Agentic AI in Customer Experience: What Business Leaders Must Know in 2025

Customer service is broken-and CX leaders don’t realize it yet.

Over the past few years, organizations have doubled down on AI chatbots and virtual assistants. Today, 76% of businesses report using AI in customer service. On the surface, that sounds like progress. But here’s the reality: only 19% of organizations leverage AI for feedback analytics and customer understanding.

This creates a dangerous imbalance. You’ve automated the front door of customer service but ignored the intelligence behind it. The result? Teams are drowning in interactions but starving for insight. You may know how many tickets were resolved, but not:

• What customers actually feel

• Why they churn

• Which issues silently erode loyalty

This is the blind spot that agentic AI closes. Unlike static chatbots, agentic systems are:

• Contextual – understanding signals across systems

• Predictive – anticipating problems before customers report them

• Adaptive – learning continuously from interactions

• Autonomous – acting proactively to achieve CX goals like higher NPS, reduced churn, and faster resolutions

For CX leaders, this isn’t about chasing another tech trend. It’s about recognizing a strategic shift: customer experience is no longer about speed alone—it’s about creating an intelligent ecosystem where every interaction drives insight, action, and measurable growth.

TL;DR

• Customer service is broken: automation surged, but true customer understanding lagged.
• Agentic AI ≠ generative AI: agents act across systems to resolve issues and drive outcomes—not just generate replies.
• Traits that matter: contextual, predictive, adaptive, autonomous—improving NPS, cutting churn, and accelerating resolutions.
• CX is shifting from reactive to proactive: real-time detection, closed-loop actions, and mission-led execution.
• Momentum is building: 81% of CX leaders now prioritize AI for feedback analytics to uncover the “why” behind customer behavior.
• Zonka Feedback’s AI Feedback Intelligence helps you close this gap by unifying feedback across channels, surfacing real-time insights, and enabling autonomous action to improve satisfaction, retention, and growth. Schedule a demo today to see it in action.

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Understanding Agentic AI: A New Era for CX

The numbers tell a compelling story. Research from Sandpiper Research & Insights reveals that decision-makers expect 56% of customer interactions to be handled by agentic AI within 12 months. That percentage jumps to 68% within three years. Agentic AI, a form of artificial intelligence, is already making a significant impact on customer experience by automating tasks, improving decision-making, and enhancing customer interactions. Customer experience teams worldwide are moving fast—but many still don’t understand what makes AI truly “agentic”.

Beyond customer service, agentic AI is transforming industries by fundamentally changing how sectors such as retail, healthcare, finance, and travel operate. This broader industry impact highlights the potential of agentic AI to revolutionize operational processes and customer experience management across various fields.

What makes AI 'agentic'

Agentic AI isn’t just smart—it’s autonomous, proactive, and outcome-driven. These four traits set it apart from traditional AI and make it a game-changer for customer experience.

Multi-Step Process Execution

AI agents can handle entire workflows, not just single tasks. It plans and executes complex processes—like verifying account details, processing refunds, and notifying customers—without human intervention. This makes resolutions faster, more consistent, and less dependent on manual handoffs.

System Integration

Instead of working in isolation, agentic AI connects across the 7–10 platforms most businesses use daily, from CRM and helpdesk tools to billing and inventory. This integration enables it to resolve issues end-to-end without forcing agents or customers to jump between systems.

Continuous Learning

Agentic AI improves with every interaction. It adapts from real-world conversations, support tickets, and feedback loops—refining how it responds and how it makes decisions. Over time, the system becomes more accurate, more personalized, and better aligned with customer expectations.

Contextual Decision-Making

Unlike rule-based bots, agentic AI uses customer history, preferences, and sentiment to shape its actions. For example, it may prioritize a high-value account for faster resolution or escalate a recurring issue proactively. Agentic AI can make autonomous decisions and act independently to resolve customer issues without human intervention. Every decision is informed by context, not just static scripts.

Autonomous Action

Agentic AI doesn’t stop at recommendations—it takes action. Whether it’s resolving disputes, scheduling callbacks, or escalating urgent cases, the system acts within defined parameters to complete the task. This autonomy reduces delays and improves both customer and business outcomes.

Agentic AI vs. Generative AI: Why CX Leaders Must Know the Difference

The confusion between agentic AI and generative AI creates implementation problems for business leaders. Here’s the distinction:

• Generative AI creates content.

• Agentic AI drives outcomes.

Agentic AI is an AI-driven approach focused on executing actions and driving outcomes in customer experience.

Where generative AI helps you respond, agentic AI helps you resolve—autonomously, across systems, and with purpose.

Why Agentic AI Matters for CX Leaders in 2025?

The shift toward agentic AI isn’t theoretical—it’s already underway, and your customers are ahead of you. Research shows that by 2026, 68% of customer interactions will be handled by AI agents, up from 56% in just 12 months. Organizations are under increasing pressure to deliver faster, smarter, and more personalized customer experiences due to rising customer expectations. The pressure to evolve is real.

While most organizations are still tinkering with chatbots and pilot programs, forward-thinking CX leaders are going further: They’re deploying AI agents to not only respond faster, but to learn from every interaction, automate resolutions, and drive strategic decisions across the org.

Here’s what this means for you:

• The window for competitive advantage is narrowing: A company that moves now gains the speed, insight, and cost-efficiency edge that will be hard to replicate later.

• Customer expectations are accelerating: Customers no longer tolerate bouncing between departments, repeating themselves, or waiting days for answers. They expect intelligent, outcome-driven service—at scale.

• The real differentiator is back-end intelligence: Most businesses have invested in front-end automation. Few have unified feedback, CRM, and support data to drive real insight. That’s the gap agentic AI closes.

• Metrics are impacted across the board: From faster resolutions and fewer escalations to improved NPS, retention, and even product adoption—agentic AI impacts what matters most to CX teams and their boards.

Agentic AI is not just another AI upgrade—it’s the foundation for modern, intelligent customer experience. The longer you wait to deploy it across your CX stack, the harder it will be to catch up with organizations that are already scaling smarter, faster, and more customer-aware.

Customer Experience Has Come a Long Way

Customer experience has always evolved in response to customer expectations—and today, those expectations are changing faster than ever. Instant replies, 24/7 availability, and omnichannel presence are no longer differentiators—they’re table stakes. What customers want now is intelligent, proactive, and frictionless service that solves their problems before they even realize there’s an issue. Agentic AI enables brands to deliver more personalized and seamless customer experiences, meeting the evolving demands of today's consumers.

That shift requires more than just faster response times or better-trained agents. It demands a new kind of intelligence across the entire CX ecosystem. That’s exactly where agentic AI steps in—ushering in a new era where customer experience is no longer reactive, but continuously learning and improving in real time.

From Reactive Support to Proactive Engagement

Traditional customer service is built around a reactive model: something breaks, the customer complains, and the brand responds. But even when handled well, this model means you’re always one step behind the customer’s expectations—and often, too late to prevent dissatisfaction.

Agentic AI flips this model entirely. These systems don’t wait for customer input—they monitor feedback, behavior, and sentiment continuously, identifying potential issues early and acting to resolve them before the customer ever hits submit on a ticket. For example, brands in retail and SaaS are already using AI to spot friction in returns, cancellations, or onboarding journeys, and automatically improve these flows to reduce drop-offs and support volume. Agentic AI can also efficiently handle routine customer queries, freeing up human agents to focus on more complex or sensitive issues.

This isn’t just faster support—it’s invisible resolution that happens upstream, where customer loyalty is won or lost.

From Human-Only to Hybrid Teams

Human agents were once the cornerstone of customer support, but scalability and consistency became challenges. Hybrid models solve this by combining the empathy and problem-solving skills of the human agent with AI efficiency. Agentic AI handles high-volume, repetitive tasks while surfacing insights that help agents focus on complex issues. This collaboration ensures better resolution quality, faster response times, and a consistent experience across channels—without overwhelming your team.

From Customer Conversations to Strategic Intelligence

Every conversation holds valuable insight, but most businesses let it go unused. Most companies have rich customer data buried in tickets, chats, emails, and feedback forms—but it’s rarely analyzed, and even more rarely used to drive action. That’s a missed opportunity.

Agentic AI changes this by turning every customer interaction into actionable intelligence. It identifies trending issues, emerging pain points, recurring themes, and sentiment patterns—and connects them directly to your CX KPIs. By learning from past interactions, agentic AI continually improves its future responses and personalizes customer service, leading to greater efficiency and accuracy. What once took months of manual analysis is now surfaced in real time, enabling you to adapt faster, fix what’s broken, and even influence product, operations, and marketing decisions.

Rather than letting feedback sit in a dashboard no one checks, agentic AI feeds it directly into your systems, teams, and strategy—closing the loop at scale.

From Cost Center to Command Center

Support has historically been viewed as a cost center, focused on reducing overhead. But as customer experience becomes a competitive differentiator, CX teams are evolving into command centers. Agentic AI helps reduce operational costs by automating routine support tasks, lowering expenses associated with human labor and increasing efficiency. With agentic AI driving automation and insight, support can directly influence metrics like retention, NPS, and revenue. Instead of reacting to issues, CX becomes a proactive engine that drives business growth through intelligence-led action.

Where Agentic AI Fits in the CX Ecosystem

Most companies are chasing AI in the wrong places. They’re building chatbots when they should be building intelligence. They’re automating tasks when they should be automating insights.

AI-driven solutions that automate, personalize, and optimize customer service processes are transforming the way businesses operate, enabling greater efficiency and improved customer engagement.

The reality? AI Agents don’t just replace human agents—they create entirely new possibilities for how businesses understand and serve customers.

Current Use in Customer Service (Chatbots, Virtual Agents)

Here’s a sobering fact: 95% of organizations remain stuck in pilot phase with their AI implementations. Only 5% achieve scaled deployment. The gap between ambition and execution is massive.

Agentic AI is transforming the contact center by automating complex workflows and improving efficiency, allowing businesses to handle customer service operations with far less manual intervention.

But when companies get it right, the results are striking.

Klarna’s agentic AI system processes refunds, returns, and payment issues in over 35 languages—end to end, without human intervention. Tickets that once took 11 minutes now complete in under 2 minutes. That’s not incremental improvement; that’s transformation.

BoldDesk represents another breakthrough. Their platform creates customized AI agents tailored to specific workflows and brand voice. Properly implemented systems resolve up to 70% of customer interactions without human handoff.

Why it matters: These aren’t just efficiency gains. They’re competitive advantages. While your competitors struggle with basic chatbot deployments, agentic AI handles complex, multi-step processes autonomously.

Emerging Role in Feedback Analytics

Customer service gets the headlines, but feedback analytics is where the real opportunity lies. Organizations are finally recognizing that understanding customer sentiment at scale requires the same intelligent capabilities that power service automation.

Smart companies are connecting the dots:

• AI analyzes sentiment across thousands of interactions simultaneously

• Advanced systems spot patterns and emerging issues before they spread, using real-time data to identify problems as they arise

• Feedback insights drive proactive improvements across all touchpoints

Traditional analytics tools simply aggregate data. Agentic AI interprets emotions, routes critical insights automatically, leverages real-time data, and detects potential issues before they generate negative feedback.

The shift is already happening. Companies are moving beyond reactive analysis to predictive intelligence that shapes strategy in real-time.

Bridging the Gap Between Service and Insight

The most powerful application? Connecting service delivery with customer intelligence.

Right now, most agents juggle 7-10 different systems during customer interactions. Data lives in silos. Context gets lost. Customers repeat themselves endlessly.

Siemens solved this by deploying multilingual AI inside Microsoft Teams that resolves IT issues instantly while proactively detecting and fixing problems before tickets are raised. One system, unified experience, dramatic time savings.

This approach creates something remarkable: truly omnichannel experiences where context follows customers everywhere. Phone calls flow into chats, which continue in visual portals—all guided by AI that maintains complete context throughout. Agentic AI helps create seamless customer journeys across channels, ensuring smooth transitions and consistent experiences for every interaction.

The result? Customer interactions generate insights that enhance service delivery, which produces more valuable data for analysis. Each conversation makes the system smarter. Each resolution teaches the AI something new.

AI Customer Experience Examples That Inspire

The proof is in the results.

Companies across industries are deploying agentic AI systems that deliver measurable business outcomes—not just theoretical potential. These systems are also delivering a better customer experience by providing faster, more personalized service. The best implementations combine service automation with analytics capabilities, creating systems that solve problems and generate valuable insights simultaneously.

Klarna's Financial Services Breakthrough

Klarna processes refunds, returns, payment issues, cancelations, and disputes in over 35 languages without human intervention. The results are striking: tickets that once took 11 minutes now resolve in under 2 minutes—an 82% efficiency gain that directly boosts customer satisfaction.

This isn’t just about speed. Klarna’s system makes contextual decisions across multiple platforms, handling complex financial workflows that traditionally required human expertise. By analyzing purchase histories, Klarna's agentic AI delivers more personalized service and enhances fraud detection based on prior customer transactions.

BoldDesk's Customizable AI Agents

BoldDesk enables businesses to create AI assistants tailored to specific workflows and brand voice. Once implemented, these systems typically resolve 70% of customer interactions without human handoff while maintaining 24/7 availability with zero downtime.

BoldDesk's platform provides the essential building blocks organizations need to develop and scale agentic AI solutions, allowing them to start with foundational systems and expand as their needs grow.

What makes BoldDesk different? The agents learn continuously from every interaction, addressing customer needs before issues escalate into bigger problems.

Healthcare Automation at Scale

Air AI runs human-like calls across more than 5,000 applications to book, reschedule, and log appointments proactively. The platform syncs with electronic health records, Outlook, and patient apps—eliminating double-booking issues that plague traditional scheduling systems.

The integration capabilities extend far beyond single-channel interactions. Customers can start on a phone call, continue via chat, and finish in a visual portal—all guided by the same AI that maintains context throughout the entire journey. Air AI supports customers throughout the entire customer journey, from initial contact to post-appointment follow-up, ensuring a seamless and personalized experience at every touchpoint.

Siemens' Proactive IT Support

Siemens deployed multilingual AI inside Microsoft Teams that resolves IT issues instantly, triggers appropriate workflows, and even detects problems before tickets are raised. The AI system is capable of making decisions autonomously to resolve IT issues before they impact users. This unified approach replaced fragmented support portals and dramatically reduced resolution times.

Why it matters: Instead of reactive problem-solving, the system anticipates needs and fixes issues before customers experience them.

The Strategic Intelligence Layer

Here’s where it gets interesting: 81% of organizations are now prioritizing AI for feedback analytics—an area traditionally underserved by automation efforts. These systems don’t just aggregate data; they actively interpret customer emotions and route critical insights to appropriate stakeholders. The rise of the ai agent is transforming customer experience and driving strategic intelligence by enabling autonomous, goal-driven systems to proactively handle complex tasks and optimize both customer and operational workflows.

The impact is profound. Agentic AI transforms customer experience in two fundamental ways:

Service delivery - Wait times drop from seven minutes to thirty seconds, first-contact resolution rates soar, and every interaction receives personalized attention.

Strategic intelligence - Customer interactions become actionable insights that drive continuous improvement across all business functions.

Benefits That Go Beyond Speed

Speed matters, but it’s not the whole story.

The real power of agentic AI lies in how it changes the fundamental relationship between businesses and customers. Agentic AI is becoming deeply embedded in customer experience strategies, fundamentally transforming how businesses operate. These systems don’t just handle inquiries faster—they create experiences that feel remarkably human despite being powered by code.

Hyper-personalization at scale

Every customer gets white glove treatment. Whether you’re serving one caller or ten thousand, each person feels like they have a dedicated concierge. Agentic AI enables personalized experiences by tailoring interactions to individual customer preferences, ensuring every engagement feels unique and relevant. This isn’t theoretical—it’s what happens when AI systems understand customer context, history, and preferences at an individual level.

The impact is immediate. One great interaction can flip brand perception overnight, turning frustrated customers into advocates. Klarna’s implementation shows this perfectly—their system handles everything from refunds to invoice inaccuracies in 35+ languages, but what matters is how each interaction feels personal and contextual.

Why it matters: Personalization at scale was previously impossible without massive human resources. Now it’s table stakes for competitive customer experience.

Always-on support with zero downtime

Peak season traffic spikes? System outages? Labor shortages? Agentic AI handles it all without breaking a sweat. Whether 1,000 or 10,000 customers need help simultaneously, each gets instant attention. Next generation agentic AI systems are setting new standards for always-on, seamless customer support, ensuring businesses can deliver uninterrupted service at scale.

The conversations feel natural too. According to Zendesk, 56% of customers believe bots will conduct natural conversations by 2026, creating experiences that flow seamlessly across channels with regional accents and cultural awareness.

Operational efficiency that scales

Behind the scenes, the numbers are striking:

• 90% of routine calls automated—freeing human agents for complex, high-value interactions

• Agentic AI does the heavy lifting—automating both routine and complex workflows so human agents can focus on higher-value tasks

• Dramatic reduction in handle time—while improving first-contact resolution rates

• Buffer against labor constraints—when hiring can’t keep pace with demand

• Cost center becomes profit driver—through operational improvements and strategic insights

Economics academics Erik Brynjolfsson, Danielle Li, and Lindsey Raymond documented a 15% increase in issues resolved per hour across 5,172 agents using AI assistance. That’s measurable productivity improvement at scale.

Strategic Intelligence

Here’s where things get interesting. Customer support stops being a cost center and becomes a strategic command center. Real-time insights reach business leaders immediately instead of disappearing into call logs nobody reads.

This creates an entirely new feedback engine for continuous improvement. Each interaction generates data that makes the next interaction smarter. It’s a virtuous cycle that compounds over time.

The shift is already happening. While most companies focused narrowly on service automation, 81% are now prioritizing AI for feedback analytics. They’re realizing that understanding customer sentiment at scale requires the same agentic capabilities that power service delivery.

Through this integration of service and analytics, businesses gain unprecedented visibility into customer needs. Every conversation becomes a learning opportunity that drives better products, smarter processes, and stronger relationships. To unlock the full potential of agentic AI for customer experience, organizations must prioritize leadership and process redesign alongside technology adoption.

From Pilot to Scale: Agentic AI Playbook for CX Leaders

Deploying agentic AI isn’t about installing a new tool and expecting transformation. It requires a practical roadmap that helps leaders start small, prove value, and expand responsibly. Organizations looking to deploy AI agents effectively should follow a structured roadmap that addresses implementation at scale, orchestration, and integration into existing processes. Here’s how CX leaders can move from experimentation to scaled adoption.

Step 1: Prioritize High-Impact Use Cases

Don’t try to automate everything at once. Focus first on workflows that are repetitive, high-volume, and customer-critical. By prioritizing these high-impact use cases, agentic AI can drive outcomes that matter most to the business, such as faster issue resolution and streamlined operations. Examples include returns and refunds, multilingual support, and simple account updates. These areas create visible wins—improved resolution times, higher CSAT—and help build internal momentum for broader AI adoption.

Step 2: Ensure Data Readiness

Agentic AI can only be as effective as the data it accesses. Most CX data lives across CRM, billing, support platforms, and feedback tools. Many organizations struggle to unify customer data across multiple platforms, which can significantly impact the effectiveness of AI solutions. Unifying these systems into a single view is critical. Documenting undocumented agent knowledge is equally important—so the AI learns from institutional expertise, not just system records. Think of this as laying the foundation for intelligence.

Step 3: Launch Pilots With Clear KPIs

Run small-scale pilots in controlled areas, but with clear success metrics such as first-contact resolution, average handle time, or CSAT improvement. Product management plays a key role in overseeing these pilot programs, ensuring data quality and robust feedback mechanisms are in place to optimize outcomes. Use these pilots to refine workflows and gather evidence of business impact. Small, targeted implementations reduce risk and create momentum for scaling. Pilots should run long enough to surface learning, but narrow enough to minimize risk. The goal is to prove ROI quickly.

Step 4: Scale in Layers, Not All at Once

Once a pilot delivers measurable ROI, expand gradually. Add more workflows, introduce new channels, and extend coverage to more complex customer journeys. For example, after refunds and cancellations, move into proactive churn prevention or onboarding support. Scaling in layers helps the organization adapt, while ensuring governance and oversight keep pace. This layered approach is especially effective for transforming contact centers, enabling organizations to leverage agentic AI to enhance customer experience and streamline support operations.

Step 5: Embed Feedback Intelligence at the Core

The real leap happens when agentic AI goes beyond support tasks and becomes the engine for feedback intelligence. Connect it to customer surveys, chat logs, and tickets. Use it to surface themes, detect emerging risks, and feed insights directly into product and operations. Integrating AI tools into feedback intelligence processes enables continuous improvement and smarter decision-making. This closes the loop—transforming CX from a reactive function into a strategic growth driver.

This playbook ensures CX leaders don’t just deploy agentic AI, but do it in a way that proves ROI, reduces risk, and creates a path to scale.

The Current Limitation: Agentic AI Is Mostly in Service Ops

So far, the strongest adoption of agentic AI has been in customer service operations—chatbots, service bots, and agent-assist copilots. These use cases are easier to trial because they target high-volume, repetitive tasks where efficiency gains are immediate.

But the reality is clear: most implementations haven’t scaled. Research from Cisco and McKinsey shows that 95% of chatbot and voice bot deployments remain stuck in pilot phases. The gap between pilots and production reflects two challenges: businesses often underestimate the complexity of redesigning processes, and they overestimate AI’s ability to replace human empathy.

Scaling has also been held back by safety, compliance, and governance concerns. Leaders need to ensure data privacy, avoid biased decision-making, and maintain transparency around when and how AI takes action. Without strong guardrails, organizations risk reputational and regulatory damage.

Digital transformation efforts are essential for embedding agentic AI beyond service operations, as they require strategic investments in AI, process redesign, and close collaboration between CIOs and COOs to drive enterprise-wide change.

This is why many companies are treating agentic AI as an operations efficiency tool, rather than embedding it into broader CX and intelligence strategies. Until businesses can integrate governance, process redesign, and trust-building, agentic AI will remain concentrated in service ops—powerful, but not yet transformative.

The Future Outlook: From Service Ops to Strategic Intelligence

The next wave of agentic AI adoption will extend far beyond chatbots and service automation. Instead of being confined to ticket resolution and efficiency plays, AI agents will operate as strategic CX partners—analyzing feedback, shaping decisions, and orchestrating customer journeys end to end. A key enabler of these advancements is natural language processing, which allows agentic AI systems to understand, interpret, and respond to human language, making customer interactions more intelligent and personalized.

Over the next three to five years, several shifts will define this transition:

• Personal AI Concierges: Customers will interact with intelligent agents that accompany them across every channel, maintaining full context from first contact to resolution. Instead of isolated conversations, customers will experience continuous, personalized journeys.

• Predictive and Proactive CX: Systems won’t just react to customer issues—they’ll anticipate them. Agentic AI will identify churn risks, detect dissatisfaction in real time, and trigger corrective actions before customers even consider leaving.

• Feedback as a Growth Engine: Feedback analysis will move from static quarterly reports into real-time intelligence streams. Agentic AI will connect sentiment, themes, and product signals directly to business outcomes, enabling leaders to adapt strategies continuously.

• Unified CX Intelligence Layer: Rather than managing disconnected systems, organizations will rely on a shared AI backbone that integrates data from CRM, surveys, support platforms, and product analytics. This unification will give every department—from service to product to marketing—a consistent, customer-driven source of truth.

• Balance of Autonomy and Human Oversight: As agentic AI takes on more decision-making power, governance will remain critical. The future belongs to organizations that combine autonomous execution with transparent processes and human judgment where it matters most.

This shift marks a turning point for CX leaders. Agentic AI won’t just be about improving service metrics—it will become the intelligent backbone that drives customer loyalty, product direction, and competitive advantage. Those who adopt early will move from firefighting to foresight, positioning customer experience as the engine of growth.

Conclusion: Turning Agentic AI into a CX Growth Engine

Agentic AI isn’t just the next technology wave—it’s the foundation for how customer experience will be delivered going forward. While most organizations today are experimenting with AI in service operations, the true competitive advantage lies in moving beyond faster replies to intelligent, proactive, and outcome-driven experiences.

CX leaders face a choice: continue investing in automation that speeds up the front door, or build an intelligence layer that learns from every interaction and drives change across the business. With more than half of customer interactions expected to be AI-handled within the next year, the window for advantage is narrow. The leaders who act now will shift CX from a cost center into a growth engine powered by intelligence, not just efficiency.

This is exactly where Zonka Feedback’s AI Feedback Intelligence comes in. By unifying surveys, tickets, chats, and reviews into a single platform, it helps organizations close the loop between service and strategy. Instead of static dashboards, CX leaders get real-time insights, proactive alerts, and automated actions that improve satisfaction, reduce churn, and align teams on what matters most.

The future of CX belongs to organizations that combine agentic AI in customer experience with intelligence in feedback. Zonka Feedback makes this shift possible—helping leaders deliver not only faster resolutions, but smarter, more customer-centric outcomes.