From Configuration to Adoption: AI Deployment Best Practices for Contact Centers
March 24, 2026
Getting AI into your contact center is no longer a question of if — it’s a matter of how. But the ROI of AI isn’t guaranteed. A rushed or poorly planned rollout can create more problems than it solves.
In a recent Five9 webinar, Josh Sosebee, Five9 Senior Director, AI & Custom Services, and I walked through what it actually takes to go live successfully.
Here are the key AI deployment best practices every CX leader should know before flipping the switch.
Tip #1: Get speech-to-text right
Before any AI model can route a call, qualify a lead, or surface a knowledge article, it has to accurately understand what was said. That means speech-to-text accuracy isn’t just a technical detail; it’s the single most critical foundation of any voice AI application.
“You won’t be able to self-service a call if the speech-to-text mistranscribes,” Sosebee said.
The industry standard for measuring this is word error rate (WER) — simply put, how often the model gets a word wrong. Even a small uptick can send calls to the wrong place and frustrate callers before a human ever gets involved.
The challenge is that speech-to-text performance isn’t one-size-fits-all. What works for podcast recordings often struggles with real contact center audio — background noise, accents and mobile connections all change that equation.
The Five9 professional services team maintains its own internal testing framework, built on real phone call audio, to identify the best speech-to-text model for each customer’s specific environment. And for use cases with unique terminology or challenging audio conditions, they can build a custom model from scratch.
Getting this foundation right is an unglamorous but non-negotiable first step.
Tip #2: Embrace an engine-agnostic AI strategy
Most organizations pick a speech-to-text or LLM vendor and stick with it. The problem is the market moves fast: the model winning accuracy tests today may not be the best option in six months.
Five9 takes an engine-agnostic AI strategy, meaning customers aren’t tied to a single provider.
Right now, DeepGram leads in many of our internal Five9 AI Agents accuracy tests, while Amazon has shown strong results on the agent assist side. That balance can shift as models improve.
As I noted in the webinar:
“As we get more newer models, we try to integrate them into our system based on accuracy, as well as based on different features and capabilities.”
An engine-agnostic strategy also means that as better models emerge, you can always switch, without having to rebuild your entire architecture.
Tip #3: Use GenAI to accelerate time to value
One of the most dramatic shifts in modern AI deployment best practices is how generative AI has compressed implementation timelines. Tasks that once required weeks of intent training and synonym mapping can now be accomplished in days or even hours.
Josh shared two real-world examples:
Simple Deployment: A furniture delivery company needed voice call routing based on customer intent. Using a pre-configured LLM template, the Five9 team filled out a simple mapping table and went live quickly. Because the LLM was already trained on internet-scale data about furniture delivery services, the model required minimal additional training and held up even when callers mixed intents in the same sentence.
Complex Deployment: A healthcare customer came in with roughly 600 medical synonyms — drug names, symptoms, specialist categories — that needed to map to the correct routing targets in two languages.
Under the old system, this would have required extensive training data and significant time before a quality go-live was possible. With an LLM-based approach, the team ran a quick test to confirm the model handled synonyms out of the box, loaded the specialty list in a structured format, and the customer went live handling multiple languages from day one.
“The build time went from weeks to days,” Sosbee summarized.
Tip #4: Phase your rollout and empower your teams
Trying to deploy everything all at once is a recipe for disaster. Successful AI deployment best practices usually start small and expand over time.
For example:
Transcription and summarization can be live in hours
Guidance cards and checklists follow over the next weeks
More complex self-service applications can then scale in parallel
Just as important: agents need to be brought along, not just trained.
When agents worry that AI will replace them, adoption stalls. In reality, today’s AI Agents today handle the repetitive, high-volume calls nobody wants: password resets, basic account questions, etc.
When AI removes repetitive tasks, agents can focus on higher-value conversations — making this one of the key ways organizations are improving agent productivity with AI.
Getting the rollout right the first time is a customer retention issue as much as an operational one. According to a Five9 survey of over 1,000 global consumers, 36% of customers say they won’t use a chatbot again after a bad experience.
Take custom call summaries, for example. Instead of spending the final 30 seconds of every interaction frantically typing notes, agents can focus on the conversation. In contact centers with tight after-call work windows, that change alone can improve both agent experience and operational efficiency.
Many organizations jump straight into AI deployment without fully understanding what’s happening inside their customer conversations. That’s why many CX leaders begin by building an AI blueprint — using conversation insights and operational data to identify the highest-impact opportunities before launching new automation. When teams understand where friction exists and what customers are really asking for, AI deployment best practices become much easier to execute.
The takeaway
Organizations succeeding with AI aren’t simply moving faster or chasing the newest models.
They’re focusing on the fundamentals:
Accurate speech-to-text and strong word error rate performance
An engine-agnostic AI strategy that keeps options open
A phased rollout that builds confidence
And agents who are prepared to work alongside AI
That’s what turns an AI investment into real results.
