While the power of technology is at its apex, so are expectations. And the gap between the two is where revenue goes to die.

Brands using AI to deepen human connection by freeing up sellers, marketers, and service agents to focus on empathy, trust, and complex problem-solving are pulling ahead. Those still bolting AI onto fragmented systems, or using it purely as a cost-cutting measure, risk falling further behind.

To help organizations address the rapidly changing factors impacting CX, we鈥檙e proud to debut our first quarterly 鈥淪tate of鈥� reports. Designed to pull together the latest research and data across sales, service, marketing, and e-commerce, these thought leadership pieces are available without a gate because we know that experience and expertise matter, and we want to share ours with you. (You can find links to the reports at the end of this post.)

The key takeaways from each report demonstrate that CX is no longer a single department’s job. Rather, it’s a cross-functional discipline, and the stakes for getting it wrong are higher than ever.

The cost of bad experiences is staggering

Let鈥檚 start with the number that should be pinned at the top of every leadership deck this quarter: bad customer experiences have put $3 trillion in global sales at risk in 2026, with consumers actively cutting back $2.1 trillion in spending and walking away entirely from $865 billion worth of it, according to Forbes data cited in our . That’s not churn. That’s customers voting with their wallets in real time.

And they’re not quiet about why. A striking 82% of consumers say a brand has disappointed them, and 60% admit they don’t pay attention to brands even when their product needs are being met. Translation: satisfying the transaction isn’t enough anymore. If the experience around it feels disorganized or impersonal, customers check out.

On the service side specifically, 75% of consumers say they’re put off by disorganized brands that pass them between multiple people or teams just to solve one problem, and 46% say service flat-out feels too impersonal.

The combustible combination of sky-high financial risk and low tolerance for friction is forcing every customer-facing function to rethink how it operates.

That’s the backdrop. Now let’s talk about what’s actually happening in each corner of the customer journey.

AI is everywhere, trust in it is not

Every function is racing to embed AI, with good reason. AI-driven traffic to U.S. retail sites is up 4,700% year over year, and almost 60% of consumers have already used AI to shop, as cited in our .

On the marketing side, notes that AI has moved from experiment to foundational: 33% of marketing leaders are using it for hyper-personalized engagement, and 31% say predictive insights and personalization have been a top priority all year.

Meanwhile the report points out that digitized suppliers leveraging AI are outperforming their peers on sales goals by a jaw-dropping 110%.

But here’s the twist that ties it all together: customers don’t actually love the AI they’re interacting with.

report underscores that point specifically:

• 79% of Americans say they strongly prefer human support over an AI agent
• 63% don’t believe AI can replace humans in service roles at all
• 89% believe brands should always offer the option to talk to a real person
• 81% believe AI is primarily being used to save the company money, not to improve their experience.

That’s the tension every function needs to sit with. AI is delivering real, measurable business value, but if customers believe that AI was deployed to cut costs rather than serve them, you might win efficiency but lose the relationship.

Trust is the currency, and it’s getting harder to earn

Each one of the reports circles back to the same word: trust. In marketing, 61% of B2B buyers say trust and credibility are the most important thing content can deliver, ranking above lead generation. Consumers are leaning on peer and creator trust more than brand messaging, too: 76% of brands report that sponsored content with creators now outperforms traditional advertising, and in social commerce, 45% of Gen Z shoppers say they’re more likely to trust a product once it goes viral.

On the e-commerce side, trust shows up as transparency. With tariffs pushing import costs up 15-30% across major categories, brands that explain price increases rather than quietly passing them along are building goodwill that pays off in retention. And in sales, the numbers show that your existing customers–the ones who already trust you–are an underused asset: 45% of revenue leaders are now focused on improving handoffs across marketing, sales, and service, while 39% are chasing expansion and upsell revenue instead of only hunting net-new logos.

The people problem behind the technology story

There’s one more thread that doesn’t get enough attention in the AI headlines: the humans delivering these experiences are stretched thin. Call center turnover is running 40-45% in 2026, spiking to 55-60% in high-stress sectors, while replacing a single agent can cost up to $46,000 when accounting for lost productivity.

As AI absorbs the easy tickets, what lands on human agents is the hard stuff: it鈥檚 complex, is emotional with high-stakes, and burnout is quietly eating away at CSAT and first-contact resolution scores. This matters because customer experience isn’t just a technology stack or a personalization engine. It’s fundamentally delivered by people; whether that’s a service agent handling an escalation, a seller navigating a buying committee, or a marketer trying to sound human in an AI-saturated feed. Protecting the people doing that work isn’t a wellbeing initiative separate from CX strategy. It is CX strategy.

Fragmentation is the enemy of good CX

If you weave every stream together, the state of CX has one clear directive: stop treating AI, data, and channels as separate initiatives owned by separate teams, and start treating the customer experience as the single thread that runs through all of them.

That means AI-shopping agents that use clean, structured product data. It means first-party data strategies that replace the crumbling third-party targeting most marketing was built on. It means service and sales teams that know exactly when to step back and let self-service work, and exactly when to step in and be human. And it means service organizations that give their people better tools instead of just more automation.

Future success won鈥檛 be shaped by the brands with the most AI; it will favor the brands that make customers feel understood, even as the experience becomes more automated. That’s not a technology bet. That’s a trust bet, and right now, trust is in short supply.

You can find our 鈥淪tate of鈥� reports here:

Jessica Keehn is chief marketing officer of 麻豆原创 Customer Experience.

This makes a lot of sense given that today However, the results aren鈥檛 always matching the hype.

A recent found that while enterprise AI adoption is rising, real impact is often elusive. The reason? Many businesses are still operating with disconnected systems and disjointed data. Without a strong foundation, AI can鈥檛 deliver what it promises.

Siloed systems aren鈥檛 just a technology problem鈥攖hey鈥檙e a business barrier.

The CX Disconnect: When Fragmentation Undermines Intelligence

Too many organizations still rely on a patchwork of tools for customer experience, supply chain, finance, and HR. While these point solutions solve individual challenges, they create friction and disconnect across the business. In an AI-powered world, friction is the enemy.

AI thrives on complete, clean, and . If your marketing, sales, service, and fulfillment teams cannot see the same data in real time, or trust that it鈥檚 accurate, your AI strategy will not be set up to succeed.

With the best intentions to embrace AI in an effort to achieve incredible efficiency, instead, customers will still lose valuable time on manual integration, inconsistent customer experiences, and AI outputs that are only as good as the (fragmented) feeding them. The delightful experience aspirations turn into trust lost and frustration all around.

Modular Innovation, Meet Enterprise Intelligence 

麻豆原创 has reimagined enterprise management with , representing a fundamental shift from traditional ERP systems to a modular, composable architecture that integrates AI, data, and applications into a unified platform.  

Grounded in harmonized, semantically rich data, this architecture allows businesses to make sense of data that has traditionally been scattered across systems and trapped in silos, so AI has the comprehensive data it needs to quickly generate meaningful insights.

麻豆原创 Business Data Cloud (麻豆原创 BDC) with native integration of 麻豆原创 Databricks, serves as a data backbone for business AI. It seamlessly connects all 麻豆原创 data and third-party data and provides integrated governance to enable real-time AI-driven decision making. 聽

Companies do not lose precious time locating and preparing data for AI. AI systems work on trusted, contextualized data, not just generic data. This produces accurate, reliable, and actionable AI recommendations that enable organizations to scale AI innovation rapidly across business domains.聽

麻豆原创 BDC is the foundation for , 麻豆原创鈥檚 AI copilot that acts as an intelligent orchestrator across the entire business suite. 麻豆原创 BDC ensures that Joule has structured business context for natural language processing and that its outputs are accurate so that Joule can provide always-on assistance to break down silos between business operations.聽

For example, when a customer service or sales representative handles a complex order issue, Joule can: 

• Check real-time supply chain constraints

• Respond to RFPs faster
• Personalize the response by pulling in relevant customer history from
• Speed response with automated case routing and research

The results are faster resolutions, happier customers, empowered employees, and incredible business outcomes with less effort and overhead.

CX + AI + ERP = Real Results

Integrating CX AI with core ERP systems enables end-to-end process optimization that was previously impossible with fragmented systems. When CX systems connect natively to back-office systems, organizations gain:聽

• Real-time personalization powered by operational data
• Intelligent workflows that prioritize high-value customers
• Predictive insights that help teams act before issues arise

The numbers speak for themselves. According to an , customers using this approach reported these benefits:

• Up to 60% reduction in the number of issues service and support teams deal with due to fewer manual errors, automated self-service support functions, automated self-service, and AI chatbots
• 25% to 50% improvement in time to resolution for issues that did require service or support resources
• 25% to 70% improvement in productivity of digital marketing and customer operations teams
• 50% to 90% improvements in sales team productivity by offloading smaller transactional sales, faster quote generation, and streamlined order management
• 20% to 40% increase in productivity of business operations due to less time spent on invoices, payments, shipments, and returns and more informed decision-making

This is not just incremental change; it鈥檚 enterprise transformation, driven by customer needs and powered by AI.

The Future of Intelligent Enterprise Operations 

Embedded within a composable business suite represents a bright future that takes the possibility of AI and makes it a reality.聽

• Businesses can seamlessly orchestrate intelligence across all functions, delivering experiences that feel effortless to customers while optimizing operations behind the scenes.聽
• Artificial intelligence won鈥檛 just automate individual tasks, but also orchestrate entire business ecosystems to deliver superior outcomes.聽聽
• Maintaining enterprise-grade reliability and enabling modular innovation will allow organizations to adapt to changing market conditions while creating competitive advantages.聽

With the rise of AI, businesses face a pivotal moment in time. Taking advantage of all that technology has to offer demands more than point solutions and departmental optimizations; it requires unified platforms, complete clean underlying data, and a clear unified strategy.

Jessica Keehn is chief marketing officer of 麻豆原创 Customer聽Experience.