But within weeks of go-live, the cracks appeared. Customers quickly got stuck in endless loops with bots that couldn’t escalate properly. Agents were juggling more systems than before. Supervisors became overwhelmed with rework. What was pitched as innovation quickly became a daily fire drill.

That story isn’t unique. Metric Sherpa’s 2025 research with Glia, based on 945 contact center leaders, shows the pattern: over 75% of contact centers already use AI, and 83% expect to expand usage this year. Yet only 16.6% of customer-facing AI purchasing decisions are led by the contact center. The very function that must deliver the outcomes is too often absent from the table.

The Price of Leaving the Contact Center Out of AI Decisions

The study surfaced the real costs of leaving contact center voices out of AI governance. Each one hits the business harder than executives expect.

Broken Rollouts

AI chosen without frontline input fails to align with actual customer journeys. The data shows long wait times, repeated transfers, and inconsistent responses remain the leading sources of customer frustration. When new tools don’t solve these basics, customers notice immediately.

Contracts in Crisis

Leaders we interviewed described renegotiations and scope changes that sent professional services fees soaring. Every missed requirement that surfaces late creates delays and drains resources.

Agent Disengagement

Adoption falters when employees feel AI has been imposed on them. Instead of empowerment, agents experience distrust. Our study found nearly a third of leaders believe AI’s ROI is underestimated—a direct reflection of stalled adoption.

Missed Opportunities

Thirty-eight percent of executives admit they undervalue the intelligence the contact center gathers from customer interactions. Without that input, AI decisions fail to capture one of the richest sources of growth and innovation.

Each outcome traces back to one choice: excluding the people closest to the work.

Why Contact Center AI Decisions Are a C-Suite Issue

Metric Sherpa and Glia’s research also found that 90% of leaders rate customer value as critically important, and 86% say the same of strategic value. These outcomes are decided in the contact center every day.

This is where customer loyalty is reinforced or lost in moments of truth. It is where feedback reveals product flaws and unmet needs. It is where coaching and development either scale a workforce or stall it. And it is where the brand either earns credibility or erodes it.

When customer-facing AI decisions bypass the contact center, organizations weaken their ability to achieve these priorities. AI investments end up disconnected from the realities of service delivery, making them harder to prove and harder to sustain. Nearly a third of leaders already acknowledge that AI’s return is underestimated. That admission reflects a gap in governance, not in potential.

Four Steps to Bring the Contact Center Into AI Decisions

The Metric Sherpa + Glia study didn’t just highlight the risks. It pointed to a clear path forward for leaders who want to close the governance gap and capture AI’s potential.

1. Establish Cross-Functional Governance

Executives or IT alone make nearly half of AI decisions.. The fix is creating councils that blend strategic oversight with frontline expertise. This ensures investments are measured not only in technical capability but also in customer and employee impact.

2. Prioritize Friction Reduction

The study identified the top pain points for both customers and employees: long wait times, transfers, system switching, and manual data entry. These are the pressure points where AI must prove itself first. Addressing them early builds credibility across the enterprise.

3. Measure What Matters

Leaders in our research said they define AI’s value through customer satisfaction (75%), employee productivity (61%), and cost savings (60%). These metrics resonate with executives and can be expanded to include loyalty and strategic insight generation. The more visible the wins, the more influence the contact center gains.

4. Pilot with Purpose

Executives in the study stressed that small, frontline pilots revealed gaps long before formal deployments. Pilots don’t just de-risk investments—they also give employees a voice, increasing trust and adoption.

The Core Truth About AI and the Contact Center

The research keeps pointing to the same conclusion: ignoring the contact center in customer-facing AI decisions creates costs that ripple through the organization. The price is visible in broken rollouts, ballooning contracts, disengaged employees, and missed opportunities.

The upside of including the contact center is equally clear. Decisions are grounded in operational reality, adoption improves, and the full value of AI shows up across customer, employee, and strategic outcomes.

AI Governance Starts With the Contact Center Voice

Our latest research confirms that AI is already here. The focus now must be on how to govern it.

Executives and IT leaders play vital roles. But unless contact center leaders claim their seat and ensure their perspective is heard, the cycle of underperforming AI will continue.

The cost of ignoring the contact center is already being paid in wasted investments and disappointed customers. The organizations that win the next era of customer experience will be those where the contact center is not a bystander but a trusted stakeholder in shaping customer-facing AI.

In this exclusive CX Today interview, we sit down with Sanas to explore the cutting-edge world of AI-powered accent translation.

From improving customer experience to tackling ethical concerns, we dive deep into the implications of reshaping the way we communicate.

Join us as we discuss:

• How AI accent translation enhances global communication
• The ethical debate around voice modification and identity
• Real-world applications for CX and business operations
• What does AI-driven accent translation mean for the future of customer experience?

Subscribe for the latest insights on AI, CX, and digital innovation.

From hallucinations to compliance breaches, the pressure to deploy customer-facing AI agents at scale has collided with a lack of control, transparency, and testing.

Cyara’s AI Trust suite was born from this friction, and it’s quickly becoming essential in turning generative AI (GenAI) promise into production-grade reality.

Origins Rooted in Risk

The AI Trust suite emerged in response to a now-familiar pain point: bots veering off-script with misleading or unsafe responses.

Infamous gaffes that include virtual agents swearing at customers, taking offence, and even telling people to break the law exemplify the risk within the service space.

To address this, Cyara developed the AI Trust testing suite, an AI testing solution with modules designed to expose the unique risks of generative AI. The latest module, AI Trust Misuse, detects and flags inappropriate or off-brand bot behavior in the development stage.

Complementing this is the AI Trust FactCheck module, which identifies factual inaccuracies and hallucinations that LLMs are known to produce.

Speaking to CX Today, Christoph Börner, VP of Engineering at Cyara, explained: “Trust is the main currency for AI-driven customer engagements or experiences.

As AI continues to reshape, the contact center landscape will also reshape. We know that new challenges will keep emerging, and we will evolve our approach to these new challenges.

FactCheck: Validating AI Responses Against Real Data

FactCheck is one of the suite’s powerful modules, a reality check for LLM outputs.

The concept is simple but critical: validate AI-generated responses against a “source of truth,” whether it’s a product knowledge base, policy library, or technical manual. Responses are audited with color-coded feedback to flag factual errors and partial matches, which teams can use to QA and refine their models.

FactCheck most frequently finds issues involving fabricated product specifications, outdated policy terms, and incorrect procedural guidance.

Bridging the Proof-of-Concept Gap

Despite surging investment in AI-powered CX, only a fraction of projects cross the chasm into production. Indeed, approximately 70 percent are still stuck in the pilot or testing phase, according to the Wall Street Journal.

The AI Trust suite offers much-needed scaffolding, helping organizations build confidence by exposing hidden risks before customers do.

On this, Börner added: “One of the biggest problems for our clients is the ‘what to do next question.’ Especially when it’s about testing AI, these language models are extremely big, and running a test here could end up with 10,000 issues being found.”

Helping contact centers take the leap of faith from proof of concept, the AI Trust Misuse module evaluates customer interactions to identify hate speech, fraud, and other topics contact centers restrict, empowering them to detect and prevent incidents of malicious intent or harmful content generation.

Speed vs. Assurance: No Longer a Trade-Off

Generative AI demands agility, but that shouldn’t come at the cost of accuracy or safety. Cyara approaches testing as part of the development lifecycle, not an afterthought.

Automated evaluations allow teams to iterate quickly while maintaining tight governance over conversational AI performance and compliance. As Börner summarized:

“We are not building these things just because we think that’s the next big thing to do; we’re building them based on the challenges that our clients face.”

To check out the full AI Trust suite, find out more here: https://cyara.com/products/cyara-ai-trust/

This can be a roadblock for enterprises looking to implement AI, especially when there are constant reports about these hallucinations hitting the headlines.  

While some are on the comical side, like when a Virgin Money customer was warned against hateful speech for using the word “virgin,” some are more serious.  

Take Cursor, for example. The AI-powered software coding assistant from AI startup Anysphere went viral due to a chatbot hallucination in response to a recent customer service query.  

When customers got logged out of their accounts and asked customer support for assistance, an AI chatbot named ‘Sam’ told them that this was “expected behavior” under a new policy — a policy the chatbot had simply invented all on its own. 

This led to confusion and distrust among the company’s customers, with some even cancelling their accounts. 

While no one is denying the benefits of AI-powered tools, the simple truth is Large Language Models (LLMs), which can be used to develop chatbots, are powerful, but they can also produce these types of frustrating hallucinations if proper guardrails are not put in place.  

Speaking to CX Today,  Nikola Mrkšić, CEO and Co-Founder of PolyAI, explained how his company has “been able to constrain the behavior of LLMs and use them in places where it makes the most sense to drive customer service conversations.” 

The team at PolyAI helps enterprises speak with customers through voice AI agents, and it’s important that these agents not only say, but also do the right things, rather than hallucinate responses or tell you they’ve taken an action when they really haven’t. 

Some of the guardrails in place with PolyAI’s agents are powered by retrieval-augmented generation, or RAG, a technique that enables AI agents to cross-reference knowledge from a generative model with a knowledge base. 

This ensures that an AI agent checks its generated responses against information the enterprise has confirmed as factual. 

In doing so, it prevents inaccurate, irrelevant, and inappropriate responses, and keeps customer conversations within established limits. 

Where Enterprises Can Make a Difference Today with AI

In pursuing seamless CX, businesses must evaluate how AI and automation support accuracy, trust, transparency, operational costs, and efficiency.  

According to PolyAI’s Mrkšić, enterprises considering where to start implementing AI for CX should consider sophisticated voice AI agents among their first real-world deployments.  

Mrkšić said, “AI is such a big and monumental thing, and many people can’t resist mounting these large offensives. What they need is a lot of probing attacks on different front lines. 

Large cloud providers or CCaaS vendors will tell enterprises to start with Agent Assist capabilities, and then down the road think about automation, but we take a different approach.

Mrkšić also noted that while AI assistance for human agents certainly brings business benefits, it does not always help address wider automation plans.  

“The model proposed by CCaaS vendors will not lead to the future these enterprises need, where 90% of calls are automated.” 

How PolyAI is Addressing the Challenges of Providing Good CX through Voice AI

Despite the benefits of CX over the phone, there are some considerations for deploying this type of technology, too.  

Voice interactions remain central to CX but face obstacles such as:

• Latency and speech recognition errors: frustrating delays and misinterpretations can degrade customer experiences. 
• Lack of contextual awareness: AI systems may struggle with complex queries requiring historical context. 
• Limited conversational flexibility: rigid AI scripts reduce adaptability in dynamic interactions.

However, PolyAI specializes in AI-driven voice assistants designed to address these challenges by maintaining natural, human-like conversations, providing accurate, contextual responses, and enhancing scalability.  

Their AI agents can handle the complexities of real-world conversations, understanding diverse accents, navigating interruptions, and adapting to shifts in context.  

PolyAI can resolve between 50% and 75% of inbound calls entirely autonomously.

At the core of PolyAI’s tech is a powerful blend of spoken language understanding, speech synthesis, and intelligent dialogue management.  

By combining retrieval-based and generative AI models, their system delivers fast, accurate, and natural-sounding responses that adapt to each caller’s needs. 

Integration is refreshingly straightforward: PolyAI’s platform works out of the box with systems like Salesforce, Twilio, and Amazon Connect, and its agents can be deployed in under six weeks, without the need to replace existing systems. 

Designed for enterprise environments, PolyAI’s voice agents meet the highest standards of security and compliance, and they’re fluent in more than a dozen languages. 

To see a demo of this technology in action, check out this video.

Unlike CRM systems that attempt to centralize all data, CDPs take a more pragmatic approach by creating unified customer views while allowing data to remain in existing systems. This approach is particularly valuable for organizations managing multiple legacy systems—with some contact center agents dealing with nearly 14 different systems of record daily.

The benefits extend beyond operational efficiency. Taylor shares compelling examples from the DVLA, where CDP implementation has transformed workplace satisfaction so dramatically that it now outperforms the entire civil service as a place to work. By reducing training time from months to days and eliminating complex system navigation, agents can focus on what matters most: delivering exceptional customer service.

As organizations embrace omni-channel strategies and AI-powered experiences, the quality and accessibility of customer data becomes critical. CDPs don’t just solve today’s integration challenges—they create the foundation for tomorrow’s AI-driven customer experiences, making them an essential investment for any organization serious about CX transformation.

Join Floyd March in this insightful conversation with Cyara’s Christoph Börner, as they unpack the launch of Cyara’s new platform designed to transform CX testing and monitoring.

From AI-powered innovations to real-time feedback loops, they explore how this latest evolution aims to boost customer satisfaction and reduce risk for enterprises.

What to Expect:

• The strategic vision behind Cyara’s new platform
• Key features that elevate CX assurance
• Real-world applications and ROI potential
• Industry trends shaping automated testing

Whether you’re a CX leader, tech strategist, or simply curious about the future of experience assurance, this episode delivers bold insights and practical takeaways.

With 40 percent of contact centers reporting increased demand for 24/7 availability, and over a third noting heightened expectations around personalization, speed, and transparency, the sector is in the midst of a fundamental shift.

From traditional support models to always-on, AI-enabled, experience-driven service that demands smarter technology, greater agility, and empowered frontline teams.

Challenges to meet this shift include the fact that nearly three-quarters of contact center leaders believe that ethical concerns, data privacy, and regulations will limit AI uptake.

Regarding the Calabrio State of the Contact Center report results, CX Today spoke to Magnus Geverts, VP Product Marketing at Calabrio.

He explained, “The one [finding] that struck the most is that 98% of the leaders surveyed use it [AI] in the contact center. It’s not a question of if it happens; it has happened, and it is here.

“New questions are being asked: are we scratching the surface? How far will we go? They’re all fascinating and exciting questions to try to answer.

“But organizations must rethink workforce strategies to address these growing demands, balancing automation with human-led problem-solving.”

AI Isn’t the Future, It’s Already Here

The report makes one thing clear: almost all contact centers are already using AI, whether through chatbots, AI-driven analytics, or automated workflows.

Yet, there’s a noticeable gap: with the increasing complexity of customer interactions, only 28% plan to invest in bot analytics.

Despite this, the optimism remains clear from a Calabrio perspective. Geverts added, “We are just scratching the surface with AI. Personalization will be a big lever in this. AI is a natural solution that helps improve CX and meet customers where they are.”

However, he conceded, “Some of the things stopping this from happening are integration and back-end technology, but we will get over those hurdles.”

While many organizations aren’t optimizing it to its full potential, addressing this analytics gap could unlock greater efficiency and improve customer outcomes.

AI Growing Pains Remain

Despite AI’s undeniable presence, barriers to adoption persist.

Cost, integration challenges, and trust issues continue to hinder progress, with 71% of contact center leaders citing ethical concerns, data privacy, and regulations as limiting factors.

Geverts encouraged leaders to consider AI being “used to benefit agents in the contact center, whether that’s to improve coaching, automatically analyze interactions, or provide a better way to target training.”

“For AI to be truly effective, businesses must navigate regulatory landscapes while fostering trust among employees and customers alike.”

“When contact centers dare to try, they will see the benefits more clearly,” he added.

The Agent Experience Disconnect

Leaders acknowledge the importance of skills like emotional intelligence and adaptability, yet 64% of organizations aren’t prioritizing empathy training, even though it is the most lacking skill.

“It’s quite a disconnect. There’s a lot of talk, but there isn’t a lot of action. Over 80% of leaders explained that offering support is important, but less than 40% of agents say they have any social activities.”

“We have seen improvements overall in how we treat agents in the contact center and the need for ongoing training and coaching, but with AI, a technology shift inside the contact center means new things to learn. We haven’t caught up with that yet.”

This disconnect raises key questions: Are contact centers preparing their workforce adequately for the AI era? If emotional intelligence is critical to success, why is training lagging?

This is exacerbated when considering 59% fail to provide ongoing coaching and support to help agents navigate AI-driven workflows.

By addressing the AI disconnect, enhancing analytics, and strengthening agent training, businesses can unlock AI’s true value, balancing efficiency with deeper customer engagement.

Full details of the report can be found here

We’ve had chat and voice bots in customer service, sales, and marketing for a long time. They started primarily as rules-based bots. These tend to be based on tech that follows rule-based scripts and logic and, as a result, have rigid flexibility. They also have limited contextual awareness, are not proactive, don’t have the capability for decision-making, their integration with other systems is limited or siloed; they have static/no ability to learn, and use cases are narrow and predefined.

Then, there were conversational AI chat/voice bots. Based on machine learning, NLP, and NLU. They have higher flexibility, contextual awareness, broader and more adaptive use cases, and more dynamic learning capabilities that can improve with data. However, they offered limited proactivity and only a reactive decision-making capacity, with moderate integration capabilities and adaptive use cases.

Agentic AI describes the next level of capabilities in chat/voice bots.

What Are AI Agents?

AI Agents are transformative, proactive, and autonomous bots capable of taking action and achieving goals without constant user input. They bring conversational systems closer to fully independent problem solvers or assistants.

Additionally, they are based on advanced AI with autonomy, reasoning, and planning. Meanwhile, their flexibility is dynamic and proactive, with deep contextual understanding and foresight.

Furthermore, AI Agents are goal-driven, with the ability to autonomously make decisions, and are – in many cases – capable of acting across many systems.

The main issue with rule-based or even conversational AI bots is that doing the business process mapping takes a long time. As such, it takes a lot of effort to get them set up, and – other than for super simple workflows – they end up frustrating customers.

Most of us have found ourselves wanting to scream at the bot because it doesn’t understand what we need or want, and we end up in an endless loop. We often figure out the workaround so that we don’t even have to be disappointed and get ourselves straight to an agent.

Avoiding the tech, of course, defeats the company’s idea that they will get an ROI out of self-service. Indeed, the results are often so disappointing that we have essentially trained customers to hit zero, pound, or whatever it takes to bypass the “system.”

Here’s why customer satisfaction (CSAT) and FCR (first contact resolution) are poised to increase with Agentic AI:

• Time Savings: By anticipating and resolving issues autonomously, Agentic AI drastically reduces the time customers spend resolving issues or navigating complex systems
• Empowered Customers: Customers feel taken care of when the system addresses their needs proactively, which builds loyalty and trust.
• Enhanced User Experience: Seamless, intuitive, and emotionally intelligent interactions provide a sense of ease and reliability.
• Action-Oriented Support: The ability to take meaningful actions ensures that customers don’t feel stuck or left without solutions.

The 18 Agentic AI Use Cases

Retail

1. Proactive Cart Recovery

A customer abandons their cart on an e-commerce site. The AI Agent identifies the behavior, sends a reminder with a discount offer, and even allows the customer to complete the purchase via a single click in the email.

• • Benefit: Reduces abandoned carts, increases sales, and enhances customer convenience.

2. Post-Purchase Assistance

After a customer buys a smart device, the AI sends setup instructions, schedules a tutorial call, or offers accessories for their purchase.

• • • Benefit: Builds customer confidence in the product and fosters loyalty.

3. Supply Issue Alerts

A popular item is out of stock, and the AI proactively informs customers on the waitlist when it’s back, providing pre-order options.

• • Benefit: Keeps customers informed and engaged without them having to follow up.

Healthcare

4. Appointment Management

The AI monitors a customer’s health records and sends reminders for regular checkups or follow-ups. It also adjusts appointments based on availability and patient preferences.

• • Benefit: Streamlines appointment scheduling, reduces missed visits, and enhances preventive care.

5. Medication Compliance

AI tracks prescribed medication schedules, reminds patients to take their doses, and even checks for pharmacy refills or delivery options.

• • Benefit: Improves health outcomes and patient satisfaction by minimizing human error.

6. Health Crisis Intervention

Detects abnormal symptoms during a telehealth conversation and escalates the case to a specialist or recommends visiting an emergency room.

• • Benefit: Offers timely interventions, potentially saving lives.

Travel and Hospitality

7. Real-Time Flight Management

A customer’s flight is delayed. The AI rebooks connecting flights, updates the itinerary, and arranges hotel stays or transportation, notifying the customer immediately.

• • Benefit: Provides peace of mind and seamless travel adjustments.

8. Personalized Itineraries

Based on a customer’s travel history, the AI suggests a customized itinerary, books local experiences, and offers restaurant reservations.

• • Benefit: Enhances the travel experience and reduces planning effort.

9. Crisis Handling

During a weather emergency, the AI proactively communicates updates, reschedules bookings, and ensures the customer is aware of safety protocols.

• • Benefit: Builds trust by showing care and foresight.

Banking and Financial Services

10. Fraud Prevention

The AI detects unusual activity on a customer’s account, temporarily freezes it, and notifies the customer with steps to confirm or dispute the transaction.

• • Benefit: Enhances security and trust.

11. Proactive Savings Advice

Based on spending patterns, the AI suggests a monthly savings plan, sets up automatic transfers, and identifies areas for cost reduction.

• • Benefit: Empowers customers to make better financial decisions.

12. Loan and Credit Management

The AI identifies when a customer qualifies for a better loan rate and provides a detailed breakdown of how to switch, including pre-filling applications.

• • Benefit: Saves money and reduces the complexity of financial decisions.

Construction and Field Services

13. Proactive Equipment Maintenance

AI detects signs of wear in rented construction equipment and schedules maintenance before it fails.

• • Benefit: Minimizes downtime and improves operational efficiency.

14. Worker Support

AI monitors worker health and fatigue in harsh environments, recommending breaks or escalating emergency interventions when needed.

• • Benefit: Enhances worker safety and well-being.

15. Site Monitoring

AI keeps track of project timelines and proactively informs the customer of potential delays, providing alternative solutions.

• • Benefit: Keeps clients informed and reduces miscommunication.

Education and Learning

16. Proactive Learning Pathways

Based on a student’s progress, the AI recommends tailored study plans, extra resources, or skill-building courses.

• • Benefit: Boosts engagement and enhances learning outcomes.

17. Early Intervention

AI identifies when a student is struggling with a topic and schedules a tutoring session or provides targeted exercises.

• • Benefit: Prevents frustration and improves academic performance.

18. Career Planning

AI assesses a student’s interests and strengths, suggesting potential career paths, internships, or workshops.

• • Benefit: Provides clarity and actionable steps for future success.

In a recent webinar with Observe.ai, I discussed how companies like Accolade Health and Affordable Care are beginning to automate their contact center with Voice AI Agents.

Vendors like Observe.AI have seen success in the AI space for contact centers. They are now entering the agentic AI space with the ability to automatically create, test, and deploy bots efficiently and effectively.

For more from Dr. Natalie, you can follow her on LinkedIn.

Dr. Natalie Petouhoff wrote the #1 book on customer experience and AI: Empathy in Action: How to Create Great Customer Experiences at Scale. She helps brands understand how to use AI and vendors to explain why their solution is unique.

Introducing Agentic AI

Agentic AI is more than a technological advancement – it’s a fundamental shift in customer service. It merges human insight with AI’s predictive power to automate more of the customer-service provider interaction. Agentic AI doesn’t replace human agents but enhances their ability to provide exceptional service, creating a seamless, intuitive experience.

Imagine setting up a router. Instead of navigating complex manuals, you receive a link via SMS. One-click connects your phone camera to our visual platform, where our AI-powered agent guides you through each step. Visual intelligence transforms the interaction from “Tell me about your problem” to “Show me your issue” and “Here’s how we solve this, together.”

Empowering Human-AI Collaboration

The essence of Agentic AI lies in empowering agents and customers. It frees agents to focus on high-value tasks by automating everything from routine to the most complex issues. Multimodal AI technology understands customers’ needs accurately and efficiently, offering a smart pair of eyes and ears, while Agentic AI enables the AI to take action for the user. This fusion of multimodal AI, which powers context-rich, human-like visual, voice, and text interactions, and agentic AI, where the AI completes actions like ordering a replacement or updating a record, represents a fundamental shift in CSX.

This transition moves from asking the customer to work around the company’s limitations (e.g., describe your problem in a way our chatbot will understand) to a more customer-centric approach. Having trouble describing your issue? Share a video. Does the AI need to offer guidance? Use AI to visually guide the customer. Need step-by-step guidance? Today’s advanced cognition or logic capabilities make this vision a reality. Need to activate or provision that customer’s account? Now this, too, is attainable.

In high-stakes industries like home security, telecom, smart home, and automotive, the precision and speed offered by visual intelligence redefine CX, turning support interactions into human-centered, precise problem-solving sessions and reducing technician dispatches.

Sophie AI: Elevating Customer Support

Sophie AI is TechSee’s flagship solution and exemplifies this balance. Sophie enhances agents’ ability to deliver exceptional service by leveraging visual intelligence. It guides customers through issue resolution independently while providing agents with critical insights when needed. This ensures customer support remains personal and scalable.

For instance, a smart home provider in the Fortune 500 achieved up to an 80% reduction in truck rolls, demonstrating how visual intelligence drives both efficiency and sustainability.

Unlocking Full Potential with Visual Components

Automation alone isn’t enough for true transformation. Integrating a visual component allows AI to understand and respond to the customer’s environment in real-time. This visual layer is critical for realizing Agentic AI’s full potential, enabling a multimodal system utilizing video, voice, and data for comprehensive, user-friendly support.

This visual integration is revolutionizing industries like telecommunications and home security, making it necessary for those aiming to scale while maintaining high customer satisfaction.

The Future of Customer Support

The market for visual augmentation is reaching a critical turning point. Companies must thoughtfully adopt emerging technologies to meet evolving customer expectations and strategic goals. TechSee’s research reveals that over 70 percent of industry leaders plan to integrate real-time visual data into customer support in the coming years. This shift toward Agentic AI is set to redefine the future of customer interactions.

Tomorrow’s customers expect seamless, guided experiences that build trust and loyalty. The future of customer support is visual, intelligent, and built on trust.

Find out more about TechSee here.

Complex journeys are also expensive: according to Gartner research, average costs per resolution start at £7.18 for a service journey involving just one channel before rising to £13.85 where two channels are involved (the most common scenario), and £20.53 where three channels are used.

Many organisations have tried to improve customer service journeys, engaging in activities like journey mapping, but these approaches often fail to deliver long-term results.

Customer journey management refers to the discipline of designing, deploying, and continuously improving customer journeys to drive seamless CX, supporting enterprise objectives around growth, and managing costs.

Instead of handling journey improvement through isolated projects managed by siloed teams, journey management centers the entire service strategy around optimizing the customer journey. This approach requires dedicated ownership, management, accountability, and commitment to continuous improvement.

Establishing a Journey Management Office

Establishing a journey management office is the largest operational change needed to start a journey management discipline. The office should be structured around a small set of high-priority customer journeys, with individual journey teams taking ownership of a single journey.

Each journey should have a dedicated journey manager, whose primary responsibility is to optimize their assigned journey and coordinate the team – otherwise largely comprised of part-time stakeholders representing multiple functions.

Hiring a full-time journey manager who you can trust to take decisions that impact your CX, channels, and operations without the need to confirm every move with leadership first can be the difference between success and failure – this must not be seen as a side-of-desk or part-time role.

Prioritize and Analyze Your Journeys

Improving every service and service support journey simultaneously is impossible, so it’s important to prioritize the journeys with the greatest potential impact on financial outcomes (loyalty, revenue growth, and cost reduction). Then, consider journey complexity, likely time to improve, resource availability, technology requirements, and other dependencies.

Once you’ve identified the journeys to improve, ask your data and analytics team to answer the following questions using channel, voice of the customer (VoC), and operational data:

• Which elements of the customer service journey have the greatest impact on customer perceptions of satisfaction/effort?
• What does the customer journey look like today?
• What channels would be the best fit considering the customer effort and cost to serve this journey?
• How do customers feel about our current state journeys?

These insights will be critical in supporting the next phase: journey mapping.

Develop Key Journey Maps

Journey maps serve as a reference point for discussions about journeys and become the platform upon which to design and optimize service and support experiences.

First, ask your teams to create an initial set of current-state journey maps to fully understand the prioritized journeys. Ensure that this captures the customers’ end-to-end need for service and how their journeys span multiple channels.

Using the current-state journey map as a guide, discuss areas of potential improvement and produce a list of solutions. Consider options to improve both the front-end journey steps and the back-office process supporting the journey. Prioritize these solutions based on their expected impact and viability before producing future-state journey maps. Prototype and test the potential future-state journeys, both internally and with customers and third parties, before producing the business and technical requirements needed to develop and deploy the updated journey. Once complete, ensure the journey maps are updated to reflect the new journey.

Develop, Deploy, and Continuously Improve

Once you’ve designed a new or improved journey, begin developing the journey across your service channels. The actual process of development isn’t markedly different in the journey management process than how it takes place today. But there are two differences in how this process is managed:

1. Split design from development. Today, many organizations jump into development, or mapping and development are done in tandem. Splitting these activities ensures that teams have space to incorporate innovative ideas into journey designs.
2. The journey manager will help coordinate channel teams and partners in IT to ensure that all business and technical requirements are delivered on time and risks or issues are mitigated.

Once journeys are deployed, leaders must avoid falling into the trap of assuming that the journey is “finished.” Instead, journey teams should analyze how the journey is performing, identify and prioritize new opportunities based on the updated journey map, and repeat the cycle to continuously improve the journey time.

Thanks to Christopher Sladdin and Daniel O’Sullivan, Director Analysts in the Gartner Customer Service & Support Practice, for submitting this article.