out your company鈥檚 policy for business travel. See what costs they鈥檒l cover and how to track your expenses for reimbursement.

You can prepare for networking opportunities by reading up on the conference speakers, sponsors, and confirmed attendees in advance. While you鈥檙e at it, now is also a great time to make any updates to your LinkedIn profile, so you can put your best face forward as you meet and make new connections during the event.听

What Can I Expect at a Customer Experience Conference?

A well-organized customer experience event will offer a balanced mix of keynotes, brand speaking sessions, and practitioner training opportunities.听

Attendees should leave a CX conference with practical takeaways and best practices that you can implement immediately, as well as a vision to take back to your organization to better plan for the future of customer experience鈥攅specially as AI becomes a foundational tool.

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.