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Month: July 2025

Machine Learning and AI (Artificial Intelligence): What CX Leaders Need to Know

Posted on Bill O’Neill

Before the rise of generative AI, most people didn’t think twice about what powered the systems behind their daily digital experiences. However, as artificial intelligence increasingly plays a more active role in how brands engage with customers, it’s worth understanding what makes it all work.

The terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but they’re not the same. ML is a subset of AI. It’s a method for achieving intelligence, not a competing approach.

To draw a parallel: if AI is the goal, building systems that can act with intelligence, then machine learning is one of the most successful means of getting there. Just as early aviation pioneers tried to mimic birds with flapping wings, symbolic AI attempted to replicate reasoning with hard-coded rules. Machine learning emerged like the modern jet engine: more powerful, more scalable, and better suited to complex tasks.

Understanding this relationship isn’t just academic. It has real consequences for how CX leaders evaluate automation strategies, allocate budget, and deploy tools.

“AI will automate customer interactions, capture customer intent, and route inquiries to the right skilled agent,” notes Forrester Vice President Kate Leggett.

What Is Artificial Intelligence?

Artificial Intelligence is the broader goal: building systems that can reason, plan, adapt, and interact intelligently with people. It includes everything from early rule-based expert systems to today’s large language models (LLMs). AI may use ML, but it doesn’t have to.

In CX, AI enables agents to interpret context, understand nuances, and take meaningful action. This often involves a mix of language processing, orchestration, and AI engineering to align system behavior with business goals. LLMs like GPT, Claude, and Gemini are examples of generative AI that support open-ended conversation, intelligent routing, and dynamic task execution.

What Is Machine Learning and Deep Learning?

Machine learning is a method that enables computers to enhance their performance on a task by analyzing and identifying patterns in data over time, thereby improving with each new example. It’s particularly good at sorting information into categories, drawing conclusions, and making useful predictions through repeated exposure to examples.

In the early days of CX automation, ML drove use cases like spam filtering and sentiment analysis. Today, it continues to power background classifiers in Quiq’s Reporting & Analytics capabilities, enabling smarter routing, trend detection, and operational insights. While expectations have evolved, the fundamentals haven’t changed. ML still learns from labeled examples and fine-tunes performance through ongoing iteration.

Machine learning continues to play a central role in enabling modern AI. The large language models that drive today’s most advanced agents are trained using extensive ML techniques on vast datasets. While users experience a fluid, human-like interface, that outcome depends on the layered ML systems powering it behind the scenes.

Deep learning (DL) builds on machine learning by stacking layers of artificial neurons that learn from data step by step. It’s especially effective at tackling more complex problems, such as deciphering what’s being said in a conversation or recognizing faces in a photo. It’s the method behind things like voice assistants, language translation, and image recognition. While it falls under the umbrella of ML, it’s the approach that powers many of the most impressive applications we associate with AI today.

Visual diagram showing AI as the overarching field, with machine learning as a subset and deep learning as a further specialization, highlighting their hierarchical relationship in modern CX applications.
Understanding how AI, machine learning, and deep learning relate to one another is essential for CX leaders evaluating automation tools. This visual outlines their hierarchy, with AI as the umbrella, ML as a method within it, and DL as an advanced technique used for complex tasks, such as language and image recognition.

Specialized Uses of Machine Learning and LLMs in CX

Within CX applications, AI systems often combine multiple techniques, including ML and large language models (LLMs). While ML and LLMs are both forms of AI, they serve different functions. ML focuses on pattern recognition and prediction; LLMs excel at language-based reasoning and adaptability.

Accuracy and Use Case Fit

When you need something done with precision, like detecting whether a message contains a regulated complaint, machine learning is often the best tool for the job. These models improve through learning from labeled examples and are evaluated using performance metrics such as accuracy, recall, and precision to assess their consistency in delivering the correct result. That consistency makes them ideal for repeatable tasks, such as sorting messages or handling routine routing decisions.

When the goal shifts to flexibility, such as interpreting a customer’s vague product question, LLMs are often a better fit. They’re built to handle open-ended questions, follow nuanced directions, and communicate in a way that feels more conversational than scripted. That strength, though, can be a trade-off. Because they’re not trained on tightly defined tasks, their output can vary, and that makes them less reliable when precision is critical.

Take one Quiq client, a major airline. They used an ML classifier to spot Department of Transportation (DOT)-regulated complaints. The model was tuned to flag anything even remotely risky. An LLM could have offered a more nuanced explanation, sure, but when compliance is on the line, nuance isn’t always the goal. Precision is.

Data Requirements

ML models are only as good as their training data. Each use case demands curated examples—often annotated by hand—to ensure the model learns the right patterns. This makes them reliable but rigid.

LLMs are trained on large volumes of text, both publicly available and proprietary, to help them recognize language patterns and produce human-like responses. Once deployed, these models rely on prompts and context windows to guide their behavior, rather than requiring retraining. This approach enables faster adaptation, even in environments where data is limited. But the quality of the results still depends on the clarity of the inputs and the trustworthiness of reference material, such as the documents used in a retrieval-augmented generation (RAG) pipeline.

Cost, Time-to-Market, and Scalability

ML is expensive to train and deploy at scale. Each new classifier can require model training, infrastructure setup, and ongoing maintenance.

LLMs are also expensive to train, but that cost is typically handled upstream by companies like OpenAI or Google. When Quiq uses an LLM, it leverages an already-trained model and customizes it via prompt engineering or retrieval techniques. This drastically reduces time-to-value.

Quiq’s AI Studio shows how quickly brands can deploy new agents without needing to rebuild a model from scratch. In client examples like Molekule, AI agents went live in days, not months, and scaled dynamically as customer needs changed.

According to an article in CX Today, “Agentic AI has emerged as a game‑changer for customer service, paving the way for autonomous and low‑effort customer experiences.” They predict up to 80 percent of customer issues could be resolved without human intervention by 2029.

Benefits of Layering Machine Learning Within AI for Smarter Automation

Together, AI and ML create a flexible, multi-layered CX stack:

  • ML handles the behind-the-scenes work: session detection, message classification, and inappropriate content filtering.
  • AI handles the dynamic conversations: resolving issues, escalating to agents, and following instructions in natural language.

ROI (Return On Investment)

The value of combining AI and ML becomes apparent quickly, particularly in areas that matter most to CX leaders. Contact centers see shorter average handle times, more accurate routing, and fewer escalations to human agents. This leads to lower cost per contact and more efficient workforce planning.

But the ROI isn’t just operational. Customers get faster answers, fewer dead ends, and smoother transitions between AI agents and human support. For example, Quiq clients that use both ML classifiers and LLM-based agents have reported improved CSAT scores and reductions in abandonment rates.

When you can solve issues faster, with fewer touches and less friction, the return compounds over time, especially at scale.

Reliability

When accuracy matters most, like meeting compliance standards or executing a high-stakes task, ML is still the safer bet. Its outputs are predictable, measurable, and repeatable. That makes it ideal for situations where consistency and precision can’t be compromised.

LLMs, on the other hand, are incredibly useful for flexibility and language understanding, but they aren’t perfect. They sometimes “hallucinate.” That is, generate content that sounds plausible but is factually wrong. They can also stray off-topic or misinterpret subtle context.

That’s why Quiq uses a layered approach: ML handles the validation, checks, and behind-the-scenes classification that support the LLM’s more open-ended reasoning. For example, after an LLM generates a response, an ML model may be used to confirm that the resolution offered aligns with business rules or that next steps were taken. This redundancy helps ensure that the AI behaves responsibly, even in complex workflows.

Speed to Launch

Thanks to LLMs and tools like AI Studio, brands can deploy fully functional AI agents in just days. There’s no retraining required; just clear instructions. This is made possible by prompt-based AI, where detailed prompts guide behavior without altering the underlying model. Teams can rapidly test, revise, and deploy updates simply by refining the prompts.

According to McKinsey’s 2024 State of AI report, 65% of organizations that have adopted AI report that it has already helped increase revenue in at least one part of their business. The value becomes even more tangible when AI and ML are used together to streamline customer-facing operations.

Final Takeaways for CX Leaders

AI and ML don’t compete; they complement each other. Machine learning is one of the most effective techniques used in modern AI systems, especially when precision and repeatability are key. When layered with conversational AI agents, ML helps teams scale automation intelligently and reliably.

For CX leaders, knowing when to apply ML-based automation versus when to use a more adaptable AI agent is the difference between just automating and truly improving the customer experience.

Ready to put AI and ML to work in your CX strategy?

Customer Service Strategies: 5 Effective Ways to Improve eCommerce Support

Posted on Michael Hartsog

More than ever before, eCommerce businesses’ overall revenue is directly tied with the quality of their customer service. Today’s customers value shopping experiences more than price or product selection and can easily transition to one of many competitors. 86% of consumers say they would spend more for a better experience, and 76% of consumers said it’s far easier now to take their business elsewhere than ever before.

According to the 2020 ROI of Customer Experience Report, 94% of consumers outstanding experience with a brand say they would recommend it to family and friends. In contrast, only 13% of consumers who had an abysmal customer experience with a brand would recommend it.

From these statistics, it’s possible to conclude that marketing and sales alone aren’t enough for eCommerce businesses to remain competitive, maintain a substantial market share, and grow their brand reputation. They need to go above and beyond to implement outstanding customer service strategies, before and after closing the sale.

Here are 5 successful customer service strategies that eCommerce brands can quickly implement to improve their overall customer experience.

1. Build a Strong Customer Service Team

Start by hiring an excellent customer service team and creating an environment that promotes staff retention.

Conversational Engagement Platforms, like Quiq, are meant to augment human agents, not replace them. When offering top-notch customer support, software can’t replace the need for well-trained and skilled customer representatives.

Here are six strategies to attract talented, customer service-minded professionals.

  • Hire the right people: Hire for attitude. Look for employee representatives with empathy, patience, and excellent communication skills.
  • Train employee representatives: eCommerce hiring managers must train their employee representatives to understand their products and services and connect with their customers on an emotional level.
  • Equip customer service agents: Brand leaders should provide a platform to offer the best consumer experience without stress, overwork, or burnout. For example, Quiq’s Conversational Customer Engagement Platform enables companies to engage with customers across different channels in one centralized place, providing easy, simultaneous responses.
  • Track agents’ performance: Using surveys, management teams should collect feedback from customers on their experiences with employees and use the insights from these ratings to evaluate each representative’s performance.
  • Reward excellent performance: Incentivize top-performing employee representatives to motivate others to do more.
  • Listen to employee representatives: Ask for and listen to worker feedback to understand their needs.

Customer service is often the first area considered for budget cuts. However, this strategy is counterproductive. According to customer experience research, 50% of consumers would switch to a competitor after a bad experience, and 80% would switch after more than one bad experience. To keep consumers from switching to competitors, managers must prioritize customer service.

When it comes to the finesse and care it takes to navigate complicated customer inquiries or assist distressed customers, nothing beats a knowledgeable, well-trained, and accessible human agent. That’s why many channels, like Apple Messages for Business and Google Business Messages require brands to have live agents to escalate conversations to.

2. Personalize Every Conversation With Consumers

customer service improvement strategies

Personalizing customer conversations means tailoring support and service to their exact needs and expectations.

Customizing services to meet consumer demand gives eCommerce businesses a competitive advantage in their industries, helps deliver faster support from team members, makes customers feel more connected, and reinforces a consistent sense of satisfaction.

Here are a few ways to offer personalized service:

  • Engage with consumers where they already are
  • Transfer consumers smoothly across employee representatives
  • Mention people by their name in every conversation
  • Make recommendations when the requested product or service is unavailable
  • Offer free demonstrations and training to educate customers

Shoppers look toward eCommerce providers to know their needs and provide what they want. Research shows that 80% of consumers are more likely to make a purchase when brands offer personalized experiences, while 72% of consumers say they only engage with personalized messaging. To win and keep business, eCommerce employees must treat consumers as people — not numbers in a sales report — with unique needs and expectations.

A conversational engagement platform can help employees provide highly personalized experiences. For example, Quiq clients, like Stio, can send outbound messages to segmented customers and offer them targeted promotions. They can use Quiq’s intelligent routing feature to provide VIPs, who may spend at a certain level or who are part of their Pro Purchase Program with priority support.

3. Collect and Use Customer Feedback

Continually gathering feedback from shoppers on their experience can help eCommerce business leaders:

  • Understand their consumers’ needs, challenges, and pain points
  • Identify the positive and negative experiences shoppers have with their brand
  • Locate the cracks in customer service
  • Provide a more personalized experience for shoppers
  • Build trust and make shoppers feel valued

To obtain comprehensive and useful customer feedback, company decision-makers need to implement intuitive ways for consumers to communicate with them and ensure the information provides actionable insight for improving customer service.

Here are seven ways to collect customer feedback:

  • Send customer satisfaction surveys online
  • Organize feedback focus groups
  • Read reviews from third-party review sites
  • Build an online community for customers

Gathering feedback is only the first step. Next, it’s important for managers to create an action plan on cumulative insights and train employees to leverage this information when responding to customer complaints.

Continuously requesting consumer feedback will help identify any gaps in customer service and reduce the likelihood of a shopper feeling unsatisfied with their transaction.

4. Use KPIs to Gauge Customer Service Performance

It is not enough for eCommerce managers to train and equip their customer service teams. Measuring and tracking customer experience with the right KPIs can help the entire team understand how their consumer experience ties to overall business success, and how shoppers’ interactions with them change over time.

KPIs help eCommerce leaders appreciate their shoppers’ satisfaction level and readiness to continue doing business with them.

Here are four customer experience KPIs to track as an eCommerce strategy:

  • Net promoter score
  • Customer effort score
  • Rate of returning visitors
  • Revenue per customer

See the breakdown of each KPI below.

Net Promoter Score

Net promoter score (NPS) is a reflection of an eCommerce business’s customer experience. An eCommerce business’s net promoter score shows the likelihood of shoppers referring their family and friends to do business with that brand.

To calculate NPS, send a survey including the question, “How likely are you to recommend our product?” Customers provide their answers on a scale of 1 to 10.

After collecting this information, calculate NPS by subtracting the total number of entries below 5 from the total number of entries above 5.

An NPS below zero indicates a low customer satisfaction level. An NPS between zero and 30 shows more satisfied customers than unsatisfied customers. Above 30 implies that there are far more satisfied customers than unsatisfied customers. Above 70 means that customers are loyal and will be the source of a lot of word-of-mouth referrals.

Customer Effort Score

Customer Effort Score (CES) reveals how much work consumers must put into researching products and services or completing a particular task. For example, how long does it take the average shopper to get a refund, sign up, or get a request ticket answered?

Customer effort score reflects how accessible a business is to its consumers.

As with NPS, calculate CES using a survey with the question, “How much effort did you have to put into completing this task?” Ask customers to respond on a scale of 1 to 7 or 1 to 5.

Here’s the breakdown for each scale.

On a scale of 1 to 5:

  • 1 = Very high effort
  • 2 = High effort
  • 3 = Neutral
  • 4 = Low effort
  • 5 = Very low effort

On a scale of 1 to 7:

  • 1 = Extremely difficult
  • 2 = Very difficult
  • 3 = Fairly difficult
  • 4 = Neither
  • 5 = Fairly easy
  • 6 = Very easy
  • 7 = Extremely easy

To calculate CES, divide the total effort scores by the number of responses. Measure it right after a purchase or service interaction. On a 1–7 scale, a score of 5+ means customers find your product or service easy to use; below 5 suggests they’re facing challenges.

Rate of Returning Visitors

The rate of returning visitors (RVR) reflects the effectiveness of customer success strategies and user experience for an online service. If shoppers enjoy their experience on the site, they’ll be more likely to return.

For an online service, calculate RVR by dividing the number of returning visitors by the number of unique visitors. The higher the value, the better the customer experience on the site.

While RVR can vary for different industries, a good RVR is 30% or more.

Revenue Per Customer

Revenue per customer (RPC) ties the overall consumer experience to a company’s bottom line.

To calculate RPC, divide the total revenue by the total customer count.

A high RPC means consumers have a consistently positive experience with a business. They are loyal, repeat shoppers who recommend the brand to friends and family.

Combined, these four KPIs provide insight into the quality of customer service, the satisfaction consumers derive from a product or service, and areas for improvement.

For example, a CES below 5 for contacting support could mean one or more of the following:

  • Customers don’t receive a timely response
  • Consumers must try multiple channels to access the customer service team
  • Customers must repeat themselves to every new call center agent they interact with
  • It takes a long time for representatives to resolve their issues

Company executives should dig deep to uncover the factors producing the low KPIs and benchmark their KPIs with their competitors to know where they stand.

5. Provide a Consistent Cross-Channel Experience

A cross-channel customer service strategy allows eCommerce employees to provide seamless and consistent customer support across different channels. Shoppers can switch between SMS, webchat, and social media without any service interruptions or inconsistencies in quality.

Cross-channel customer service helps shoppers get quick responses to their needs, improves the brand’s reputation and trust, and boosts consumers’ positive experiences.

Here are seven strategies to implement a cross-channel experience:

  • Ensure customers can reach employee representatives offline and online on the platform of their choice
  • Implement seamless transition when moving customers from one call center agent to another
  • Maintain comprehensive documentation on each customer to help call center agents continue conversations based on the last engagement
  • Build mobile-friendly customer support pages to provide mobile consumers a smooth experience
  • Respond on time to customer queries on all channels and present practical solutions to their needs
  • Create a comprehensive self-service solution to help customers solve their problems by themselves
  • Present a unified front and ensure every team and department collaborate and share information

Today, 95% of customers use three or more channels to connect with a company in a single service interaction, and 65% of customers expressed frustration over inconsistent experiences across channels. Establishing a presence on all available platforms might spread a company’s resources too thin and lead to inconsistent, negative experiences. Instead, focusing on meeting consumers where they are and establishing unified, consistent experiences on their most used channels will create a better overall customer experience.

With Quiq’s cross-channel digital messaging platform, employees can provide a consistent experience for consumers across different digital platforms.

Also, to implement the best customer service strategy, nothing beats working with the right conversational customer engagement platform. Ideally, interacting with consumers will be easy and fun for employees and provide frictionless support for shoppers, while increasing the brand’s competitive advantage, market share, and overall revenue.

Key Takeaways

Improving eCommerce customer service strategies doesn’t have to be complicated—it just requires consistency, the right tools, and a customer-first mindset. Here’s a quick recap of the five strategies covered:

  • Hire and retain the right people. A strong customer service team is your foundation. Invest in hiring, training, and supporting representatives who can deliver real value to your customers.
  • Personalize every interaction. Today’s shoppers expect tailored experiences. Meeting them where they are, remembering their preferences, and offering relevant recommendations all go a long way.
  • Collect and act on feedback. Listening to your customers helps identify areas for improvement and ensures your support is aligned with their expectations.
  • Measure what matters. Tracking KPIs like NPS, CES, and revenue per customer gives you the insight you need to improve and scale your support strategy effectively.
  • Provide a consistent experience across channels. Whether it’s SMS, web chat, or social media, customers expect seamless, responsive support—no matter where they reach out.

When done right, these strategies don’t just improve customer service—they help build loyalty, encourage repeat purchases, and turn satisfied shoppers into brand advocates.

Invest in a Quality Customer Engagement Platform

More than ever, consumers want to do business with brands that make it easy to browse, shop, complete transactions, and get support on the consumers’ terms. eCommerce businesses can gain a significant competitive advantage by providing these experiences through seamless engagement via digital channels.

Improving customer success and satisfaction is a long-term plan that requires buy-in and commitment from management, investment in training employee representatives, collecting feedback consistently, measuring the right things, and investing in a platform to manage everything.

Quiq is a conversational customer engagement platform that enables enterprises to unify SMS, email, chat, and social media interactions with their consumers all in one place. Request a demo to see how Quiq can help employee representatives provide a seamless customer service experience.

11 Live Chat Best Practices for Exemplary Service

Posted on Max Fortis

Don’t deliver good customer service. Aim for the exceptional service that sets you apart from your competition. Customers demand convenience, speed, and ease when they need to engage with a company. When it comes to live chat (also known as web chat), it’s critical to provide an experience that welcomes the customer to engage with your brand.

Live chat serves as your front line to the customer on your website. This messaging channel allows you to engage with your customers at their point of purchase for higher conversions. With live chat, customers can reach your brand at their convenience and receive the pre-sales support or post-sales service they need. This article gives you the 11 live chat best practices to deliver the ultimate customer experience.

What is Live Chat?

Live chat is a messaging tool integrated into a brand’s website, app, or third-party platform that enables instant communication with customers regarding orders, inquiries, or issues. Unlike email or chatbots, live chat provides a more personalized and real-time interaction.

As demand for immediate support increases, live chat software has become a vital solution for brands, allowing them to engage with users instantly. It is commonly used to resolve customer issues, offer after-sales support, and provide quick troubleshooting, all of which contribute to higher customer satisfaction and retention.

11 Live Chat Best Practices

#1 Choosing the Right Live Chat Platform

Choosing the right live chat platform is essential for great customer service. Look for one that offers real-time communication, easy integration, and intuitive design. Features like AI support, analytics, and strong security are also important. Test different platforms to find the best fit for your business needs and customer expectations.

#2: Be Transparent With Your Availability

While larger brands may have a customer support team working 24/7, other businesses may have limited hours. If you’re one of the companies that limits the hours of support, make sure that you simply disable live chat when your team is unavailable or when your company is closed. Quiq’s chat feature allows you to remove the chat bubble on your site during non-supported hours.

If your chat function isn’t available 24/7 and you prefer to receive after-hour messages, tell your customers that you’ve received their message and let them know when you will get back to them.

#3: Collect Information Upfront

Make it easy for employees to provide a more personalized experience to your customers by collecting a little information upfront. Use a short web form to collect information. This information can be used to route incoming conversations to the best queue or employee. Not only that, this extra information will help your employees identify the customer and the nature of their inquiry immediately instead of having to spend valuable time asking for it. Knowing full name, account number, topic category, or order number will help your team know who they are talking to and allow them to get a jump on helping the customer faster.

#4 Balance Personalization and Professionalism

Customers expect live chat interactions to be both personal and professional. Striking the right balance between empathy and professionalism can be challenging, but is key to a positive customer experience.

To help agents achieve this, implement features like skill-based routing to direct inquiries to the most knowledgeable team members. For example, technical support queries can be automatically sent to agents with expertise in the specific product. Additionally, using a unified inbox allows agents to view all customer messages in one place, streamlining responses and ensuring consistency.

Providing agents with complete context—such as customer history and preferences—can also improve personalization. This empowers agents to tailor their responses, maintaining a professional yet empathetic tone throughout the conversation.

#5: Always Be Ready To Respond

Customers want answers fast and at their pace. That’s one of the reasons they’re avoiding the phone and having to be tied to it. With live chat, customers can send messages at their pace, whether they do so in 3 minutes or 3 hours. Companies can set service level agreements (SLA’s) so that everyone understands what an acceptable response time is for customers when they do reach out.

Quiq helps employees meet those SLAs with our Adaptive Response Timer (ART). This feature not only provides visual cues to notify employees when a conversation needs attention, but it also automatically prioritizes multiple conversations based on how slow or fast the customer is responding to messages. This is critical because chat agents tend to handle 5 or more conversations at one time. Staying on top of the right ones is easy with Quiq.

#6: Never Get Disconnected

Your customers are busy and at times, may need to step away from a chat conversation. Sometimes, it’s only for a few minutes while they check another tab on their desktop. At other times, it may be a lot longer. When customers don’t respond after a certain time limit, most chat platforms will “time out” of chat sessions, requiring the customer to initiate a new chat session and start their entire process from the beginning.

Unlike many traditional chat tools, Quiq’s chat platform is asynchronous, which means conversations never end and never have to be restarted. This avoids customer frustration of having to restart a chat conversation and agent uncertainty when a customer goes dark. Customers can return to the chat conversation whenever it is convenient for them. This conversational continuity gives your agents and your customers peace of mind.

#7: Present The Chat Conversation History

Sure, some customers may only need to contact you once, but there are some who need to reach you on a more frequent basis. It’s important that a record is kept of all the past chat interactions you’ve had with a customer. This conversation history serves as an excellent reference point and helps agents or employees know what kind of issues the customer may have encountered previously and the guidance they were given.

Quiq presents the entire chat conversation history to the agent, along with the most recent inquiry. Let’s say a customer starts a conversation with one agent, walks away during the conversation, and comes back while that first agent is on break. The newly assigned agent will have the same latest interaction, as well as past interaction history, presented.

#8: Provide A Seamless Experience

From time to time, one of your employees may not know how to answer a specific question from the customer. So, they will need to transfer the customer to another team member. When this happens, you need to guarantee that the customer doesn’t have to explain her question or problem all over again. The new team member should have access to the previous conversation and simply continue the conversation.

Quiq’s transfer and collaboration features allow employees to ask for help behind the scenes from peers or managers. Customers can be easily transferred to other team members with or without them even knowing. Anyone invited to help with the conversation can see the entire history of the conversation and any additional information available on the customer. These features create a seamless experience for your customers while optimizing efficiency.

#9: Ensure Authentication & Data Security

As online data breaches rise, securing live chat interactions is essential to maintaining customer trust. Sensitive data exchanged through live chat is vulnerable to malicious attacks, which can damage your brand’s reputation. Implementing encryption, two-factor authentication (2FA), and single sign-on (SSO) helps protect this information and assures customers that their data is secure.

Additionally, role-based access controls limit sensitive information to authorized personnel only, preventing unauthorized access. By prioritizing robust data security in your live chat platform, you not only protect your customers but also enhance their confidence in your brand, improving conversion rates and customer loyalty.

#10: Use Sentiment Analysis

Use sentiment analysis to understand how customer conversations are going. This is particularly important for companies that may have a large number of chat conversations to manage. Managers can see at a glance which conversations are going well and which may be at risk.

Quiq uses simple visual cues that identify if customers’ mood shifts during a conversation. Agents and managers can quickly see if a conversation needs extra attention or needs to be prioritized.

#11: Ask For Feedback At The End

Your customers’ feedback or opinion about how the live chat interaction went is definitely a best practice. You trained your team members to provide the best service they could, but the ultimate test will be what your customers think about their overall experience. This is a timely way to know that you’re on the right track, as well as a great way to continuously improve your live chat experience.

Live Chat Is Your Front Line

It wasn’t so long ago that the only way customers could get in touch with a company was by picking up the phone and calling. Now, with live chat and messaging options, customers can simply click-to-chat with a representative who can provide the pre-sales support or post-sales service they need.

Being available to your customers at their “moment of need” is where businesses turn visitors to their website into customers who love their product and service and rave about their experience. Live chat may be one of the first interactions your customers have with anyone from your company. Make sure you leave a great first impression by implementing these 11 live chat best practices.

What Are Agentic AI Workflows?

Posted on John Andersen

Customer expectations have outpaced the capabilities of traditional chatbots. It’s no longer enough to provide canned responses or navigate rigid decision trees. To meet today’s demands, businesses need AI systems that can think through problems, adapt in the moment, and act with context. That’s where agentic AI workflows come in.

Agentic AI workflows are changing how customer interactions happen by putting decision-making power into the hands of AI agents. With the ability to assess context, choose the best course of action, and continuously learn, these workflows go beyond answering questions. They solve problems autonomously.

How Agentic AI Workflows Work

Agentic AI workflows represent a new frontier in how generative AI is applied. Rather than focusing solely on content creation, these systems are designed to reason, make decisions, and take action based on goals and context, what the Forbes Tech Council describes as “the next frontier: in applying GenAI to real-world problem-solving.

Agentic AI workflows refer to a new generation of AI systems that go beyond pre-programmed logic. These workflows involve AI agents that perform complex tasks by:

  • Breaking goals into sub-tasks
  • Making decisions based on context
  • Iterating through tasks using real-time feedback

With traditional automation, you define what should happen and build the system to follow that exact path. Agentic workflows, on the other hand, allow AI agents to choose from a set of available tools or actions and determine the best next step based on the situation using statistical analysis. That ability to choose and to adapt is what makes them truly agentic.

That ability to make context-driven decisions enables AI agents to engage more naturally with customers, even when the conversation goes off-script. It’s about giving them a curated set of tools and letting them determine how best to move forward within defined boundaries.

Key Differences from Traditional AI

  • Static AI follows the rules; Agentic AI decides how to follow them within safeguards.
  • Traditional bots rely on pre-defined flows and intent-mapping; Agentic agents use reasoning to decide the best course of action in context, even in dynamic or multi-intent conversations.
  • Standard workflows follow a tree; Agentic workflows adjust the path based on real-time conditions.

Example of an Agentic AI Workflow

Quiq implemented an agentic AI workflow for a cosmetic services provider. The challenge? Customers entering the chat primarily wanted pricing, while the brand prioritized collecting contact information.

Traditional bots struggled with this tug-of-war. Quiq’s agentic workflow offered a better solution:

  1. Customer asks for pricing
  2. AI agent provides a broad estimate and requests contact details
  3. Customer repeats request
  4. AI Agent gives more specific pricing and again requests information

Each time, the AI agent reevaluates the context, determines how much to disclose, and how to nudge the conversation toward scheduling a consultation, all while honoring business rules.
Another example comes from a major airline using Quiq to manage both voice and digital conversations. Their AI agent can dynamically hand off to humans, escalate when needed, and make decisions on whether to collect additional details or move forward. This reduces frustrating loops and enables smoother experiences.

Key Components of Agentic AI Workflows

According to McKinsey’s 2025 AI report, companies that are scaling AI effectively are more likely to embed agents that “act autonomously and operate across workflows.”

Agentic systems require several core components to work effectively:

  • Autonomous AI Agents: These agents can operate independently but within defined limits.
  • Goal Setting: Every successful agentic workflow starts with a clear goal. Whether it’s answering a question or resolving a complex issue, agents need a target to work toward.
  • Task Decomposition: Once the goal is set, the system breaks it down into smaller steps. That way, agents can focus on one part of the problem at a time, just like people do.
  • Feedback Loops: Agents listen to what users say and adjust their approach accordingly. If something doesn’t work, they try a different path.
  • Adaptive Decision-Making: While the agents themselves don’t autonomously rewrite their logic after each interaction, the system is built for continuous improvement through Human-in-the-Loop (HITL) processes. As explained in MLJourney, HITL ensures that AI systems evolve responsibly by combining automated learning with human review. At Quiq, real-world interaction data is regularly reviewed by experts, who update Process Guides, refine prompts, and fine-tune orchestration to improve performance and reduce risk over time. For true adaptability, workflows should be LLM agnostic — giving teams the ability to swap models as performance or cost factors change
  • Environment Interaction: Agents call APIs, fetch data, and orchestrate responses using tools like Quiq’s AI Studio.

The Process Guide serves as a digital playbook, providing succinct instructions that guide the AI agent through various types of conversations. When combined with company-specific data and the right toolset, it provides the foundation for how agents behave and make decisions in real time.

Flowchart showing how agentic AI breaks a complex goal into sub-tasks, including interpreting intent, selecting tools, and adapting to input
Agentic AI workflows use task decomposition to solve problems through a series of context-driven, autonomous decisions, constantly refined through real-time feedback.

Types of AI Agent Workflows

Agentic workflows aren’t one-size-fits-all. They come in various forms, depending on the use case, business goals, and the level of decision-making required. Some common types include:

  • Conversational AI Agents: These agents handle interactions in voice or chat. Unlike scripted bots, they understand context, manage multi-turn conversations, and adjust based on what the customer says, even if they go off-topic.
  • Multi-Agent Systems: In more complex settings, multiple AI agents—each an expert in a specific business domain—work together to solve a problem. For example, a “Retail Agent” could handle a customer’s initial product questions, then seamlessly hand off the conversation to a “Logistics Agent” to track the package and adjust the delivery. Together, they collaborate in real time to reach a shared goal that spans different business functions.
  • Robotic Process Automation with Agency: RPA has traditionally followed static rules, but when infused with agentic logic, it becomes much more responsive. These agents determine when and how to execute processes based on the current context, rather than just a script.

Benefits of AI Agentic Workflows

According to Gartner’s 2024 Hype Cycle for Artificial Intelligence, agentic AI is nearing the peak of inflated expectations, with enterprise readiness expected within the next 2 to 5 years. This signals growing momentum and urgency for companies to invest in agentic systems now.

Agentic workflows offer CX teams a smarter, more adaptable way to engage with customers:

  • Increased Efficiency: They handle both routine and complex tasks, freeing up your human agents to focus on what really needs their attention.
  • Scalability: As customer demand grows, agentic systems scale with you, eliminating the need to rebuild workflows every time something changes.
  • Improved Accuracy: Over time, agents start to notice patterns. They become better at identifying familiar issues and knowing how to respond, which results in fewer mistakes and more helpful interactions.
  • Faster Resolution: When a customer asks a question, the agent doesn’t just follow a script; instead, the agent provides a personalized response tailored to the customer’s needs. It looks at the full context and decides what to do next, making it easier for people to get what they need without delay.
  • Cost Savings: By automating more intelligently, teams reduce repetitive tasks and free up support staff to focus on more complex, higher-impact work.
  • Better Experience: Conversations feel more natural, responsive, and helpful, not robotic or rigid.

What makes agentic workflows stand out is their flexibility. Instead of sticking to a script, they respond to what the customer is actually saying. If topics change mid-conversation, the AI can adjust its approach and keep moving toward a resolution, just like a good customer service rep would. The AI can also refer to previous topics mentioned in the same conversation. Further, the AI can handle a single request from the user that contains more than one instruction.

Challenges of Agentic AI Workflows

Agentic AI systems introduce powerful capabilities, but they also require thoughtful planning and oversight. Some common hurdles include:

  • Data Availability: Without access to accurate data, even the smartest agent can hit a wall. Data gaps make it harder to respond accurately or take meaningful action.
  • Complex Integration: Connecting AI agents to legacy systems, third-party platforms, and internal tools often requires customized API work and thorough testing.
  • Ethical Oversight: It’s not just about whether an agent can do something; it’s about whether it should be done. Guardrails must be in place to prevent false promises or unintended outcomes.
  • Guardrails and Governance: AI agents require a framework that specifies what is permitted. This includes limits on actions, boundaries for language, and checks on tone and accuracy.
  • Variability in AI Models: Large language models can behave unpredictably. Without frequent calibration and prompt refinement, testing, and monitoring, output quality can drift over time.

To help businesses stay in control, Quiq uses a layered system of checks and balances. Every AI agent response is evaluated before and after it’s delivered to the user, ensuring that only approved, on-brand, and contextually appropriate messages are sent. This provides organizations with confidence that their AI agents act responsibly and remain aligned with business goals through a layered system of checks and balances.

How to Implement Agentic AI Workflows

Rolling out agentic AI workflows doesn’t have to be overwhelming, especially with the right framework in place. At Quiq, implementation centers on clarity, control, and collaboration at every step:

1. Define the Problem: Start by identifying the customer experience gap you aim to solve. This ensures the agent is aligned with actual business needs.

2. Establish Goals: Set measurable success metrics and clear behavioral boundaries for the AI agent to work within. Crucially, define which decisions are safe for the AI to make autonomously and which require human oversight.

3. Design Workflows and Human Touchpoints: Create Process Guides that serve as coaching instructions. These guides must not only direct the AI but also explicitly define the human-in-the-middle interaction points:

    • Escalation Triggers: When should the agent automatically hand off to a human (e.g., customer frustration, complex out-of-scope query)?
    • Approval Gates: What critical actions (e.g., processing a large refund, modifying an account) must a human approve before the AI executes them?
    • Collaborative Inputs: Where should the AI pause and ask a human for a specific piece of information or a judgment call before proceeding?

4. Set Up Data Access: Connect the agent to relevant APIs, real-time customer data, and your company knowledge base so it can take meaningful action.

5. Test Rigorously: Use conversation simulations with defined assertions to validate expected behavior. This phase must include both automated testing and thorough human review to check for nuance, tone, and brand alignment that automated tests might miss.

6. Deploy and Iterate: Once live, monitor performance closely and refine as needed. Regular reviews ensure the agent continues to meet both user expectations and business standards.

Quiq’s AI Studio serves as the orchestration layer behind it all, connecting to any LLM, integrating with your systems, and offering platform-agnostic flexibility. With built-in testing and ethical safeguards, teams can launch with confidence and scale intelligently, integrating with any LLM or custom model and supporting platform-agnostic deployments.

Use Cases Across Industries

Agentic AI workflows can be applied in nearly every industry where real-time decisions, context awareness, and dynamic action matter. Here are a few examples of how different sectors are putting them to work:

  • Customer Service: Agents do more than deflect calls. They resolve tickets, identify customer sentiment mid-conversation, and escalate issues when needed without waiting for human intervention.
  • Retail & eCommerce: From “Where is my order?” inquiries to adjusting delivery addresses and cross-selling accessories, agentic workflows help retailers respond faster, reduce cart abandonment, and personalize experiences.
  • Airlines: Agents handle everything from booking changes to baggage tracking. They can ask clarifying questions, navigate loyalty programs, and escalate only when absolutely necessary across both voice and chat.
  • Healthcare: Patient interactions require a balance of caution and efficiency. Agentic workflows assist with triage, scheduling, and responding to common questions based on historical data, while flagging sensitive cases for human review.
  • Financial Services: AI agents assist with account management, fraud alerts, loan application updates, and more while ensuring all actions stay within compliance frameworks.

In every case, the AI agent isn’t just reacting; it’s analyzing the context, choosing the right tools, and proactively guiding each interaction toward a resolution.

Moving Forward with Agentic AI

Agentic AI workflows represent a meaningful leap forward in automation. Rather than relying on rigid logic, these systems adapt, make decisions, and deliver value in real time.

AI doesn’t have to be intimidating. The best way to begin is by identifying one problem worth solving, building a focused solution around it, and expanding from there.

With Quiq’s orchestration tools, business-ready data, and ethical guardrails, organizations can confidently embrace agentic AI.

Ready to get started?

  • Schedule a Demo: See how agentic workflows operate in real time with Quiq’s AI Studio.
  • Talk to a Solutions Expert: Let us help you identify the highest-impact use case for your business.
  • Explore Quiq’s AI Agents: See how our AI Agents operate autonomously within smart guardrails to solve customer problems quickly and accurately.

Agentic AI: Tangible Use Cases to Bring the Art of the Possible to Life

Posted on Lauren Winder

Gartner recently predicted that agentic AI will automatically resolve 80% of common customer service issues by 2029. Sounds exciting, right? But you may also be asking yourself…’what exactly would this look like?’

The possibilities of agentic AI — a type of AI designed to autonomously execute tasks, make decisions, and take action, all while adapting to evolving conditions in real time — are practically endless. And it’s easy to feel overwhelmed by too many options. In fact, that’s why geniuses like Einstein and Obama prefer to have closets full of the same color clothes!

So rather than letting your imagination run wild with hypothetical scenarios, we’ve decided to highlight a few transformative and totally tangible agentic AI applications used by real CX teams. Because choosing the right agentic AI use case for your CX organization is much more critical than deciding what shirt to wear. Or, at least we think so.

Orchestrate Reward Programs Across Systems

This use case involves an AI agent that can combine knowledge of loyalty program policies with specific customer data from across systems to provide highly personalized service and rewards. It can dynamically automate key decisions and processes to issue credit, offer discounts, upgrade shipping, and more based on a customer’s tenure, rewards card status, loyalty points tier, purchase history, return frequency, etc. Any actions taken or additional data points gathered are recorded in the appropriate systems to help eliminate information silos.

What Makes This Agentic

In addition to offering custom, bi-directional integrations with other CX tools, true agentic AI platforms provide an orchestration layer that guides the conversation flow. The AI agent leverages various Process Guides to dynamically adapt and respond to users’ questions.

These Process Guides provide general instructions for handling particular tasks, while also specifying which tools and knowledge the agent can invoke when appropriate. In this refund scenario, the AI agent can:

  • Look up customer information, including order status and membership level
  • Access knowledge bases containing return and warranty policies
  • Generate a personalized response based on all available context
  • Initiate the return process on the customer’s behalf

Rather than relying on predefined, rigid if/then logic, the AI agent determines which actions to take based on conversational context. Every interaction passes through a series of pre- and post-generation guardrails that combine LLMs, business rules, and other validation mechanisms to ensure the exchange is appropriate, on-brand, accurate, and genuinely resolves the customer’s issue.

Diagnosis and Routing via Image Recognition

In this scenario, the agentic AI agent is used to accurately diagnose product issues and route customers to the appropriate human agents for troubleshooting. The AI agent does this by leveraging sophisticated image recognition and combining it with both general product and account-specific information gathered during the conversation and from across other systems.

What Makes This Agentic

Not only does this AI agent use the communication and reasoning power of LLMs to fully comprehend language and users’ inquiries, but it also leverages image processing as part of its Process Guide. It’s similar to how human technicians are taught to know when to ask for an image and look for visual cues to accurately diagnose an issue. The AI agent can then factor this knowledge into the context of the conversation and take the appropriate next step by escalating the customer to the right human agent.

The best part? All the information the AI agent collects over the course of the conversation, including the image and data accessed from other systems, is passed to the human agent at the time of escalation. In addition to receiving an AI-generated summary, the human agent is also able to read back over the full conversation in detail. This seamless handoff ensures the customer never has to repeat themselves, and significantly accelerating time to resolution.

Proactive Service Recovery

Using this agentic AI service enables travel and hospitality companies to detect customer patterns that typically lead to negative experiences based on historical data, and proactively address them to improve guests’ experiences. For example, imagine a guest arrives late, the hotel restaurant is closed, and the pool cabana they want is sold out the next day.

A front desk employee may receive an automated email alert flagging these issues due to the effects they had on previous guests. To proactively improve this customer’s experience and increase the chances they will re-book with the hotel in the future, it’s recommended they bring a bottle of wine and hand-written thank you note up to the guest’s room. Or, the agentic AI service could automatically address these concerns by text messaging a coupon for a free wine bottle to the guest directly.

Example Agentic AI Workflow

Discover More Agentic AI Use Cases

Did you find these agentic AI use case examples insightful? We’re just getting started! Our latest guide is meant to help bring the art of the possible to life by exploring:

  • Agentic AI applications across three key industries: Retail (including eCommerce), Travel & Hospitality, and Consumer Services
  • Nine increasingly complex use cases, including a review and feedback analysis engine, cross-channel service orchestration, and many more
  • Real success stories from a top furniture retailer and other early adopters of agentic AI for CX

Download now