Stefani Barbero

Get past the vague language

Every vendor who sells a generative AI agent for contact centers makes the same big claims about what you can achieve with their product – smarter automation, increased productivity, and satisfied customers. That language makes all the solutions sound pretty much the same, which makes a fair comparison more difficult than it ought to be. 

If you want to get past the vague language, take control of the conversation by asking these key questions. The answers will help you spot the differences between solutions and vendors so you can make the right choice for your business.

1. What exactly does your AI agent do?

Some AI agents simply automate specific processes or serve up information and other guidance to human agents, while others can operate independently to talk to customers, assess their needs and take action to resolve their issues. Ask these questions to distinguish between them.

• Can your genAI agent handle customer interactions from start to finish on its own? Or does it simply automate certain processes?
• How do your agents use generative AI?
• What channels does your AI agent support?

Look for a solution that uses the full range of generative AI’s capabilities to power an AI agent that can work independently to fully automate some interactions across multiple channels, including voice. This type of agent can listen to the customer, understand their intent, and take action to resolve the issue.

2. Is there more to your solution than a LLM + RAG?

Retrieval augmented generation (RAG) grounds generative AI agents on an authoritative source, such as your knowledge base. That helps the solution produce more accurate and relevant responses. It’s a dramatic improvement that’s invited some to ask whether RAG and a foundational model is all you need. The simple answer is no. Ask these questions to get a fuller picture of what else a vendor has built into their solution.

• Which models (LLMs) does your solution use? And why?
• Besides a LLM and RAG, what other technologies does your solution include? And how is it structured?
• Will I get locked into using a specific LLM forever? Or is your solution flexible enough to allow changes as models evolve?

Look for a solution that uses and orchestrates a wide variety of models, and a vendor that can explain why some models might be preferred for certain tasks and use cases. In addition to the LLM and RAG, the solution should include robust security controls and safety measures to protect against malicious inputs and harmful outputs. The vendor should also offer flexibility in which models are chosen and should allow you to swap models later if another would improve performance. 

3. How will your solution protect our data (and our customers’ data)?

Security is always a top concern, and generative AI adds some new risks into the mix, such as prompt injection, which could allow a bad actor to manipulate the AI into leaking sensitive data, granting access to restricted systems, or saying something it shouldn’t. Any AI vendor worth considering should have strong, clear answers to these security questions. 

• How do you ensure that the AI agent cannot be exploited by a bad actor to gain unauthorized access to data or systems?
• How do you ensure that the AI agent cannot retrieve data it is not authorized to use?
• How does your solution maintain data privacy during customer interactions?

Look for a solution that can detect when someone is trying to exploit the system by asking it to do something it should not. It should also have strong security boundaries that limit the AI agent’s access to data (yours and your customers’). Security and authentication in the API layer are especially critical for protecting data. And all personal identifiable information (PII) should be redacted before data is stored.

4. How do you keep your AI agent from ticking off my customers or damaging my brand?

We’ve all heard stories of bots that spouted offensive language, agreed to sell pricey products for a pittance, or encouraged people to do unsafe things. Solution providers worth considering should have robust safety mechanisms built in to ensure that the AI agent stays on task, produces accurate information, and operates ethically. Get the details on how a vendor approaches AI safety with these questions.

• How do you mitigate and manage hallucinations?
• How do you prevent the AI agent from sharing misinformation with our customers?
• How do you prevent jailbreaking?

Look for a solution that grounds the AI agent on information specific to your business, such as your knowledge base, and includes automated QA mechanisms that evaluate output to catch harmful or inaccurate responses before they are communicated to your customer. The solution should also incorporate a variety of guardrails to protect against people who want to exploit the AI agent (jailbreaking). These measures should include prompt filtering, content filtering, models to detect harmful language, and mechanisms to keep the AI agent within scope.

5. How hard will the solution be to use and maintain?

Conditions in a contact center can change quickly. Product updates, new service policies, modified workflows, revised knowledge base content, and even shifts in customer behavior can require your agents to adapt – including your AI agents. Ask these questions to find out how well a solution empowers your team to handle simple tasks on their own, without waiting on technical resources. 

• What kinds of changes and updates can our contact center team make to the solution without pulling in developers or other technical resources?
• What will it take to train our supervisors and other CX team members to work with this solution?

Look for a vendor who has invested in user experience research to ensure that their solution’s interfaces and workflows are easy to use. The solution should have an intuitive console that empowers non-technical business users with no-code tools to manage changes and updates on their own. 

6. How will we know what the AI is doing – and why?

When a human agent performs exceptionally well – or makes a mistake – you can ask them to explain their reasoning. That’s often the first step in improving performance and ensuring they’re aligned with your business goals. It’s equally important to understand how an AI agent is making decisions. Use these questions to learn how a solution offers insight into the AI’s reasoning and decision-making.

• How will we know what specific tools and data the AI agent is using for each customer interaction?
• In what ways do you surface information about how the AI agent is reasoning and making decisions?

Look for a vendor who provides a high degree of transparency and explainability in their solution. The AI agent should generate an audit trail that lists all systems, data, and other information sources it has accessed with each interaction. In addition, this record should also include an easily understood explanation of the AI agent’s reasoning and decision-making at each step.

7. How does your solution keep a human in the loop?

Solution providers acknowledge the importance of keeping a human in the loop. But that doesn’t mean they all agree on what that human should be doing or how the solution should accommodate and enable human involvement. These questions will help you assess how thoroughly the vendor has planned for a human in the loop, and how well their solution will support a cooperative relationship between the AI and your team.

• What role(s) do the humans in the loop play? Are they involved primarily during deployment and training, or are they also involved during customer interactions?
• When and how does your genAI agent hand off an interaction to a human agent? 
• Can the AI agent ask the human agent for the input it needs to resolve the customer’s issue without handing over the interaction to the human?
• What kind of concurrency can we expect with a human in the loop?

Look for a solution with an intuitive interface and workflow that allows your human agent to provide guidance to the AI agent when it gets stuck, make decisions and authorize actions the AI agent is prohibited from doing on their own, and step in to speak with the customer directly as needed. The AI agent should be able to request guidance and then resume handling the interaction. The solution should be flexible enough to easily accommodate your policies for when the AI agents should ask its human coworker for help.

8. Why should we trust your team?

Trust depends on a number of factors, but it starts with expertise. What you really need to know is whether a vendor has the expertise to deliver a reliable solution now – and continue improving it for the future. These questions will help you determine which solution providers are best equipped to keep up with the pace of innovation. 

• What components of your solution were developed in-house vs. acquired from third-parties?
• What kind of validation can you share from third-parties?
• Can you point me to your team’s research publications and patents?

Look for a vendor with a strong track record of in-house development and AI innovation. That experience is a good indicator of the vendor’s likelihood of continuing to expand their products’ capabilities as AI technologies evolve. Patents, published research, and third-party validation from industry experts and top-tier analysts underscore the vendor's expertise.

This list of questions is not exhaustive. There’s a lot more you could – and should – ask. But it’s a good start for rooting out the details you’ll need to make a fair comparison of generative AI agents.

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Path toward a reimagined contact center

The state of AI in contact centers is at a critical juncture. Generative and agentic AI have forever altered the CX tech landscape and presented a new set of choices for customer service leaders. After incorporating a bevy of AI solutions to improve efficiency in recent years, they now face a fork in the road. Down one path is the familiar strategy of continuing to optimize existing processes with AI. This path has its charms. It’s well-trod and offers predictable rewards. 

The other path is new, only recently created by the rapid evolution of generative and agentic AI. This path enables bold steps to radically transform the way the contact center operates. It might be unfamiliar, but it leads to spectacular benefits. Instead of incremental improvements with basic automation and agent support, it offers a more substantive transformation with generative AI agents that are capable of resolving customer issues independently.

At a recent Customer Contact Week (CCW) event, Chris Arnold, VP of Contact Center Strategy for ASAPP joined Wes Dudley, VP of Customer Experience for Broad River Retail (Ashley Furniture) to discuss this fork in the road and what it takes to travel the new path created by generative and agentic AI. Their conversation boiled down to several key points that translate into straightforward steps you can take now to start down the path toward a reimagined contact center that delivers much bigger benefits for the business.

You can also listen to the full conversation moderated by CCW's Managing Director of Events, Michael DeJager.

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Step #1: Understand your customer journeys and pinpoint what’s not working

Up to this point, the primary goal for AI in the contact center has been to make existing processes faster and more efficient. While efficiency gains provide incremental benefits to the bottom line, they often do little to improve the customer experience. Simply swapping out your current tech for generative AI might buy you yet another small efficiency gain. But it won’t automatically improve the customer’s journey.

A better approach is to incorporate generative and agentic AI solutions where they can make a more significant impact. To do that, you have to pinpoint where the real problems are in your end-to-end customer journeys. That’s why mapping those journeys is a critical first step. As Wes Dudley explained,

One of the first things we did is start customer journey mapping to understand the points in our business of purchase, delivery, repair, contacting customer service. With that journey mapping with all of our leaders, we were able to set the roadmap for AI.

By identifying the most common pain points and understanding where and why customer journeys fail, you can explore how generative and agentic AI might be able to address those problem areas, rather than simply speeding everything up. As a first step, you don’t have to map everything in excruciating detail. You just need to identify specific issues that generative and agentic AI can solve in your customer experience. Those issues are your starting point.

Step #2: Make your data available for AI

There’s a lot of focus on making your data AI-ready, and that’s crucial. But too many customer service leaders interpret that message to mean that their data must be pristine before they can count on generative AI to use it well. There are two problems with that interpretation. First, it creates a roadblock with a standard for data integrity that is both impossibly high and unnecessary. The most advanced AI solutions can still perform well with clean but imperfect data.

The second problem with this narrow focus on data integrity is that it overlooks the question of data availability. An AI agent, for example, must be able to access your data in order to use it. As Chris Arnold noted,

We're finally to a place where if you think about the agents' work and the conversations that they manage, agentic AI can now manage the vast majority of the conversation, and the rest of it is, how can I feed the AI the data it needs to really do everything I'm asking my human agents to do?

Ensuring that your data is structured and complete is only part of the availability equation. You’ll also need to focus on maintaining integrations and creating APIs, which will allow AI solutions to access other systems and data sources within your organization to gather information and complete tasks on behalf of your agents and customers. By all means, clean up your data. At the same time, make sure you have the infrastructure in place to make that data available to your AI solutions. 

Step #3: Align stakeholders and break down silos

AI implementation isn’t just about technology—it’s also about people and processes. It’s essential to align all stakeholders within your organization and break down silos to ensure a unified approach to AI adoption. As Chris Arnold explained, “Historically, we've [customer service] kind of operated in silos. So you have a digital team that was responsible for chat, maybe for the virtual assistant, but you've got a different team that's responsible for voice. And you create this fragmented customer experience. So as you're laying out the customer journey, begin with the customer in mind, and say, what are all the touch points? Include the website. Include the mobile app. Include the IVR. We no longer have to operate in silos. We shouldn't think of voice versus digital. It's just one entry point for the customer.”

If your goal is to continue optimizing existing processes with AI point solutions, then aligning stakeholders across the entire customer journey is less critical. You can gain efficiencies in specific parts of your process for digital interactions without involving your voice agents or the teams that support your website and mobile app. But if your goal is to achieve more transformative results with generative and agentic AI, then a holistic strategy is paramount. You’ll need to bring together all of your stakeholders to identify the key touchpoints across the customer journey and ensure that AI is integrated into the broader business strategy. This collaboration will help ensure that AI is used to complement existing technologies and processes in a way that yields measurable results for both the bottom line and the customer experience.

Step #4: Embrace the human-AI collaboration model

Much of the work that AI currently performs in contact centers is a supporting role. It offers information and recommendations to human agents as they handle customer interactions. That improves efficiency, but it doesn’t scale well to meet fluctuating demand. 

One of the most exciting developments in AI for customer service flips the script on this dynamic with AI agents that handle customer interactions independently and get support from humans when they need it. ASAPP’s GenerativeAgent® can resolve a wide range of customer issues independently through chat or voice. It’s also smart enough to know when it needs help and how to ask a human agent for what it needs so it can continue serving the customer instead of handing off the call or chat. 

“We are of the mindset that, without exaggeration, generative agents can replace 90% of what humans do – with supervision,” says Arnold. “So maybe you don't want your customers to be able to discontinue service without speaking to a human. GenerativeAgent can facilitate the conversation… but it can come to the human-in-the-loop agent and ask for a review so that the [AI agent] doesn't get stuck like it does today and then automatically escalate to an agent who has to then carry on the full conversation. We can now commingle the [GenerativeAgent] technology, the GenerativeAgent with the human, and you can have just about any level of supervision.”

Right now, we have AI that supports human agents. As we move forward, we’ll also have humans who support AI agents. As the human-AI balance shifts toward a more collaborative relationship, we’ll see radical changes in processes, workflows, and job functions in contact centers. The sooner you embrace this human-AI collaboration model, the better equipped you’ll be for the future.

Step #5: Get started now

The future of customer service won’t just be elevated by AI. It will be completely redefined by it. Contact centers will look – and function – very differently from the way they do now. And this future isn’t far away. We’re already at the fork in the road where you have a clear choice: stick with the familiar strategy of using AI to optimize existing processes, or take steps toward the future that generative and agentic AI have made possible. The path is there. It’s just a matter of getting started. You don’t have to do it all at once. You can go one step at a time, but it’s time to take that first step.

As Chris Arnold said at CCW,

Do it now. Don’t wait. Don’t be intimidated. Start now. Start small because all of us who have worked in the contact center for a long time, we know that small changes can lead to great big results. Just start now.

Get past the vague language and ask the right questions when choosing an AI agent solution

Learn why these key questions are critical

The latest crop of AI agents for the contact center can engage in fluid conversation, use reasoning to solve problems, and take action to resolve customers’ issues. When they work in concert with humans, their capabilities are maximized. That makes the human in the loop a critical component of any AI agent solution – one that has the potential to drive significant value.  

Most solution providers focus on the human in the loop as both a safety measure and a natural escalation point. When the AI fails and cannot resolve a customer’s issue, it hands the interaction to a human agent.

Many contact center leaders see this approach as appropriately cautious. So, while they steadily expand automated self-service options, they tend to keep human agents front and center as the gold standard for customer service.

But here’s the catch: It also imposes significant limitations on the value AI agents can deliver. 

Fortunately, there’s a better approach to keeping a human in the loop that drives the value of an AI agent instead of introducing limitations. 

The typical human-in-the-loop roles

You probably won’t find a solution provider who doesn’t acknowledge the importance of having a human in the loop with a generative AI agent. But that doesn’t mean they all agree on exactly what that human should be doing or how the solution should enable human involvement. For some, the human in the loop is little more than a general assurance for CX leaders that their team can provide oversight. Others use the term for solutions in which AI supports human agents but doesn’t ever interact with customers. 

Beyond these generalities, most solutions include the human in the loop in one or more of these roles:

1. Humans are directly involved in training the AI. They review performance and correct the solution’s output during initial training so it can learn and improve.
2. Humans continue to review and correct the AI after deployment to optimize the solution’s performance.
3. Humans serve as an escalation point and take over customer interactions when the AI solution reaches the limits of what it can do. 

The bottleneck of traditional escalation

Involving members of your team during deployment and initial training is a reliable way to improve an AI agent’s performance. And solutions with intuitive consoles for ongoing oversight enable continued optimization.

But for some vendors, training and optimizing the AI is largely where the humans’ role ends. When it comes to customer interactions, your human agents are simply escalation points for when the AI agent gets stuck. The customer experience that generates is a lot like what happens when a traditional bot fails. The customer is transferred, often into a queue where they wait for the next available agent. The human in the loop is just there to pick up the pieces when the AI fails.

This approach to hard escalations creates the same kind of bottlenecks that occur with traditional bots. It limits containment and continues to fill your agents’ queues with customers who have already been let down by automation that fails to resolve their issue.

The incremental improvements in efficiency fall short of what could be achieved with a different human-AI relationship and an AI agent that can work more independently while maintaining safety and security.

Redefining the role of the human in the loop

The first step to easing the bottlenecks created by hard escalations is to redefine the relationship between humans and AI agents. We need to stop treating the humans in the loop as a catch-all safety net and start treating them as veteran agents who provide guidance to a less experienced coworker. But for that to work, the AI agent must be capable of working independently to resolve customer issues, and it has to be able to ask a human coworker for the help it needs. 

With a fully capable autonomous AI agent, you can enable your frontline CX team to work directly with the AI agent much as they would with a new hire. Inexperienced agents typically ask a supervisor or more experienced colleague for help when they get stuck. An AI agent that can do the same thing is a more valuable addition to your customer service team than a solution that’s not much more than a better bot. 

This kind of AI agent is able to enlist the help of a human whenever it

• Needs to access a system it cannot access on its own
• Gets stuck trying to resolve a customer’s issue
• Requires a decision or authorization by policy

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The AI agent asks the human in the loop for what it needs – guidance, a decision, information it cannot access, or human authorization that’s required by policy. Once the AI agent receives what it needs, it continues handling the customer interaction instead of handing it off. For added safety, the human can always step in to speak with the customer directly as needed. And a customer can also ask to speak to a human instead of the AI agent. In the ideal scenario, you have control to customize the terms under which the AI agent retains the interaction, versus routing the customer to the best agent or queue to meet their needs.

Here is what that could look like when a customer calls in.

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The expansive value of human-AI collaboration

With this revised relationship between humans and AI agents, the human in the loop amplifies the impact of the AI agent. Instead of creating or reinforcing limitations, your human agents help ensure that you realize greater value from your AI investments with these key benefits:

1. Faster resolution times

When an AI agent can request and get help – and then continue resolving the customer’s issue – customers get faster resolutions without transfers or longer wait times. That improves First-Contact Resolutions (FCR) and gets customers what they need, faster.

2. More efficient use of human agents

In the traditional model, human agents spend a lot of time picking up the pieces when AI agents fail. With a collaborative model, agents can focus on higher-value tasks, such as handling complex or sensitive issues, resolving disputes, or upselling services. They are not bogged down by routine interactions that the AI can manage.

3. Higher customer satisfaction

Customers want quick resolutions without a lot of effort. Automated solutions that cannot resolve their issues leave customers frustrated with transfers, additional time on hold, and possibly having to repeat themselves. An AI agent that can ask a human coworker for help can successfully handle a wider range of customer interactions. And every successful resolution improves customer satisfaction.

4. Scalability without compromising quality

The traditional model of escalating to humans whenever AI fails simply doesn't scale well. By shifting to a model where AI can consult humans and continue working on its own, you ensure that human agents are only involved when they are uniquely suited to add value. This makes it easier to handle higher volumes without sacrificing quality or service.

5. Continuous learning to optimize your AI agent

Interactions between the AI agent and the human in the loop provide insights on the APIs, instructions, and intents that the AI needs to handle similar scenarios on its own in the future. These insights create the opportunities to continue fine-tuning the AI agent’s performance over time.

Generating value with the human in the loop

By adopting a more collaborative approach to the human-AI relationship, contact centers can realize greater value with AI agents. This new model allows AI to be more than just another tool. It becomes a coworker that complements your team and expands your capacity to serve customers well.

The key to implementing this approach is finding an AI solution provider that has developed an AI agent that can actively collaborate with its human coworkers. The right solution will prioritize flexibility, transparency, and ease of use, allowing for seamless integration with your existing CX technology. With this type of AI agent, the humans in the loop do more than act as a safety net. They drive value.

See how GenerativeAgent® works with a human in the loop in the contact center

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Empathy in customer service doesn’t always look the way we expect. Sometimes it wears a disguise.

A few years ago, I bought my daughter a new mobile phone for Christmas. She planned to make a 3-hour drive, mostly through rural areas, to visit her cousins the next morning. We needed to get the phone working before she pulled out of the driveway. 

But nothing we tried did the trick. So, late in the afternoon on Christmas day, we needed customer support.

Our service provider’s website offered two options, phone or chat. I hate chat for support, but the wait time for a phone call was more than I could commit to while getting ready for a family dinner.  So, I fired up the chat and asked for help. At first, I wasn’t sure whether it was a bot or a human. I didn’t care either way as long as we got the phone working. Over nearly two hours, I alternated between the chat and my family. And in the end, the problem was fixed. 

What does empathy actually look like in customer experience?

I don’t recall the agent (human after all) saying anything particularly compassionate. And yet, this was one of the most empathetic customer service experiences I’ve ever had. Here’s why:

1. The interaction resolved my problem on my schedule without requiring a call or visit to the store.
2. I had a clear choice between phone and chat and knew the current wait times.
3. I got the problem resolved without missing Christmas dinner with my family.

The bottom line is that my service provider gave me options for how to engage and a convenient way to get what I needed.

This is how empathy sometimes wears a disguise. It masquerades as efficiency, convenience, and ease. 

In an industry hyper-focused on the emotional side of empathy, we too often overlook this crucial practical side. But we shouldn’t. It matters to customers, a lot. 

• 93% of customers expect their issue to be resolved on the first call
• 62% of customers would prefer to “hand out parking tickets” than wait in an automated phone tree for service or have to repeat themselves multiple times to different team members
• 80% of American consumers say efficiency is the most important factor in the customer experience, and more than half say it’s worth paying more for

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The often-overlooked practical side of empathy in CX

In recent years, the CX industry has focused intently on empathy. Businesses spend time and resources to upskill agents on active listening, emotional intelligence, and expressing care and compassion. They even provide lists of empathetic phrases their agents can use. And a growing number of contact centers use AI to detect customer sentiment throughout each interaction. All of that is great. It reminds the agents that customers are human, too, and they need to hear that someone cares about their problem.

Validating a customer’s feelings is an important component of putting empathy into practice. But it’s only one component.

Caring alone doesn’t resolve a customer’s issue, and it doesn’t automatically make the process of reaching a resolution easy or convenient. 

Long wait times, multiple interactions, and chatbot failures are not empathetic. Many CX leaders view those points of friction through the lens of contact center efficiency with metrics like transfer rates and digital containment. But friction also increases customer effort, which is an important component of empathy in CX. And too many contact centers deliver experiences that require a lot of customer effort – ineffective self-service, complicated IVR menus, disconnected channels, and more. An agent who says they understand your frustration can’t erase all that effort and wasted time.

Empathy in CX strategy: Are we making it too complicated?

The concept of empathy is somewhat vague and squishy, so it’s not surprising that CX leaders sometimes convert it into something else when crafting CX strategy. The problem is, they often convert empathy into the equally vague concept of customer-centricity. What does that mean? Keeping the customer front and center at all times, sure – but how? It isn’t always clear how centering the customer translates into actions and processes for the contact center to follow. 

The vague nature of both empathy and customer-centricity tends to give rise to complex frameworks that attempt to make the strategy more concrete. For example, a framework might categorize elements in the CX ecosystem into systems of listening, understanding, action, and learning. Those frameworks can help shape perspectives within your business, but they still require additional translation to make them actionable for your frontline CX team. 

Here’s a simpler approach. Embedding empathy into your CX strategy means consistently aiming to do these four things:

1. Resolve the customer’s issue in the first interaction.
2. Take up as little of the customer’s time as possible.
3. Make the entire process easy and convenient.
4. Treat your customers and employees like the human beings they are.

Getting to the point of empathy with generative AI

In contact centers, early AI implementations increased efficiency, but employees felt the impact more than customers. In some cases, AI deployments actually increased frustration by raising customers’ hopes with big promises of faster, more convenient service that didn’t ever materialize. Consider chatbots. Even with improved language processing, bots can’t take action to resolve a customer’s issue. So, they require time and effort from the customer but often, can’t truly help. When it comes to the practical side of empathy, they fail to deliver. 

But that was then, and this is now. The technology has matured, and current implementations of generative AI are improving contact centers’ performance on both the emotional and practical sides of empathy. AI solutions increasingly take over repetitive and time-consuming tasks, freeing agents to focus more effectively on the customers they’re serving. This shift makes space to engage with more empathy across the board. 

Customer-facing AI agents will generate a larger, even seismic, shift in how empathy is embedded into customer experiences. Generative AI agents can listen, understand, problem-solve, and take action to resolve customers’ issues. That ticks all the empathy boxes for me. This massive leap forward lays the groundwork for CX leaders to shift the emphasis of their AI investments toward solutions that do more than talk in a natural way.  

Practical empathy that’s just a chat or call away

That Christmas a few years ago when I needed customer service, I didn’t care whether I chatted with a human or AI. I just wanted my problem resolved before my daughter left town, preferably without having to call or visit the store the next day. I got lucky that time. My service provider had agents available. But we all know that’s not always the case. With a generative AI agent ready to respond 24/7/365, the customer’s luck never runs out. Effective, efficient, and convenient service will always be just a call or chat away. For me, that’s the part of empathy in CX that too many businesses are missing today. But I suspect that’s about to change. 

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Ever heard the phrase, "Customer service is broken?" 

It's melodramatic, right? —something a Southern lawyer might declaim with a raised finger. Regardless, there’s some truth to it, and the reason is a deadly combination of interaction volume and staffing issues. Too many inbound interactions, too few people to handle them. The demands of scale do, in fact, break customer service. 

This challenge of scaling up is a natural phenomenon. You find it everywhere, from customer service to pizza parlors.

Too much appetite, too little dough

If you want to scale a pizza, you have to stretch the dough, but you can't stretch it infinitely. There’s a limit. Stretch it too far, and it breaks. 

Customer service isn't exactly physical, but physical beings deliver it— the kind who have bad days, sickness, and fatigue. When you stretch physical things too far (like balloons, hamstrings, or contact center agents), they break. In contact centers, broken agents lead to broken customer service.

Contact centers are currently stretched pretty thin. Sixty-three percent of them face staffing shortages. Why are they struggling? Some cite rising churn rates year after year. Others note shrinking agent workforces in North America and Europe. While workers flee agent jobs for coding classes, pastry school, and duck farming, customer request volumes are up. In 2022, McKinsey reported that 61% of customer care leaders claimed a growth in total calls. 

To put it in pizza terms (because why not?), your agent dough ball is shrinking even as your customers' insatiable pizza appetite expands.

What’s a contact center to do? There are two predominant strategies right now: 

• reduce request volumes (shrink the appetite)
• stretch your contact center’s service capacity (expand the dough)

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Contact centers seem intent on squeezing more out of their digital self-service capabilities in an attempt to contain interactions and shrink agent queues. At the same time, they’re feverishly investing in technology to expand capacity with performance management, process automation and real-time agent support. 

But even with both of these strategies working at full force, contact centers are struggling to keep up. Interaction volume continues to increase, while agent turnover carries on unabated. Too much appetite. Not enough dough to go around. 

How do we make more dough?

Here’s the harsh reality – interaction volume isn’t going to slow down. Customers will always need support and service, and traditional self-service options can’t handle the scope and complexity of their needs. We’ll never reduce the appetite for customer service enough to solve the problem.

We need more dough. And that means we need to understand the recipe for customer service and find a way to scale it. The recipe is no secret. It’s what your best agents do every day:

1. Listen to the customer
2. Understand their needs
3. Propose helpful solutions
4. Take action to resolve the issue

The real question is, how do we scale the recipe up when staffing is already a serious challenge?

Scaling up the recipe for customer service

We need to scale up capacity in the contact center without scaling up the workforce. Until recently, that idea was little more than a pipe dream. But the emergence of generative AI agents has created new opportunities to solve the long-running problem of agent attrition and its impact on CX.

Generative AI agents are a perfect match for the task. Like your best human agents, they can and should listen, understand, propose solutions, and take action to resolve customers’ issues. When you combine these foundational capabilities into a generative AI agent to automate customer interactions, you expand your contact center’s capacity – without having to hire additional agents. 

Here’s how generative AI tools can and should help you scale up the recipe for customer service:

1. Generative AI should listen to the customer
   Great customer service starts with listening. Your best agents engage in active listening to ensure that they take in every word the customer is saying. Transcription solutions powered by generative AI should do the same. The most advanced solutions combine speed and exceptional accuracy to capture conversations in the moment, even in challenging acoustic environments. 

2. Generative AI should understand the customer’s needs
   Your best agents figure out what the customer wants by listening and interpreting what the customer says. An insights and summarization solution powered by generative AI can also determine customer intent, needs, and sentiment. The best ones don’t wait until after the conversation to generate the summary and related data. They do it in real time.

3. Generative AI should propose helpful solutions
   With effective listening and understanding capabilities in place, generative AI can provide real-time contextual guidance for agents. Throughout a customer interaction, agents perform a wide range of tasks – listening to the customer, interpreting their needs, accessing relevant information, problem-solving, and crafting responses that move the interaction toward resolution. It’s a lot to juggle. Generative AI that proposes helpful solutions at the right time can ease both the cognitive load and manual typing burden on agents, allowing them to focus more effectively on the customer.

4. Generative AI should take action to resolve customers’ issues
   This is where generative AI combines all of its capabilities to improve customer service. It can integrate the ingredients of customer care—listening, understanding, and proposing—to safely and autonomously act on complex customer interactions. More than a conversational bot, it can resolve customers’ issues by proposing and executing the right actions, and autonomously determining which backend systems to use to retrieve information and securely perform issue-resolving tasks.

Service with a stretch: Expanding your ball of dough

Many contact centers are already using generative AI to listen, understand, and propose. But it’s generative AI’s ability to take action based on those other qualities that dramatically stretches contact center capacity (without breaking your agents). 

A growing number of brands have already rolled out fully capable generative AI agents that handle Tier 1 customer interactions autonomously from start to finish. That does more than augment your agents’ capabilities or increase efficiency in your workflows. It expands your frontline team without the endless drain of recruiting, onboarding, and training caused by high agent turnover. 

A single generative AI agent can handle multiple interactions at the same time. And when paired with a human agent who provides direct oversight, a generative AI agent can achieve one-to-many concurrency even with voice interactions. So when inbound volume spikes, your generative AI agent scales up to handle it. 

More dough. More capacity. All without stretching your employees to the breaking point. For contact center leaders, that really might be as good as pizza for life. 

Want more? Read our eBook on the impact of agent churn

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Instead of incremental changes to timeworn tools, what if we could bypass the problem altogether?

Download the Agent Churn: Go Through It or Around It? eBook to learn why traditional strategies for agent retention aren't working, and how generative AI enables a radical new paradigm.

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