Affordable AI for Contact Centers: 8 Strategies That Won’t Break the Bank

The biggest concern we hear from contact centers is this:

AI sounds great, but the price tag doesn’t.

We get it.

Between per-minute transcription fees, high-tier licensing, and surprise overages, it can feel like AI is out of reach for everyone but the largest enterprises.

In this scenario, how do you make intelligent automation both powerful and wallet-friendly /

We work with more than 1500 customers worldwide, ranging from regional customer experience desks to global support hubs.

Based on this experience, we have pulled together eight strategies that help you unlock AI’s benefits without blowing your budget.

1. Offer Tiered Accuracy Plans

Does your provider give you the option to choose from standard, business, and enterprise transcription tiers?

Each can come with its own accuracy, latency, and price point.

Why does this make sense?

You don’t need 99.9% accuracy for every use case. For routine call logging or coaching insights, the standard option would be good enough. It would come with, say, 85 to 90% accuracy and would be 40% cheaper than the enterprise version.

Only when you need verbatim transcripts for compliance can you subscribe to an enterprise plan for those critical calls.

The way it would work is you would subscribe to 80% standard credits and 20% enterprise credits.

A regional insurance customer of ours subscribed to the standard option for 80% of its call volume and the enterprise option for 20% of its call volume, which is primarily for high-value claims inquiries. They were able to cut their transcription spend by 50% while ensuring top-tier accuracy where it mattered most.

2. Design Modular, Pay-as-You-Go Feature Packs

No customer wants everything you package as a part of your AI offering. You can separate your core services, such as transcription, sentiment analysis, and topic extraction, into standalone modules with individual pricing.

Would it work better?

Your customers would only subscribe to those services that make sense for them. They only pay for the capabilities that drive value for them.

You don’t have to pay for the full AI suite when all you want to do is automate your note-taking.

A fast-growing fintech customer utilized the sentiment API exclusively for their high-risk escalation queue. With this approach, they onboarded the sentiment API alone with a low entry barrier. Once it started producing results, they confidently rolled out sentiment analysis across all queues in the next quarter.

3. Prioritize High-Impact Use Cases

Look at two or three workflows that will yield the fastest results and ROI. It can be auto-logging calls, surfacing coaching insights, or deflecting routine inquiries.

How does this help?

This would allow you to see real savings early, which will make you more confident in exploring broader AI rollouts.

One of our healthcare customers used AI for their post-call summarization. This allowed them to reduce after-call work by 50% within six weeks. They were able to see a positive return on investment on their AI subscription cost during the trial period itself.

4. Promote Hybrid Human-AI Collaboration

How about having AI-generated summaries and next-best action suggestions directly into your agents’ workflow? And provide the agents with an option to review and approve the summaries or suggestions.

In most cases, you don’t require 100% accuracy, and your agents will continue to handle the nuance. This would keep your costs down while boosting quality.

One of our telecom customers adopted this “draft-and-approve” mode, which reduced their average handling time by 20%.

5. Automate Off-Peak Batch Processing

What is the biggest cost in any AI implementation? It is the computing bill.

Have you ever considered queuing bulk sentiment jobs during off-peak hours when compute rates are lower?

You get cost-effective analytics on your full call history without impacting live-call performance or incurring peak-hour surcharges.

One of our travel customers set their weekly call analytics reports to run at 2 am on preemptible instances. This reduced their compute bill by 30% with no difference in report turnaround time.

6. Cultivate Internal AI Champions

Does your vendor offer free certification and a sandbox environment, allowing you to train a small team to serve as your AI ambassadors?

They can help you build and share best-practice templates.

With centralized AI champions, you can prevent each department from spinning up its own costly proof-of-concept. Your champions become in-house experts, reducing vendor support needs and redundant cloud spend.

One of our automobile dealership customers designated two agents for our certification program. They created pre-built workflows in our low-code interface, slashing new project setup time from weeks to days.

7. Embed Real-Time Cost Monitoring and Alerts

Does your dashboard track spends by feature, queue, and time of day, with custom thresholds that trigger alerts when you approach your budget limits?

This would allow you to understand the spikes and take corrective actions before costs escalate. For instance, if a misrouted test environment floods your enterprise model, you can rectify it immediately with real-time visibility.

An eCommerce customer of ours had set a $1000 daily spend cap on its premium transcription tier. They received an alert at 7 am stating that 30% of the cap was utilized, and upon further investigation, they discovered that one of their training bots was misconfigured, auto-transcribing every live call. This allowed them to reset the auto-transcription and save on unexpected charges. 

8. Leverage Volume Commitments

Does your vendor offer volume discounts? For instance, do they offer a steep discount off the list rates when you subscribe for 12 months?

This would make your costing predictable and help you avoid huge unplanned costs.

Many of our customers have secured a 40% reduction on per-minute fees during volume commitments.

Whenever you embark on any new initiative, what are the three things that you look at?

They would require flexibility, control, and flexible pricing, especially with AI projects. By following these eight strategies, you can achieve these three objectives.

The ideal way to approach your AI implementation is to start small, make gains, and roll out to broader use cases.

You should view your AI initiatives as a core operational expense, rather than a discretionary project funding.