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Economic uncertainty is changing the way enterprises think about service delivery. When inflation spikes, labor costs rise, and demand becomes harder to forecast. During these times of uncertainty, the default question to contact center leaders is usually: How can we do more with less?
Budget cuts in contact centers are nothing new. However, today’s budget cuts come with higher expectations, driven by AI hype. It’s no longer just about doing more with less. It’s about doing it better, too.
For many, cost-cutting has resulted in offshoring, outsourcing, and investing in basic voice automation. But these tactics are showing their limits, and CX usually takes a hit. Rigid scripts, robotic workflows, and long wait times reinforce the barriers between customers and genuinely helpful support, leaving customers shouting “Agent!” into the phone.
Enterprises need to rethink the economics of customer service. Some are overcorrecting by cutting headcount aggressively and expecting AI to fill the gap. But the most resilient companies are learning that balance, not replacement, drives long-term ROI and better CX.
Pressure is up, patience is down: The cost of getting CX wrong
We’re already seeing what happens when companies lean too far into automation without rethinking the bigger picture. AI can reduce costs, but when cost-cutting becomes the primary goal, CX often suffers.
In 2024, financial services company Klarna announced that its new AI agent handled two-thirds of customer service chats in its first month. In a time when AI agents were making news for all the wrong reasons, Klarna seemed to implement AI to truly transform customer service operations.
Just a year later, Klarna reversed course, with its CEO, Sebastian Siemiatkowski’s refreshingly honest explanation of what went wrong: “As cost unfortunately seems to have been a predominant evaluation factor when organizing this, what you end up having is lower quality.”
When AI implementation is fundamentally grounded in cutting costs, contact center success metrics (containment rates, AHT, and even CSAT) can look great in the short term. However, without real, intentional investment in improving CX with automation, you lose business.
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Start where voice AI drives real operational ROI
Voice AI should be deployed where it drives real operational ROI. Blanket automation and jumping into large, complex projects too early often leads to short-term pilots that don’t scale. If they’re too small, they won’t make an impact or validate the investment.
Voice AI delivers the most value when it’s used to automate high-volume, low-value tasks—like password resets or identity verification—that take up as much as 60% of agent time. These calls clog up phone lines, increase wait times, and, according to a recent study, cost up to $7.16, with a call duration of 7 minutes, which equates to about $1.02 per minute.
The costs are significant. Contact centers spend roughly $12bn per year just checking callers’ security details (ID&V), making voice the most expensive service channel. Automating the repetitive parts of a call—like password resets or ID&V—can reduce average handle time by 20 to 30 percent, free up agent capacity, and improve the experience for customers who actually need support.
Balancing efficiency with customer experience
Automating repetitive tasks is a good place to start. It frees up agents, shortens wait times, and cuts costs. But to get the full value from voice AI, enterprises need to think beyond efficiency. The real impact comes when automation also improves the customer experience.
Contact center operations have historically focused on lowering call volumes, optimizing agent utilization, and improving routing accuracy. Meanwhile, CX teams have worked to build loyalty and grow lifetime value.
These goals aren’t in conflict. Voice AI can help achieve both.
With AI agents, enterprises can reduce handle times, improve resolution rates, and lower costs while delivering a smoother, faster, enjoyable customer experience.
When AI is designed around real customer needs, it resolves issues without escalating unnecessarily, freeing up agents for high-value conversations. By implementing solutions that focus on the customer, enterprises can drive engagement that naturally creates efficiencies.
Quicken quadruples containment, with 2,500 calls resolved daily and not a single customer complaint.
Read the case studyHandling peak seasons without a spike in operating costs
Many contact centers see call volumes surge by 60% to 200% due to seasonality, changes in consumer behavior, or events outside their control.
Historically, the response has been to hire temporary staff or outsource overflow. While these approaches can provide coverage, they come at a cost and often increase monthly operating expenses.
Voice AI gives enterprises a third option: dynamic capacity. AI agents don’t need to be trained or scheduled. They can scale up instantly during peak periods, handling common call types without sacrificing service quality or exceeding your budget.
How to quantify the ROI of an AI agent
The case for AI doesn’t rest on cost-cutting alone. When deployed thoughtfully, voice AI drives ROI on two fronts:
1. Increased Revenue: Capturing missed revenue calls
2. Operational Efficiency: Cutting costs through automation & better resource allocation
Let’s take “Ralph’s Retail” as a hypothetical example:
- Annual call volume: 1 million
- Abandonment/missed call rate: 25%
- Call to sale conversion rate: 15%
- Average order value: $55
- Average handle time: 7 minutes
- Cost per minute: $1.02
- Cost per minute range: $5.11-$7.16 (5-7 minutes per call)
- Cost per missed call: $8.25
Increased revenue
No action | Base case (12.5%) | High case (7%) | |
Total calls | 1,000,000 | 1,000,000 | 1,000,000 |
Missed calls | 250,000 | 125,000 | 70,000 |
Cost per missed calls | $8.25 | $8.25 | $8.25 |
Total revenue missed | $2,062,500 | $1,031,250 | $577,500 |
Increased revenue | $1,031,250 | $1,495,000 |
Key takeaways
- Ralph’s Retail currently has ~$2M of revenue at risk each year
- Implementing an AI agent would enable them to recoup ~$1-1.5m of missed revenue
Operational efficiency
Base case | High case | |
Total calls | 1,000,000 | 1,000,000 |
Cost to serve | $5,114,286 | $7,160,000 |
Call reduction rate | 35% | 50% |
Calls reduced | 350,000 | 500,000 |
Cost reduced | $1,790,000 | $3,580,000 |
The quantitative case justifies implementing new technology as a smart business decision, but the “softer” impacts are often those that the team feels the most.
Even modest reductions in missed calls or call duration translate into significant savings and revenue. The ROI isn’t just theoretical; it’s measurable and repeatable.
A Forrester Study revealed PolyAI's role in boosting revenue and reducing operational costs to drive a 391% return on investment.
Resilience starts with the right strategy
In uncertain times, it’s tempting to chase quick fixes, especially when AI promises scale at a lower cost. But the real winners in this economic cycle won’t be the ones who automate the fastest. They’ll be the ones who automate intelligently, using voice AI to create service models that are not only more efficient but also more human, more consistent, and more resilient.
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