Before the rise of generative AI, most people didn’t think twice about what powered the systems behind their daily digital experiences. However, as artificial intelligence increasingly plays a more active role in how brands engage with customers, it’s worth understanding what makes it all work.
The terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably, but they’re not the same. ML is a subset of AI. It’s a method for achieving intelligence, not a competing approach.
To draw a parallel: if AI is the goal, building systems that can act with intelligence, then machine learning is one of the most successful means of getting there. Just as early aviation pioneers tried to mimic birds with flapping wings, symbolic AI attempted to replicate reasoning with hard-coded rules. Machine learning emerged like the modern jet engine: more powerful, more scalable, and better suited to complex tasks.
Understanding this relationship isn’t just academic. It has real consequences for how CX leaders evaluate automation strategies, allocate budget, and deploy tools.
“AI will automate customer interactions, capture customer intent, and route inquiries to the right skilled agent,” notes Forrester Vice President Kate Leggett.
What Is Artificial Intelligence?
Artificial Intelligence is the broader goal: building systems that can reason, plan, adapt, and interact intelligently with people. It includes everything from early rule-based expert systems to today’s large language models (LLMs). AI may use ML, but it doesn’t have to.
In CX, AI enables agents to interpret context, understand nuances, and take meaningful action. This often involves a mix of language processing, orchestration, and AI engineering to align system behavior with business goals. LLMs like GPT, Claude, and Gemini are examples of generative AI that support open-ended conversation, intelligent routing, and dynamic task execution.
What Is Machine Learning and Deep Learning?
Machine learning is a method that enables computers to enhance their performance on a task by analyzing and identifying patterns in data over time, thereby improving with each new example. It’s particularly good at sorting information into categories, drawing conclusions, and making useful predictions through repeated exposure to examples.
In the early days of CX automation, ML drove use cases like spam filtering and sentiment analysis. Today, it continues to power background classifiers in Quiq’s Reporting & Analytics capabilities, enabling smarter routing, trend detection, and operational insights. While expectations have evolved, the fundamentals haven’t changed. ML still learns from labeled examples and fine-tunes performance through ongoing iteration.
Machine learning continues to play a central role in enabling modern AI. The large language models that drive today’s most advanced agents are trained using extensive ML techniques on vast datasets. While users experience a fluid, human-like interface, that outcome depends on the layered ML systems powering it behind the scenes.
Deep learning (DL) builds on machine learning by stacking layers of artificial neurons that learn from data step by step. It’s especially effective at tackling more complex problems, such as deciphering what’s being said in a conversation or recognizing faces in a photo. It’s the method behind things like voice assistants, language translation, and image recognition. While it falls under the umbrella of ML, it’s the approach that powers many of the most impressive applications we associate with AI today.
Specialized Uses of Machine Learning and LLMs in CX
Within CX applications, AI systems often combine multiple techniques, including ML and large language models (LLMs). While ML and LLMs are both forms of AI, they serve different functions. ML focuses on pattern recognition and prediction; LLMs excel at language-based reasoning and adaptability.
Accuracy and Use Case Fit
When you need something done with precision, like detecting whether a message contains a regulated complaint, machine learning is often the best tool for the job. These models improve through learning from labeled examples and are evaluated using performance metrics such as accuracy, recall, and precision to assess their consistency in delivering the correct result. That consistency makes them ideal for repeatable tasks, such as sorting messages or handling routine routing decisions.
When the goal shifts to flexibility, such as interpreting a customer’s vague product question, LLMs are often a better fit. They’re built to handle open-ended questions, follow nuanced directions, and communicate in a way that feels more conversational than scripted. That strength, though, can be a trade-off. Because they’re not trained on tightly defined tasks, their output can vary, and that makes them less reliable when precision is critical.
Take one Quiq client, a major airline. They used an ML classifier to spot Department of Transportation (DOT)-regulated complaints. The model was tuned to flag anything even remotely risky. An LLM could have offered a more nuanced explanation, sure, but when compliance is on the line, nuance isn’t always the goal. Precision is.
Data Requirements
ML models are only as good as their training data. Each use case demands curated examples—often annotated by hand—to ensure the model learns the right patterns. This makes them reliable but rigid.
LLMs are trained on large volumes of text, both publicly available and proprietary, to help them recognize language patterns and produce human-like responses. Once deployed, these models rely on prompts and context windows to guide their behavior, rather than requiring retraining. This approach enables faster adaptation, even in environments where data is limited. But the quality of the results still depends on the clarity of the inputs and the trustworthiness of reference material, such as the documents used in a retrieval-augmented generation (RAG) pipeline.
Cost, Time-to-Market, and Scalability
ML is expensive to train and deploy at scale. Each new classifier can require model training, infrastructure setup, and ongoing maintenance.
LLMs are also expensive to train, but that cost is typically handled upstream by companies like OpenAI or Google. When Quiq uses an LLM, it leverages an already-trained model and customizes it via prompt engineering or retrieval techniques. This drastically reduces time-to-value.
Quiq’s AI Studio shows how quickly brands can deploy new agents without needing to rebuild a model from scratch. In client examples like Molekule, AI agents went live in days, not months, and scaled dynamically as customer needs changed.
According to an article in CX Today, “Agentic AI has emerged as a game‑changer for customer service, paving the way for autonomous and low‑effort customer experiences.” They predict up to 80 percent of customer issues could be resolved without human intervention by 2029.
Benefits of Layering Machine Learning Within AI for Smarter Automation
Together, AI and ML create a flexible, multi-layered CX stack:
- ML handles the behind-the-scenes work: session detection, message classification, and inappropriate content filtering.
- AI handles the dynamic conversations: resolving issues, escalating to agents, and following instructions in natural language.
ROI (Return On Investment)
The value of combining AI and ML becomes apparent quickly, particularly in areas that matter most to CX leaders. Contact centers see shorter average handle times, more accurate routing, and fewer escalations to human agents. This leads to lower cost per contact and more efficient workforce planning.
But the ROI isn’t just operational. Customers get faster answers, fewer dead ends, and smoother transitions between AI agents and human support. For example, Quiq clients that use both ML classifiers and LLM-based agents have reported improved CSAT scores and reductions in abandonment rates.
When you can solve issues faster, with fewer touches and less friction, the return compounds over time, especially at scale.
Reliability
When accuracy matters most, like meeting compliance standards or executing a high-stakes task, ML is still the safer bet. Its outputs are predictable, measurable, and repeatable. That makes it ideal for situations where consistency and precision can’t be compromised.
LLMs, on the other hand, are incredibly useful for flexibility and language understanding, but they aren’t perfect. They sometimes “hallucinate.” That is, generate content that sounds plausible but is factually wrong. They can also stray off-topic or misinterpret subtle context.
That’s why Quiq uses a layered approach: ML handles the validation, checks, and behind-the-scenes classification that support the LLM’s more open-ended reasoning. For example, after an LLM generates a response, an ML model may be used to confirm that the resolution offered aligns with business rules or that next steps were taken. This redundancy helps ensure that the AI behaves responsibly, even in complex workflows.
Speed to Launch
Thanks to LLMs and tools like AI Studio, brands can deploy fully functional AI agents in just days. There’s no retraining required; just clear instructions. This is made possible by prompt-based AI, where detailed prompts guide behavior without altering the underlying model. Teams can rapidly test, revise, and deploy updates simply by refining the prompts.
According to McKinsey’s 2024 State of AI report, 65% of organizations that have adopted AI report that it has already helped increase revenue in at least one part of their business. The value becomes even more tangible when AI and ML are used together to streamline customer-facing operations.
Final Takeaways for CX Leaders
AI and ML don’t compete; they complement each other. Machine learning is one of the most effective techniques used in modern AI systems, especially when precision and repeatability are key. When layered with conversational AI agents, ML helps teams scale automation intelligently and reliably.
For CX leaders, knowing when to apply ML-based automation versus when to use a more adaptable AI agent is the difference between just automating and truly improving the customer experience.
Ready to put AI and ML to work in your CX strategy?
- Explore how Quiq’s AI Studio, AI Agents, and Reporting & Analytics tools help you scale automation with speed and intelligence.
- Schedule a demo to see it in action.
