Schedule a call with Nova Cloud to turn your contact center into a data-driven, AI-powered operation.
When we say AI in contact centers, we don’t mean replacing your agents. But we do mean giving them the right tools to resolve issues faster and with less effort.
Your clients expect 24/7 customer service that feels personal and immediate. Falling short means higher costs and lower loyalty.
Amazon Connect, backed by the strength of AWS services, gives you a foundation that scales and integrates with your existing systems. In this playbook, you’ll see how to connect real goals with measurable results and take steps you can defend to leadership.
Pro tip: See how Nova implements Connect + GenAI to cut AHT, raise FCR, and lower cost per contact without ripping and replacing your stack. Explore Nova’s Amazon Connect services.
First, let’s look at the challenges that usually hold your contact center back.
Running a call center at scale brings a set of challenges that cut into margins, strain teams, and weaken customer loyalty. These aren’t abstract issues because you deal with them daily.
Here are the most pressing ones.
Extended call durations drain productivity and delay responses for customers waiting in the queue.
Longer handle times mean higher costs per contact and greater frustration on both sides of the line. Without better tools, shaving those minutes down becomes nearly impossible.
High-pressure environments wear agents down fast, increasing burnout and turnover rates.
Each departure forces you to invest in recruitment, onboarding, and training, only to risk repeating the cycle. High churn also erodes institutional knowledge, which makes it harder to deliver consistent service.
Well-trained staff struggle when knowledge is fragmented, too. That’s even when companies invest in contact center knowledge bases.
When agents can’t access accurate answers quickly, you see longer calls, inconsistent outcomes, and lower customer satisfaction scores.
Static IVR processes frustrate customers who expect conversational support rather than endless option trees. That leads to abandoned calls and poor experiences. Each abandoned call represents lost revenue and lasting damage to loyalty.
Lack of Actionable Analytics
Data overload doesn’t equal insight. Sometimes, we even see a data underload. The numbers back this up because few organizations turn contact center data into actionable insight.
Without enough actionable data on AHT, first call resolution, or sentiment, you can’t target improvements or justify technology spend. This leaves leadership questioning ROI and teams stuck reacting instead of improving.
Pro tip: If you’re struggling with blind spots beyond the contact center, you’ll want to look at how Datadog-powered DevOps improves monitoring for eCommerce systems. It shows how real-time visibility cuts downtime and reduces cost across complex stacks.
Generative AI addresses your most pressing challenges by strengthening both efficiency and customer trust. These are the areas where it creates a measurable impact.
Static IVR trees push customers away, but bots built with Amazon Lex and Amazon Bedrock handle real conversations. AI-powered chatbots let most users access services instantly, improving satisfaction and reducing call volume.
That shift reduces call volume and keeps your agents focused on high-value issues.
The same source shows 56% of customers prefer self-service for plan selection, while 77% use it for bill payments and recharge. Deploying smarter self-service experiences allows you to shorten queues and improve customer experiences with faster resolutions.
Your agents waste time searching for the right answer. With agent assistance tools tied into your knowledge bases, responses surface instantly.
And Generative AI helps you cut handle time while improving agent performance. These are two levers that directly reduce costs per contact.
In fact, a 2023 SurveyMonkey report found that customers valued AI because it improved availability, speed, and accuracy.
Summarization + After-Call Notes: Auto-Generate Call Transcripts + CRM Updates
Manual note-taking slows down wrap-up time.
To solve this, many companies use generative AI to create transcripts and post-contact summaries automatically. This solution works well, reducing the average call duration by about three minutes per call in some cases.
That kind of time savings can compound across thousands of interactions for your company, too. It also frees your agents to handle more calls without extending shifts.
You can’t risk missing early warning signs in a conversation.
Your competitors understand this as well and increasingly use sentiment analysis tools to analyze customer interactions.
You don’t want to be left behind.
Real-time monitoring helps you act before a customer threatens to leave. That’s how you can protect your company’s loyalty and revenue.
Instead of waiting for the next inbound call, AI can prepare personalized follow-ups by predicting customer needs in advance.
For you, that level of foresight means better routing, shorter calls, and higher satisfaction without scaling headcount.
At Nova, we partnered with Diri Telecomunicaciones to modernize their contact center. The challenge was that customers typically faced wait times between 43 and 80 minutes for support. That delay created frustration, increased costs, and left agents struggling to keep up with demand.
We built an intelligent contact center stack using Amazon Connect, Amazon Lex, and Amazon Bedrock. The system supported both voice and WhatsApp channels, which allowed customers to receive immediate attention.
Wait times dropped from more than an hour to near-instant responses. Query resolution also improved, with agents working in an AI-assisted workspace that surfaced knowledge base answers in real time. This sped up resolutions and reduced training overhead.
Scalability was another outcome. By using serverless architecture, Diri removed over-provisioning and cut operating costs. The solution also supported multiple brands (Diri, Turbocel, and Pillofon) through custom prompts. This made sure that each brand delivered a consistent but different experience.
The results were measurable, such as:
This project shows how generative AI, layered on Amazon Connect, can deliver both efficiency gains and stronger customer loyalty.
Pro tip: See the results behind the numbers. Dive into Nova’s case studies to see more architectures, KPIs, and before/ after metrics you can take to leadership.
In this section, we’ll help you implement generative AI in your contact center and set you up for the kind of wins we’ve already achieved. You need a clear sequence that connects business goals to measurable outcomes. Without a structured approach, you risk pilot projects that stall or investments that never return value.
These are the practical steps that help you move from strategy to results.
Before you deploy AI, you need clarity on what success looks like. That starts with KPIs. Average handle time (AHT), first call resolution (FCR), customer satisfaction (CSAT), and cost per contact are the benchmarks your leadership will ask about.
Each one ties directly to operational efficiency and financial impact.
Take CSAT as an example.
CMSWire reports that scores typically land between 75% and 85%, with banking near 79%. Sprinklr reports e-commerce averaging 80%, retail around 75%, and automotive at 77%.
Tracking those differences gives you context for where your own numbers should improve.
You also need to consider the cost per call.
Industry reports show the average ranges from $2.70 to $5.60. For a center handling millions of interactions annually, shaving even a fraction of a dollar per call translates into seven-figure savings.
That said, we always advise our clients to follow KPIs that connect your goals to your pain points.
For example, high agent turnover, long wait times, and low NPS can raise costs and weaken loyalty. With tools like Amazon Connect Contact Lens, you can measure sentiment in real time and link it back to FCR or CSAT to make your goals measurable and defensible.
Setting clear KPIs tied to pain points allows you to create a baseline that makes it easier to prove ROI when you bring AI into the picture.
Once you’ve set clear goals, you need the right platform to anchor them.
Amazon Connect, built on Amazon Web Services, gives you a cloud-native, pay-per-use foundation that scales across omnichannel experiences (voice, chat, and SMS). Instead of long implementation cycles, you can deploy quickly and expand as demand grows.
The results posted on Amazon Connect Customers are measurable.
Fujitsu, for example, rolled out Amazon Connect across global contact centers, supporting more than 5,000 agents for 450 companies. They achieved over 96% accuracy in intraday forecasting, 15% efficiency gains in quality assurance, and a 10% increase in customer satisfaction by using its omnichannel capabilities.
These numbers show how forecasting accuracy reduces staffing costs and how higher QA scores protect consistency across large teams.
Cost efficiency is another differentiator.
Because of its consumption-based design, Amazon Connect can reduce overall experience costs by up to 80% compared to on-premises systems. For contact centers managing millions of interactions annually, that translates into huge operational savings without sacrificing service quality.
Integration is also important.
Many deployments link Amazon Connect directly with Salesforce or other CRMs. In another case cited on Amazon Connect Customers, a company cut average handle time by 15% and doubled contacts handled per hour by fully leveraging native CRM integration and self-service automation. That kind of performance lift directly impacts your ability to scale without increasing headcount.
Starting with Amazon Connect means you’re moving to the cloud and building a foundation. This will align cost control, scalability, and integration with your broader customer journey strategy.
Pro tip: Integrating Amazon Connect with your existing CRM isn’t just about speed. For Salesforce-heavy teams, the right commerce integration consulting services can stabilize your stack while you modernize the contact center.
Once you have Amazon Connect in place, the next move is adding generative AI where it drives measurable impact. You don’t need to implement every feature at once, but you can start with focused use cases that improve efficiency and demonstrate ROI.
Begin with self-service.
Combining Amazon Lex with Amazon Bedrock allows you to replace rigid menus with natural language conversations. That change reduces friction and keeps your agents free for complex issues.
And don’t wait until all your competitors start doing it first, because early adopters are more likely to report high ROI from AI tools in customer experience.
Next, improve live interactions with agent assistance tools.
In a field study of more than 5,000 agents, issues resolved per hour rose by 15% on average when they had access to generative AI suggestions during calls. Real-time retrieval of documents and answers:
Finally, reduce post-call work.
As we explained above, generative AI can create transcripts and post-contact summarization synced directly to CRM systems. And research shows that AI-generated call summaries eliminate about 5% of after-call work time. Across high contact volume, that translates into thousands of hours reclaimed for actual customer interactions.
Layering these use cases allows you to improve AHT, FCR, and other essential KPIs. You’ll also be creating visible wins that build executive confidence for broader AI adoption.
Pro tip: ROI from generative AI is only part of the equation. Retail and CPG teams are also unlocking value by leaning into AWS cloud optimization consulting firms that pair cloud savings with performance gains.
Rolling out AI across your entire operation at once is risky.
A smarter path is to start small. Choose one queue or region, then measure outcomes before expanding. This gives you a controlled environment where you can validate assumptions and track ROI with confidence.
Call deflection is usually the first target.
According to DB Kay & Associates, strong deflection rates cut service costs while maintaining satisfaction.
Handle time and sentiment are other metrics you should capture.
You can use tools like Contact Lens for real-time sentiment tracking and compare before-and-after results. If you see shorter handle times and fewer escalations, that’s evidence you can present directly to leadership.
The baseline matters. Collect historical data on AHT, CSAT, and cost per contact before your pilot begins. Then measure again once AI is in place. Creating a clear before-and-after comparison allows you to reduce skepticism and make your case with numbers.
This focused rollout builds credibility, reduces risk, and positions you for broader adoption across the contact center.
Pro tip: Pilots in customer service mirror pilots in migration. Retail IT teams under budget pressure often test cloud moves with AWS migration partners to validate savings before scaling.
After a pilot proves value, the next challenge is sustaining performance. That requires ongoing monitoring.
Platforms like Datadog and CloudWatch give you visibility into latency, uptime, and CX KPIs in real time. Without that layer, small problems (like delayed response times) can snowball into higher costs and customer churn.
Industry data backs this up.
Gains include improved uptime, fewer outages, and measurable increases in customer satisfaction. Those improvements protect your operation and have a direct impact on revenue and retention.
But visibility alone isn’t enough. You need to act on what you see.
That means fine-tuning prompts, retraining workflows, and aligning AI models to changing business rules.
And the truth is, too many organizations fail here.
According to McKinsey reports, fewer than 20% track well-defined KPIs tied to generative AI tools. The consequence is wasted investment, because leadership has no data to defend ROI.
Combining Datadog or CloudWatch with conversational analytics allows you to create a feedback loop that keeps models aligned with business outcomes. That process helps you correct drift, prevent performance drops, and provide leadership with defensible, KPI-driven reports.
With monitoring and structured feedback in place, you move from a working pilot to a repeatable, scalable system that can be trusted at enterprise scale.
Once you’ve proven success in a pilot, the next step is scaling across more channels and regions. That means extending from voice into chat, SMS, WhatsApp, and even social platforms where your customers already expect service. Without a unified approach, you risk fragmented support and inconsistent results.
Unfortunately, very few organizations have reached this level of maturity.
Research shows that only 31% of businesses have fully implemented omnichannel contact centers that unify interactions across channels. This gap creates an opportunity for you to stand out by delivering a consistent experience regardless of the entry point.
The benefits are clear.
According to SQM Group, true omnichannel service can lift CSAT scores to 67%, compared with just 28% for multichannel setups. That’s because customers don’t have to repeat themselves when moving between channels. Their contact details, history, and context travel with them. This reduces frustration and handle time.
The long-term financial case is even stronger.
Companies with effective omnichannel strategies see a 91% higher year-over-year customer retention rate compared to those without. Retention means lower acquisition costs, steadier revenue, and stronger lifetime value from your customer base.
Scaling also means preparing your workforce, though.
Tools like Amazon QuickSight can give leaders real-time visibility into performance across geographies. This can help you identify gaps and replicate what’s working. Scaling with structure allows you to protect quality while expanding coverage.
Pro tip: Extending to omnichannel is part of a larger shift. Many retailers are tying contact center strategy into digital transformation playbooks that drive measurable ROI.
To apply generative AI effectively in your contact center, you need to know which tools actually move the needle. These are the core AWS services that work together to deliver measurable outcomes:
Using these tools in combination allows you to create a stack that reduces handle time, improves accuracy, and provides leadership with defensible results. Each piece serves a specific role, but together they give you a system you can scale with confidence.
Defining the right KPIs gives you evidence that AI is driving measurable value. These are the metrics that connect operational improvements to financial results and executive-level reporting:
AI in contact centers means giving them tools that shorten handle time, improve accuracy, and reduce turnover risk. The real opportunity lies in augmentation because it can help your agents serve customers better while protecting your bottom line.
At Nova, we design, deploy, and integrate solutions built on Amazon Connect and the wider AWS ecosystem. We combine generative AI with governance, analytics, and optimization so you see results that leadership can defend.
If you’re ready to turn strategy into measurable impact, schedule a call with our team today.