Editorial
The Gist
- The human gap. AI budgets are growing, but most CX teams lack the skills and governance to use it responsibly.
- Strengths and limits. AI excels at scale and knowledge retrieval, but humans remain essential for empathy and nuance.
- Human-guided AI. Embedding skilled agents in the AI lifecycle ensures accuracy, ethics, and brand alignment.
- Readiness framework. Audit skills, establish governance, invest in learning, and use agent-assist tools to bridge the gap.
- The CX future. Value comes from uniting AI’s precision with human expertise to deliver trust and better outcomes.
Table of Contents
- The Human Gap in AI Adoption
- AI as a Journey, Not a Destination
- Human-Guided AI: A Strategic Imperative
- Building Human-Guided AI Readiness: A Practical Framework
- Bridging the Gap, Building the Future for AI in CX
The Human Gap in AI Adoption
As customer expectations rise and digital interactions multiply, organizations are racing to integrate AI into their customer experience (CX) strategies. Yet, despite growing tech budgets, many CX teams find themselves unprepared to harness AI effectively. The gap isn’t just technical — it’s human. Closing this readiness gap requires more than deploying smart tools; it demands a thoughtful blend of human empathy and AI precision.
Forrester’s latest research reinforces this point: while CX leaders are ramping up AI investments, many lack the internal skills to deploy these tools responsibly. This underscores the case for human-in-the-loop strategies, where human expertise guides AI to deliver real value.
This builds on the thinking I laid out in a prior CMSWire piece, “Flashy AI Pilots Don’t Build CX Value.” I made the case that governance and strategy — not technology demos — are the true starting points for AI success.
Let’s dive deeper.
Where Humans Shine, and Where AI Falls Short
While AI can process vast datasets and deliver instant responses, it lacks the emotional intelligence that defines great customer service. Humans excel at providing care and empathy — the emotional glue of customer relationships. But when faced with uncommon or complex issues, agents may struggle, especially when the knowledge required is obscure or has faded in their memory since initial training. This divide creates friction — and opportunity with AI.
With access to vast repositories of engagement-specific information, AI can deliver accurate answers quickly. This is where generative AI shines. Yet, it still struggles with the nuance and emotional intelligence that define meaningful human connection.
Related Article: Research Shows Human-Centered AI Key to CX Success
AI as a Journey, Not a Destination
That’s why organizations should treat AI implementation as a journey — one that begins with a deep understanding of customer interactions and the drivers behind them. By analyzing engagement patterns, CX leaders can identify which inquiries are best suited for AI deflection. Typically, these are straightforward or knowledge-heavy use cases that don’t require emotional nuance or human reasoning. Deploying conversational AI to handle these types of interactions first allows human agents to focus on higher-value customer engagements.
Forrester concurs with this approach, advising leaders to begin their AI journey by targeting mature, well-defined use cases — such as knowledge-heavy inquiries — before expanding into more nuanced interactions.
Here, it’s also helpful to reinforce advice from my last article; CX leaders should prioritize AI intervention at bottlenecks that truly impact loyalty or productivity/efficiency — not just what’s technically feasible. Just because you can apply AI at a point in your customers’ journey with you doesn’t mean you should. It’s important to invest in implementations that will move the needle for your business.
Best Fit for Humans vs. AI in Customer Experience
Understanding the division of strengths ensures CX leaders deploy AI responsibly while preserving the value of human empathy.
| Scenario | Best Approach | Reason |
|---|---|---|
| Simple, repetitive inquiries | AI-powered self-service | Faster response times and reduced agent workload |
| Complex, high-value interactions | Human agent support | Requires empathy, problem-solving and nuance |
| Urgent or VIP requests | Human agent support | High-touch interactions improve satisfaction and loyalty |
| Knowledge-heavy FAQs | AI copilots and conversational AI | Instant knowledge retrieval improves accuracy |
| Agent training and onboarding | AI copilots assisting humans | “Next best action” boosts speed and confidence |
AI Copilots Boost Speed and Confidence for Customer Service Agents
The case for deploying AI and human agents to the types of calls they are best suited for is made, but let’s also consider the value of AI copilots. Human agents are adept at guiding conversations, but they don’t always recall the information they need and may spend valuable time searching for answers. Here, AI can instantly surface relevant data, helping agents resolve issues more efficiently.
For newer agents, AI-powered “next best action” tools can streamline decision-making by narrowing options and guiding them toward optimal outcomes — improving both speed and confidence. As Microsoft’s corporate VP of customer service Mala Anand put it, “With Copilot we’re able to resolve each customer case faster, automate routine support interactions, and, most importantly, improve the customer experience.”
Human-guided AI fits naturally into this framework — with AI copilots assisting agents in real time, accelerating resolution, reducing training overhead and ultimately strengthening the customer experience.
Related Article: Agentic AI and the Future of Customer Support: What CX Leaders Need to Know
Human-Guided AI: A Strategic Imperative
Human-in-the-loop (HITL) AI offers a middle ground. By embedding skilled agents into the AI lifecycle — from training to real-time oversight — organizations can ensure that AI tools remain accurate, ethical and aligned with customer expectations. This model transforms AI from a standalone tool into a collaborative partner.
Maintaining that balance requires a continuous improvement loop, where humans monitor and refine AI performance to ensure responses are accurate, contextually appropriate and aligned with brand standards. As AI models evolve, performance can drift — and sometimes not always for the better. Continual oversight, as well as automated testing tools, helps refine AI behavior over time, preventing frustrating customer experiences caused by misaligned or ineffective automation.
Industry leaders agree that the most effective CX strategies keep humans front and center. As participants of the Deloitte CX Roundtable observed, “The most successful CX leaders will likely be those who can use AI to enhance a human-centered customer experience.”
Similarly, CMSWire’s Scott Clark noted that “AI is no longer a futuristic concept; it’s deeply embedded in how businesses operate. However, the most impactful AI systems are not those that replace human input but those that amplify it.”
Building Human-Guided AI Readiness: A Practical Framework
To operationalize human-guided AI, CX leaders must invest in both technology and talent. Here’s a practical roadmap:
- Audit AI readiness: Evaluate team skills in prompt engineering, bias detection and ethical oversight.
- Establish governance: Define clear use cases and guardrails to ensure responsible AI deployment.
- Invest in learning: Dedicate a portion of your services budget to upskilling and continuous education.
- Promote data storytelling: Help teams translate insights into compelling narratives that drive executive buy-in.
- Leverage agent-assist tools: Equip agents with real-time guidance — such as suggested responses, knowledge surfacing and “next best action” prompts -- to help them resolve issues faster and more confidently.
- Ensure human oversight: vigorously monitor and continuously assess how AI is performing in your business and adjust based on data. This is not a set-it-and-forget-it exercise.
It’s critical that the human element isn’t overlooked. Forrester reports that just one-third of CX leaders feel confident in their teams’ data literacy — a foundational skill for responsible AI use.
Sentiment, journey friction and customer trust are the true metrics of CX success – and the ultimate test of whether AI is helping or hurting the customer experience.
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Human-Guided AI Readiness Framework
A structured approach helps CX leaders close the skills gap and ensure AI strengthens — not weakens — customer trust.
| Step | Action | Outcome |
|---|---|---|
| 1. Audit readiness | Evaluate team skills in prompt engineering, bias detection and data literacy | Clear view of gaps and training needs |
| 2. Establish governance | Define guardrails, ethical standards and approved use cases | Responsible and compliant AI use |
| 3. Invest in learning | Upskill agents and leaders through continuous education | Build long-term AI fluency across teams |
| 4. Promote data storytelling | Translate AI insights into compelling narratives for executives | Stronger buy-in and investment alignment |
| 5. Leverage agent-assist | Deploy copilots, suggested responses and real-time prompts | Faster resolutions and reduced training overhead |
| 6. Ensure human oversight | Continuously monitor and refine AI performance | Accurate, ethical and customer-aligned outcomes |
Bridging the Gap, Building the Future for AI in CX
AI is not a shortcut. It is a force multiplier — accelerating insights, decisions and execution at a scale humans alone cannot achieve. The opportunity is in uniting AI’s computational power with human expertise to create systems that continuously learn, adapt and improve.
For customer experience leaders, this means more than efficiency gains — it means unlocking new models of engagement, anticipating needs before they surface, and delivering outcomes with unmatched speed and precision. The future of CX is not humans adapting to AI. It is AI and humans evolving together to set new standards for customer value.
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