Generative AI in the contact center has moved from a hype-driven concept to a business reality. What started as a tech trend has quickly become one of the biggest shifts in how support teams work.
The recent CX Today report on AI in Customer Experience found that 46% of businesses now invest more in AI for customer service than they do for sales, marketing, or commerce. Additionally, more than 90% of CX teams are planning on centralizing customer-facing solutions to ensure AI applications can influence every part of the customer journey.
The tools are already everywhere, from virtual assistants, AI copilots, auto-reply apps, to smart routing systems. But customer satisfaction levels still aren’t improving as much as expected.
In fact, Forrester’s Customer Experience Index hit a record low in 2024. So, while AI is certainly changing how things get done, it’s still unclear whether it’s making things better for the people on the other end of the call. So, what can CX leaders learn from all this, and what’s next?
Further reading:
- What Can AI and Automation Actually Do for Your Contact Center?
- Will your CFO Approve Agentic AI?
- Are You Building AI Reliability Debt?
What’s the Current State of Generative AI in the Contact Center?
It’s wild to think about how quickly generative AI in the contact center went from an interesting concept to “already everywhere”. Just two years ago, most contact centers were still playing around with basic chatbots. Now, nearly 80% have implemented some form of generative AI, from agent copilots to auto-summarization tools.
For most companies, the early go-to was AI copilots, built to sit alongside agents during live interactions. These tools help with drafting replies, pulling up relevant articles, and summarizing calls afterward. They’re fast, low-risk, and popular. 82% of CX teams say they’re already using some kind of copilot, and three out of four say it’s delivering real value.
But the tech is moving fast. Many companies are now experimenting with autonomous AI agents, tools that don’t just assist agents but actually handle full conversations on their own.
Some can answer customer questions using internal documentation. Others can triage issues and escalate them if needed.
Despite some very public AI misfires (remember the delivery bot that started swearing at customers?), the confidence in these tools is surprisingly high. According to the same CX Today report, 79% of CX leaders say they’d trust an AI agent to talk to customers without any prior training.
That’s interesting, when 66% of businesses still admit their customers prefer talking to humans over bots. And 61% of industry pros believe the government should step in and guarantee people the right to speak to a human if they want to.
What Are the Use Cases for Gen AI in the Contact Center?
So, what’s generative AI actually being used for in contact centers today? Quite a lot. Some of the most common use cases, as mentioned above, focus on helping agents move faster, avoid admin work, and make fewer mistakes. The CX Today report highlights a few popular options:
Writing Replies for Agents
This is the big one. Half of all contact centers are using generative AI to draft replies for agents. The AI figures out the customer’s intent, pulls relevant info from internal systems, and writes a suggested message. Agents can tweak it or send it as is. It saves time and helps teams stay consistent in tone and language.
Auto-QA and Coaching
Quality assurance is another go-to. Around 45% of teams use AI to automatically review conversations, flag good and bad moments, and even generate coaching tips. It’s a lot faster than handling manual reviews, and it’s helping managers spot trends they might miss otherwise.
Creating Knowledge Articles on The Fly
Tools like NICE Enlighten AI are excellent for this. Instead of waiting for someone to write up a new help article, the AI listens to real conversations and creates articles based on what agents are already doing. Around 39% of teams are using this to keep their knowledge bases fresh and useful.
Post-Call Summaries and CRM Updates
Fewer teams are using AI here (about 38%), but demand is still high. Instead of spending a few minutes writing a call summary, the AI does it instead. It fills out CRM fields, updates tags, and gets the agent ready for the next call in seconds.
Virtual Assistants and Copilots
Over 80% of contact centers now use some kind of copilot. These tools help agents find answers, suggest next steps, or guide them through processes. They’re everywhere, and for good reason: most teams say they’re working well.
Full-Service AI Agents
This is where generative AI in the contact center is really starting to evolve. Some businesses are testing fully autonomous agents that don’t just assist, they act as digital team members. They can answer customer questions, escalate complex cases, and even improve themselves over time by learning from past interactions.
Additional Use Cases
Of course, lots of other generative AI use cases are beginning to emerge too, particularly as AI continues to be baked into endless tools and platforms. Companies are exploring:
- Real-time language translation for global support teams.
- AI that understands sentiment and escalates emotional or frustrated customers faster.
- Bots that talk to other bots as “machine customers” become more common.
- Compliance-checking tools that flag risky language or policy violations.
- Predictive tools that spot early signs of churn and trigger personalized retention efforts.
What Should Leaders Learn from Gen AI in the Contact Center?
There’s no shortage of excitement around generative AI in the contact center, even now. But companies aren’t just focusing on the hype anymore, either. They’re paying attention to the reality. Now that the tech is living in thousands of contact centers, patterns are starting to emerge, and they’re not all good.
We’re learning that:
More AI Doesn't Automatically Mean Better CX
Despite all the investment, customer satisfaction scores aren’t going up. At least not consistently. In fact, Forrester’s CX Index hit its lowest level ever in 2024. Throwing AI at the problem without a clear strategy doesn’t guarantee positive results.
A Gartner study found 64% of customers would actually prefer it if companies didn’t use AI in their service strategy at all. That doesn’t necessarily mean AI doesn’t have a place in the contact center, but it might not always need to be customer-facing.
Another major issue is that AI and humans aren’t always working together effectively. Bot-to-human handoffs are still messy. Some people still have to repeat themselves when they get to a live agent. If a bot hands over a conversation, the agent needs the full backstory, otherwise the customer experience falls apart.
Companies Need to Build the Right Foundations
For all the talk about AI transforming customer service, many contact centers are still skipping the groundwork. One common issue is rushed investments, often driven more by C-suite pressure than by actual customer needs. When the goal is just to cut costs or ride the AI trend, the resulting deployments tend to underdeliver or backfire.
But even well-intentioned projects can stumble without a clear understanding of customer demand. Many teams still don’t fully grasp why customers are reaching out. Without good journey mapping or conversation analytics, there’s a real risk of automating the wrong things.
On top of that, governments are starting to enforce guardrails. Spain’s three-minute response rule and California’s AI Act are just the beginning. Brands will soon need to prove that their AI systems are fair, accessible, and respectful of customer rights. Future-proofing means thinking about compliance from the very beginning, not scrambling after a policy change.
Get the Data Right, or Everything Falls Apart
There’s a saying in tech: garbage in, garbage out. And that applies heavily to generative AI in the contact center. Even the best generative AI won’t deliver results if it’s working with bad or incomplete data.
Outdated knowledge articles, siloed customer histories, and missing context can all lead to poor recommendations or wrong answers. That erodes both trust and efficiency.