How to Use AI for Customer Engagement
TL;DR: AI for customer engagement means using artificial intelligence to improve how your contact center interacts with customers, both through AI agents that handle conversations independently and through real-time assistance that helps your human agents perform better. Organizations that succeed invest in change management, build data infrastructure that connects conversations to outcomes, and typically start with augmentation before attempting full automation.
Contact center leaders face intense pressure. Your team handles thousands of interactions every week, but scaling agent performance remains elusive. Agent attrition continues as your best-trained people leave, while customer volumes keep climbing. The pressure from executives to deliver more with the same budget compounds the challenge, forcing you to choose between service quality and operational costs.
The challenge is that most AI initiatives fail to generate measurable results. Success separates from wasted budget when you understand what AI actually does, how it works in practice, and which strategies deliver real outcomes. Success also requires investing in change management and data infrastructure while establishing strong governance alongside technology selection.
This guide covers what AI for customer engagement actually means, proven strategies that work in real contact center environments, and how to make AI work for your organization without the common pitfalls that derail most implementations.
What is AI for customer engagement?
AI for customer engagement means using artificial intelligence to improve how your organization interacts with customers, whether through voice calls, chat, messaging, or any other channel. AI for customer engagement works in two fundamental ways.
First, AI helps your human agents during live conversations with real-time suggestions and support. Tools like Cresta Agent Assist provide instant information retrieval while offering behavioral coaching and multilingual support.
This technology gives agents instant knowledge base access with AI-generated summaries during conversations, plus real-time guidance and multilingual translation support. Agent Assist AI supports your team during the moments that matter most, helping agents say the right thing at the right time while reducing the mental load of juggling multiple systems.
Second, AI agents can talk directly with your customers. These AI agents manage conversations independently across voice, chat, and messaging without human intervention. Unlike traditional chatbots that follow scripts, AI agents understand natural language and handle complex, multi-intent conversations. They can manage tasks like processing returns, scheduling appointments, updating account information, and troubleshooting technical issues while maintaining natural conversations that match your brand voice.
Some platforms integrate both customer-facing AI and agent assist into a single system. When your AI agent hands off a complex issue to a human agent, that transfer preserves full context so customers don't repeat themselves. The human agent receives a complete summary of what the AI has already covered, including:
- Conversation history
- Customer information
- Attempted solutions
This prevents customers from repeating themselves and gives your agent everything needed to resolve the issue on the first try.
How does AI improve customer engagement?
AI improves customer engagement by making both automated and human-assisted interactions more effective.
On the efficiency side, AI agents resolve routine inquiries without human involvement, reducing wait times and freeing agent capacity. And on the quality side, real-time assistance helps human agents resolve issues faster and more thoroughly by surfacing the right information at the right moment.
These gains mean you can handle more customers without hiring proportionally more people. According to the 2024 CCW Digital Market Study, most contact center leaders see AI as a way to absorb simple tasks so agents can focus on complex work, with only about 20% expecting AI to lead to significant headcount reduction. This shows that AI primarily helps you grow and scale, rather than replace humans.
But handling more volume only matters if you're handling it well. First call resolution (FCR) is the metric that customers actually care about. Customers consistently prefer waiting longer for an agent who can resolve their issue on the first attempt rather than getting a quick response that doesn't solve anything.
AI improves FCR in two ways. Customer-facing AI agents can handle straightforward issues completely, freeing up human agents to focus on complex problems that require judgment and relationship skills. And when human agents handle interactions, real-time AI assistance gives them instant access to the right information and guidance, increasing their ability to thoroughly solve problems during the first conversation.
5 proven AI customer engagement strategies
Here’s how you can use AI for your customer engagement strategies:
1. Start with real-time agent augmentation before full automation
Real-time agent augmentation delivers results faster than full automation because you enhance existing workflows rather than rebuilding them completely. Companies that get AI right usually start by helping their human agents first, rather than trying to automate everything on day one.
To support this, the 2024 Cresta State of the Agent Report found that 81% of agents report performing better because of the technology made available to them, and 79% say good software makes or breaks whether an agent is good at their job.
Real-time agent assist tools provide behavioral coaching along with instant knowledge searches and automated summaries to cut after-call work. This augmentation strategy works because it builds on what your agents already do well rather than replacing them. Your experienced agents become more effective when AI provides real-time guidance and context, freeing their capacity for relationship building and complex problem solving. And your newer agents benefit from AI-powered coaching during live interactions, so they perform at higher levels faster while they build their skills.
By applying this principle, United Airlines achieved a 15% reduction in average handle time (AHT) after deploying Cresta Agent Assist. The airline also saw 90% positive agent experience scores and 97% employee satisfaction, demonstrating that augmentation improves both efficiency and agent engagement.
2. Deploy conversational AI for routine interactions
Once you've proven AI works for your team through agent augmentation, you can identify routine interactions that AI can handle entirely. Success requires identifying situations where customer needs are predictable, the required information is structured, and conversation flows follow consistent patterns.
The challenge is that most organizations rely on guesswork and incomplete data to make these decisions, leading to false starts, wasted budget, and subpar bots that frustrate customers. Cresta Automation Discovery solves this by analyzing your historical conversations to build a blueprint for automation. The tool identifies which conversation topics are best fit for AI agents based on complexity, deviation patterns, and resolution rates, then assigns an Automation Readiness score to each. You can map conversation flows from greeting to resolution, see where deviations typically occur, and export AI agent prompts directly for rapid prototyping, moving from insight to effective automation without starting from assumptions.
Snap Finance, a consumer financing provider experiencing 40-50% year-over-year growth, increased their deflection rate by 5.5x after implementing Cresta AI Agent. They also achieved a 40% reduction in AHT and 23% higher customer satisfaction (CSAT) scores. The key was identifying which interactions were truly ready for automation rather than trying to automate everything at once.
3. Implement human oversight for AI quality
AI agents won't be perfect every time, so you need oversight systems in place. Human-in-the-loop oversight creates a supervisory layer where human operators can provide real-time monitoring, with the ability to "whisper" guidance or fully intervene in AI agent conversations when needed.
The challenge is doing this at scale without creating a bottleneck. Cresta Agent Operations Center solves this by uniting oversight and intervention for both human and AI agents in a single command center.
Supervisors can monitor hundreds of live conversations simultaneously and step in when it matters, guiding AI agents with instructions that shape their next response, sending direct messages relayed to customers verbatim, or taking over entirely when higher-risk moments arise. The system detects emotional cues, compliance risks, and low-confidence signals that could derail conversations, alerting supervisors when intervention can add value or when an AI agent needs support on complex or high-stakes interactions.
When AI escalates to humans, full context transfers, including conversation history, customer information, and attempted solutions. This prevents customers from repeating themselves and gives your agent everything needed to resolve the issue.
The 2024 CCW Digital Market Study found that contact center leaders worry most about security risks, misinformation, and biased responses when implementing AI. Oversight systems address these concerns directly by keeping humans in the loop for situations that require judgment.
4. Use conversation intelligence to drive continuous improvement
Analyzing 100% of conversations reveals patterns that remain invisible when relying solely on small sample reviews. The smartest companies treat AI deployment as the beginning of an ongoing optimization process rather than a one-time implementation.
Tools like Cresta Conversation Intelligence analyze all interactions to reveal patterns invisible in sample-based monitoring. Ask questions in natural language, like "Why are customers calling about billing this week?" and get evidence-backed answers in minutes.
For a broader view of what's driving contact volume, Topic Discovery shows conversation themes through visual clustering, overlaid with metrics like resolution rates and sentiment, so you can see not just what customers are talking about but how those conversations are going.
This shift from sampling to complete visibility changes how fast you can act. CVS Health went from scoring just 5% of calls to 100% after implementing Cresta Conversation Intelligence. According to Srikant Narasimhan, VP and Head of Enterprise Customer Experience & Insights at CVS Health, "We don’t need to ask. We know what's wrong." What used to take weeks of manual analysis now surfaces in real time.
Once you can see everything, the next question is what actually matters. Outcome Insights answers that by connecting specific agent behaviors to business results, showing how much revenue you lose when agents skip discovery questions or fail to address objections. This moves beyond correlation to causation, identifying which actions actually drive outcomes rather than just tracking activity.
5. Balance automation with authentic human connection
Every contact center leader faces the same question: what's the right mix of AI and human agents? The answer depends on your customers, your industry, and what you're actually trying to accomplish. Most agents are open to handling more complex work when AI takes routine tasks off their plate, but that willingness often depends on whether the shift comes with real career growth or just harder work for the same pay.
Some customers will always prefer human interaction for complex or emotionally charged situations. Even when AI could technically handle an issue just as quickly, many people simply feel better talking to a person, especially when something has gone wrong or when the stakes feel high.
The most effective strategies give customers choice while designing intelligent routing that guides them toward the most effective channel based on their specific situation and history. If someone has called three times about the same issue, routing them to your most skilled human agent makes more sense than another AI interaction.
The strategic question becomes what human agents should focus on rather than cutting headcount. As AI handles routine transactions, human agents can focus on relationship building, complex problem solving, and situations that require empathy and judgment. This approach positions your contact center as a strategic capability that drives customer lifetime value rather than a cost center to be minimized.
Making AI work for your contact center
Contact centers today look completely different from how they did three years ago. AI handles routine interactions autonomously while human agents receive real-time guidance that makes them dramatically more effective.
The companies building these capabilities now are setting themselves up to win as customer expectations keep rising and budgets stay tight.
But getting there requires more than technology selection. You need governance that's robust but agile, automation that improves efficiency without degrading experience, and executive commitment to change management.
Cresta brings together Conversation Intelligence, Agent Assist, and AI Agent on shared infrastructure, so data, models, and governance work together rather than fragmenting across point solutions. Cresta Conversation Intelligence analyzes 100% of interactions and surfaces what's driving customer contacts. Cresta Agent Assist provides real-time guidance and knowledge retrieval during live conversations, plus automated summaries that eliminate after-call work.
Cresta AI Agent handles voice and digital conversations with intelligent escalation to humans when situations require judgment, and the Agent Operations Center gives supervisors visibility to monitor and intervene when needed.
What makes this architecture different is that insights feed directly into action. The platform identifies which interactions are ready for automation, which behaviors drive outcomes, and where human judgment matters most. Organizations can progress from analytics to augmentation to automation without losing visibility across their operation.
Visit our resource library to explore more guides on AI customer engagement strategies, or request a Cresta demo to see how these capabilities work together in your specific contact center environment.
Frequently asked questions about AI for customer engagement
What's the difference between AI agents and AI-assisted agents?
AI agents handle customer conversations independently without human intervention, managing tasks like scheduling, account updates, and troubleshooting on their own. On the other hand, AI-assisted agents are human agents who receive real-time support from AI during conversations, including behavioral hints, knowledge retrieval, and compliance reminders.
What percentage of customer interactions should be automated?
There's no universal answer. The right percentage depends on your customer base, interaction complexity, and competitive positioning. Some organizations automate 30-40% of interactions successfully, while others find 60-70% appropriate for their use case. The key is using conversation intelligence to identify which specific interactions are actually ready for automation based on complexity, deviation patterns, and customer acceptance, rather than setting arbitrary targets.
What skills do agents need in an AI-augmented contact center?
As AI handles routine work, the conversations that reach human agents tend to be more complex, more emotional, or both. That means agents need stronger problem-solving abilities, emotional intelligence, and judgment for non-standard situations.
They also need comfort working across multiple channels and alongside AI tools rather than seeing them as separate from their workflow. The good news is that AI assistance actually helps agents develop these skills faster by providing real-time coaching during live conversations.
How do I measure the success of AI customer engagement initiatives?
Focus on business outcomes rather than technology metrics. Track resolution rates, customer satisfaction, handle time, and revenue impact rather than just containment percentages or automation rates. You also need to connect conversation data to downstream results like retention, expansion, and lifetime value.
