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Month: April 2025

7 Effective Ways to Communicate with Customers

Posted on Cristina Bravo-Olmo

Key Takeaways

  • Personalize every message: Reference customer history to show understanding.
  • Stay consistent across channels: Tone and timing should reflect your brand voice everywhere.
  • Balance automation with empathy: Use bots for speed but humans for nuance.
  • Measure and adapt: Track sentiment and resolution metrics to refine communication.
  • Build a communication-first culture: Every department should speak the same customer-centric language.

When customer service is at its busiest, it might be time to get back to the basics: Your team’s ability to effectively communicate with customers.

What does customer communication entail? A lot more than you might think. From error messages and initiating returns with customer service to marketing emails and social media, it’s all a part of the customer experience.

Knowing how to communicate with customers is essential to your customer service team. In this article, we will discuss strategies that will help you enhance your customer communication and overall workplace operations.

Why Does Communication With Customers Matter?

Customer communication matters because it’s at the heart of every great experience. It’s how people get to know your brand, decide if they can trust you, and ultimately choose whether to stick around. When you respond quickly, clearly, and with a human touch, customers feel heard and valued. That kind of connection builds trust and turns simple interactions into lasting relationships, bringing customers back to your brand and product.

But great communication isn’t just about replying fast—it’s about being thoughtful and proactive. Today’s customers expect you to understand their needs and meet them where they already are, no matter the channel. When your communication feels easy and consistent, everyone wins. Customers stay happier, your team becomes more efficient, and every conversation has the potential to drive real business growth impact.

7 Strategies to Improve Customer Communication

Improving customer communication isn’t about doing more—it’s about doing it better. From the tools you use to the tools you use to the tone you set, every interaction is an opportunity to build trust and loyalty. Here are seven practical strategies to help your team communicate more clearly, efficiently, and authentically with every customer.

1. Start With Simple Communication Skills

Even with advanced technology, effective communication begins with timeless fundamentals. No matter the channel, these core techniques set the tone for trust and clarity.

Active Listening Techniques When You Communicate with Customers

  • In-person: Nod when appropriate. Smile or show concern based on the conversation, and make eye contact throughout the discussion.
  • Over the phone: When you can’t make gestures to show you’re listening, give the person on the other end auditory cues. Use phrases like “I understand” or try repeating back what they just said.
  • Over messaging: This can be a little trickier with asynchronous messaging. Customers who employ messaging to resolve a problem often want it solved quickly without much back-and-forth conversation. In this scenario, the best way to show you’re listening is by responding quickly and addressing the problem head-on.

Mirror Your Customer’s Communication Style

  • In-person: Rely on body language and facial expressions to tell you how your customer is feeling. Are they fidgety and in a hurry? Are they relaxed and looking for some small talk? Show your customer you understand them by taking their visual cues and using them to determine whether to give quick, to-the-point answers or spend time chatting about the weather. You can also mirror body language as a way to connect with your customer—just don’t go too overboard. You don’t want to make them feel uncomfortable.
    Woman texting customer service
  • Over the phone: Take your cues from the tone of their voice. If they’re leisurely answering your initial questions (and peppering in some jokes), but you try to rush them through the conversation, that’ll leave them feeling unsatisfied.
  • Over messaging: Are they writing formally or using shorthand and emojis? Try to match your tone to theirs. Find a balance between the brand communication style and your customers’ expectations to make the strongest connection.

Show Patience

Especially during busy seasons, patience builds goodwill. Let customers finish explaining before jumping to a solution. Whether customers are coming to you angry and upset or coming to you confused and in need of direction, it’s important to show understanding. Don’t interrupt, and wait until they’ve completed their thought before jumping in with the solution. Patience will help you solve your customers’ problems and win them over time and time again.

Practice Conflict Resolution

  • Stay calm and validate feelings.
  • Focus on solutions, not blame.
  • Use team training to practice these skills before high-stress moments occur.

These fundamentals build confidence, loyalty, and trust—both within your team and with your customers. When agents know how to navigate tense situations with empathy and composure, customers walk away feeling respected and supported, even when the outcome isn’t perfect. Over time, this approach turns potential conflicts into moments that strengthen relationships rather than strain them.

2. Get proactive with outbound communications

Many businesses think customer service involves waiting around until customers come to them with a problem. The problem with this type of reactive service? Customers are already facing an issue when they come to your team. They may be agitated, upset, disappointed, and filled with tons of other unpleasant emotions by the time they ask for help. Not only does this make it more difficult for your customers to communicate with you, but it also creates a poor impression of your brand that can have long-lasting effects.

A text message interaction between customer service and a customer on an iPhone

Instead, get proactive with your customer service by using outbound communications.

Getting in front of a customer service issue before it happens can be as straightforward or as complex as your business dictates.

Here are some examples of how you can solve a customer’s problem before it happens:

  1. Software-as-a-service products can include helpful tips to direct customers on how to use the product.
  2. E-commerce retailers can send an SMS message with a track-my-package link to reduce inbound “Where’s my order?” calls.
  3. An online business can send out a notification for planned website maintenance.

And customers appreciate proactive service. 

Being proactive reduces inbound issues and strengthens customer trust before problems arise.

3. Establish a Consistent Brand Voice Across Channels

Customers interact with brands across a variety of channels, whether through chat, email, SMS, or social media, and they expect a consistent experience every time. Inconsistent messaging or tone can quickly break trust and create confusion, making it harder for customers to connect with your brand.

Why Consistency Matters

Consistency reinforces identity, builds trust, and creates familiarity. When your tone and message align across every channel, customers know what to expect, which builds loyalty.

How to Create Consistency

  • Develop a brand voice guide with tone, vocabulary, and do’s/don’ts.
  • Train all customer-facing teams (support, sales, social).
  • Use tools like Quiq to apply consistent templates and tone settings.

Consistency doesn’t just look professional, it ensures customers always feel connected to your brand.

4. Know When to Automate vs. When to Humanize

Not all customer communication requires a human touch, but not everything should be automated either. Striking the right balance is essential to providing fast and efficient service without sacrificing empathy.

When to Automate

  • Routine tasks (order updates, password resets, appointment reminders)
  • After-hours FAQs or triage workflows
  • Simple follow-ups or satisfaction surveys

Automation improves efficiency and availability. Tools like Quiq’s AI-powered Agentic AI make it easy to automate these interactions without compromising customer satisfaction.

When to Humanize

  • Complex, emotional, or high-stakes issues
  • Billing questions, cancellations, complaints, or onboarding
  • Scenarios that require empathy or nuanced understanding

Why This Matters

Over-relying on automation can frustrate customers who need empathy or more personalized assistance. By thoughtfully combining automation and human interaction, you can deliver both speed and satisfaction, ensuring a seamless experience in all forms of customer communication.

5. Enhance Communication Through Personalization and Measurement

Customers appreciate clarity and empathy, but they expect personalization.

Personalization

Go beyond first names. Use data to tailor conversations:

  • Reference past interactions (“I see you contacted us about your return last week …”).
  • Suggest solutions based on purchase history.
  • Time outreach around relevant events or renewals.

Measurement

Track communication performance through these metrics:

  • Sentiment analysis
  • First-contact resolution (FCR)
  • Customer Effort Score (CES)

Use surveys, feedback loops, or AI text analysis to identify friction and improve tone and timing.

Culture of Purpose

Empower every team member from marketing to support to communicate with intention. When everyone understands the “why” behind your brand’s message, every interaction feels genuine and aligned. A shared sense of purpose ensures customers get consistent, thoughtful communication no matter who they’re speaking with. Over time, this creates trust, strengthens brand loyalty, and turns everyday conversations into meaningful connections that reinforce your company’s values.

6. Empower Your Agents to Communicate with Customers

Since your customer service agents are at the frontline of customer communications, it’s important to empower them with everything they need to be successful when they communicate with customers.

Give Agents Access to Information

Agents can only deliver great communication when they have the right information at their fingertips. Equip your team with a centralized knowledge base, customer history, and real-time context so they can respond quickly and confidently. When agents don’t have to dig for details or wait on other departments for answers, they can focus on what matters most

Redefine Agents Role

AI has transformed what it means to be a customer service agent. Instead of spending time on repetitive questions or transactional tasks, agents can now focus on the conversations that really matter—those that require critical thinking, empathy, and human connection. With AI managing routine inquiries, routing conversations, and even suggesting next-best responses, your team is free to handle complex or relationship-driven interactions like upselling, retention, or pre-purchase guidance.

This is where Agentic AI takes communication to the next level. By working alongside human agents, Agentic AI doesn’t just automate—it collaborates. It provides real-time insights, surfaces relevant data, and helps agents understand customer intent faster. The result is a team that’s more informed, more confident, and more capable of delivering meaningful, high-value conversations that strengthen relationships and drive revenue.

7. Build a Culture of Effective Customer Communication

The customer isn’t always right, but they should always be heard. Creating a culture of communication starts with how your team listens, responds, and learns from every interaction. Encourage employees at every level to see communication not as a task, but as an opportunity to understand your customers better.

Create Clear Expectations

Set standards for tone, response time, and ownership across every department to ensure every customer interaction feels consistent and aligned with your brand’s values. When everyone knows what “good communication” looks like, customers experience your company as one cohesive, trustworthy voice instead of a collection of disconnected departments.

Train and Reinforce

Offer workshops, peer reviews, and feedback sessions to maintain strong communication habits and keep your team aligned as expectations evolve. These touchpoints create space for continuous learning—allowing agents to share best practices, refine tone and empathy skills, and stay confident when handling new challenges. Regular training also reinforces your company’s commitment to growth and consistency, ensuring that great communication becomes a lasting part of your culture, not just a one-time initiative.

Align Teams Around the Same Goal

From product to marketing, make sure everyone understands that how they communicate directly impacts how customers perceive your brand. When internal teams align on messaging, tone, and responsiveness, customers experience a consistent, trustworthy brand voice at every touchpoint. This shared understanding turns communication from a departmental task into a company-wide commitment to excellence.

Power Your Customer Service Team with Quiq

Effective communication starts with empowered teams, and Quiq makes that possible. With our Agentic AI solution, your agents can engage customers seamlessly across every messaging channel while maintaining the consistency and empathy that drive lasting loyalty. Quiq connects your CRM, knowledge base, and workflows so every conversation is informed, efficient, and on-brand.

The result? Faster resolutions, stronger relationships, and communication that feels effortless for both your agents and your customers. Learn more about how to implement agentic AI and what to look for in our free buyer’s kit.

Frequently Asked Questions (FAQs)

What are the 5 C’s of communication skills?

The 5 C’s of communication are clarity, consistency, creativity, confidence, and connection. Clear messaging helps customers understand you. Consistency builds trust. Creativity keeps conversations engaging. Confidence reassures customers that they’re in good hands. And connection, rooted in empathy, turns one-time interactions into lasting relationships. Together, these principles form the foundation of effective customer communication.

What is the best way to talk to customers?

The best way to talk to customers is with clarity, empathy, and intent. Meet them where they are, whether that’s through chat, SMS, or social messaging, and keep your tone conversational and human. Listen actively, personalize responses, and focus on resolving issues rather than following scripts. Tools like Quiq’s Agentic AI make this easier by giving agents the context, data, and AI support they need to respond quickly and thoughtfully.

How does Agentic AI help customer communication?

Agentic AI transforms communication by acting as a true partner to human agents. Instead of just automating tasks, it collaborates by understanding context, predicting intent, and suggesting next-best responses in real time.

What are the benefits of good customer communication?

Good communication builds trust, loyalty, and efficiency. When customers feel heard and understood, they’re more likely to stay engaged and advocate for your brand. Internally, it helps teams work faster and with fewer errors, improving metrics like response time, CSAT, and NPS. In short, strong communication isn’t just good service, it’s good business.

How to measure customer communication?

You can measure customer communication through both quantitative and qualitative metrics. Key indicators include response time, resolution rate, customer satisfaction (CSAT), Net Promoter Score (NPS), and sentiment analysis. You can also analyze conversation quality: tone, empathy, and clarity to understand how your communication impacts customer perception. With Quiq, these insights are built into the platform, helping teams continuously improve how they connect and communicate.

AI Benchmarking Best Practices: A Framework for CX Leaders

Posted on Greg Dreyfus

Key Takeaways

  • Effective AI benchmarking converts your AI from a “black box” into a measurable asset – it helps prove value, spot gaps, and guide improvements.
  • Benchmark at multiple levels (internal, competitive, industry, and customer) using operational, customer-experience, financial, and AI metrics.
  • Key metrics include AI deflection/containment rate, average handle time (AHT) reduction, first-contact resolution (FCR), CSAT lift, cost-to-serve reduction, and ROI.
  • Benchmarking must be iterative: review and update metrics regularly, ground AI responses in real data, and guard against data inconsistency, bias, and hallucinations.

Is your AI investment delivering provable value, or is it still operating like a black box?

In today’s rapidly evolving customer experience (CX) landscape, where Artificial Intelligence (AI) promises transformative results, like decreasing service costs by up to 30% and yielding an average ROI of $1.41 for every dollar spent, simply implementing AI isn’t enough. You need to measure its impact. AI benchmarking holds the key.

Effective AI benchmarking is critical for evaluating progress, sustaining momentum, and refining your AI initiatives. By comparing performance internally and against industry standards, organizations ensure their strategies are competitive, effective, and aligned with evolving customer expectations. Robust benchmarking also builds credibility by quantifying success and providing a clear narrative for stakeholders. This is vital, as industry projections suggest AI could handle a significant majority of customer interactions, potentially between 70% (per Gartner) and 95% by 2025.

This article cuts through the complexity to deliver actionable AI benchmarking strategies specifically designed for CX professionals who need to demonstrate tangible results. Whether you’re just beginning your AI journey or looking to optimize existing implementations, you’ll learn how to develop an AI benchmarking framework aligned with your strategic goals. I’ll walk you through selecting the right metrics, establishing meaningful baselines, and creating a continuous improvement cycle that drives CX excellence. By the end, you’ll be equipped with practical tools to quantify AI’s impact, turning data into compelling narratives that secure stakeholder buy-in and position your organization as a CX leader. Let’s get started.

The Role of Benchmarking in Al-Driven CX

AI benchmarking goes beyond measuring outcomes; it establishes a clear context for performance. It highlights where AI initiatives deliver value and identifies gaps that require attention. In an era where AI investment is accelerating (98% of leaders plan to boost AI spending in 2025), benchmarking is vital for several reasons:

  • Identifying Best Practices: Learning from internal successes or external examples to guide future improvements.
  • Gaining Buy-In: Demonstrating progress and ROI with data-driven insights helps secure support from leadership and operational teams.
  • Driving Innovation: Comparing results against industry leaders inspires new strategies and reinforces a commitment to continuous improvement.

Understanding why AI benchmarking matters sets the stage. Now, let’s look at what top performance actually looks like in the current landscape.

What Good Looks Like in 2025

Based on current AI benchmarks and successful implementations, “good” Al-powered CX in 2025 isn’t just about isolated metrics. It’s about a holistic transformation that delivers significant, measurable value across the board. Here’s a snapshot:

Substantial Automation & Efficiency

Leading organizations achieve high AI Deflection Rates, with virtual agents fully resolving significant portions of inquiries without human intervention. Reported rates vary widely based on industry and use case, often ranging from 43% to over 75%.

This translates to significant reductions in Average Handle Time (AHT), sometimes resulting in 5x faster resolutions, and major Agent Productivity gains, often between 15-30%. Operational costs see marked decreases, potentially reaching the significant levels mentioned earlier.

Enhanced Customer Experience

Critically, efficiency gains do not come at the expense of satisfaction. Top performers maintain or even improve CSAT scores, often seeing lifts like Motel Rocks’ 9.44 point increase or Quiq clients like Accor achieving 89% CSAT. This is achieved through faster responses, 24/7 availability, increased personalization, and effective Human-Al Orchestration, ensuring empathy for complex issues. Improved First Contact Resolution (FCR) is key, with reductions in repeat contacts of 25-30% reported.

Tangible Business Outcomes & ROI

Success is measured in clear financial terms. Organizations demonstrate strong ROI, often reaching the average levels noted earlier, and achieve significant cost savings (Gartner projects $80 billion globally by 2026). Furthermore, Al is leveraged for revenue growth through Conversational Commerce, turning service interactions into sales opportunities, as seen with Klarna projecting $40M in additional profit, or Quiq clients attributing 10% of daily sales to chat.

Strategic & Integrated Approach

Excellence involves strategically deploying Al within asynchronous messaging channels (SMS, web chat, etc.) favored by customers. It requires robust Al Governance, seamless integration with existing systems, continuous iteration based on data, and commitment to agent training.

Leveraging Advanced, Accurate Al

Successful implementations increasingly use sophisticated conversational Al, often incorporating Large Language Models (LLMs) enhanced with techniques like Retrieval-Augmented Generation (RAG) for factual accuracy grounded in company knowledge. Agent-Assist tools are widely used to empower human agents.

“In essence, ‘good’ in 2025 means Al is deeply embedded, driving efficiency, enhancing customer satisfaction, delivering clear financial returns, and strategically positioning the organization for future innovation…” – Greg Dreyfus, Head of Solution Consulting at Quiq

Achieving this level of success requires a structured approach to measurement. Let’s look at the different ways you can benchmark your progress.

Types of AI Benchmarking

Internal Benchmarking

Focuses on comparing Al-driven performance within the organization to establish a baseline and track improvements over time.

  • Example: Compare resolution times and CSAT scores for Al versus human-handled inquiries.
  • Benefits: Highlights immediate wins, uncovers inefficiencies, and ensures alignment with goals.

Competitive Benchmarking

Involves comparing your organization’s metrics against direct competitors.

  • Example: Evaluate how your Al adoption impacts NPS or cost-per-interaction relative to others in your sector.
  • Benefits: Identifies competitive gaps or advantages, informs positioning strategies.

Industry Benchmarking

Assesses performance against general industry standards and best practices.

  • Example: Use analyst reports to compare your productivity gains (e.g., aiming for the 15-30% range) with sector leaders.
  • Benefits: Provides a macro view, uncovers broad trends for innovation.

Customer-Centric Benchmarking

Focuses on measuring outcomes that directly impact customer perceptions and loyalty.

  • Example: Compare Customer Effort Scores (CES) before and after implementing Al.
  • Benefits: Ensures CX initiatives genuinely improve the customer experience.

With these benchmarking types in mind, how do you build a practical framework for your organization?

Building an Al Benchmarking Framework

1. Establish Al Governance & Define Scope (Foundation)

Before deploying Al widely, create a clear Al Governance framework. Assemble a cross-functional team (CX, IT, Legal, Compliance) to define responsible usage policies, ethical guardrails, and risk protocols. Determine which metrics are most relevant to your goals and tie them to business outcomes like cost reduction, revenue growth, or retention.

2. Set Benchmarks at Multiple Levels

Establish benchmarks evaluating:

  • Operational Impact: FCR, Deflection Rate, AHT, Agent Productivity.
  • Customer Impact: CSAT, NPS, CES, Churn.
  • Financial Impact: ROI, Cost Savings, Revenue Influence.
  • AI Agent Mechanics: Evaluate core components like routing accuracy (did the right skill get called?), skill/tool correctness (did the skill/tool execute properly?).

3. Leverage Tools and Technology

Use appropriate tools to gather and analyze data efficiently. This includes:

  • Analytics Platforms: To track KPIs and visualize trends.
  • Customer Feedback Tools: For CSAT, NPS, CES surveys.
  • CX Automation Platforms (like Quiq): That often have built-in reporting and facilitate AI deployment, especially in asynchronous messaging channels.
  • Ensure robust integration with existing systems (CRMs, order management, etc…) to avoid data silos and enable personalized experiences.

4. Regularly Review and Update Benchmarks

Metrics and goals must evolve as AI capabilities mature. Schedule regular reviews (e.g., quarterly) to assess performance and adjust strategies. Stay current with industry reports, as benchmarks change rapidly.

Take our free AI readiness assessment to discover where you are on the AI maturity path.

Now that the framework is outlined, let’s dive deeper into the specific metrics you should be tracking, along with current industry benchmarks.

Key Metrics for Al Benchmarking in CX (with 2024-2025 Benchmarks)

Here are top metrics across key categories, updated with recent industry benchmarks:

1. Al Performance & Adoption Metrics

  • Al Deflection / Containment Rate: Percentage of inquiries handled or fully resolved by Al without human intervention.
    • Benchmark: Highly variable based on industry, use case complexity, and AI maturity.
      • Commonly reported rates range from 43% (e.g., Motel Rocks) up to 70-75% for specific sectors (e.g., AirAsia, some telcos).
      • For routine, high-volume tasks, AI may handle up to 80%.
      • Top-performing implementations can achieve even higher containment, such as Quiq client BODi® reporting 88%.
  • Self-Service Resolution Rate: Percentage of customer issues fully resolved via AI self-service without any human agent involvement.
    • Benchmark: Varies; examples include Sony at 15.9% and Quiq client Molekule achieving a 60% resolution rate for interactions handled via self-service AI. Industry average projections evolve (e.g., ~20% now, projected higher).
  • Agent Assist Utilization: Frequency agents leverage Al tools. Crucial for measuring adoption of augmentation tools.
  • Al Adoption / Interaction Handling: Percentage of total interactions involving Al.
    • Benchmark: Projected Al handling 70% (Gartner) to 95% of interactions by 2025.
  • Task Convergence / Reliability: Measures the consistency and predictability of the AI agent in completing a specific task within an expected number of steps or interactions. High convergence indicates a more reliable and less error-prone process.

2. Efficiency Metrics

  • Average Handle Time (AHT) Reduction: Decrease in average interaction time.
    • Benchmark: 25-30% range reported. Specifics: 27% (Agent Assist), 30% (Republic Services), 33-sec absolute drop (Camping World), 5x faster resolution (Klarna).
  • Agent Productivity Gain: Increase in agent efficiency (e.g., inquiries/hr).
    • Benchmark: Avg. 15-30% from GenAl. Agents using Al: +13.8% inquiries/hr. Camping World: +33% efficiency. Quiq client (National Furniture Retailer): 33% fewer escalations.
  • First-Response Time (FRT): Speed of initial reply. Al excels here for instant answers.
  • Escalation Rate: Percentage of Al interactions needing human help. Depending on the use case, lower is better however some use cases require human escalation.

3. Customer Experience Metrics

  • First-Contact Resolution (FCR): Percentage issues resolved on first interaction.
    • Benchmark: AI contributes significantly to improving FCR by reducing repeat contacts.
    • Examples of FCR Improvement: Klarna reported 25% fewer repeat inquiries (effectively a +25% FCR impact); Republic Services saw 30% fewer repeat calls.
    • Note: This differs from AI-specific resolution rates. For instance, while Quiq client Molekule achieved a 60% AI self-service resolution rate for the contacts handled by AI, the impact on overall FCR depends on the percentage of total contacts handled by AI.
  • CSAT Lift / Score: Change in customer satisfaction.
    • Benchmark: Often maintained or improved. Klarna: Parity with humans. Motel Rocks: +9.44 points. Any Al use: +22.3% lift avg. Quiq Clients: Accor (89%), BODi® (75%), Molekule (+42% lift).
  • Customer Effort Score (CES): Measures ease of resolution. Lower effort = higher loyalty.
  • Net Promoter Score (NPS): Likelihood to recommend.

4. Financial Metrics

  • Cost Per Contact / Cost-to-Serve Reduction: Decrease in interaction handling cost.
    • Benchmark: Reductions align with AI’s potential for significant operational savings, potentially reaching up to the 30% mark mentioned previously. Gartner projects $80B projected savings globally by 2026.
  • Return on Investment (ROI): Financial return from Al investment.
    • Benchmark: As highlighted earlier, the average ROI often reaches $1.41 per $1 spent, with 92% of early adopters seeing positive ROI.
  • Revenue Influence / Conversational Commerce: Added revenue via Al assistance.
    • Benchmark: Klarna: Projected +$40M profit. Retailers: 5-15% conversion lift. H&M: Higher AOV. Quiq clients: Accor (2x booking click-outs), National Furniture Retailer (10% daily sales via chat).

5. Operational Metrics

  • Error Reduction Rate: Decrease in mistakes vs. manual processes.
  • Training Time Reduction: Faster onboarding with Al tools.
  • Knowledge Creation Efficiency: Speed of turning interactions into reusable knowledge.

While these results are impressive, achieving them requires navigating potential pitfalls. Let’s examine the common challenges.

Common Challenges in Al Benchmarking and How to Overcome Them

While the benefits are clear, organizations face hurdles:

1. Accuracy and “Hallucinations”

  • Challenge: Generative Al can sometimes produce incorrect answers.
  • Solution: Implement RAG to ground Al responses in verified knowledge; use hybrid approaches; ensure human oversight.

2. Lack of Consistent Data

  • Challenge: Comparing performance requires standardized data collection.
  • Solution: Develop uniform data practices; use centralized dashboards; ensure robust integration with existing systems (CRM, etc.).

3. Bias and Fairness

  • Challenge: Al models can perpetuate biases.
  • Solution: Use diverse training data; continuously monitor outputs via observability (clear box); establish clear ethical guidelines; ensure human oversight.

4. Data Privacy and Security

  • Challenge: Al often needs sensitive data, increasing risks.
  • Solution: Ensure strict compliance (GDPR, CCPA); anonymize data; vet vendors; work with legal teams.

5. Benchmarking in a Rapidly Changing Landscape

  • Challenge: Benchmarks quickly become outdated.
  • Solution: Stay connected with analyst reports; update benchmarks regularly; focus on continuous improvement relative to your baseline.

6. Balancing Internal and External Comparisons

  • Challenge: Internal focus may miss competitive shifts.
  • Solution: Use internal benchmarks for initial wins; incorporate external insights as Al matures.

7. Change Management & Skills Gap

  • Challenge: Implementing Al requires organizational change and new skills.
  • Solution: Communicate clearly; invest in agent training/upskilling (empathy, complex problem-solving); position Al as augmentation; address job fears proactively.

8. Evaluating Multimodal Interactions:

  • Challenge: Benchmarking AI that handles complex interactions involving voice, visuals, or other modalities requires specific metrics and approaches beyond text-based analysis (e.g., audio chunk analysis for voice agents).
  • Solution: Develop modality-specific evaluation criteria; ensure benchmarking tools can capture and analyze multimodal data; maintain focus on the overall user experience across modalities.

Download our comprehensive, 102-page guide on AI change management, AI-Ready CX: A Leader’s Guide for Change, Adoption, and Impact. Get the guide >

Continuous Improvement and Outcome-Based Optimization

Benchmarking is not a static report card; it’s a dynamic tool for driving ongoing refinement. Furthermore, consistent evaluation at multiple levels serves as a crucial diagnostic tool, enabling teams to more effectively debug issues and pinpoint root causes when performance deviates from expectations. Organizations must move beyond measurement to action. This involves:

  • Regularly analyzing gaps between current performance and benchmarks.
  • Establishing feedback loops: Use analytics, customer surveys, and agent input.
  • Iterating continuously: Use insights to update AI training, rules, and workflows. Treat AI as a product that requires ongoing improvement.
  • Focusing on outcomes: Evolve measurement beyond operational metrics to track key business outcomes (CSAT, LTV, retention, revenue).
  • Engaging cross-functional teams (including an AI governance team) to implement changes and oversee evolution.

Strategic Recommendations for CX Leaders

Based on 2024-2025 trends and AI benchmarks, consider these strategic steps:

  1. Prioritize Asynchronous Messaging Channels (0-6 Months Start): Embrace channels like web chat, SMS, WhatsApp, etc., where customers prefer to interact and Al integrates effectively. [Impacts: CSAT, Agent Productivity, Deflection Rate]. Quiq specializes in optimizing these channels.
  2. Implement Al Agent Deflection for Tier-1 (0-6 Months Start): Focus Al automation on high-volume, low-complexity inquiries first to achieve quick ROI and free up human agents. [Impacts: Deflection Rate, Cost Per Contact, AHT].
  3. Leverage Agent-Assist Tools (6-12 Months+): Augment human agents with Al suggestions, knowledge surfacing, and task automation. [Impacts: AHT, Agent Productivity, FCR, Training Time].
  4. Master Human-Al Orchestration (Ongoing): Design seamless handoffs between Al and humans, ensuring context is preserved. Define clear escalation rules. [Impacts: CSAT, FCR, Agent/Customer Experience]. Quiq’s platform excels at this.
  5. Invest in Data Integration & Agent Training (Ongoing): Break down data silos for a unified customer view. Upskill agents for complex issues and Al collaboration. [Impacts: Personalization, Agent Effectiveness, CSAT].
  6. Explore Conversational Commerce Responsibly (Ongoing): Use Al to offer relevant recommendations during service interactions, prioritizing problem-solving first. Track conversion and sentiment carefully. [Impacts: Revenue Influence, AOV, CSAT (if done well)]. Quiq supports this blend.
  7. Stay Ahead of Technology (Ongoing): Keep an eye on advancements like RAG for accuracy and Agentic Al for future autonomous task handling. [Impacts: Future-proofing, Accuracy, Advanced Automation].

The Path Forward

Implementing robust AI benchmarking is about embedding a culture of data-driven decision-making and continuous improvement within your CX organization. By setting clear goals, leveraging the right metrics, learning from both internal and external examples, and strategically applying AI through platforms designed for effective orchestration like Quiq, CX leaders can move beyond the hype.

You can demonstrate significant value, enhance customer loyalty, contain costs, and ultimately, drive tangible business results in the evolving landscape of AI-powered customer experience. The time to measure, refine, and prove the impact of your AI strategy is now.

Frequently Asked Questions (FAQs)

What is AI benchmarking?

AI benchmarking is the process of measuring your AI system’s performance against internal goals, industry standards, or competitors. It helps you understand how well your AI is performing and where to improve.

Why is AI benchmarking important?

Benchmarking ensures your AI investments deliver measurable value. It identifies performance gaps, validates ROI, and guides optimization efforts to improve efficiency, accuracy, and customer experience.

What metrics are used to benchmark AI performance?

Common AI benchmarking metrics include deflection rate, containment rate, first-contact resolution (FCR), average handle time (AHT) reduction, customer satisfaction (CSAT) lift, and cost-to-serve improvements.

How often should AI performance be benchmarked?

AI performance should be reviewed regularly to capture changes in customer behavior, technology updates, or new business priorities.

What are the biggest mistakes to avoid when benchmarking AI?

The most common mistakes include using inconsistent data, ignoring bias or hallucinations in AI responses, and failing to adjust benchmarks as systems evolve.

How does AI benchmarking improve ROI?

By tracking operational and customer-experience metrics, benchmarking shows how AI contributes to faster resolutions, lower costs, and better customer satisfaction – directly tying performance to ROI.

What’s the difference between internal and external benchmarking?

Internal benchmarking compares performance over time within your organization, while external benchmarking measures your results against competitors or industry leaders.

Citations List

  1. “61 AI Customer Service Statistics in 2025.” Desk365.
  2. “Snowflake Research Reveals that 92% of Early Adopters See ROI from AI Investments.” Snowflake.
  3. “Generative AI in Customer Experience: Real Impact, Key Risks, and What’s Next.” Conectys.
  4. “Future of AI in Customer Service: Its Impact beyond 2025.” DevRev.
  5. “Call Center Reporting: Your Definitive Guide (2025).” CloudTalk. 
  6. “Elevating Customer Support in Healthcare.” Alvarez & Marsal.
  7. “Call Center Performance Metrics Examples for Success.” Call Criteria. 
  8. “Key Benchmarks Should You Target In 2025 for your Contact Center.” NobelBiz. 
  9. “10 Call Center Metrics to Track in 2025.” Call Criteria. https://callcriteria.com/call-center-metrics-2/
  10. “What Call Center Benchmarks Should You Target In 2025?” Nextiva. 
  11. “AI in Customer Service Statistics [January 2025].” Master of Code.
  12. “Superagency in the workplace: Empowering people to unlock AI’s full potential.” McKinsey.
  13. “5 AI Case Studies in Customer Service and Support.” VKTR.
  14. “The Evolving Role of AI in Customer Experience: Insights from Metrigy’s 2024-25 Study.” Metrigy.
  15. “Customer experience trends 2025: 6 analysts share their predictions.” CX Dive.
  16. “AI in Customer Service Market Report 2025-2030.” GlobeNewswire.
  17. “5 AI in CX trends for 2025.” CX Network.
  18. “How organizations are leveraging Generative AI to transform marketing.” Consultancy-ME.
  19. “IT and Technology Spending & Budgets for 2025: Trends & Forecasts.” Splunk.
  20. “How AI is elevating CX for financial services firms in 2025 and beyond.” CallMiner.
  21. “The Top 14 SaaS Trends Shaping the Future of Business in 2025.” Salesmate.
  22. “Real-world gen AI use cases from the world’s leading organizations.” Google Cloud Blog.
  23. “Phocuswright’s Travel Innovation and Technology Trends 2025.” Phocuswright.
  24. “50+ Eye-Popping Artificial Intelligence Statistics [2025].” Invoca.
  25. “51 Latest Call Center Statistics with Sources for 2025.” Enthu AI.
  26. “Artificial Intelligence Archives.” FutureCIO.
  27. “NLP vs LLM: Key Differences, Applications & Use Cases.” Openxcell.
  28. “LLM vs NLP: Understanding The Top Differences in 2025.” CMARIX.
  29. “Compare Lunary vs. Private LLM in 2025.” Slashdot.
  30. “Five Trends in AI and Data Science for 2025.” MIT Sloan Management Review.
  31. “Five Trends in AI and Data Science for 2025 From MIT Sloan Management Review.” PR Newswire.
  32. “5 Tech Trends to Watch in 2025.” Comcast Business.
  33. “Top 2025 Trends in Customer Service.” Computer Talk.
  34. “AI Governance Market Research 2025 – Global Forecast to 2029.” GlobeNewswire.
  35. “The state of AI: How organizations are rewiring to capture value.” McKinsey.
  36. “The 2025 CX Leaders Trends & Insights: Corporate Edition Report.” Execs In The Know. 
  37. “Predictions 2025: Tech Leaders Chase High Performance.” Forrester.
  38. “Management Leadership Archives.” FutureCIO.
  39. “Explore Gartner’s Top 10 Strategic Technology Trends for 2025.” Gartner.
  40. “Leadership and AI insights for 2025.” NC State MEM.
  41. “Tackling the Challenges and Opportunities of Generative AI in Financial Services.” Spring Labs.

Unlock Agent Potential with Quiq’s Real-Time Agent Assist Capabilities

Posted on Max Fortis

Customer service is evolving, and with it, the demands placed on service agents are rapidly increasing. From managing complex inquiries to delivering personalized, high-quality customer experiences, agents are under constant pressure to perform at their best. This is where Quiq’s Real-Time Agent Assist comes into play. With AI-driven insights, real-time guidance, and cutting-edge automation, this powerful tool doesn’t just support agents—it transforms them into top performers.

In this blog, we’ll explore precisely how Quiq’s real-time agent assist capabilities—part of our overall AI contact center offering—can revolutionize your customer service operations by boosting efficiency, reducing costs, and delighting customers.

Transform agent productivity with real-time AI insights

Agents are at the heart of your customer interactions, and giving them the tools they need to succeed can make all the difference. Quiq’s real-time agent assist AI is designed to empower agents with in-the-moment guidance and actionable insights during live interactions. These agent tools mean faster resolutions, greater confidence, and improved productivity for your team.

With Quiq, agents no longer have to second-guess their responses or scramble to find the right information. Instead, AI steps in to provide precise recommendations and cues at just the right time.

Take action today
Experience the future of customer service firsthand. Get a demo of Quiq’s real-time agent assist offering today and see how it can transform your support team.

AI-powered efficiency for every role, every conversation

Whether it’s advising agents on complex issues, streamlining onboarding, or cutting operational costs, Quiq’s real-time agent assist offering delivers impactful benefits across the board.

Here’s how it works for your business:

1. Optimize decision-making

Equip your agents with real-time insights and recommended actions, enabling them to resolve issues with precision. Whether handling a challenging customer inquiry or upselling products,

Quiq ensures that agents make the best decisions in every interaction. Agents get real-time suggested responses as the conversation progresses, which leverage the same underlying knowledge and systems that power AI agents. Think: knowledge bases, product catalogs, CRM data, and any other data sources that might be helpful in the context of agentic AI systems. AI Assistants don’t just suggest responses; they can also act on an agent’s behalf—like automatically starting a warranty claim, or updating a customer’s flight, without making the agent do the work manually.

2. Streamline training and onboarding

AI-powered coaching is a game changer for new agents. With Quiq, your team gains access to on-the-job guidance that accelerates learning. New hires ramp up faster, while experienced agents refine their skills, creating a consistently high-performing team. New agents get the same great suggested responses and actions that a high-performing human or AI agent would have.

It makes a brand-new agent as good as an AI agent, because they’re working off the same datasets, integrations and responses.

3. Reduce operational costs

Achieve more with fewer resources. Quiq automates routine inquiries and streamlines workflows, freeing up your agents to focus on high-value interactions. This means fewer hiring needs and a leaner operational model. In addition, AI Assistants can gather extra key pieces of data during a conversation, add them to specific ticket fields or append them to a case or conversation, reducing the amount of manual entry an agent has to do.

4. Enhance customer satisfaction

Quiq’s agent-facing AI empowers agents to provide accurate, instant, and personalized support, leading to faster resolutions and happier customers. The result? Higher CSAT scores and stronger customer loyalty. This is done through a combination of response suggestions, real time feedback, and taking action on the agent’s behalf.

5. Insights into agent performance

Quiq’s robust agent analytics give contact center leaders deep insight into how human agents are performing. In our experience, this is critical to ensuring that real-time agent assistance does its job and helps agents in the most effective way possible.

Watch this video to learn how it works >

Key features of real-time agent assist with Quiq

At the core of Quiq’s real-time agent assist lies a suite of innovative features designed for seamless customer interactions. See it in action:

1. In-the-moment guidance and coaching

Built in Quiq’s AI Studio, AI assistants can leverage data from any enterprise system and combine that with conversational context to suggest responses and provide recommendations, or coaching, during a conversation. Agents thrive with support that adapts in real time. Quiq provides targeted coaching during live conversations, using AI to deliver hints, reminders, and workflows tailored to each interaction.

For instance, in a case study with an office supply retailer, Quiq’s assist feature was so effective it allowed associates to get immediate answers to questions 2 out of 3 times. This led to a whopping 68% self-service rate resolution rate.

2. Automated post-conversation summary and analysis

After-conversation work can be a major time sink—but not with Quiq. Using AI-generated summaries, agents can cut down on post-interaction tasks, allowing them to focus on the next customer. Customers get faster service, and agents stay productive.

Importantly, summaries are also available for the agent right when they take over a conversation. For example, if the user has been talking with an AI agent, the human agent will get a summary of the conversation, creating a seamless experience for the end customer.

Beyond summarization, Quiq can also extract key pieces of information and automatically update CRMs or other enterprise systems with the appropriate information.

3. Smart routing and prioritization

Not all customer inquiries are created equal. Quiq’s intelligent routing ensures that inquiries are directed to the best-suited agents based on real-time data like expertise, workload, or customer urgency. This minimizes wait times and optimizes outcomes.

Real results with AI assistants: Office supplier case study

When a leading office supply retailer integrated Quiq’s agent-facing AI Assistant, they saw impressive improvements in just a few weeks.

  • Increase in containment rates: 35% (with a 6-month average containment rate of 65%)
  • Associates got immediate answers: 2 out of 3 times
  • Self-service resolution rate: 68%
  • Associate satisfaction with AI: 4.82 out of 5

The AI ensured that each employee was guided toward resolving customer issues promptly while automating laborious and repetitive inquiries. This created a win-win for both customers and the team itself. Read full case study >

Elevate customer support with Quiq’s real-time agent assist offering

Imagine a team where every agent operates at their peak potential, guided by AI that backs their every move. Quiq’s real-time agent assist isn’t just an upgrade for your service department—it’s a revolution that touches every part of your customer experience.

If you’re ready to unlock your agents’ potential and take your customer service to the next level, now is the time to act.

Key Questions to Anticipate from Stakeholders About the AI Impact on Business

Posted on Lauren Winder

As AI transforms business operations across industries, leadership teams face increasing pressure to make informed decisions about technology investments. According to McKinsey’s January 2025 report, Superagency in the workplace: Empowering people to unlock AI’s full potential, the “long-term AI opportunity” represents “$4.4 trillion in added productivity growth potential from corporate use cases.”

Yet, despite these compelling advantages and massive increases in AI investments from companies like yours, that same McKinsey report highlights that “only 1 percent of leaders call their companies ‘mature’ on the deployment spectrum, meaning that AI is fully integrated into workflows and drives substantial business outcomes.”

This implementation gap shows the crucial need for business leaders to address stakeholder concerns thoroughly and strategically when proposing AI initiatives. And a primary avenue through which to do so is by aligning on metrics early on.

When presenting the business impact of AI, stakeholders will likely have a range of questions based on their priorities and concerns. Preparing thoughtful responses to these inquiries will demonstrate your expertise and build trust in your proposed approach.

Here are some key questions to anticipate:

Revenue and Growth Projections

  • What specific assumptions are driving the revenue growth projections?
  • Are these growth metrics sustainable, or are they tied to short-term trends?
  • How does this initiative impact key revenue drivers like upsell, cross-sell, and average order value?

Customer Impact and Engagement

  • How will this solution improve the customer journey and address pain points?
  • What measures will ensure that the personalization efforts resonate with customers across diverse segments?
  • How will you address privacy concerns related to AI-driven personalization?

Competitive Positioning

  • How does this AI initiative position us against competitors adopting similar technologies?
  • What unique capabilities or differentiators does this solution bring to the market?

Scalability and Adaptability

  • How scalable is this solution as customer needs and expectations evolve?
  • Can the AI system adapt to support new products, markets, or customer demographics?

Risk and Contingency Planning

  • What risks could arise if the AI fails to meet customer expectations or delivers subpar results?
  • How will you manage potential reputational risks tied to AI missteps?

Customer Metrics and Validation

  • What customer feedback mechanisms will be in place to measure the impact of AI on loyalty and satisfaction?
  • How will improvements in metrics like CSAT, NPS, and retention directly link to financial outcomes?

Operational Feasibility

  • How will AI integrate with our existing systems and processes?
  • What level of disruption can we expect during implementation?
  • How will this impact current employee roles, and what retraining or re-skilling will be required?

Cost and ROI Clarity

  • How accurate are the projected cost savings, and what assumptions were used in the calculations?
  • What is the anticipated timeframe to achieve ROI?
  • Are there hidden costs or unexpected expenses we should anticipate?

Time to Value

  • How quickly will customers notice and benefit from these enhancements?
  • What is the expected timeline to achieve meaningful improvements in retention and loyalty?

Final Thoughts

Anticipating these stakeholder questions isn’t just about having ready answers—it’s about demonstrating comprehensive strategic thinking in your AI implementation approach. Gartner research demonstrates that “organizations where the AI team is involved in defining success metrics are 50% more likely to use AI strategically than organizations where the team is not involved.”

As we touch on in another recent article, the landscape of AI implementation is rapidly evolving. Organizations that proactively address stakeholder concerns while maintaining a clear vision of their AI strategy will be positioned for competitive advantage in the coming years.

The journey to successful AI implementation begins with honest assessment. Is your organization prepared to navigate these complex stakeholder conversations?

Beyond Rules: Agentic AI Orchestration and the Dawn of Emergent Intelligence

Posted on Bill O’Neill

Key Takeaways

  • Agentic orchestration enables collaboration: Multiple AI agents can coordinate, delegate, and work together toward shared goals rather than following fixed, rule-based workflows.
  • Hybrid multi-agent systems perform better: Combining agents powered by different LLMs under a supervisory agent can produce more accurate, context-rich outcomes.
  • Scalability and flexibility are crucial: Orchestrated systems should easily add, modify, or remove agents without disrupting performance.
  • The future is emergent intelligence: Agentic orchestration moves AI beyond automation toward systems that learn, evolve, and self-optimize over time.

In the world of software and automation, “orchestration” is a familiar term. At its simplest, an orchestration tool says, “do this, then do that, and if something goes wrong, do this other thing.” It’s a manager for computers, software, and automated sequences of tasks, often spanning multiple systems. Think of it like a digital composer in front of an orchestra, ensuring each instrument (or in this case, system) plays its part in harmony to create a cohesive whole. Traditional orchestration tools handle predefined workflows, executing tasks in a set order. But the world of automation is changing, leading to the need for better AI Orchestration.

The Shift to Agentic AI: Autonomous Agents

Enter agentic AI. Instead of following rigid instructions, agentic AI systems consist of agents – individual components capable of autonomous decision-making. Imagine a single agent designed for a specific task. Now, imagine another agent designed for a different task, also acting autonomously, making its own decisions based on its training and the information it receives. Each agent operates independently, yet they need to work together. This brings us to the core challenge: how do you orchestrate all this together? How do you get AI agent orchestration right?

What is AI Orchestration? Coordinating the Agentic

Agentic AI orchestration is the art and science of managing, coordinating, and monitoring multiple autonomous AI agents that make their own decisions. It’s about creating a system where these independent agents can collaborate effectively to achieve a common goal. Key concepts include:

  • Delegation: The ability of one agent to recognize when a task is best handled by another agent and seamlessly hand it off.
  • Shared State: Maintaining a common understanding of the situation across all agents. This might involve shared data, context, or goals.
  • Inter-Agent Communication: Establishing channels for agents to exchange information, requests, and results.
  • Decision Hierarchies: Defining how agents interact and who (or what) makes final decisions when conflicts arise.
  • Dynamic Adaptation: The ability to add new agents, retire old ones, or modify existing agents without disrupting the overall system. The system should learn and adapt over time.
  • Tool/Function Calling: Agents dynamically invoke tools, which wrap APIs to perform specific functions like flight rebooking or baggage fees. This simplifies workflows, while the orchestration platform ensures state and context continuity across tasks.

A Real-World Example: Quiq’s Retail Orchestration

Quiq has built advanced agentic orchestration systems for real-world applications. One example involves a high-end retail customer. The goal was to provide personalized product recommendations, mimicking the experience of interacting with expert salespeople. The solution? A multi-agent system:

  • The Problem: The retailer needed a way to offer highly personalized recommendations to online customers.
  • The Solution: A system of interacting AI agents, managed by an AI Orchestration platform.
  • Agent 1 (OpenAI-based): This agent listens to the customer’s requests and preferences and, based on that understanding, searches a “virtual back room” to suggest relevant items (in this case, jewelry).
  • Agent 2 (Gemini-based): This agent performs the same task as Agent 1, but utilizes a different large language model (LLM). This provides a different perspective and potentially different recommendations.
  • Supervisor Agent (Different LLM): This agent receives the recommendations from both Agent 1 and Agent 2. It analyzes these suggestions and, using its own LLM, makes a final, consolidated recommendation to the customer.

This system demonstrates the power of agentic orchestration. By combining the strengths of multiple LLMs and specialized agents, the system delivers a richer, more nuanced experience than any single agent could provide on its own. This is an example of “simple” orchestration, where the interaction pathways are relatively well-defined by an AI orchestration platform.

Moving from Simple to Complex: Orchestrating Chains of Agents

Agentic AI orchestration isn’t just about simple, isolated interactions between a few agents, it’s about building systems that tackle increasingly complex challenges. This involves creating chains of agents, where tasks are dynamically delegated across multiple autonomous components.

Unlike traditional workflows with rigid pre-defined steps, these systems operate based on the autonomous nature of the agents. Each agent decides when to delegate a task, and the agent receiving the task is also autonomous, making its own decisions based on its specific capabilities and real-time information. These interconnected, agentic chains allow organizations to break down complex workflows into manageable, intelligent components.

Dynamic tool calling is also central to enabling complex agent chains. Tools serve as functional abstractions around APIs, providing capabilities that bridge agent needs with external systems or processes. These tools offer the agent flexibility—not only access to functions but the ability to decide how and when to use them.

For example, if a customer-facing agent receives a request to reschedule a flight, the agent evaluates the user’s goal, analyzes the available tools (e.g., a flight rebooking tool, a pricing calculation tool, and a baggage fee tool), and autonomously decides which tools to invoke and in what sequence. Instead of being explicitly told which steps to follow, the agent uses its own judgment to determine how the task should be fulfilled based on context and real-time information. The delegated agent then calls the relevant functions to accomplish each subtask, while maintaining a shared state to ensure the results are presented cohesively to the user. This modular and context-driven orchestration eliminates unnecessary repetition, allowing agents to collaborate effectively while offering dynamic and highly personalized solutions to tasks.

However, as systems grow more intricate, AI orchestration becomes critical to ensure smooth coordination, robust communication, and predictable outcomes. Managing these systems requires an orchestration platform capable of providing visibility, control, and alignment with the organization’s goals.

Let’s take another example, this time in the airline industry, and illustrate a multi-hop chain:

  • Customer: “I need to change my flight to next Tuesday and add a checked bag.”
  • Customer-Facing Agent: Recognizes two distinct tasks: flight change and baggage addition.
  • Customer-Facing Agent: Delegates the flight change request to the Rebooking Agent.
  • Rebooking Agent: Searches for available flights on the new date, calculates any price differences.
  • Rebooking Agent: Recognizes the need to handle baggage, and delegates this sub-task to the Baggage Agent.
  • Baggage Agent: Calculates the baggage fees based on the airline’s policies.
  • Baggage Agent: Returns the baggage fee information to the Rebooking Agent.
  • Rebooking Agent: Combines the flight change information and baggage fees, returning a complete price quote to the Customer-Facing Agent.
  • Customer-Facing Agent: Presents the options and the total price to the customer.

This demonstrates how a single customer request can trigger a cascade of interactions between multiple specialized agents. The power comes from the agents’ ability to dynamically delegate tasks based on their capabilities and the context of the request. This illustrates the complexity and need for specialized tools in AI agent orchestration.

Delegation Strategies

The “intelligence” of an agentic system often lies in how it chooses to delegate. Sophisticated strategies include:

  • Capability-Based Routing: Agents advertise their skills (e.g., “I handle baggage”), and tasks are routed accordingly.
  • Resource-Based Biding: Find the least expensive agent to complete the task if multiple agents are available, offering similar services.
  • Load Balancing: Distributing work to the least-busy agents to ensure efficiency.
  • Context-Aware Delegation: Choosing the best agent based on the specific details of the request.
  • Learned Delegation: The system learns over time which agents are best suited for different tasks, optimizing delegation based on past performance.
  • Hierarchical Delegation: Agents delegating to sub-agents forming a tree of task execution.

Key Components of an Agentic AI Orchestration System

The “plumbing” that enables agentic agents to communicate and work together is critical. This involves several foundational components, and our platform provides a best-of-breed AI Orchestration platform designed to unify these elements.

Communication Layer: Agentic systems rely on robust communication mechanisms to connect agents internally and externally. APIs are fundamental to this process, allowing agents to interact with other systems, external data sources, and business processes. APIs enable agents to access the information they need to make decisions and seamlessly integrate their outputs into workflows.

State Management: Effective state management ensures agents maintain context and consistency across interactions. Whether handling customer preferences in retail or flight details in an airline system, shared information must be stored and accessed efficiently. This can involve shared databases, distributed caches, or more sophisticated state management systems tailored for agentic AI.

Decision-Making Logic: Decision logic governs how agents determine when to delegate and to whom, exemplifying the emergent intelligence of agentic systems. In addition to and in place of static decision trees and hard-coded logic, agents make dynamic judgment calls based on the state of the conversation. These decisions are driven by real-time inputs, statistical probabilities, and predicted outcomes. For example, an agent might weigh context, historical data, and immediate priorities to select the best course of action. External inputs through APIs and enterprise integrations further enrich these decisions, providing the necessary data for agents to optimize outcomes.

Monitoring and Analytics: Monitoring tools provide visibility into agent operations and interactions, ensuring workflows remain aligned to business value and high-performing as systems evolve. In many cases, an observer agent, such as an external LLM-based system, plays a crucial role in this orchestration. This agent analyzes ongoing conversations, identifies interesting or alarming behaviors, and generates insights such as customer sentiment, emerging topics, and other actionable analytics. This is yet another case of what it means to have AI orchestration, where agents—including observer agents—collaborate not only to act, but also to monitor, interpret, and provide valuable feedback to improve the system. By leveraging observer agents, businesses gain deeper visibility and ensure their systems continuously adapt to real-world complexity.

Deployment and Management: Deployment tools simplify the lifecycle of agent operations. Agents can be deployed, updated, and retired while integrating with external systems and business processes. This ensures seamless orchestration and alignment between agentic systems and enterprise needs.

The Importance of Observability, Debugging, and Guardrails

In a complex system with multiple interacting autonomous agents, things may go wrong. This is where deep analytics, observation tools, and robust guardrails become absolutely essential. We need to understand not just how each individual agent behaved, but also how the data flowing between agents and the shared state influenced their actions. We also need mechanisms to keep the system operating within defined boundaries. These aspects are critical in any effective AI Orchestration strategy.

Observability and Debugging

Effective observability requires:

  • Individual Agent Monitoring: Detailed logging of each agent’s decisions, actions, and internal state. Our platform provides comprehensive analytics for understanding agent behavior.
  • Inter-Agent Communication Tracing: Visualizing the flow of data and requests between agents. This helps pinpoint bottlenecks and identify unexpected interactions.
  • Shared State Analysis: Understanding how changes in the shared state (e.g., customer preferences, inventory levels) affect agent behavior.
  • Root Cause Analysis: Tools to quickly identify the underlying cause of unexpected behavior or errors. This is crucial for debugging and improving the system. Our platform offers advanced diagnostic capabilities to streamline troubleshooting.
  • Explainability: The system should be designed in a way that allows a human to understand, in detail, how the agentic AI agents are making their decisions and how they interact with each other. Our platform prioritizes transparency and provides tools to explain agent behavior.

Emergent Behavior and Guardrails

As agentic systems become more complex, they can exhibit emergent behavior, characteristics that arise from the interactions of agents but were not explicitly programmed. Emergent behavior can be a powerful asset, such as discovering novel solutions or optimizing workflows in unexpected ways. However, it can also result in negative outcomes, such as oscillations, inefficiencies, or harmful outputs. Establishing robust guardrails is therefore critical to ensure agents stay aligned with organizational goals while minimizing risks.

Effective platforms must provide a comprehensive suite of tools for managing emergent behavior and ensuring system safety. These include mechanisms for enforcing boundaries, maintaining operational integrity, and addressing sensitive data concerns.

Explicit Constraints:
Platforms must establish clear operational constraints to control agent behavior and resource consumption.

  • Role-Based Access Control (RBAC): Limit what actions each agent is allowed to perform based on its role and capabilities. This ensures agents only operate within their intended boundaries.
  • Resource Limits: Constrain resources such as computational power or LLM tokens that each agent can consume, preventing excessive use that could destabilize the system.
  • Rate Limiting: Prevent agents from making too many requests or delegations within a short time frame, protecting the system from overload or abuse.
  • Validation Rules: Define rules that all agent outputs must satisfy to ensure alignment with business aims and compliance requirements.

Sensitive Data Handling and Isolation:
Agentic systems often deal with sensitive data, making it critical to enforce policies for its usage and sharing.

  • Authentication Between Agents: Require agents to authenticate their identities when communicating, ensuring secure exchanges and preventing unauthorized actions.
  • Policy Enforcement for Sensitive Data: Enable policies that dictate how sensitive data—such as customer information or proprietary business details—is accessed, processed, and shared by agents. This helps control data exposure and aligns with regulatory standards.
  • Data Isolation: Provide mechanisms to isolate certain types of data between agents to ensure information is only accessible to agents specifically authorized to see it. This enables hierarchical or segmented workflows where certain agents are excluded from viewing or interacting with datasets containing sensitive information.

Monitoring and Intervention:
Monitoring and intervention systems offer real-time oversight of agent activities and allow for swift intervention in case of unexpected behaviors.

  • Anomaly Detection: Use advanced algorithms to detect unusual agent behavior that may indicate operational issues or risks.
  • Circuit Breakers: Automatically halt or slow interactions between agents when errors or anomalies exceed predefined thresholds, mitigating cascading failures.
  • Human Override: Provide mechanisms for human operators to intervene and manually take control of key systems when necessary.
  • Kill Switches: Offer immediate shutdown capabilities for individual agents or subsystems to address emergencies or critical failures.

Governance and Policy:
Governance tools ensure agentic systems operate within defined organizational, regulatory, and ethical constraints.

  • Policy Engines: Implement rules that enforce compliance across all agent activities, ensuring alignment with legal, regulatory, and ethical standards.
  • Auditing and Compliance: Enable detailed auditing and reporting tools to maintain transparency, document actions, and ensure compliance with industry standards or regulations.

By combining these guardrails with robust authentication, sensitive data policies, and monitoring, organizations can unlock the full potential of AI Orchestration while controlling risks and maintaining alignment with operational goals. Proper safeguards allow agentic systems to operate dynamically and intelligently while remaining constrained by the principles of safety, security, and compliance.

Challenges and Considerations

Agentic AI orchestration, while powerful, presents several challenges:

  • Complexity: Managing the interactions of many autonomous agents can quickly become overwhelming. Careful design and modularity are essential. Our services team can help you architect and implement robust, scalable solutions.
  • Explainability: As mentioned above, understanding the emergent behavior of a complex agentic system is a major challenge. Our platform and expertise provide the tools and guidance needed to address this.
  • Debugging: Identifying and resolving issues in a distributed system of autonomous agents requires specialized tools and techniques. We provide the necessary debugging capabilities for AI orchestration.
  • Security: Protecting against malicious agents or vulnerabilities in the communication and state management layers is crucial. Our platform incorporates robust security measures.
  • Ethical considerations: Ensuring responsible use of agentic systems, particularly in areas like decision-making and autonomy, is paramount. We are committed to helping you build ethical and responsible AI solutions.

Benefits of Implementing AI Orchestration

AI orchestration unlocks intelligent responsiveness, moving beyond simple automation. By embracing AI Orchestration, organizations gain:

  • Dynamic Adaptability and Emergent Solutions: Systems dynamically adapt and discover emergent solutions. Autonomous agents respond to the unexpected, innovating beyond pre-programmed systems, orchestrated by AI Agent orchestration.
  • Increased Resiliency Through Decentralization: Decentralization enhances resilience. Agents continue operating during failures, re-routing tasks for robust reliability.
  • Optimized Resource Utilization Through Autonomous Allocation: Dynamic resource allocation optimizes resource utilization, minimizing waste and driving cost savings.
  • Enhanced Agility Through Flexible Integration: Modular systems allow seamless integration of new components, quickly adapting to evolving needs. This agility is powered by an AI Orchestration platform.
  • Improved Scalability Through Agent Replication and Delegation: Distributed architecture enables seamless scaling. New agents handle growing workloads through dynamic delegation.
  • More Efficient Task Distribution Through Agentic Task Selection: The orchestrating agent ensures the most efficient sub-agent is selected for each task.
  • Empowered Innovation Through Decentralized Decision-Making: Decentralized decision-making fosters innovation. Autonomous agents explore new approaches, accelerating iteration and creative solutions. Our platform provides guardrails and safety checks for AI Orchestration, keeping everything aligned with business policies.

Other Use Cases (Brief Examples)

Beyond retail and airlines, agentic AI orchestration has potential applications in many areas:

  • Customer Service: Multi-agent chatbots that can handle complex inquiries, escalating to different specialized agents as needed.
  • Data Analysis: Collaborative agents that can analyze different aspects of a dataset and combine their insights to generate a more comprehensive understanding.
  • Process Automation: Automating complex workflows that involve multiple AI systems, each performing a specific task.

The Future of Agentic Orchestration – Towards Self-Healing and Self-Optimizing Systems

Agentic AI orchestration offers tremendous potential for building more scalable, resilient, adaptable, and efficient AI systems. By allowing autonomous agents to collaborate and specialize, we can tackle complex problems that were previously intractable.

Our vision is to create systems that are not just intelligent, but also self-healing and self-optimizing – able to automatically detect and recover from failures, and continuously improve their performance over time. We aim for a future where agentic systems become “invisible infrastructure,” seamlessly automating tasks and empowering businesses to achieve new levels of efficiency and innovation. The “composable AI” capabilities are a game changer.

However, realizing this potential requires careful attention to the challenges of observability, explainability, and the need for robust guardrails. Our platform and services team are dedicated to providing the tools, expertise, and support you need to build and deploy these next-generation AI systems successfully and responsibly. Contact us to learn more about how we can help you harness the power of AI Orchestration.

Empowering Businesses with Agentic AI Orchestration

At Quiq, delivering exceptional customer experiences has always been our mission, and we’ve been building these experiences for years. Over time, we’ve learned what it takes to design, deploy, and scale intelligent agentic systems that seamlessly blend automation, autonomy, and efficiency.

These hard-won insights led us to develop AI Studio, a next-generation AI Orchestration tool that encapsulates everything we’ve learned. With AI Studio, businesses can harness the full potential of agentic AI to achieve scalable, intelligent automation while simplifying the complexity and overhead traditionally associated with orchestration systems.

We aim to give our customers the tools they need to succeed:

  1. Build sophisticated agents that are dynamic and adaptable to your workflows.
  2. Debug issues with clarity, ensuring agents perform consistently and reliably.
  3. Monitor agent behavior, inter-agent workflows, and emerging trends with powerful observability tools.
  4. Improve and iterate agents in real-time with transparency and confidence.

AI Studio is more than just a platform. It is the orchestration engine that empowers your team to move beyond rigid automation and build next-generation systems that dynamically respond to your business needs. By enabling your agents to communicate, collaborate, and scale, we help businesses unlock truly transformative customer experiences. Here’s a walk through on how orchestration works in AI Studio:

When your team has the right tools, there is no limit to what you can build. Let’s evolve the future of orchestration together. Contact us today to see how AI Studio can transform the way your business approaches intelligent automation.

Frequently Asked Questions (FAQs)

What is agentic AI orchestration?

Agentic AI orchestration is the process of coordinating multiple AI agents so they can collaborate, delegate tasks, and make autonomous decisions to achieve complex goals without relying on rigid, rule-based workflows.

How does agentic AI differ from traditional automation?

Traditional automation follows fixed logic and workflows, while agentic AI adapts dynamically –  enabling agents to reason, learn from context, and evolve their behavior over time.

What are the key components of an orchestrated AI system?

Effective orchestration depends on core principles like delegation, shared context between agents, communication protocols, and flexible decision hierarchies that allow systems to adapt in real time.

Why use multiple AI agents instead of one large model?

Multi-agent systems combine the strengths of different models. For example, pairing agents specialized in reasoning, summarization, or creativity to produce more nuanced and accurate results than a single model can deliver alone.

How does agentic AI support scalability?

Because orchestrated systems are modular, you can add, update, or remove agents without rebuilding the entire system, making it easier to scale and evolve with business or technical needs.

What is meant by “emergent intelligence”?

Emergent intelligence refers to the collective capability that arises when multiple AI agents interact effectively. This results in smarter, more adaptive behavior than any single agent could achieve independently.

How can organizations start implementing agentic AI orchestration?

 Start by identifying repetitive, high-impact workflows that could benefit from autonomy, then layer in specialized agents with clear communication pathways and a supervisory agent to oversee collaboration.

Omnichannel Messaging: What It Is and How It Works

Posted on Allyson Scott

KEY TAKEAWAYS

  • Omnichannel messaging unifies communication channels (SMS, email, chat, social, etc.) into one seamless platform, ensuring context carries across every interaction.
  • Unlike multichannel (which focuses on availability), omnichannel prioritizes consistency so customers never have to repeat themselves when switching channels.
  • Benefits include smoother customer experiences, stronger personalization, faster resolutions through AI automation, and more efficient team workflows.
  • A successful strategy focuses on knowing customer journeys, integrating key channels, unifying data, maintaining a consistent voice, and measuring results.
  • Choosing the right platform means ensuring broad channel coverage, strong CRM integrations, scalability, analytics, and compliance with data security standards.
  • Tools like Quiq make omnichannel adoption easier with AI automation, CRM integrations, and scalability—helping businesses deliver better experiences at scale.

Effortless communication is the backbone of today’s leading businesses, especially in industries like e-commerce and retail. Customers expect quick, personalized interactions that fit their needs and, more importantly, their preferences and busy schedules. That’s where omnichannel messaging comes in—a game-changer for businesses looking to nurture meaningful customer relationships, solve queries faster, and deliver exceptional experiences across all touchpoints.

What does omnichannel messaging mean, and how can it reshape how companies connect with their customers? This comprehensive guide explores its definition, benefits, strategies, and touches on how Quiq’s omnichannel messaging platform can turn it into a competitive edge.

What is Omnichannel Messaging?

At its core, omnichannel messaging is a strategy that integrates communication channels, including SMS, email, live web chat, social media, and more, into a unified platform. Unlike multichannel messaging, where interactions across channels are siloed, omnichannel messaging ensures customer interactions are seamless, connected, and context-aware no matter which channel they use or move to while trying to resolve an issue.

For example, a customer might start a conversation on Instagram Messenger, continue it via email, and then complete their inquiry over SMS, all while a single thread of past messages and intent follows them. Omnichannel messaging keeps everything cohesive, eliminating repetition or confusion during customer interactions.

Omnichannel messaging has become a vital tool for creating exceptional customer experiences, offering businesses a way to engage customers directly on their preferred platforms while maintaining a consistent and unified voice.

Omnichannel vs Multichannel: Key Differences

At first glance, omnichannel and multichannel messaging might sound like the same thing, as they both involve using multiple communication channels to reach customers. But the way they connect those channels makes all the difference.

  • Multichannel messaging means offering customers different ways to interact with your brand (SMS, email, live chat, social media, etc.). Each channel operates independently, and the customer experience can vary depending on where the interaction takes place. For example, a conversation started in a web chat might not carry over to an SMS thread.

  • Omnichannel messaging takes things a step further by unifying all channels into a single, continuous experience. No matter where the conversation starts or shifts, the context follows the customer. An AI agent can see the full conversation history across SMS, chat, email, and even social platforms, allowing for smoother handoffs and more personalized interactions.

The bottom line: multichannel is about availability, while omnichannel is about consistency. A multichannel strategy ensures customers can reach you where they want, but an omnichannel strategy ensures they never have to repeat themselves or start over when they switch channels.

Key Characteristics and Benefits of Omnichannel Messaging

With customer expectations soaring and new channels seeming to pop up each day, omnichannel messaging offers businesses tangible advantages that impact everything from customer satisfaction to long-term loyalty.

1. Seamless Customer Experience

The greatest strength of omnichannel messaging lies in providing a frictionless experience for customers.

  • By unifying communication across platforms, businesses ensure conversations are not interrupted, even when customers switch from one platform to another.
  • Integration with customer support tools, such as CRMs, centralizes interactions, offering teams a full view of previous conversations. This leads to higher clarity and faster resolutions.

Imagine a shopper reaching out via web chat with a query about your e-commerce selection. Later, when they message again through WhatsApp, your support team already knows their query history, saving time for both the customer and your team.

2. Personalized Interactions

Personalization has become the gold standard for successful customer engagement and customer retention. Omnichannel messaging takes it to the next level by utilizing collected customer data to craft highly relevant and timely messages.

  • Tailor communications based on customer preferences, buying habits, or interaction history. For example, send product suggestions and reminders relevant to their recent purchases.
  • Maintain message context across channels to avoid frustrating scenarios where customers have to repeat their queries.

This kind of personalization doesn’t just benefit customer satisfaction; it also improves lead conversion and builds layers of trust.

3. Enhanced Customer Satisfaction

Customer satisfaction relies heavily on businesses’ ability to respond quickly and resolve issues efficiently.

  • With omnichannel messaging, faster response times are possible through features like AI automation, smart ticket routing, and quick action suggestions for human agents.
  • Seamless resolutions across any platform create a lasting impression of reliability and commitment toward customer care, further building loyalty.

When customers feel heard and valued, they are more likely to become repeat buyers and brand advocates.

4. Streamlined Communication

Managing numerous communication channels often feels overwhelming for customer service teams. Omnichannel platforms rectify this chaos.

  • By centralizing interactions on a single platform, teams simplify their workflows and enhance communication efficiency.
  • AI-powered automation handles repetitive tasks, like sending order confirmations or appointment reminders, enabling your agents to focus on higher-value conversations.

Efficiency leads to reduced operational costs without sacrificing quality service, something no forward-thinking business can afford to overlook.

5. Improved Customer Retention

Research consistently shows that satisfied customers are more likely to stay with a brand.

  • Omnichannel messaging fosters emotional loyalty by delivering consistent, valuable experiences.
  • Proactive engagement, such as personalized birthday discounts sent via the customer’s favorite channel, keeps your brand top of mind.

Over time, this combination of trust and tailored care translates to greater lifetime customer value.

6. Consistent Brand Voice

Your brand’s voice is the essence of its identity. Omnichannel messaging ensures it remains unified across all platforms.

  • Whether communicating on Instagram, through email, or via SMS, the tone, style, and messaging are consistent.
  • This consistency reinforces your brand identity, making it recognizable and reliable.

Large-scale enterprises and startups can harness this benefit to build a stronger market presence and leave no room for mixed business messaging.

7. Competitive Advantage

Adopting omnichannel messaging separates your business from competitors still relying on siloed, fragmented communication strategies.

  • A seamless, personalized approach encourages stronger customer relationships and establishes your business as innovative and customer-focused.
  • Businesses leveraging platforms with real-time solutions and AI tools are seen as more responsive and resourceful, which proves to be a clear differentiation in today’s crowded market.

For example, incorporating communication options like WhatsApp in regions where SMS usage is limited makes your business accessible to untapped customer bases.

How to Create an Omnichannel Messaging Strategy

An effective omnichannel strategy is less about being on every channel and more about creating seamless, connected experiences. Here’s how to get started:

  1. Know Your Customers: Map their journey and identify which channels they use most.

  2. Integrate Key Channels: Focus on the ones that matter and connect them so context carries over.

  3. Unify Data: Centralize customer information so teams see the full conversation history.

  4. Stay Consistent: Use a unified voice, tone, and brand style across all touchpoints.

  5. Measure & Improve: Track KPIs like CSAT and response times to refine your approach.

A successful omnichannel strategy prioritizes connection and consistency, ensuring customers never feel like they’re starting over when they switch channels.

How to Choose an Omnichannel Messaging Platform

The right omnichannel messaging platform ensures your strategy feels seamless rather than fragmented. Focus on these essentials:

  1. Channel coverage: Supports the channels your customers use most.

  2. Integrations: Connects with your CRM and support tools for unified data.

  3. Ease of use: Simple for teams to adopt with automation and clear dashboards.

  4. Scalability: Grows with your business and new channel needs.

  5. Analytics: Delivers insights on response times, CSAT, and conversions.

  6. Security: Meets industry compliance and data protection standards.

Our biggest tip is to choose a platform that not only offers features but also unifies channels, data, and teams to deliver a truly seamless customer experience.

Tackle omnichannel messaging with Quiq

Creating an omnichannel messaging strategy is easier with modern tools like Quiq. Designed to integrate seamlessly across your ecosystem, Quiq offers several standout features:

  • Easy integration with platforms like Salesforce, SAP, and Shopify, uniting customer touchpoints under one interface.
  • AI-powered agents and automation to handle repetitive queries and speed up response times while improving accuracy.
  • Unmatched scalability for growing businesses ready to expand to more channels without increasing complexity.

With Quiq, businesses access a world-class omnichannel communication platform that simplifies messaging while amplifying customer satisfaction.

FAQs

What’s the difference between omnichannel and multichannel messaging?

Multichannel messaging means being available on different platforms (SMS, email, chat, social, etc.), but each channel functions independently. Omnichannel messaging connects those channels into one continuous experience, so context and conversation history follow the customer wherever they go.

What channels should I include in my omnichannel strategy?

It depends on your customers. Our advice would be to start by mapping their journey and identifying the platforms they use most (e.g., SMS, WhatsApp, live chat, or social messaging). Focus on connecting those first to maximize impact.

How does agentic AI fit into omnichannel messaging?

Agentic AI enhances omnichannel platforms by automating common tasks, offering smart routing, and personalizing responses at scale. This reduces wait times and frees up human agents to focus on complex or high-value interactions.

How can I measure the success of an omnichannel messaging strategy?

Key metrics include customer satisfaction scores (CSAT), Net Promoter Score (NPS), response times, and customer retention rates. Improved consistency and fewer customer complaints are also strong indicators of success.

How to Automate Customer Service – The Ultimate Guide

Posted on Michael Hartsog

From graph databases to automated machine learning pipelines and beyond, a lot of attention gets paid to new technologies. But the truth is, none of it matters if users aren’t able to handle the more mundane tasks of managing permissions, resolving mysterious errors, and getting the tools installed and working on their native systems.

This is where customer service comes in. Though they don’t often get the credit they deserve, customer service agents are the ones who are responsible for showing up every day to help countless others actually use the latest and greatest technology.

Like every job since the beginning of jobs, there are large components of customer service that have been automated, are currently being automated, or will be automated at some point soon.

That’s our focus for today. We want to explore customer service as a discipline and then talk about how Agentic AI can automate substantial parts of the standard workflow.

What is Customer Service?

To begin with, we’ll try to clarify what customer service is and why it matters. This will inform our later discussion of automated customer service and help us think through the value that can be added through automation.

Customer service is more or less what it sounds like: serving your customers – your users, or clients – as they go about the process of utilizing your product. A software company might employ customer service agents to help onboard new users and troubleshoot failures in their product, while a services company might use them for canceling appointments and rescheduling.

Over the prior few decades, customer service has evolved alongside many other industries. As mobile phones have become firmly ensconced in everyone’s life, for example, it has become more common for businesses to supplement the traditional avenues of phone calls and emails by adding text messaging and chatbot customer support to their customer service toolkit. This is part of what is known as an omni-channel strategy, in which more effort is made to meet customers where they’re at rather than expecting them to conform to the communication pathways a business already has in place.

Naturally, many of these kinds of interactions can be automated, especially with the rise of tools like large language models. We’ll have more to say about that shortly.

Why is Customer Service Important?

It may be tempting for those writing the code to think that customer service is a “nice to have”, but that’s not the case at all. However good a product’s documentation is, there will simply always be weird behaviors and edge cases in which a skilled customer service agent (perhaps helped along with AI) needs to step in and aid a user in getting everything running properly.

But there are other advantages as well. Besides simply getting a product to function, customer service agents contribute to a company’s overall brand, and the general emotional response users have to the company and its offerings.

High-quality customer service agents can do a lot to contribute to the impression that a company is considerate and genuinely cares about its users.

What Are Examples of Good Customer Service?

There are many ways in which customer service agents can do this. For example, it helps a lot when customer service agents try to transmit a kind of warmth over the line.

Because so many people spend their days interacting with others through screens, it can be easy to forget what that’s like, as tone of voice and facial expression are hard to digitally convey. But when customer service agents greet a person enthusiastically and go beyond “How may I help you” by exchanging some opening pleasantries, they feel more valued and more at ease. This matters a lot when they’ve been banging their head against a software problem for half a day.

Customer service agents have also adapted to the digital age by utilizing emojis, exclamation points, and various other kinds of internet-speak. We live in a more casual age, and under most circumstances, it’s appropriate to drop the stiffness and formalities when helping someone with a product issue.

That said, you should also remember that you’re talking to customers, and you should be polite. Use words like “please” when asking for something, and don’t forget to add a “thank you.” It can be difficult to remember this when you’re dealing with a customer who is simply being rude, especially when you’ve had several such customers in a row. Nevertheless, it’s part of the job.

Finally, always remember that a customer gets in touch with you when they’re having a problem, and above all else, your job is to get them what they need. From the perspective of contact center managers, this means you need periodic testing or retraining to make sure your agents know the product thoroughly.

It’s reasonable to expect that agents will sometimes need to look up the answer to a question, but if they’re doing that constantly it will not only increase the time it takes to resolve an issue, but it will also contribute to customer frustration and a general sense that you don’t have things well in hand.

Automation in Customer Service

Now that we’ve covered what customer service is, why it matters, and how to do it well, we have the context we need to turn to the topic of automated customer service.

For all intents and purposes, “automation” simply refers to outsourcing all or some of a task to a machine. In industries like manufacturing and agriculture, automation has been steadily increasing for hundreds of years.

Until fairly recently, however, the technology didn’t yet exist to automate substantial portions of customer service worth. With the rise of machine learning, and especially large language models like ChatGPT, that’s begun to change dramatically.

Let’s dive into this in more detail.

Examples of Automated Customer Service

There are many ways in which customer service is being automated. Here are a few examples:

  • Automated questions answering – Many questions are fairly prosaic (“How do I reset my password”), and can effectively be outsourced to a properly finetuned large language model. When such a model is trained on a company’s documentation, it’s often powerful enough to handle these kinds of low-level requests.
  • Summarization – There have long been models that could do an adequate job of summarization, but large language models have kicked this functionality into high gear. With an endless stream of new emails, Slack messages, etc. constantly being generated, having an agent that can summarize their contents and keep agents in the loop will do a lot to boost their productivity.
  • Classifying incoming messages – Classification is another thing that models have been able to do for a while, and it’s also something that helps a lot. Having an agent manually sort through different messages to figure out how to prioritize them and where they should go is no longer a good use of time, as algorithms are now good enough to do a major chunk of this kind of work.
  • Translation – One of the first useful things anyone attempted to do with machine learning was translating between different natural languages (i.e. from Russian into English). Once squarely in the purview of human beings, this is now a task that machines can do almost as well, at least for customer service work.

Should We Automate Customer Service?

All this having been said, you may still have questions about the wisdom of automating customer service work. Sure, no one wants to spend hours every day looking up words in Mandarin to answer a question or prioritizing tickets by hand, but aren’t we in danger of losing something important as customer service agents? Might we not automate ourselves out of a job?

Because these models are (usually) finetuned on conversations with more experienced agents, they’re able to capture a lot of how those agents handle issues. Typical response patterns, politeness, etc. become “baked into” the models. Junior agents using these models are able to climb the learning curve more quickly and, feeling less strained in their new roles, are less likely to quit. This, in turn, puts less of a burden on managers and makes the organization overall more stable. Everyone ends up happier and more productive.

So far, it’s looking like AI-based automation in contact centers will be like automation almost everywhere else: machines will gradually remove the need for human attention in tedious or otherwise low-value tasks, freeing them up to focus on places where they have more of an advantage.

If agents don’t have to sort tickets anymore or resolve routine issues, they can spend more time working on the really thorny problems, and do so with more care.

Strategies for Implementing Automated Customer Service

Once you’ve decided to bring automation into your customer service strategy, the next step is implementation. Here are some key strategies to help you get started and ensure a smooth transition that benefits both your team and your customers.

Assess Your Current Customer Service Needs

Start by reviewing your support data. Which questions pop up most often? Where do your agents spend the most time? Identifying these patterns will help you pinpoint which tasks can—and should—be automated. Look for high-volume, repetitive inquiries that don’t require much nuance. These are prime candidates for automation that won’t sacrifice the quality of your customer experience.

Choose the Right Automation Tools

Not all automation tools are created equal. Consider solutions like AI agents, automated ticket routing, or self-service portals. The key is to choose platforms that work well with your existing CRM and communication tools, so everything stays connected. Look for tools that are flexible, scalable, and easy for your team to manage over time.

Develop a Knowledge Base and Self-Service Options

A well-organized knowledge base can deflect tickets before they ever hit your queue. Build out FAQs, how-to articles, and video tutorials that answer your customers’ most common questions. Use AI-powered search features to surface the right content quickly. And don’t forget to update your content regularly based on feedback and emerging issues—your knowledge base should evolve alongside your customers.

Set Up Automated Responses and Workflows

Automation isn’t just about answering questions—it’s about streamlining entire workflows. Set up automated messages for order updates, appointment reminders, or common troubleshooting steps. Use branching logic and triggers to guide customers through resolutions, and ensure these flows are intuitive. The goal is to help customers solve issues faster, without needing to wait on hold.

Balance Automation with Human Support

Even the best bots have their limits. Make sure customers can easily escalate to a live agent when necessary—especially for complex or sensitive issues. Train your human support team to step in smoothly when automation reaches its edge. And whenever possible, personalize the experience by using data to greet customers by name or tailor responses based on their history.

Monitor Performance and Continuously Optimize

The work doesn’t stop after launch. Keep an eye on key metrics like resolution time, deflection rate, and customer satisfaction scores. Collect feedback from users to understand where automation is helping—or where it might be falling short. With the right data, you can train your AI and machine learning models to recognize patterns, refine workflows, and improve response accuracy—so your automated service keeps getting smarter with every interaction.

Moving Quiq-ly into the Future!

Where the rubber of technology meets the road of real-world use cases, customer service agents are extremely important. They not only make sure customers can use a company’s tools, but they also contribute to the company brand through their tone, mannerisms, and helpfulness.

Like most other professions, customer service agents are being impacted by automation. So far, this impact has been overwhelmingly positive and is likely to prove a competitive advantage in the decades ahead.

If you’re intrigued by this possibility, Quiq has created a suite of industry-leading agentic AI tools, both for customer-facing applications and agent-facing applications. Check them out or schedule a demo with us to see what all the fuss is about.

Request A Demo

Rich Communication Services – A Guide for CX Leaders

Posted on Max Fortis

Key Takeaways

  • What RCS Adds: An upgrade to SMS/MMS with rich media, read receipts, typing indicators, and interactive elements, built right into native messaging apps.
  • Apple’s Big Step: With iOS 18 (2024), Apple joined Google in supporting RCS, enabling seamless, feature-rich messaging between iPhone and Android users.
  • Why It Matters for CX: Businesses can deliver branded, interactive experiences, like verified agent chats, surveys, carousels, and order updates, without requiring extra app downloads.
  • The Bigger Picture: RCS combines the reach and trust of SMS with the engagement of OTT apps (WhatsApp, iMessage), making it a strong contender for the global messaging standard.

If by chance you haven’t heard of this new frontier in text-based customer communication, your first question is probably, “What is rich messaging?”

Well, you’re in luck! We wrote this piece specifically to get to the bottom of this subject. Here, we offer a deep dive into rich messaging, the capabilities it unlocks, and its implications for CX. By the time they’re done, CX directors will better understand why rich messaging should be central to their customer outreach strategy and the many ways in which it can make their job easier.

What Is Rich Messaging?

Rich messaging aims to support person-to-person or business-to-person communication with upgraded, interactive messages. Senders can attach high-resolution photos, videos, audio messages, GIFs, and an array of other media to enhance the receiver’s experience while conveying a lot more information with each message.

Google’s Rich Communication Services (RCS), for example, is one approach to rich messaging, but it is not the same thing as rich messaging in general.

For a number of reasons, rich messaging applications have supplanted SMS in both personal and professional outreach. SMS messages simply do not support many staples of modern communication, such as group chats or “read” receipts. What’s more, the reach of SMS will remain limited because it requires a cellular connection, whereas rich messages can be sent over the internet.

Though SMS will probably be around for a while, rich messaging is becoming increasingly popular as companies have been trending toward greater use of applications like WhatsApp.

With RCS, businesses don’t need a separate app —modern features like typing indicators, read receipts, and media sharing are available directly in the phone’s native messaging app (Google Messages, Apple Messages). This allows CX leaders to deliver rich, branded, and secure experiences tied to a customer’s mobile number without forcing app downloads.

Armed with these and similar channels, CX directors can now:

  • More easily capture new customers with compelling outreach.
  • Resolve customer issues directly via text, chat, or social media messaging (a huge advantage given how obsessed we’ve all become with our phones);
  • Interact with customers in real-time, which is a capability more and more people are looking for when seeking help.
  • Gather and act on analytics.
  • Scale their communications while simultaneously reducing the burden on contact center agents.

Given these facts, it’s no surprise that more and more CX leaders are making texting a key component of building lasting customer relationships.

What is Rich Messaging on Different Platforms?

Now that you have more perspective on what rich messaging is and what it offers, let’s spend some time talking about which platforms you should focus on.

There are a few major providers of rich messaging, but we’ll focus on Apple and WhatsApp. Apple has long been a communication giant, but with billions of users worldwide, Meta’s WhatsApp has certainly earned its spot at the table.

The sections below provide more details about how rich messaging works on each platform.

What is Rich Messaging on Apple?

Through Apple Messages for Business, contact centers can offer their customers a direct line of communication. This allows for far greater speed and convenience, to say nothing of the personalization opportunities opened up by artificial intelligence (more on this shortly).

What is Rich Messaging on WhatsApp?

WhatsApp is a widely used application that uses rich messaging for texts, voice messages, and video calling for over two billion users worldwide. Utilizing a simple internet connection for its services, WhatsApp allows users to bypass the traditional costs associated with global communication, making it a cost-effective choice.

It supports integration with tools like the Quiq agentic AI platform, which can automatically transcribe voice messages and allows for the export of these conversations for analysis using technologies like natural language processing.

For more information, check out our dedicated article on WhatsApp Business.

RCS vs. SMS vs. MMS vs. OTT: Understanding the Key Differences in Messaging

As consumer expectations have grown, so has the need for messaging technology to evolve. Businesses can no longer rely solely on traditional SMS to deliver standout experiences, especially when customers are used to rich, app-like interactions. That’s where understanding the differences between messaging types comes in.

From basic texts to interactive messages with buttons, images, and videos, here’s how SMS, MMS, RCS, and OTT messaging stack up:

What is SMS?

SMS (Short Message Service) is the most basic form of text messaging. It has a 160-character limit and doesn’t support media like images or videos. Despite its simplicity, it’s still widely used for short, timely notifications and reminders.

What is MMS?

MMS (Multimedia Messaging Service) expands on SMS by allowing users to send pictures, videos, and audio. It relies on mobile data and carrier support, and can be more expensive to send—especially at scale.

What is RCS?

RCS (Rich Communication Services) takes messaging to the next level with features like read receipts, typing indicators, carousels, high-resolution media, interactive buttons, and branded customer experiences via verified RCS Agents. It’s carrier-dependent and still gaining traction, but it offers a powerful upgrade for business-to-customer communication while running over Wi-Fi or data networks instead of the cellular voice network

What is OTT Messaging?

OTT (Over-the-Top) messaging apps like WhatsApp, Facebook Messenger, and Apple’s iMessage work over the internet and bypass traditional carriers altogether. These platforms offer end-to-end encryption, rich media, and global reach, making them a go-to for brands looking to meet customers where they are.

RCS also enables businesses to create branded, interactive customer journeys. Features like carousels, surveys, order updates, and suggested reply buttons allow for more engaging experiences while increasing trust with verified sender IDs.

Key Features and Benefits of Rich Messages

Whether on Apple, WhatsApp, or another channel, rich messaging is one of the best ways of interacting with customers; it’s convenient and powerful enough to help a CX leader rise above their competition.

At its core, rich messaging is defined by key features such as high-resolution photos and videos, read receipts, typing indicators, branded buttons, quick reply options, and interactive carousels, all within the customer’s native messaging app. These features create smoother, faster, and more trusted interactions compared to traditional SMS or MMS.

Below, we will get into more specifics about the advantages to be had from using rich messaging.

1. Cost-Effectiveness

Because it works over the internet, rich messaging is a great way for CX directors to connect with customers without breaking the bank.

But it can also help your organization save money by reducing customer support costs. When consumers need to talk to someone at your business, they can speak to knowledgeable agents (or a large language model trained on those agents’ output) through your rich messaging platform.

In this same vein, rich messaging makes it far easier to engage in asynchronous communications. This means agents are able to handle multiple conversations at the same time, resulting in further savings.

Finally, rich messaging is far more scalable than almost any other approach to customer outreach, especially when you effectively leverage AI.

2. Real-Time Insights

When they integrate rich messaging with a platform offering excellent support for real-time analytics, companies gain access to conversation analytics that provide the insights they need to improve contact center performance.

They can generate reports on click rates and other helpful interaction metrics, for instance, giving CX leaders a feedback loop they can use to test changes and see what improves customer satisfaction, loyalty, and lifetime value.

3. Rich Messaging is Native to the Devices Customers are Already Using

You could pay for the most compelling billboard in the history of marketing, but if it’s on the moon where no one will see it, it’s not going to do you much good. For this reason, we’ve long pointed out that it’s important to meet your customers where they are – and these days, they’re on their phones.

When combined with the statistics in the following section, we think that the case for rich messaging as a central pillar in the CX director’s communications strategy is very strong.

4. Increased Engagement

As it turns out, text messaging consistently achieves higher open and response rates compared to other methods. Adding elements like tappable carousels, suggested replies, and rich media further boosts engagement by making every message interactive and visually compelling.

This high level of engagement demonstrates the significant potential of text messaging as a communication strategy. Considering that only about 25% of emails are opened and read, it becomes clear that investing in text messaging as a primary communication channel is a wise decision for effectively reaching and engaging customers.

5. The Human Touch (but with AI!)

Rich messaging lets brands personalize conversations at scale with the help of AI. Machine learning powers this personalization, the same tech behind Netflix recommendations. Now, thanks to advancements in agentic AI, this same technology is being integrated into text messaging.

Previously, language models lacked the necessary flexibility for personalized customer interactions, often sounding mechanical and inauthentic. However, today’s models have greatly enhanced agents’ abilities to adapt their conversations to fit specific contexts. While these models haven’t replaced the unique qualities of human interaction, they mark a significant improvement for CX directors aiming to improve the customer experience, keep customers loyal, and boost their lifetime value. What’s more, when used over time, these innovations will help a CX leader stand out in a crowded marketplace while making better decisions.

To make use of this, though, it helps to partner with a platform that offers this functionality out of the box.

6. Security

Trust and transparency have always been important, but with deepfakes and data breaches on the rise, they’re more crucial than ever. Some rich messaging applications, like WhatsApp, support end-to-end encryption, meaning your customers can interact with you knowing full well that their information is safe.

But, to reiterate, this is not the case for all rich messaging services, so be sure to do your own research first.

What is Rich Messaging? It’s the Future!

Rich messaging is the technology that makes this possible, and it’s even more impactful when you partner with a platform like Quiq that enables personalization, analytics, and better engagement with your customers. Read more here to learn about the communication channels we support!

Frequently Asked Questions (FAQs)

What exactly is Rich Communication Services (RCS)?

RCS is the modern upgrade to SMS and MMS. It lets you send high-resolution photos and videos, see when someone is typing or has read your message, and interact through branded buttons or carousels, all from the phone’s built-in messaging app.

How does RCS differ from SMS or MMS?

SMS is limited to short text, MMS adds basic media, but RCS goes further. It creates an app-like experience inside the default inbox, complete with interactive elements, analytics, and branding opportunities that drive stronger engagement.

Does Apple support RCS now?

Yes. With iOS 18 (2024), Apple rolled out RCS support. That means iPhone users can finally exchange rich messages with Android users, bringing features like upgraded group chats, read receipts, and high-quality media into play.

How does RCS compare with apps like WhatsApp or Messenger?

WhatsApp and Messenger require separate downloads, while RCS is built right into your phone number and default messaging app. OTT apps still shine with global reach and encryption, but RCS offers something unique: direct, branded communication in the channel customers already use every day.

How are businesses putting RCS to work?

Companies are using it to design interactive journeys, think order updates, quick-reply buttons, customer surveys, or scrolling product carousels. Paired with verified business profiles, RCS builds trust while keeping conversations efficient and engaging.

 Is RCS secure?

Security varies by implementation, but many carriers and providers now offer end-to-end encryption for RCS. Combined with verified sender IDs, it helps ensure customers can trust the conversations they’re having with your brand.

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