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.聽

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

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聽

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聽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 鈥斅爐hey operate on real-time data, or they operate incorrectly聽
• 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聽

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.聽

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 糖心原创鈥檚 intelligence with confidence, rather than assumption.聽

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.聽

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 鈥斅爄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.聽

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.聽

This is an area where 糖心原创鈥檚 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 糖心原创鈥檚 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 糖心原创鈥檚 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.聽

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聽
• Root Cause Assist that identifies systemic issues before they escalate聽
• 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.聽

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.聽

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聽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,聽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.聽

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.

We鈥檝e been taking to the blog to highlight the key use cases of our most recent updates. Today we鈥檙e exploring Total Experience Profiles (TX Profiles), which unifies customer feedback with operational data to provide a single, 360-degree view of a customer鈥檚 complete history with a brand. 

We鈥檙e going to share the feature in action as our fictional home improvement retailer, GoodHome, uses Total Experience Profiles to see what鈥檚 happening for a VIP customer whose overall satisfaction score (OSAT) has dropped from a 7.6 to a 3. 

The Character and the Conflict

Meet Gabby, the contact center manager at GoodHome. Gabby鈥檚 team has their plates full, each handling dozens of customer service calls, emails, live chats, social media interactions, and texts on a daily basis. It鈥檚 up to her to make sure that issues that need extra attention, and high-value customers at risk of churning, don鈥檛 slip through the cracks.

In the past, keeping up with everything was an almost impossible task. That鈥檚 because GoodHome lacked a central platform where everyone in the contact center could easily see everything they needed to know about their customers鈥攁nd what was happening for them across their interactions with the brand鈥攊n one single place. 

Social interactions lived in one tool. Survey data lived in another. Call transcripts were stored somewhere else altogether. These kinds of silos created hassles and headaches for everyone, and led to costly customer turnover. You know the struggle.

Finally, when GoodHome started using 糖心原创鈥檚 Total Experience Profiles, things got a lot less complicated for Gabby and her colleagues. 

The Moment of Truth

Total Experience Profiles create single, unified customer profiles, bringing together customer data from across sources (operations, transactions, surveys, CRM, and app, website, social media, and customer service interactions). Employees can use TX Profiles to zoom in on a single customer journey and see everything they鈥檝e experienced over their entire history with the brand.

Recently, Gabby used TX Profiles to check in on what was going on for a VIP customer, Jamie, when Gabby received an automated alert from 糖心原创 letting her know that this loyalty program member needed extra attention. 

Jamie鈥檚 customer satisfaction score had just dropped to a troubling 3 out of 10. What had happened?

Gabby was able to get answers quickly using Total Experience Profiles.

The Solution: Total Experience Profiles

In just one quick scroll of Jamie鈥檚 TX Profile, Gabby was able to determine the reason why her satisfaction had declined so dramatically: GoodHome鈥檚 app was acting up. 

Jamie couldn鈥檛 check on the status of her order, leaving her no choice but to call the contact center for support. But that鈥檚 the exact opposite of what she was hoping to happen when using the app鈥攕he wanted to save time and shop all on her own. 

Not only could Gabby find out the reason Jamie鈥檚 score took a nosedive, she could dig deeper into Jamie鈥檚 customer profile and see over the last year that Jamie had been an active (and satisfied) customer, completing 194 transactions and averaging an OSAT of 7.6. 

Gabby could also easily find a transcript of Jamie鈥檚 call into the contact center in her TX profile that offered even more details regarding her issues when using the app.

The Resolution and Business Impact

Thanks to Total Experience Profiles, Jamie鈥檚 issue didn鈥檛 slip through the cracks. Gabby was able to follow up, apologize for the experience, and earn this VIP customer鈥檚 loyalty back. Not only that, she was able to see that Jamie鈥檚 experience wasn鈥檛 due to user error and was, in fact, the result of an underlying issue that needed to be escalated to the app team鈥攕omething Gabby did right away, connecting the dots between the in-store, contact center, and digital experience. 

All of these seemingly small interventions can add up to a world of difference for brands like GoodHome and their customers. 

These days, Gabby and her coworkers spend a lot less time trying to find information, their customers spend a lot less time repeating themselves, and the team is able to serve customers faster and better. That鈥檚 something that鈥檚 paying off. 

After all, a 糖心原创 Market Research study, The State of Brand Loyalty, finds that 80% of consumers say they feel more loyal to brands when they deliver a better experience than their competitors do. Two customer experience factors that drive up loyalty, according to the same study? When companies deliver a consistently seamless experience and go above and beyond to help their customers. And that鈥檚 precisely what Total Experience Profiles are designed to help contact center agents and managers make happen. 

See Total Experience Profiles in action

Watch our to discover how this feature can give your team the full picture of the customer experience at the individual level and power frontline actions that move the needle for each and every customer based on their unique histories with your brand.

For years, 糖心原创鈥檚 mobile app, 糖心原创 Mobile, has empowered frontline managers to move on time-sensitive customer experience (CX) opportunities to make money and reduce risk. In fact, users log in to our app hundreds of thousands of times on a weekly basis, viewing up to nearly two million different tabs filled with alerts, insights, and actions that power frontline operations. 

That鈥檚 why we鈥檙e excited about two new features we debuted in late 2025 specifically for 糖心原创 Mobile.

The first is , which instantly alerts frontline leaders and teams right as their important metrics are changing, so they can jump in, fix issues fast, and keep experiences on track. 

The second is , which uses AI to pinpoint the underlying factors driving these shifts in CX metrics, enabling frontline managers and regional leaders to determine what鈥檚 working and what needs attention鈥攐n the go. 

Here on the blog we鈥檝e been shining a light on our Frontline-Ready AI features, and today we鈥檙e going to give you a look at what these two 糖心原创 Mobile upgrades can do for you. See them in action as our fictional home improvement retailer, GoodHome, uses both features to detect an urgent dip in the in-store pickup experience and uncover and rectify the root cause faster than ever before.

The Character and the Conflict

Meet Nathan, a manager at one of the busiest locations of GoodHome. 

In the past, Nathan and his team of frontline employees didn鈥檛 have a reliable way to get a pulse on what was happening for customers in the store, in the moment. Sure, team members could gauge the sentiment of 1:1 interactions as they were happening, but they lacked a bigger-picture view of how customers collectively felt and the issues they were facing in the aggregate. Yes, the team received reports summarizing the feedback customers shared via surveys. Eventually. After the fact. When it was too late to address the challenges customers were facing.

In the absence of actionable, timely data, employees made adjustments based on gut intuition, dated customer feedback from days or weeks earlier, and the way things had simply always been done.

Nathan knew there had to be a better way. Fortunately, that better way became a reality when GoodHome rolled out 糖心原创 Mobile to the frontline.

The Moment of Truth

Now that Nathan and his team have access to 糖心原创 Mobile, they can keep tabs on their in-store customer experience KPIs and get notified the moment important metrics change, thanks to Mobile Scorecard Notifications. When fluctuations in metrics like customer satisfaction and NPS庐 occur, they can quickly figure out what鈥檚 causing these scores to go up or down with Root Cause Assist on Mobile. 

That鈥檚 exactly what happened recently when Nathan received an automated alert letting him know that the rating for his location鈥檚 in-store pick-up experience score had fallen to a troubling -3.08. 

What was going on with in-store pickup?

The Solution: Mobile Scorecard Notifications and Root Cause Assist on Mobile

In the past, the team might have only become aware鈥攁nd been able to get to the bottom鈥攐f this type of issue after calls to the store spiked or tons of customers began lining up at customer service to ask for help with the same problem.

This time, Mobile Scorecard Notifications paired with Root Cause Assist on Mobile meant Nathan was notified and able to  intervene as the issue was unfolding. 

Here鈥檚 how. 

First, Nathan used Mobile Scorecard Notifications to set the specific metrics he wanted to be notified about and how he wanted to receive updates (daily, or in real time as scores dropped by a certain percentage or number of points).

Then, as soon as there was a change in one of those metrics, he received a notification like the one mentioned earlier. 

By tapping on the alert, Nathan was taken to 糖心原创 Mobile, where he was able to drill into additional details about the declining KPI and see a GenAI-powered summary of the underlying cause powered by Root Cause Assist on Mobile. Root Cause Assist on Mobile analyzes all of a brand鈥檚 data from survey responses, call and chat transcripts, social media interactions, employee notes, and more, to reveal the 鈥渨hy鈥� behind score changes.

In this instance, customers were having a hard time trying to view and modify their pickup orders within GoodHome鈥檚 app. 

Nathan was also able to see the impact of this issue on the store鈥檚 other KPIs: The lower in-store pickup rating was linked to a drop in the store鈥檚 overall satisfaction rating, which fell by 3.2 points from a strong 8.6 rating to a worrisome 5.4 out of 10. 

This is an example of the insights GoodHome is able to uncover using Mobile Scorecard Notifications and Root Cause Assist on Mobile.

With Root Cause Assist on Mobile, Nathan discovered additional details about the error with the app and how it was affecting overall satisfaction by various customer segments, including by rewards program member status and customer age. 

Root Cause Assist on Mobile uses all recent survey responses, call and chat transcripts, social media interactions, employee notes, and more to reveal the causes of the company鈥檚 CX score changes. 

The Resolution and Business Impact

Within seconds, Nathan could easily see there was an issue with GoodHome鈥檚 app and escalate the problem to the company鈥檚 app team, who were able to implement a fix. At the same time, the rest of the frontline was aware of the bug and was better able to assist customers affected by the app error. 

Frontline teams from the world鈥檚 leading brands, including Hyatt, Panera, and Dick鈥檚 Sporting Goods, use 糖心原创鈥檚 Mobile Scorecards every day to stay on top of customer sentiment and rising issues with live data at their fingertips. With the addition of Mobile Scorecard Notifications and Root Cause Assist on Mobile, now employees can take action to address issues as they arise faster than ever, helping their organizations make more money, reduce the risk of churn, and drive loyalty.

See these features in action

Watch our demos of and to get a look at how these capabilities put the power of AI into the hands of the frontline, enabling them to drive meaningful business outcomes鈥攏o complicated prompt engineering skills required.

As part of our series highlighting our Frontline-Ready AI features, we鈥檒l explore the key use cases and benefits of . 

Building on 糖心原创鈥檚 Digital Experience Analytics (DXA), DXA for mobile analyzes every behavior and technical experience signal into intelligence to create and deliver better experiences.

Ahead, we鈥檒l show you the solution in action. You鈥檒l see how a fictional home improvement retailer, GoodHome, uses DXA for mobile to monitor their digital experience score, identify an error that鈥檚 causing user frustration to spike, and resolve the issue鈥攁ll within minutes. 

Let鈥檚 dive in. 

The Character and the Conflict

Meet Taylor, the Digital CX Manager at GoodHome.

Her retail company鈥檚 apps and websites have hundreds of millions of users annually, and she knows that every swipe, scroll, form fill, hover, and tap across the digital experience tells a story. Collectively, these digital behaviors reveal what鈥檚 fueling sales, repeat visits, and longer-term loyalty. 

The GoodHome team has been using 糖心原创 DXA for their websites, and recently enabled DXA for their mobile app for a complete view of the digital customer journey. 

Digital Experience Analytics for mobile apps offers a balanced user experience score, highlights top friction points for prioritization and associated action recommendations, visualizes and summarizes the customer journey by page or across every page to enable action, and triggers alerts and workflows to ensure that action happens.

The Moment of Truth

Things have been going well for GoodHome鈥檚 digital channels. Really well. There鈥檚 been a 30% increase in purchases made through their app this year. 

But Taylor knows that even the smallest bump in the digital experience has the potential to derail customers from successfully completing their journeys.

That鈥檚 why now that she has access to DXA for mobile apps, she鈥檚 constantly keeping tabs on her brand鈥檚 Digital Experience Score (DXS), an AI rules-based score that evaluates activity and events online across five key pillars鈥攅ngagement, technical performance, form effectiveness, frustration, and navigation鈥攖o produce a 0-10 perspective on the health of the experience.

Turns out, GoodHome鈥檚 overall DXS has recently dropped by a staggering 3.7 points, from a 7.8 out of 10 to a failing 4.1 out of 10. The main driver? The brand鈥檚 frustration score is getting worse. Much worse. Customer frustration with the app is at a high of 8.4 out of 10, and unlike every other pillar, you do not want a high score here!

The Solution: Digital Experience Analytics for Mobile Apps in Action

In the past, the GoodHome team might have remained in the dark about what was happening for mobile app customer experiences for hours or days until survey responses, negative reviews, and contact center emails, calls, and live chat sessions spiked. 

Now, anyone with permission at the retailer can see what鈥檚 happening by drilling into the details available for each DXS pillar to identify root cause experience issues and pages where improvement is needed most. 

They can also size the affected customer journeys and view an experience. And CX teams can drive the change without having to wait for busy data scientists and technologists to engage in lengthy data investigation. 

When Taylor views the details for the frustration score, she can see that the top driver of frustration is unresponsive touches, with 39K instances occurring recently. From her 糖心原创 self-service training鈥攁nd personal experience鈥攕he knows nothing is more frustrating than when something is clicked or interacted with and doesn鈥檛 do anything.

Taylor also sees that the page in the mobile application causing the most frustration is the 鈥楳y Order鈥� page available within the 鈥楢ccount鈥� section of the app.

Diving deeper to understand why this is happening, Taylor knows she has an easy path to see any and all experiences where this frustration was shown, but she isolates it to just those on the page in question. 

Each session comes with everything she could ever want to know about the device, the customer, and their journey across the mobile app. Taylor even has the ability to replay the session and walk in the digital shoes of the customer based on the depth of data captured by 糖心原创 Digital Experience Analytics. 

But actually, she sees everything she needs in the AI-generated summary about this customer鈥檚 experience.

In this case, she discovers that unresponsive touches are happening on the 鈥淢y Orders鈥� screen of the GoodHome app, where those unresponsive touches are actually due to a native app error鈥攁 technical issue鈥攁s users like this one are trying to check on their pickup orders. DXA reporting reveals that once a customer鈥檚 order status changes to 鈥減rocessing,鈥� a bug is occurring that is preventing users from reviewing, changing, canceling, or checking on the status of their orders.

And even though the overall technical pillar score for the mobile app was great鈥攕uggesting product management and development teams might not have cause for concern鈥擳aylor knows better based on just how much frustration is happening here. She has all the evidence to partner with her colleagues to return the GoodHome app back to a level of technical and experience excellence their customers expect.

The Resolution and Business Impact

If left unresolved, this type of app error can have ripple effects across the business. Impacted customers will likely end up reaching out to customer support for help or showing up in person to check on the status of their orders, an added operational burden鈥攁nd cost for the retailer. 

Increased expenses aren鈥檛 the only concern. Broken experiences can erode customer trust and loyalty leading to less repeat activity or even no future engagement at all. In fact, a 糖心原创 Market Research study, Latest Trends on the Digital Customer Experience, finds that 51% of customers say that in the past, they have stopped being a customer of a company because of encountering too many difficulties using their website or mobile app. So GoodHome doesn鈥檛 just stand to lose app users but actually potential forever customers without action.

But thanks to using DXA for mobile apps to monitor what鈥檚 happening with GoodHome鈥檚 app, Taylor is able to share her findings with the app developers, who are then able to push out a fix, and create a score-based alert program to provide proactive behavioral experience insights for future improvement and innovation opportunities. All within minutes.

See 糖心原创鈥檚 Digital Experience Analytics for Mobile Apps in Action

Watch our demo to discover how this new solution enables you to turn taps, swipes, focused attention, and errors into meaningful customer insights to deliver experiences that reduce friction, increase engagement, and generate sustainable revenue.

In the last year, we introduced a suite of powerful new Frontline-Ready AI features that seamlessly integrate into existing customer experience workflows and shorten the path from insight to action to improve experiences for customers.

Here we鈥檙e shining a light on these solutions, highlighting how these tools can help teams act faster, solve problems smarter, and deliver results in the moment, without the wait, noise, or guesswork. 

In this first post in the series, we鈥檒l walk you through the key customer experience use cases and benefits of . 

Intelligent Summaries for text analytics harnesses the power of GenAI to help brands understand the top themes and sentiment of thousands of customer interactions as they鈥檙e unfolding across channels in real time鈥攚ithout anyone having to manually listen in, read a transcript, or analyze a single conversation.

Ahead, we鈥檒l show you the feature in action. See a fictional home improvement retailer GoodHome unlock immediate value with our new solution to get an update on the brand鈥檚 CX KPIs in seconds, figure out what has caused one of the retailer鈥檚 most important metrics to decline, and implement a solution to address the root cause of the problem ASAP. 

Let鈥檚 get to it.

The Character and the Conflict

Meet Sara, the CX Manager at (the fictional composite) home improvement retailer, GoodHome.

She used to spend hours manually reviewing mountains of data to make sense of what was happening for her customers. She鈥檇 have to sift through social media conversations, online reviews, and customer support calls, emails, and live chat sessions to figure out the top issues they were struggling with and what her team needed to do to intervene.

It took so long to find answers, she wasn鈥檛 able to act in the moment, when it actually mattered. 

That all changed when GoodHome invested in Intelligent Summaries for text analytics, a solution from 糖心原创 that cuts through mountains of unstructured data (including comments, reviews, and transcripts) instantly with GenAI-powered summaries.

The Moment of Truth…

By turning to Intelligent Summaries for text analytics, Sara is now able to quickly spot what鈥檚 going on for GoodHome鈥檚 customers by getting a real-time pulse on the retailer鈥檚 most important customer experience metrics.

Recently, she used it to detect a critical drop in the company鈥檚 in-store overall satisfaction (OSAT) score. The KPI had fallen from a strong 8.6 out of 10 to a just-above average 5.4. 

What was going on?

For answers, Sara was able to ditch her typical (tedious) process of manual review, and get the information she needed in just one click.

The Solution: Intelligent Summaries for Text Analytics

Intelligent Summaries for text analytics provides a GenAI-powered analysis of the most recent, relevant customer comments on this topic, giving an executive-level summary of the issue in just seconds. 

Intelligent Summaries for text analytics provides a summary of the topic in just one click. 

Without having to do any digging into the data, she was able to get this one-click generative AI-powered summary.

Intelligent Summaries for text analytics explains shifts in metrics by summarizing recent and most relevant customer interactions for that specific topic or theme.

Sara was also able to view the Intelligent Summary on the topic when she clicked into the Topic Deep Dive for a bit more analysis. The Topic Deep Dive also revealed co-occurring themes related to the topic, that indicated that the app was crashing, customers were experiencing issues with their orders being processed, and the app wasn鈥檛 stable. 

Upon seeing the impact of 鈥渞ecurring app crash,鈥� Sara was able to view a summary of this theme that helped her identify the why: A confusing app experience was preventing GoodHome customers from viewing their orders, leading to a poor pickup experience. 

When teams dig into co-occurring themes, they can quickly understand the most impactful themes with a Summary of the most recent interactions. 

All that, without ever having to manually read or review a single comment or conversation. 

The Resolution and Business Impact

By quickly identifying the problem in the moment, Sara was able to alert the company鈥檚 app developers who intervened, implemented a fix, and stopped the app issues in its tracks.

Customers were once again able to seamlessly pick up their orders without any issues and the company鈥檚 in-store OSAT score bounced back to the average 8.6 rating the brand had achieved over the previous three months.

By acting in the moment, Sara stopped the issue from spiraling out of control鈥攑reventing customers from becoming detractors and avoiding a mountain of negative reviews that might have otherwise deterred future customers from using the app or doing business with GoodHome. 

See 糖心原创鈥檚 Intelligent Summaries for Text Analytics in Action

Watch our to discover how this GenAI solution can translate your company鈥檚 unstructured data points into actionable 10-second summaries.