Quiq

Month: March 2025

8 Customer Experience Metrics Every CX Leader Should Be Tracking

Posted on Cristina Bravo-Olmo

Delivering a remarkable customer experience (CX) is no longer optional—it’s essential. It can be the defining factor that sets your business apart, fosters loyalty, and drives growth. To truly understand and elevate your CX, tracking the right customer experience KPIs is critical.

Customer experience metrics offer clear and quantifiable insights into how your customers perceive your business, empowering you to identify strengths and address gaps effectively. But what are these key metrics, and how can they guide your strategy?

This guide will explore eight essential customer experience metrics, unpack their significance, and show you how to leverage them to improve satisfaction, loyalty, and overall business success.

What are customer experience metrics?

Customer experience metrics are quantifiable indicators that reflect the success of your business in meeting, and preferably exceeding, customer expectations. They go beyond traditional customer service metrics to evaluate every touchpoint of the customer journey, offering a comprehensive view of satisfaction, loyalty, and engagement.

Unlike operational metrics, which measure backend efficiency, CX metrics focus on the customer’s perception of interactions with your brand—both emotional and rational. When tracked effectively, measuring customer service metrics highlights gaps in your service and offers actionable insights to refine your strategies.

Why CX metrics matter

Metrics aren’t just numbers—they’re a reflection of your customers’ thoughts, feelings, and behaviors. Focusing on CX metrics allows you to:

  • Boost retention by building stronger relationships with your customers.
  • Optimize processes to reduce bottlenecks and frustrations.
  • Drive revenue by improving loyalty and attracting referrals.

Key customer experience metrics

Every organization needs to assess CX from multiple angles. Here are the eight metrics every CX professional should be tracking to create measurable and meaningful improvements.

  1. Customer Satisfaction Score (CSAT) measures a customer’s overall happiness with a specific product, service, or interaction on a scale of 1-5.
  2. Net Promoter Score® (NPS) measures customer loyalty and willingness to recommend a company to others using a scale of 0-10.
  3. Customer Effort Score (CES) measures the ease of a customer’s experience with a company or specific task.
  4. Customer Churn Rate measures the percentage of customers lost over a specific period.
  5. Customer Retention Rate measures the percentage of customers a company retains over a specific period.
  6. Customer Lifetime Value (CLV) predicts the total revenue a customer is expected to generate throughout their relationship with a company.
  7. First Response Time (FRT) measures the time it takes for a customer to receive an initial response to their inquiry.
  8. Average Resolution Time (ART) measures the average time it takes to completely resolve a customer’s issue.

Let’s take a look at them one by one.

1. Customer Satisfaction Score (CSAT)

A Customer Satisfaction Score (CSAT) measures how satisfied customers are with a specific interaction, product, or service. It offers a direct look into how your brand meets immediate customer needs.

How to measure CSAT

Customers are typically asked, “How satisfied were you with your experience?” and rate their satisfaction on a scale of 1 to 5. The CSAT formula is simple:

CSAT (%) = (Number of Satisfied Responses / Total Responses) × 100

For instance, if 80 out of 100 customers rate their experience as satisfied (4-5), your CSAT is 80%.

Why CSAT is important

Tracking CSAT lets you pinpoint issues right away and focus on areas where customers expect immediate improvements. For example, customer service teams can use CSAT to evaluate agent performance and streamline workflows.

qualtrics graph

Source: Qualtrics

How to improve CSAT

  • Immediacy: Address customer feedback on the spot. If there’s an issue with an order, for example, resolve it as quickly as possible to the customer’s satisfaction.
  • Ask for feedback in the context of the experience: Use surveys directly after an experience and within the channel it occurred in to capture the customer’s sentiment on the highest, most honest note possible.
  • Proactive support: Anticipate issues through data-driven analytics.
  • Employee training: Equip your team with the skills to deliver exceptional service.

    Learn how BODi® achieved a 75% CSAT rating with Quiq’s AI. See case study >

2. Net Promoter Score (NPS)

Net Promoter Score® (NPS) reveals how likely customers are to recommend your business to others, serving as a long-term loyalty indicator.

How to measure NPS

nps example

Source: Lumoa

Ask your customers, “How likely are you to recommend [brand/product/service] to a friend?” Customers respond on a scale of 0-10. Responses fall into three categories:

  • Promoters (9-10): Likely to recommend.
  • Passives (7-8): Neutral.
  • Detractors (0-6): Unlikely to recommend.

Calculate NPS as follows:

NPS = % of Promoters – % of Detractors

Why NPS is crucial

A rising NPS indicates growing customer loyalty, while a low or declining score signals dissatisfaction that needs urgent attention.

How to enhance NPS

  • Engage promoters: Encourage them to share referrals or write reviews.
  • Address detractor concerns: Reach out to unhappy customers to understand issues and resolve them.
  • Build real connections: Use insights to deepen customer relationships.

“BRINKS has been a happy Quiq customer since November 2017. We started by implementing two-way, asynchronous messaging for sales and customer support, which reduced our call volume YoY, including 30% in just the past 3 years. In that same timeframe, we had increased our NPS scores by a staggering 90+ points.” —Brian Lunseth, Director, Digital Customer Experience & Dev at BRINKS

3. Customer Effort Score (CES)

Customer Effort Score measures how easy it was for customers to complete a specific action, such as resolving an issue or making a purchase.

How to measure CES

A common CES survey asks, “How easy was it to accomplish [specific task]?” Responses typically range from 1 (very difficult) to 5 (very easy). Calculate an average CES by dividing the total score by the number of responses. For instance:

CES example

Source: Responsly

Why CES matters

Effortless experiences lead to higher satisfaction and loyalty. Studies show that reducing customer effort has a direct impact on repeat business.

How to improve CES

  • Streamline navigation: Simplify the process for high-friction actions like payments or returns.
  • Invest in automation: Self-service tools like AI agents can make problem-solving quicker.
  • Proactive customer service: Reach out before issues escalate. Proactive AI can do this for you on your website, using information about the customer’s previous orders, shopping behaviors, and more.

4. Customer Churn Rate

Churn Rate tracks the percentage of customers who stop doing business with you during a given period.

How to measure churn

Calculate churn by dividing the number of customers lost during a specific period by the total number of customers at the beginning of that period, then multiply by 100.

Why reducing churn is key

Churn directly impacts revenue. Retaining existing customers is far more cost-effective than acquiring new ones, making churn reduction a high priority for CX professionals.

How to minimize churn

  • Identify pain points: Use surveys to understand why customers leave.
  • Deliver value: Ensure customers feel they’re getting more than they paid for.
  • Reward loyalty: Offer exclusive benefits or personalized outreach to high-value customers.

5. Customer Retention Rate

Retention Rate measures your ability to keep customers over time, reflecting satisfaction and trust.

How to measure retention

Retention Rate = ((# of Customers at End – # of New Customers) / # of Customers at Start) × 100

Why retention matters

A high retention rate drives repeat purchases, referrals, and long-term profitability.

How to improve retention

  • Personalized communication: Use customer data for tailored messaging.
  • Loyalty programs: Reward continued engagement with meaningful incentives.
  • Listen & adapt: Act on feedback to show customers their voice matters.

6. Customer Lifetime Value (CLV)

CLV estimates the total revenue a customer will bring to your business throughout their relationship with your brand.

How to measure CLV

CLV = (Average Purchase Value × Purchase Frequency) × Customer Lifespan

Why CLV is critical

CLV provides insights into the long-term value of different customer segments, helping you allocate resources more effectively.

How to increase CLV

  • Upsell opportunities: Introduce complementary products.
  • Exceptional CX: Maintain service quality at every touchpoint.
  • Proactive retention: Address issues that could lead to churn.

7. First Response Time (FRT)

FRT measures the average time it takes for customer service teams to respond to inquiries.

How to measure FRT

Divide the total time to first response by the number of support tickets answered.

Why FRT matters

Customers expect fast responses. A quick first response fosters trust and improves customer sentiment.

Tips to improve FRT

  • Automate responses: Use AI to acknowledge tickets instantly.
  • Efficient routing: Ensure tickets reach the right teams quickly.
  • Track trends: Identify recurring delays and resolve the root cause.

8. Average Resolution Time (ART)

ART measures the average time needed to resolve customer issues fully.

How to measure ART

Total resolution time / Total number of cases resolved = ART

Why ART is essential

Highly efficient resolutions ensure a smooth customer experience, demonstrating your service team’s competence.

How to reduce ART

  • Incorporate AI to handle routine questions: Use artificial intelligence to automatically solve more Tier 1 inquiries.
  • Comprehensive training: Equip agents to solve issues faster, boosting their capabilities with technology that helps them do their jobs more efficiently.
  • Knowledge bases: Offer customers easy access to self-help resources.
  • Cross-team collaboration: Enable teams to share insights to address complex issues efficiently.

Learn how Molekule achieved 60% resolution rates with Quiq’s AI. See case study >

Improving CX metrics one step at a time

Knowing how to measure customer experience metrics and tracking them is not enough—you need to act on what the data reveals. Each CX metric shines a light on specific aspects of the customer journey, from satisfaction (CSAT) to service efficiency (FRT and ART).

No single metric paints the full picture. Combine insights from various metrics to assess your customers’ needs holistically.

Using platforms like Quiq, you can simplify the process by uniting analytics from multiple channels. This allows you to analyze customer sentiment, improve inefficiencies, and empower teams with real-time insights.

AI Change Management: A Guide to Successful Agentic AI Adoption in CX

Posted on Lauren Winder

It probably comes as no surprise that a recent study by PwC revealed that more than 60% of employees say they experienced more changes at work in the last year than the one prior. And now the rise of agentic AI is ushering in yet another wave of change for CX teams.

Unlike first-generation AI, agentic AI holds the potential to revolutionize the customer experience, enhancing agent efficiency, building customer trust and loyalty, and driving critical business outcomes. However, along with the promise of groundbreaking improvements in customer experience, integrating agentic AI into CX also presents significant change management challenges.

Whether you’re looking to upgrade an existing chatbot solution or implement an AI agent for the first time, adoption isn’t just about technology. It’s about people. The success of any agentic AI initiative depends on CX leaders’ ability to help their teams and all other stakeholders understand what’s at stake, why they should care, and what they can expect — both good and bad.

This guide explores common AI change management challenges and best practices to help set everyone in your CX organization up for agentic AI success. But first…

What is AI change management?

AI change management is the structured process of integrating AI-driven solutions into business operations while ensuring employees, customers, and other stakeholders are aligned and supported throughout the transition.

The goal? Minimize disruption while maximizing value.

Prioritizing people-related AI change management prior to choosing a vendor makes every other step of the AI change management process significantly less stressful (and more successful). Organizations can strengthen customer trust, upskill their workforce, and innovate more quickly than their competitors — all without disrupting stakeholder morale, satisfaction, or alignment.

Why AI change management is critical for business success

It’s been reported that as many as 80% of companies worldwide now use AI-powered chat on their websites. However, the majority of these instances are “chatbots” that leverage first-generation AI, rather than AI agents that harness the latest large language models (LLMs) and generative AI (GenAI) capabilities to give AI agency — AKA agentic AI.

Here at Quiq, we define agentic AI as a type of AI designed to exhibit autonomous reasoning, proactive and goal-directed behavior, and a sense of self or agency, rather than simply following pre-programmed instructions or reacting to external stimuli. Agentic AI systems can interact with humans in a way that is similar to human-human interaction, such as through natural language processing (NLP) or other forms of communication.

Agentic AI clearly represents a major opportunity for CX leaders to finally deliver unprecedented customer experiences that previous generations of AI have been promising to power for years — one that improves agent productivity, enriches customer relationships, and delivers real results.

But implementing agentic AI without proper preparation can quickly lead to bottlenecks, resistance, and, ultimately, failure. Poor planning and misaligned strategies can lead to process disruption, human agent churn, broken customer trust, and negative ROI. In contrast, structured AI change management ensures smoother transitions. It anticipates risks, proactively addresses employee concerns about AI’s impact on their roles, and establishes clear expectations and goals.

The impact of agentic AI on business and the workforce

The role of AI in CX transformation

There are myriad ways to apply AI across the customer journey. For example, AI agents offer 24/7/365 multilingual support. This improves performance metrics and customer satisfaction while showcasing commitment to delivering personalized experiences — something that AI agents can provide by drawing on customer data from various enterprise systems.

GenAI can also automate content generation, saving time by crafting product information, summaries, and even articles. This not only reduces workloads, but also allows customer service teams to tackle more complex or sensitive tasks.

Successful AI change management in action

An established furniture brand was grappling with customer experience friction and missed sales opportunities in a fiercely competitive industry. To curb these challenges, the company partnered with Quiq to introduce a custom AI agent capable of transforming customer interactions across platforms. The successful implementation and integration of this AI agent enabled the company to drastically reduce customer support escalations to human agents by 33%. It also facilitated proactive customer engagement, leveraging a product recommendation engine that contributed to the largest sales day in the company’s history.

Similarly, a leading national office supply retailer utilized Quiq to build an AI-driven assistant for store associates in just 6 weeks. The ability to rapidly generate accurate information to help answer in-store customer queries has increased associate efficiency by 35%. The AI initiative simplified the store associate’s experience, streamlined access to information, improved customer service efficiency, and significantly boosted job satisfaction and productivity. Results include a self-service resolution rate of 68% and an associate AI satisfaction rating of 4.82 out of 5!

Roles usually involved in AI change management

Successfully integrating agentic AI into your customer experience requires AI change management across a number of key stakeholders, including:

  • Service and support agents: Your frontline service agents are the backbone of your CX strategy — and often the group most concerned about AI integration. Their core question? “Will this replace me?”
  • Marketers: Marketing teams are the storytellers of your brand. They are concerned about creating a singular brand voice and crafting messages that resonate with your audience at every touch point — especially touch points involving AI.
  • Sales representatives: Sales professionals rely on meaningful connections with prospects and customers. Their personal approach has always been a differentiator — which means change invites skepticism.
  • IT: From ensuring data privacy and security to integrating enterprise-grade solutions, IT must handle the back-end complexity of AI platforms. They’ll want assurance of reliability and room to customize configurations for ongoing scalability.
  • Executives: C-suite leaders are key decision-makers driving AI adoption from the top down. They see the bigger picture, but often want to know one thing upfront: “What’s the ROI?”

Common objections and challenges in AI change management

CX leaders encounter a number of challenges and objections when it comes to integrating and adopting agentic AI. Some of the most common that require immediate change management include:

“AI will take jobs away from human agents!”

The fear that AI will replace human agents is one of the most significant barriers to adoption. Employees may worry about their livelihoods, viewing AI as a competitor or threat, rather than a partner or resource.

“AI can’t deliver great customer service…”

Skepticism around AI often stems from previous experiences with underwhelming chatbots. Customers and team members alike may wonder if AI agents have the intelligence and nuance to handle real-world customer concerns.

“What about data privacy and security?”

AI systems require large volumes of data to function effectively, which can raise concerns about privacy, security, and compliance. Some teams may even push for custom solutions built in-house to maintain control.

“What if it damages our brand’s reputation?”

The potential for AI-related issues — misunderstood responses, hallucinations, or off-brand messaging — can trigger anxiety among stakeholders tasked with protecting the company’s public image and perception.

“AI will solve all our problems instantly!”

On the flip side, some stakeholders might naively believe AI to be a magic wand that will instantly resolve all inefficiencies and elevate CX metrics overnight. This unrealistic expectation can lead to disappointment when results are not immediate.

Best practices for AI change management

AI change management isn’t just about removing barriers — it’s about creating advocates. By understanding stakeholders’ concerns and aligning solutions with their priorities, you can demystify AI and build trust in its transformational potential.

AI isn’t a standalone fix; it’s part of a collaborative vision. Focus on education, transparency, and actionable results to align teams and embed confidence in AI’s role. Ultimately, a well-managed transition will enrich not just your CX strategy — but the experience of everyone involved.

Here are a few key best practices for getting folks on board before taking the plunge:

Highlight AI’s role in upskilling human agents

Your team needs clarity on how AI will enhance their roles, not erase them. AI is exceptionally good at automating repetitive, low-value tasks like data entry or providing scripted customer responses. AI also presents agents an opportunity to grow and develop new skills, like interpreting AI insights or managing tech-enabled workflows.

Plus, AI can make those same agents significantly more productive. How? With the automation of simple, routine tasks, combined with AI assistants helping your agents respond faster and more accurately to higher-value conversations.

Engage skeptics in the vendor selection process

Even within forward-thinking teams, some employees will approach AI with hesitation — or outright skepticism. Their reservations often stem from perceptions of over-hyped technology or negative past experiences (hello, ineffective chatbots). Turn skeptics into allies by giving them a seat at the table. Specifically, involve them in identifying and evaluating AI for CX use cases and solutions. This inclusion doesn’t just smooth over resistance, it also helps teams get excited about potential solutions.

Explore “buy-to-build” agentic AI solutions

If technical stakeholders show resistance to off-the-shelf platforms, offer a middle ground. Buy-to-build platforms offer technical teams the flexibility, visibility, and control they crave to build secure, custom experiences that satisfy business needs. At the same time, they save time, money, and resources by handling the maintenance, scalability, and ecosystem required for CX leaders to deliver impactful AI-powered customer interactions.

Invite brand experts to help build and test

Help teams understand that, contrary to popular belief, hallucinations are preventable using a combination of the latest AI technology, retrieval augmented generation (RAG), and sophisticated business logic that runs pre- and post-response generation checks. Then, involve them in the knowledge preparation and testing processes to reassure them that the AI agent is responding to customers in your unique brand voice.

Establish clearly defined objectives and KPIs

AI projects succeed (or fail) based on realistic and measurable outcomes. Clear, incremental goals ensure alignment at every level of the organization and prevent optimistic executives from expecting too much, too soon. Define objectives and KPIs, such as increased first-contact resolution (FCR) rates, improved CSAT or lower customer effort score (CES), that align with broader business goals, and establish timelines that make sense for hitting each one.

Trends in AI change management: Using AI to streamline adoption

Interestingly, AI itself can accelerate change management initiatives in several key ways. AI proves instrumental in making data-driven decisions that propel positive change, like analyzing in-depth employee surveys to identify patterns and trends that can then be proactively addressed. This allows businesses to effectively gauge employee sentiment toward change, helping to drive strategies that are tailored, engaging, and transformative.

AI platforms can sync various communication channels, automate reminders, and even draft communications based on contextual understanding. This creates a more open, transparent, and inclusive environment for all employees, making organizational change more scalable and effective. It also helps bridge any potential communication gaps, ensuring that stakeholders at all levels are aligned with the strategic vision and the change management process.

Preparing for AI-driven change

As Founder & Principal Analyst of esteemed customer experience research and advisory firm Metric Sherpa, Justin Robbins, recently said, “While AI adoption is surging, only a fraction of organizations report tangible success. Why? It’s not because the technology doesn’t work. It’s because too many organizations approach it with unrealistic expectations, incomplete strategies, or resistance rooted in fear.”

These are all issues that must be addressed before signing on the dotted line. Nobody said it was easy — we humans are complex creatures notoriously opposed to change. But there’s more that unites us than divides us, as the saying goes, which is why we were able to successfully classify your change-resistant colleagues into seven common personas.

AI Adoption in 2025: Trends, Drivers, and Implementation Tips

Posted on Mark Kowal

The exponential rise of artificial intelligence (AI) is transforming US industries, reshaping workflows, and unlocking new opportunities. Once the stuff of futuristic movies, AI is now a tangible part of business strategy, enabling companies to streamline operations, personalize customer experiences, and leap ahead of competitors.

From generative AI tools like ChatGPT to analytical AI applications driving decision-making, businesses across the United States are rapidly integrating these technologies into their core functions. According to McKinsey’s State of AI 2024 survey, AI adoption (of all AI types) skyrocketed, with 72% of organizations now using AI in at least one business function—up from 50% just two years ago. Generative AI, in particular, has grown rapidly, shifting from experimental to essential in marketing, customer service, and supply chain operations.

 

McKinsey Image - AI Adoption

Source: McKinsey

This blog takes a closer look at the unfolding AI revolution by addressing key adoption trends, drivers of growth, challenges, and practical integration strategies. Let’s dive in.

The current landscape of AI adoption

Businesses in the US are moving beyond initial experimentation and actively leveraging AI in significant ways. A recent study reveals that 65% of organizations are using generative AI tools like large language models (LLMs) in at least one functional area (trailing behind all types of AI by just seven points). It’s worth noting that the adoption of LLMs, generative AI, and agentic AI is happening far more rapidly than AI adoption as a whole, which speaks to the revolutionary capacity of this next generation of AI.

Beyond these buzzworthy tools, traditional AI applications like machine learning and data analytics remain pivotal for supply chain management, customer service, and resource optimization.

Generative AI has made substantial inroads in marketing and sales, with adoption rates doubling since last year. From automating customer segmentation to generating dynamic ad copy, marketers are using AI to drive measurable gains in ROI.

Industries such as retail, financial services, and travel and hospitality are leveraging AI to reduce operational costs and enhance employee productivity. Moreover, the emergence of agentic AI, designed to act autonomously (within predetermined parameters) and adapt dynamically, promises to further streamline complex global workflows, paving the way for even greater efficiency and innovation.

AI across sectors:

  • Sales & Customer Service: AI agents can help generate revenue in the pre-sales cycle with proactive and personalized recommendations, upsells, and more. They also provide instant support and efficient resolution of customer inquiries. AI can analyze customer interactions to identify pain points and improve service quality. Agentic AI can even handle complex customer service issues autonomously, escalating only the most challenging cases to human agents, ensuring quicker response times and increased customer satisfaction. There are also agent and employee-facing AI assistants that can supercharge humans to be faster and more efficient.
  • Product Development: AI is streamlining R&D and enabling faster innovation cycles.
  • Marketing: AI-enabled personalization and content generation unlock higher engagement and conversions. AI-driven lead generation, personalization, and predictive analytics allow organizations to deliver targeted campaigns and understand consumer behavior more effectively.
  • Supply Chain Management: Analytical AI improves demand forecasting, minimizes operational disruptions, and optimizes inventory. Real-time data analysis via AI is also mitigating disruptions more efficiently.
  • Human Resources: AI-powered recruitment tools streamline hiring processes by scanning and analyzing thousands of resumes with pinpoint accuracy.

While adoption is accelerating, the potential for AI remains vast, with McKinsey estimating that newer generative tools could add trillions of dollars in annual global productivity. This growth is further fueled by the increasing capabilities of agentic AI, promising to automate complex decision-making processes and optimize workflows on a previously unimaginable scale.

Investment trends in AI

There’s some exciting research and data around AI investments. Stanford University’s AI Index report says, “In 2023, the United States saw AI investments reach $67.2 billion…” But last year, in 2024, McKinsey’s data showed 67% of companies plan to increase AI spending significantly over the next three years, underscoring a shared belief in AI’s long-term impact.

As the numbers grow, one thing remains clear: Governments and companies continue to invest more and more in AI. And a key area of investment is focused on developing and deploying agentic AI systems, capable of learning and adapting in real-time to optimize business processes across various departments.

What’s driving AI adoption?

AI is far more than a trend at this point. It’s a response to evolving economic, competitive, and technological pressures.

1. Economic and competitive pressures

Businesses face increasing pressure to lower costs while driving innovation. AI empowers organizations to achieve these objectives by automating labor-intensive tasks, allowing human talent to focus on strategic decisions.

Whether it’s predicting customer churn or optimizing inventory, AI amplifies efficiency in ways previously unimaginable. Companies competing in fast-paced markets are finding AI indispensable for maintaining relevance and gaining a competitive edge, particularly with the advanced autonomous capabilities offered by agentic AI.

2. Technological breakthroughs

The evolution of machine learning, generative AI, and especially agentic AI has lowered the barriers to implementing AI solutions. Thanks to pre-trained, off-the-shelf LLMs, even smaller organizations can integrate cutting-edge AI into their operations without a team of data scientists.

Applications built using these models can be deployed in 1-4 months, as noted in McKinsey’s findings, reducing previous implementation delays. Agentic AI takes this a step further by making it easier for businesses to achieve significant operational improvements through a variety of high-impact use cases.

3. Operational drivers

AI’s ability to deliver faster insights and scalability is remarkable. Predictive analytics helps forecast market trends, while automation in customer service reduces response times and delights customers with personalized responses. Furthermore, the autonomous capabilities of agentic AI make it indispensable for any organization seeking operational excellence.

4. Demand for hyper-personalization

AI facilitates hyper-personalized experiences by drawing insights from real-time data analytics. For example, a retail business can use AI to customize product recommendations based on individual shopping behavior, driving higher engagement. The use of agentic AI can further enhance this, adapting interactions based on customer behavior.

5. Data-driven decision making

Data has emerged as the backbone of modern enterprises. AI excels in processing vast datasets to uncover actionable insights on the spot, transforming how businesses approach forecasting, customer engagement, and pricing strategies. The ability of agentic AI to autonomously analyze and act on this data provides a distinct competitive advantage. For example, AI can track real-time user interactions across digital touch points, predict intent, and then autonomously adapt website content and proactive AI agent responses to create more resonant experiences based on the data it’s encountering.

6. Scalability and long-term ROI

AI investments demonstrate scalability, with organizations realizing measurable cost reductions and revenue growth. The ability to scale solutions across multiple functions, such as HR, marketing, and logistics, makes AI particularly attractive in enterprise-wide applications. The autonomous operation and adaptability of agentic AI contribute to this ROI by optimizing processes. And even though it’s always best to keep a human in the AI loop, agentic reduces the need for as much manual human intervention as previous generations of the technology.

Overcoming the challenges of AI implementation

While the promise of AI is vast, its adoption comes with significant hurdles.

Common AI adoption challenges and solutions

Data quality and management:

  • Challenge: AI is only as effective as the data it’s trained on. Poor-quality or siloed data can lead to inaccurate predictions and biased outputs.
  • Solution: Deploy rigorous data governance policies to ensure clean, accessible, and secure datasets. Periodically audit systems to identify inconsistencies.

Integration into legacy systems:

  • Challenge: Many companies struggle to integrate AI into their existing infrastructure.
  • Solution: Leverage middleware like Quiq’s AI Studio to bridge AI models with legacy platforms, reducing the need for complete system overhauls. A phased implementation process allows for smoother transitions.

Navigating regulatory complexity:

  • Challenge: AI implementation is often hindered by evolving legal and ethical requirements, such as data privacy regulations.
  • Solution: Employ compliance experts to ensure adherence to data privacy laws. Transparency in AI operations, such as maintaining records of AI decisions, will aid regulatory compliance.

Building employee trust in AI:

  • Challenge: Introducing AI often sparks workforce apprehension. Employees may worry about AI replacing their jobs.
  • Solution: Successful implementation hinges on transparent leadership communication emphasizing how AI augments roles rather than replaces them. Businesses should also focus on reskilling employees, positioning them for growth in advanced roles alongside AI.

Best practices for getting AI adoption right

Adopting AI doesn’t happen overnight. Businesses need a strategic roadmap to overcome AI adoption challenges for effective implementation.

Step 1: Define clear objectives and KPIs

What measurable outcomes does your organization want to achieve with AI? Set specific targets, such as a 20% reduction in response time or a 15% increase in supply chain efficiency.

Step 2: Form a cross-functional team

AI initiatives should involve collaboration between IT, operations, and customer-centric teams. Cross-functional input ensures alignment of AI solutions with business goals. This team should also include experts who can manage the integration of agentic AI systems, ensuring alignment with overarching business objectives.

Step 3: Test and scale

Start small by rolling out simpler use cases in select departments. Use these trials to measure outcomes, refine algorithms, and tweak operational workflows before scaling across the organization. Your initial tests should also focus on evaluating the performance and adaptability of agentic AI in real scenarios.

Step 4: Conduct ongoing performance reviews

AI requires constant calibration. Ensure continued success by comparing performance metrics against initial objectives and making iterative adjustments. Continuous monitoring is crucial to ensure it aligns with ethical and operational standards.

Step 5: Invest in employee training

Knowledgeable employees are the key to leveraging AI. Offer targeted training programs to upskill staff, ensuring they can successfully operate AI tools. These training programs should also focus on how to effectively collaborate with and manage agentic AI systems.

Future trends and predictions in AI adoption

Looking ahead, the AI adoption landscape is poised to evolve fast, with several key trends:

  1. Wider adoption of agentic AI: Businesses will begin shifting from task-based Gen AI capabilities to agentic AI, which can autonomously handle intricate workflows and decision-making. This shift will drive a new wave of efficiency and innovation. (Explore Quiq’s rapid agentic AI builder tool here).
  2. Hyper-personalization: AI will continue to enhance customer experiences, delivering tailored marketing campaigns, product recommendations, and seamless omnichannel experiences. Agentic AI will play a crucial role in enabling real-time personalization by autonomously adapting to individual customer behaviors and preferences.
  3. Ethical AI governance: Organizations will need robust frameworks to ensure compliance with evolving regulatory standards while addressing concerns around bias, transparency, and environmental impact. This is particularly important with agentic AI, where autonomous decision-making requires stringent oversight.
  4. Multi-model and model-agnostic AI systems: The future lies in hybrid AI systems that combine generative AI tools with analytical and operational AI capabilities. These will offer integrated solutions across marketing, service, and production. Agentic AI will act as the orchestrator, coordinating these different AI models to optimize end-to-end processes.
  5. Advanced natural language understanding: Agentic AI will make customer interactions even more seamless and human-like, transforming industries from retail and healthcare across exciting use cases. AI will grow to autonomously manage and optimize customer interactions based on contextual understanding.
  6. AI-driven efficiency initiatives: Companies will integrate AI tools into efficiency strategies to reduce operational waste.

Accelerating your AI adoption journey

To achieve meaningful, measurable success, business leaders must act now. Said another way, AI is a competitive necessity in 2025. Clear goals, robust cross-functional teams, and iterative testing are non-negotiables for capitalizing on AI’s potential.

By implementing strategic adoption frameworks, focusing on integrating agentic AI capabilities, and staying ahead of trends, organizations can redefine industry demands. Organizations that invest in AI today will define the benchmarks for tomorrow.

What is an LLM Agnostic Approach to AI Implementation?

Posted on Bill O’Neill

The world of AI is evolving faster than ever, and businesses that want to stay ahead need to adapt just as quickly. Enter LLM-agnosticism: a flexible, future-proof approach to AI implementation that allows organizations to integrate any large language model (LLM) without being tied to a single provider or model. It’s the secret to staying nimble in a landscape where models are improving by the month, where costs are dynamic, and where risk is everywhere.

This isn’t just about avoiding vendor lock-in (though that’s part of it); it’s about setting yourself up for long-term success. Model providers often deprecate older models as technology progresses, leaving no option but to move forward. If your systems are locked into one specific model or lack the right tools to handle change, you’ll find yourself scrambling to adjust. Worse, without strong analytics and insights, you’ll be shooting blind, not knowing how the deprecation or forced upgrade impacts your business outcomes.

An AI model agnostic approach lets you navigate these inevitable transitions thoughtfully and proactively, rather than reacting under the pressure of abrupt changes. With the right infrastructure in place, you can evaluate how new models align with your business goals, make informed decisions about upgrades, and execute transitions with confidence.

With model-agnostic systems, you win on three fronts:

  1. Flexibility to adopt new technologies
  2. Lower costs by choosing models strategically
  3. Reduced risks from provider dependency

While there’s some technical heavy lifting to make it happen, the payoff is worth it, for both your present and future AI strategy. Let’s unpack why LLM-agnosticism matters, how it provides a real competitive edge, and what’s involved in making it a reality.

Why LLM Agnosticism is Crucial Right Now

The AI arms race is in full swing, and it’s changing how organizations think about their investments. Every few months, a new LLM disrupts the market. Just look at Deepseek, a fresh contender offering performance that competes with larger vendors, but at a fraction of the cost. Companies that built rigid AI systems locked to a single model? They’re stuck. Companies with an LLM-agnostic system? They can evaluate Deepseek, adopt it immediately if it’s a good fit, and move on with no headaches. That’s agility in action.

It’s not just about the excitement of new models, either. The stakes are higher than ever. If you’re building AI systems that rely on a single provider, you’re exposed to all sorts of risks: pricing changes, outages, compliance hiccups, and more. Why would you put yourself in that position when there’s a better way?

On top of risk reduction, going AI model-agnostic keeps your options open, both for today and tomorrow. You can plug in cutting-edge models or tailor your system to include proprietary models from customers, making your offerings even more valuable. And the operational benefits are clear: you avoid technical debt, scale quickly without rewriting your systems, and maintain flexibility in a market rife with change.

Adaptability, cost control, and risk reduction; it’s hard to imagine a stronger business case.

What Does It Mean to Be LLM-Agnostic?

An LLM-agnostic approach means you’re not married to any single model or provider. It gives you the freedom to adopt any LLM that works best for your needs, easily switch between options, and integrate specialized or customer-specific models when needed. Think of it as creating a universal power adapter. No matter where you go or what socket you encounter, your adapter will work.

This kind of setup goes beyond simply reducing dependency on a single provider, it opens the door to greater innovation. For example, you can integrate a highly specialized model for tasks like fraud detection or regulatory compliance without extensive reengineering. At the same time, it allows you to transition to more cost-effective providers without disrupting your operations. AI model-agnostic systems not only support your current objectives but also prepare your systems to adapt to future challenges and opportunities with ease.

The Real Benefits (And Why They Matter)

Let’s talk about big-picture outcomes. What’s the real value of an AI model-agnostic system?

First, there’s future-proofing. The fast pace of AI development means organizations can’t afford to be tied to outdated technology or locked into a provider that may not keep up. New models like Deepseek can quickly disrupt the landscape, and an LLM-agnostic approach ensures you’re ready to adopt better options as they emerge, without requiring costly or time-consuming infrastructure changes.

Next, there’s cost optimization. AI can represent a significant investment, and not every application requires the most advanced or expensive model. An AI model-agnostic framework allows you to align the right model to the right task, using high-performance options where necessary and more cost-effective models for routine tasks. Transitioning to providers that offer lower costs becomes straightforward, helping organizations save both time and money over the long term.

Finally, there’s risk mitigation. Placing too much reliance on a single provider creates vulnerabilities, whether it’s unexpected price increases, outages, or a lack of compliance with evolving regulations in your region. A model-agnostic strategy builds resilience into your system, making it easier to switch providers, integrate solutions that meet local compliance needs, and maintain steady operations regardless of external disruptions.

Add all of this up, and the takeaway is simple: You’re building an AI system with staying power.

How to Make LLM Agnosticism Happen

Getting to an LLM-agnostic architecture involves putting the right technical pieces in place. It’s not overly complicated, but it does require a little upfront effort.

The first priority is building an abstraction layer. Think of this as a bridge between your application logic and the LLMs themselves. It smooths out the differences between models, so your system can swap them in and out with little disruption. Without this layer, you’d be stuck reconfiguring everything every time you wanted to use a new model.

You’ll also need a unified API to keep your inputs and outputs consistent. Whether you’re working with one model or ten, this API ensures the system behaves the same way. That means no surprises in how data is handled, errors are flagged, or results are formatted, regardless of which LLM is doing the heavy lifting underneath.

Another critical piece is rigorous and automated regression testing. LLMs generate non-deterministic outputs, meaning the results can vary, even with the same input. On top of that, prompts will not always behave the same way across different LLMs, and each model often comes with its own tone and writing style.

Without preparation, this can lead to outcomes that feel inconsistent or unpredictable. A strong testing framework ensures that switching models doesn’t disrupt functionality or user experience. Real-world scenarios should be replayed to validate workflows, and benchmarks need to confirm that outputs meet expectations for accuracy, tone, and consistency, even as models change.

Strong analytics are also critical for ensuring a seamless and impactful transition between models. By maintaining and leveraging historical analytics, you can monitor whether your LLM-agnostic experience is meeting business objectives both before and after a switch. These analytics can reveal whether key business metrics—like response accuracy, user satisfaction, conversion rates, or operational efficiency—remain aligned with expectations or require further adjustments. Without a strong foundation of data, it’s nearly impossible to gauge whether a new model is truly driving value or inadvertently introducing blind spots. Ensuring historical analytics are built into your observability strategy creates a solid understanding of both past performance and current impact, helping the system continuously align with broader business goals.

Finally, you’ll want LLM observability tools to monitor system performance. These help you track metrics like latency and cost in real time, as well as compare new models against historical benchmarks. Observability isn’t just about catching issues; it’s about actively optimizing your model-agnostic system as things evolve.

Putting It All Together

If this sounds like a lot, don’t worry; it’s manageable when approached step by step. Start with an assessment of your current AI systems, workflows, and business needs. Determine where you might benefit from multi-model capabilities or where current vendor lock-in is limiting your options. From there, build out the abstraction layer and unified API, test rigorously, and establish monitoring for long-term optimization.

Many businesses also roll this out gradually. For instance, start with one use case where switching models might have immediate cost or performance benefits, and expand from there.

Real-World Impact

So what does this look like in practice? One company moved from ChatGPT 4.0 Mini to Gemini 2.0 Flash, attracted by its speed and lower cost. With an LLM-agnostic setup, they seamlessly integrated the new model, improving business outcomes while cutting costs.

A healthcare provider faced sudden compliance restrictions tied to geographic regions, threatening their operations. Their LLM-agnostic framework allowed them to quickly deploy domain-specific models to meet regulatory requirements, avoiding costly downtime or legal risks.

Meanwhile, a retail organization prepared for peak holiday demand by dynamically blending models from multiple providers. They routed routine inquiries to cost-effective models and escalated complex issues to higher-performing ones, scaling efficiently while keeping customer experiences seamless.

Each organization used their LLM-agnostic architecture to adapt quickly, reduce risk, and stay ahead, without rebuilding their systems or missing a step.

Wrapping Up

At its core, LLM agnosticism is about giving your business options. It’s about flexibility, adaptability, and peace of mind in a world where AI is central to success, but constantly shifting under your feet.

If your AI strategy isn’t built to handle rapid change, you’re setting yourself up for frustration, or worse, irrelevance. But by investing in an LLM-agnostic architecture, you’re making sure your systems are ready for whatever the future holds.

Adaptability isn’t just a luxury anymore, it’s the price of staying competitive with AI. And the sooner you start down this path, the better positioned you’ll be to make the most of what’s coming next.

Omnichannel vs. Multichannel: What is the Difference?

Posted on Allyson Scott

Customer preferences and behaviors continue to evolve, making the customer experience (CX) a critical battleground for businesses. Both omnichannel and multichannel marketing strategies aim to meet customers where they are, but they do so in distinct ways.

From eCommerce managers and marketing professionals to CX leaders and online retail executives, understanding these differences is essential for most people responsible for optimizing customer journeys and driving growth.

This article unpacks the nuances between omnichannel vs. multichannel strategies, explores their applications, and highlights which might work best for your business.

What is omnichannel marketing?

Omnichannel marketing is a holistic strategy that integrates all customer touchpoints into a seamless brand experience. Whether customers interact with your brand on social media, visit your website, or shop in-store, the messaging and experience remain consistent and interconnected. Said another way, omnichannel focuses on all your channels.

Rather than solely focusing on having a presence across various platforms, this strategy makes each customer interaction feel unified, regardless of the channel. Omnichannel marketing prioritizes the customer, crafting journeys that adapt dynamically to individual behavior and preferences.

How does omnichannel marketing work?

Omnichannel marketing aligns every channel to provide a cohesive, personalized experience. Let’s say a customer places items in their cart on your website, but doesn’t complete the purchase. An omnichannel strategy might trigger a personalized email reminder, followed by targeted ads on social media or a mobile app notification. Once the customer revisits your website, they might also see tailored product recommendations based on their browsing history.

The result? A fluid, customer-centric experience where transitions between channels are unnoticeable.

Brands like Nike excel at this by synchronizing their app, stores, and website to provide an experience tailored to customer preferences. For instance, users can “heart” their favorite styles on the app and access them in-store through personalized services.

Omnichannel marketing requires technology integrations, such as customer data platforms (CDPs), marketing automation, and AI tools, to monitor and adapt to customer actions in real time.

What is multichannel marketing?

Multichannel marketing, as the name suggests, involves engaging customers through multiple communication channels, such as email, social media, paid ads, and physical stores.

Understanding omnichannel vs. multichannel

 

However, unlike omnichannel marketing, multichannel strategies often lack integration between these touchpoints. The channels operate independently, each with a unique message or campaign tailored to its format and audience.

How does multichannel marketing work?

Multichannel marketing works by leveraging individual platforms to reach customers. For example, a brand might run an email campaign, while also promoting products through social media ads and display banners. Each channel operates in isolation, engaging customers at various stages of the buyer’s journey.

The focus here is on expanding brand reach across multiple platforms, rather than creating a synchronized experience. While multichannel marketing lacks the fluidity of omnichannel efforts, it can still effectively boost visibility and engagement through channel-specific strategies.

For example, Apple uses multichannel tactics by employing retail stores as experiential spaces, online platforms for eCommerce, and services like Apple TV+ to promote its ecosystem. Each channel serves its own purpose, while being loosely connected to the larger brand.

Key consideration for multichannel

Since channels in multichannel marketing work independently, businesses need to ensure that the messaging on each platform is relevant and not repetitive. The strategy ultimately aims to increase customer touchpoints, capturing attention across various platforms.

Omnichannel vs. multichannel – what’s the difference?

The primary difference between multichannel vs. omnichannel lies in their focal points. Multichannel focuses on the number of channels being used, while omnichannel focuses on creating a consistent customer experience across all channels.

Here’s a breakdown of key differences:

Feature Multichannel Marketing Omnichannel Marketing
Focus Channels and platform reach Unified customer experience
Integration Channels operate independently   Channels are interconnected
Customer Experience   Varies by channel Seamless and consistent across touchpoints
Approach Channel-first Customer-first
Personalization Limited to specific channels Extensive and tailored to individual behaviors
Technology Required Moderate High (requires AI, advanced integrations, CDPs)

 

Think of multichannel as individual branches on a tree—each branch operates independently, offering value on its own. Whether it’s a website, social media, or email, these channels function separately, each providing its own unique experience. Omnichannel, on the other hand, integrates those branches into a unified canopy, ensuring all channels work together seamlessly. This creates a more cohesive, consistent, and meaningful experience for the customer, where the journey feels connected regardless of the platform they interact with.

Examples of omnichannel marketing

Many leading brands actively leverage omnichannel marketing to enhance customer experiences.

Example 1: Starbucks

Starbucks excels at omnichannel by integrating its mobile app with in-store experiences. Through the app, customers can place orders, earn loyalty points, and reload their digital wallets. Whether they’re browsing on their phones or placing an in-store order, the data stays synchronized, ensuring a streamlined experience.

Example 2: Sephora

The beauty giant bridges online and offline worlds using personalized data. Sephora’s app allows users to book in-store consultations, check loyalty points, or virtually try products before heading into a physical store. Their cohesive blend of customer convenience and personalization is the epitome of omnichannel success.

Implementing omnichannel marketing for your business

To adopt an omnichannel strategy:

  1. Gather data: Use customer data platforms to collect and unify data from all touchpoints.
  2. Clear any roadblocks: Ensure your sales, marketing, and customer service teams collaborate for consistent messaging.
  3. Personalization tools: Invest in tools like AI to deliver tailored messages across platforms.
  4. Metrics & adaptation: Continuously measure engagement at each touchpoint to optimize experiences and anticipate customer needs.

Why omnichannel should be your long-term goal

While multichannel can be a good starting point for businesses new to digital engagement, an omnichannel customer service strategy offers long-term advantages. By integrating all your brand’s channels and centering on the customer’s needs, businesses benefit from increased loyalty, stronger engagement, and sustainable growth.

Pro tip: If resource limitations make omnichannel challenging, start by building strong, independent multichannel systems. Gradually focus on integrating these components as your team and technology stack mature.

At the end of the day, omnichannel is about creating a brand-defining experience for your customers—one that molds a memorable, enduring relationship with them.