Managing AI Like a Workforce: The Next CX Advantage

The Gist

Train AI like you train people. Most CX leaders wouldn’t unleash untrained agents on customers — yet they do exactly that with AI, skipping structured onboarding and coaching.
Continuous feedback matters. AI performance declines without ongoing review, retraining, and feedback loops between CX and data teams.
AI is a workforce, not a widget. Success depends on managing AI as part of the team — with KPIs, performance reviews, and collaboration alongside humans.

Many CX programs stumble not because AI fails, but because leaders fail to manage it like a living, learning part of the customer service workforce. Treating AI as a “hire” rather than a “tool” — with structured training, continuous feedback and shared accountability — turns it from a disappointing experiment into a dependable teammate that strengthens customer interactions over time.

Artificial intelligence has quickly become the centerpiece of modern customer experience strategies. From digital agents to predictive analytics, AI promises to make interactions faster, smarter, and more personalized. Yet for many organizations, the reality doesn’t match the hype.

After months of implementation and investment, the reports start coming in: customers are frustrated, automation rates are low, and the AI “doesn’t work as expected.” Technology gets blamed. But more often than not, the real problem isn’t the AI, it’s how we manage it.

Ironically, the same leaders who would never dream of leaving a team of human agents untrained or unsupervised often deploy AI systems with little to no structured guidance, ongoing feedback or performance review.

The Human Agent Parallel
The Same Rules Apply to AI
Why So Many Organizations Fall Into the Trap
Treat AI Like a Workforce, Not a Widget
The Future of AI in Customer Experience

The Human Agent Parallel

Think about how you hire and train live agents in your contact center. You don’t expect them to succeed immediately. Before they take their first customer call, you invest in:

Training and onboarding: Teaching your brand voice, service standards, product knowledge and escalation paths.
Call listening and quality assurance: Reviewing performance regularly, identifying what’s working and where to improve.
Coaching and feedback: Helping them refine how they communicate, empathize and solve problems.
Performance tracking: Measuring metrics like resolution rate, handle time and customer satisfaction.

Without these elements, even the most talented agents would likely fail. They’d make inconsistent decisions, deliver off-brand messages and frustrate customers. The fix isn’t to fire them, it’s to train, coach, and support them.

Now take that same scenario and apply it to your AI. Most organizations deploy AI with little of the rigor they apply to human teams. They train it once, test it briefly, and move on. Then they’re surprised when it fails to meet expectations.

The Same Rules Apply to AI

AI systems learn from data. The quality and variety of that data determine how well they perform. Yet many organizations feed AI models only with idealized documentation, static FAQs, or polished marketing language, none of which reflect how real customers actually communicate.

Just like a new employee, AI needs to be exposed to authentic, messy, real-world interactions to understand nuance, intent and emotion. And it doesn’t stop there. To stay effective, it requires continuous learning.

Train on real conversations: Use transcripts, call data, and chat logs to ground your AI in real customer language.
Monitor and measure: Review AI interactions the same way you’d review agent calls, listen, assess, and adjust.
Coach for improvement: Feed back what you learn. If the AI misunderstood a question or provided a poor response, update its training data.
Define clear success metrics: Track containment rate, escalation success, and sentiment, not just deflection or completion rates.

If you don’t do these things, your AI is like a new agent thrown onto the floor without training or feedback, a recipe for disappointment.

Why So Many Organizations Fall Into the Trap

The root cause of AI failure usually isn’t technical. It’s organizational and operational. Many companies lack clear ownership for AI performance. Once the technology is launched, it becomes everyone’s responsibility, and therefore no one’s.

In most cases, there’s no structured feedback loop connecting what customers experience to how the AI improves. Data science teams may build the model, but service or CX leaders understand the customer. Without collaboration between those groups, AI quickly drifts out of alignment with the brand experience.

Another common issue is the “set it and forget it” mindset. Unlike traditional software, AI isn’t static. Customer language, product offerings, and service expectations evolve constantly. If your AI isn’t continuously retrained and optimized, it will degrade, sometimes faster than you realize.

Treat AI Like a Workforce, Not a Widget

The organizations seeing the biggest success with AI aren’t necessarily those with the most advanced technology. They’re the ones that treat AI as part of the team.

Here’s what they do differently:

Create structured AI training programs. They plan and schedule updates to the model, just like employee training cycles.
Establish performance reviews. Regularly assess AI outputs and compare them against key CX metrics.
Build feedback loops. When the AI misses context or provides an incorrect answer, they capture it, fix it and retrain.
Blend human and AI collaboration. AI handles what it’s good at, while humans handle empathy, exceptions, and complex judgment.
Celebrate and reward improvement. Teams recognize AI enhancements the same way they’d reward process or performance gains from employees.

When you manage AI like a member of your workforce, complete with KPIs, feedback sessions and continuous learning, it not only performs better, it becomes a long-term asset rather than a failed experiment.

The Future of AI in Customer Experience

AI isn’t replacing humans; it’s reshaping how teams deliver value. But success depends on realizing that AI isn’t plug-and-play; it’s hire-and-train.

If you want your AI to sound more natural, resolve issues faster and reduce customer effort, you need to treat it with the same care and discipline as a live agent. That means structured onboarding, consistent feedback, and ongoing optimization.

In customer experience, every interaction shapes perception. Whether the “agent” on the other end is human or digital, the goal is the same: represent your brand accurately, empathetically, and efficiently.

AI doesn’t fail because it’s not capable. It fails because we don’t give it the same chance to learn that we give our people.

The future of CX belongs to organizations that understand this simple truth: training doesn’t stop when you hit “deploy.”

After months of implementation and investment, the reports start coming in: customers are frustrated, automation rates are low and the AI “doesn’t work as expected.” Technology gets blamed. But more often than not, the real problem isn’t the AI, it’s how we manage it.

Learning Opportunities

Webinar

Oct

Beyond Storage: Smarter Content, Bigger Impact with DAM + AI

Discover how the DAM + AI duo makes content smarter, stronger and more accessible.

Webinar