Customers reach out to brands on the phone, via chat, email, messaging apps, and social media. No matter how they want to connect, consumers now expect each interaction to be quick, accurate, and contextually aware of what happened previously. According to Sinch's 2025 research, 81% of consumers will respond negatively when they have to repeat themselves to multiple customer support agents. The Twilio 2025 State of Customer Engagement Report revealed that consumers only feel understood by the brands they interact with 45% of the time.

AI is a practical solution to escalating customer engagement volume. However, while CallMiner's 2026 CX Landscape Report revealed that 99% of CX organizations are using some form of automation, just 24% of these leaders reported that their customers rated their automated experiences as very positive. Customers themselves have their doubts: according to a Gartner survey, 64% of respondents want companies to avoid using AI for customer service.

Whitepaper

Empathy & Automation: Three Ways to Make Automated Customer Experiences More Human

Discover how customer intelligence and conversation intelligence can help organizations build more human customer experiences with automation. Explore three practical ways to identify the right interactions to automate, design around real customer behavior and measure automation based on customer outcomes, not efficiency alone.

Ensuring your AI delivers meaningful customer engagement requires clarity around your goals first and foremost, as well as reliable customer insights and processes to act on those insights. Below are some best practices to help you level up your customer engagement while keeping customers satisfied and maintaining human oversight.

What is AI for customer engagement?

AI for customer engagement refers to AI applied to gain insight into customer needs and enable more relevant, responsive interactions throughout the customer journey. Popular implementations include conversation analytics, AI-enabled self-service (e.g., CallMiner OmniAgent), real-time agent assistance, personalization, and proactive outreach. For more examples, see these practical use cases for AI in customer experience.

All of these solutions are complementary. Conversation analytics reveals why customers are contacting you, where engagements fall short and what types of replies lead to issue resolution. Armed with that knowledge, you can enhance both digital and human engagements.

Best practices for using AI for customer engagement

While choosing the right technology matters, how you use them is even more important. Here are 11 best practices for the entire AI lifecycle, from selecting the right problem to focus on and building with quality data to ensuring humans are always in the loop and iterating for improvement. When you follow these best practices, AI can help you deliver interactions that customers will value.

1. Start with a specific customer problem and measurable goals

In CallMiner's 2026 CX Landscape Report, 76% of organizations admitted that automation is driven by internal goals rather than customer needs. Focus on friction points instead, like repeat contacts, slow response times, inconsistent answers, or painful handoffs. Prioritize CX use cases by customer impact, feasibility, and existing data. Establish baselines and success metrics ahead of time. For instance, maybe your first project aims to reduce repeat contacts related to billing questions. Read more about how to automate CX for contact centers.

2. Ground engagement strategies in customer conversations

In CallMiner's 2025 CX Landscape Report, 72% of companies surveyed collect primarily solicited feedback. Solicited feedback only measures feedback from customers who are willing to provide it. Speech and text analytics evaluate every interaction to uncover intent, sentiment, and frustration. Layer survey feedback and operational data on top of this information, segment by reason for contact, channel, customer group and stage in journey, and then prioritize where AI will have the most impact.

3. Build a reliable foundation of customer data and knowledge

AI can only be as reliable as the information it has available. In fact, 52% of CX leaders fear AI will contribute to misinformation. Connect your customer records, interaction history, and approved knowledge articles using integrations. Remediate outdated content and inconsistent definitions, identify owners to keep knowledge up-to talent delivery and limit AI access to only the information needed for each use case.

4. Personalize interactions around customer needs and context

Research from Twilio showed that 71% of consumers abandon purchases when experiences don’t feel relevant. Leverage recent interactions and friction points to customize replies, prevent reps from making customers repeat themselves, and align outreach with what the customer is trying to accomplish now. And know when not to: only 15% of consumers fully trust brands with their data, and 84% want control of their personalization preferences.

5. Design clear paths between AI and human support

According to Gartner, customers' biggest fear about AI in the contact center is that they won't be able to reach a human. Identify what will trigger an escalation. Is it a complex request, multiple failures or detectable frustration? Ensure agents can easily ask for human help and include full context when escalating to that human agent. CallMiner's 2026 report revealed that customers who had extremely positive automated interactions were actually more likely to be escalated to a human agent (76% vs. 68%). AI agent analytics can identify where your handoffs are failing.

6. Support agents with timely, actionable guidance

Gartner predicted that 73% of service organizations would use agent assist by the end of 2025. Agent assist tools such as CallMiner RealTime can present the right knowledge and next steps at the right time during live interactions, helping agents and reducing after-call work. Offer short guidance that can be easily scanned, allow agents to quickly bypass irrelevant guidance, and use conversation intelligence to focus your AI-powered coaching efforts and bridge gaps in your knowledge base.

7. Maintain continuity across engagement channels

Sinch discovered that while 58% of consumers want to be able to transact through their preferred channel, 38% of organizations cite channel integration as one of their top-three frustrations. Maintain context throughout channels, provide consistent answers, and monitor for frustration-based channel switching due to failed self-service attempts. Evaluate the full journey rather than single interactions, since channel-level metrics can hide journey-level problems.

8. Be transparent and protect customer information

Twilio reports that 54% of consumers desire transparency into when they are speaking with artificial intelligence vs. a human. Be transparent about when you are using AI. Clearly explain your data usage practices in plain language. Enforce access controls, retention policies, and utilize safeguards like CallMiner Redact. Monitor AI outputs regularly to ensure they align with your policy and relevant regulations. CallMiner research in 2025 reported that 67% of organizations were deploying AI before having proper governance measures in place.

9. Test AI performance across realistic customer scenarios

According to CallMiner’s 2026 research, 40% of organizations reported AI struggles with nuanced, emotional, or high-risk conversations. Test AI with actual transcripts for common, unclear, and edge-case requests. Measure performance across languages, accents, and demographics, and assess precision, tone, and escalation patterns. Pilot and then expand rollout, like you’ll learn how to do in this guide on agentic AI for contact centers.

10. Measure customer outcomes alongside operational efficiency

Organizations that report extremely positive automated experiences are six times more likely to continuously optimize automation with CX insights (49% vs. 8%). Measure resolution, repeat contacts, customer effort, and satisfaction along with handle time and containment. Determine if automation actually solves a need or just moves work to another channel. Benchmark your results against segment baselines. Read more about the true ROI of CX automation.

11. Create a continuous improvement process

Just 30% of organizations act on customer insights to enhance automated experiences.Capture trends in conversation failures and unmet needs. Own the changes needed to address insights. Change knowledge, workflows, coaching, and AI based on what you learn. Then validate changes are helping the experience shift. Top performers view customer conversations as actionable enterprise intelligence shared across teams.

Common mistakes to avoid

Even well-planned AI initiatives can undermine engagement. Watch for these common pitfalls:

• Trying to automate a broken process. If your policy or workflow is already confusing your customers, AI will confuse them faster and at scale. Fix the root cause first, or address it alongside the automation.
• Viewing higher automation rates as a success metric. If all you’re measuring is containment, you’re only measuring that the customer didn’t reach a human. Measure containment along with resolution, repeat contact, and customer satisfaction.
• Personalizing with inaccurate information. Nothing kills personalization faster than referring to the wrong order number, old address, or problem that the customer already fixed.
• Making human help difficult to find. Obscuring the path to a human agent might net you better containment rates today, but it will drive away your customers tomorrow. In fact, according to Gartner Research, 53% of customers say they would switch to a competitor if a company chose to automate customer service with AI.
• Rolling out AI without committing to monitoring and accountability. Product changes, policy changes, and customer needs will cause your AI to slowly degrade over time. Set up regular reviews and assign a named owner, or you’ll only know there’s a problem when you get complaints.
• Gathering intelligence without dedicating anyone to use it. Dashboards aren’t self-help. Every repeat problem should have an owner, a corrective action plan, and a verification process.

How conversation analytics supports better AI engagement

Most of these efforts rely on knowing what’s actually happening in your customer interactions. Conversation analytics uncovers intent, sentiment, effort, and resolution across all human and AI interactions so teams can focus on what matters most, backed by data. You can use those insights to improve self-service design, coach agents, update knowledge, and repair the underlying processes that create unnecessary contacts.

CallMiner Eureka uncovers insights from 100% of omnichannel customer interactions. Use powerful analysis and visualization to fuel agent augmentation and automation, then close the loop by feeding results back into analysis. Estafeta reduced service time for their customers by 78% using this methodology.

Learn how CallMiner can help you develop your customer engagement strategy. Read about why organizations choose CallMiner or request a demo.

Frequently asked questions

When should you start experimenting with AI-powered customer engagement?

Pick one customer pain point, baseline it, test one use case well, and then scale.

How can companies humanize AI-powered engagement efforts?

Only use data that’s pertinent, be transparent about your use of AI, clearly state how you’re using customer data, respect customer preferences, and have strict boundaries around privacy. 61% of consumers don’t think brands will use their personal data for their best interest according to Twilio.

When should AI hand off to a live agent?

The four major instances are complex requests, when the customer explicitly asks for a person, when the chatbot fails multiple times and customer frustration is detected or when an issue is detected that requires human discretion. According to Gartner research, 54% of customers have more faith in an agent's ability to recommend the right product/service.

Which KPIs help measure the effectiveness of AI customer engagement?

Metrics around resolution, repeat contacts, customer effort score, customer satisfaction and use-case specific metrics such as retention/conversion are commonly used. Efficiency metrics should be considered as well.

How does conversation analytics benefit AI customer engagement?

It tells you what the customer is trying to achieve and where conversations are falling short. Use it to identify gaps in your self-service, internal knowledge base and handoff processes and continue to make improvements.