Who runs your AI after go-live? A guide to building operational readiness

You've chosen your AI platform. You've run the pilot. Leadership is bought in. Now comes the question that determines whether AI becomes a competitive advantage or an expensive experiment. Who actually runs this thing?

The operational model for AI in customer service is different from traditional software. There's no set-it-and-forget-it option. AI requires ongoing attention, tuning, and strategic oversight.

Teams that understand this build AI programs that get smarter over time. Teams that don't end up with systems that underperform, frustrate customers, and erode trust.

Here's how to build the operational foundation that makes AI work.

The skills your team needs to run AI

Running AI isn't about hiring machine learning engineers. It's about building cross-functional capability within your existing CX organization. The most successful teams develop competency across three domains.

Conversational design. Someone needs to own the quality of AI conversations. This person understands how customers actually talk, what questions they ask, and how to structure responses that feel helpful rather than robotic. They're constantly reviewing conversations, identifying gaps, and refining the AI's personality and tone.

Data analysis. AI generates enormous amounts of signal about customer behavior, intent, and satisfaction. You need someone who can translate that data into actionable insights. What topics are customers asking about most? Where is AI falling short? What patterns indicate an opportunity to expand automation?

Knowledge management. AI is only as good as the information it draws from. Someone must own the accuracy, completeness, and freshness of your knowledge base. When policies change, products launch, or processes update, that information needs to flow into your AI immediately.

Most CX teams already have people with these skills. The shift is in how those skills get deployed. A quality analyst becomes an AI conversation auditor. A training specialist becomes a knowledge architect. A team lead becomes an AI operations manager.

This mindset transforms AI from a technology project into an operational discipline.

Defining clear ownership

Ambiguity kills AI programs. When nobody owns improvement, nobody improves anything. You need explicit accountability for three functions.

Tuning is the work of making AI better at handling specific scenarios. This includes adjusting responses, adding new topics, refining intent detection, and configuring escalation rules. Tuning should be owned by someone close to your customers, typically a senior support specialist or CX operations lead who understands both the technology and the customer experience intimately.

Monitoring is about catching problems before they become crises. This means watching key metrics like resolution rate, customer satisfaction, escalation patterns, and AI confidence scores. Monitoring requires daily attention and should sit with someone who has authority to escalate issues quickly.

Improvement is the strategic layer. This involves analyzing performance trends, identifying expansion opportunities, and prioritizing the AI roadmap. Improvement ownership typically sits with a CX manager or director who can balance AI investment against other operational priorities.

Some organizations combine these roles. Others distribute them across a small team. The structure matters less than the clarity. Every team member should know exactly who to go to when AI needs attention.

98% of CX leaders recognize the need for seamless AI-to-human transitions, but only 10% have implemented these handoffs without a struggle. Clear ownership is what closes that gap.

Building capacity without building a new department

The fear of AI ownership often comes from resource anxiety. Leaders worry they'll need to hire entire teams just to manage the technology. That's rarely true if you're smart about capacity planning.

Start by quantifying the actual work. Tuning work scales with conversation volume and complexity, but most teams find that a few hours per week handles routine optimization. Monitoring can be largely automated with the right alerting setup. Improvement work clusters around major initiatives and product launches.

A realistic model for most mid-sized CX teams allocates about 10 to 15 hours weekly across all AI operations functions. That's less than half a full-time role. The key is distributing that time across people with the right skills rather than treating it as a dedicated position.

As AI handles more conversations and becomes more central to your operation, capacity needs grow. Build that into your planning. The goal is always for AI efficiency gains to outpace the investment in AI management.

Creating feedback loops that actually work

AI improvement depends on feedback. The best teams build systematic ways to capture insights from the people closest to customer conversations.

Your support team members see AI failures every day. They notice when customers get frustrated, when responses miss the mark, when escalations happen that didn't need to happen. Create a simple mechanism for them to flag issues in the moment. A dedicated Slack channel, a quick form, a tag in your system. Make it frictionless.

Customer feedback is equally valuable. Post-conversation surveys should include questions specifically about AI quality. Did the automated response help? Was it easy to reach a human when needed? Track these metrics over time and correlate them with operational changes.

Quality reviews should explicitly include AI performance. When you audit conversations, assess both the AI portions and the human handoffs. Look for patterns that indicate systematic issues rather than one-off failures.

The feedback loop only works if people see results. When a team member flags an issue that leads to improvement, close that loop. Share wins. Build a culture where AI optimization feels like a team effort.

Measuring what matters

You can't improve what you don't measure. But measuring everything creates noise that obscures signal. Focus on metrics that directly indicate AI health, and rethink what "health" actually means.

This shift matters. Deflection measures how many customers you pushed away. Resolution measures how many you actually helped. The difference shapes everything from team incentives to technology decisions.

Resolution rate tells you how often AI successfully handles conversations without human intervention. Track this overall and by topic category. A declining resolution rate in a specific area signals a problem that needs immediate attention.

Brands using Gladly are seeing strong results across channels. KÜHL achieves a 79% True Resolution Rate on email and 59% on chat. MaryRuth's sees 79.7% True Resolution Rate on email and 81% on email collaboration requests.

Containment quality measures whether AI resolutions actually satisfy customers. A high resolution rate with low satisfaction scores indicates AI is technically completing conversations while leaving customers frustrated. Only Only 32% of customers whose issues were resolved through AI support are more likely to return. This shows that resolution without relationship equity is a hollow win.

Escalation patterns reveal where AI struggles. Are certain topics always escalating? Are escalations happening too early in conversations? Too late? Understanding escalation behavior helps prioritize tuning efforts.

Time to improvement tracks how quickly your team responds to identified issues. A long cycle time from problem identification to fix suggests operational bottlenecks that need addressing.

Designing handoffs that preserve context

More than three-quarters of AI conversations eventually involve a human. Escalation is the norm, not a failure state. The question is whether the path is clear when customers need it.

But handoffs fail when context disappears. 48% of customers would abandon if they had to re-explain their issue. 40% would abandon if they had to re-verify their identity. The worst handoffs aren't the slowest ones. They're the ones that erase context.

Kate Showalter explains how Crate and Barrel approaches this.

Simplicity for the customer requires operational discipline behind the scenes. Your AI needs full context to act decisively. Your team members need that same context to add value immediately when they step in. The architecture that makes this possible isn't just a technology decision. It's an operational commitment.

Making readiness sustainable

AI readiness isn't a destination. It's an ongoing operational capability. The teams that sustain high AI performance over time share common characteristics.

They treat AI as a team member, not a tool. They talk about AI performance in team meetings. They celebrate AI wins alongside human achievements. They hold AI accountable to the same standards as any other support channel.

They invest in continuous learning. AI platforms evolve rapidly. New capabilities emerge quarterly. Teams that stay current with platform capabilities find new opportunities to improve performance.

They measure the full picture. Efficiency and cost savings are essential, but they're only one dimension. Kate Showalter captures this perfectly. "If we can do more than just solve problems, if we can think about how we're talking about a product, leveraging a selling function in our chat, our top revenue producer is our chat function. Because the customer is there, engaged on our site, shopping. We need to help them shop."

This is the difference between AI that processes volume and AI that builds relationships. The operational discipline is the same. The outcomes are dramatically different.

They maintain perspective on what AI is for. The goal isn't maximum automation. The goal is better customer experiences delivered efficiently. That sometimes means more AI. It sometimes means less. The operational mindset is about optimization, not ideology.

The bottom line

Building operational readiness for AI takes intentional effort. But the teams that get it right create compounding advantages. Their AI gets smarter. Their team members focus on higher-value work. Their customers get faster, more consistent experiences.

Among customers who hit a blocked transfer, 40% abandon or switch brands. And the damage compounds. Of those who couldn't transition from AI to a human, 47% say they won't make future purchases if it happens again. One bad experience primes customers to leave faster the next time.

These losses don't surface immediately. They accumulate quietly, conversation by conversation, before showing up as churn, compressed lifetime value, or increased price sensitivity.

Every escalation, every repeated question, every blocked exit is a loyalty decision. AI works. Whether it builds devotion is up to you, and the operational foundation you put in place to run it well.