From Overwhelmed Call Centers to an AI Workforce: What Our Real Case Studies Prove

When we talk to CX, collections, and service leaders, the skepticism is always the same:

“We’ve seen plenty of AI demos. Show us real numbers from real banks.”

So, here’s a direct answer to that request.

Instead of a hypothetical example, we’ll walk through what actual customers have achieved with Aiphoria’s voice-first, goal-oriented AI workforce—combining:

• A named, public case study: TBC Bank Uzbekistan, a digital-first bank operating in Uzbek and Russian

• Two anonymized deployments from regulated financial institutions in Europe and Latin America

• Evidence from adjacent, published work on speech tech in low-resource language environments

These stories (and the composite metrics we highlight) are the same that we shared with banking, fintech, and telco leaders during our recent LatAm workshop tour in São Paulo, Bogotá, and Mexico City.

All results are examples from real programs. Taken together, they show what an AI workforce for banks can actually do when it’s designed to think and work like people do.

1. Why real case studies matter more than AI promises

There’s no shortage of “transformational AI” stories in banking. The problem is that many of them:

• Start with a model, not a business problem

• Show impressive prototypes, then stall in production

• Talk about deflection and automation, but say very little about recovery, resolution, or revenue

Leaders we meet want something different:

• Proof that AI can handle real collections, service, and sales conversations over voice

• Clarity on what it takes to get there (data, integrations, governance)

• Numbers they can put in front of a CEO, CFO, or regulator without blushing

That’s why we lean heavily on concrete case studies, not just platform slides. Let’s start with the one we can name publicly.

2. TBC Uzbekistan: AI collections at scale, in two languages

TBC Bank Uzbekistan is a digital-first institution backed by the EBRD, operating in Uzbek and Russian. It set out to modernize early-stage collections—not with a generic chatbot, but with a dedicated AI workforce built for its reality.

The challenge

Like many growing banks, TBC faced:

• Rising consumer loan volumes

• Human collectors tied up on repetitive early-stage reminders

• High costs and limited scalability for manual dialing

Most off-the-shelf AI tools couldn’t handle the core problem: real phone calls in Uzbek and Russian with heavy accents, slang, and interruptions. Public speech models in these languages were limited, and generic bots weren’t built for regulated collections work.

The approach

Together, we co-designed a voice-first AI collections workforce around three principles:

1. Goal-oriented AI agents, not generic bots

   • Each AI worker had a clear job: reach early-stage delinquent customers, verify identity, explain status, negotiate within policy, and secure a payment or promise.

2. Speech tech tuned to local reality

   • We built and trained bespoke ASR/TTS models for Uzbek and Russian using TBC’s own call data, informed by our broader speech-tech work in low-resource languages.

3. Deep integration and governance

   • The AI workforce connected directly to core systems and dialers, with full audit trails and strict guardrails on what could be said, offered, and escalated.

The outcomes

In public materials, we’ve shared three key proof points for TBC Uzbekistan:

• Up to 10× better collection efficiency per $1,000 collected versus traditional, human-only operations

• Significant operational cost reductions in early-stage recovery, supported by third-party research showing 30–50% cost reduction potential from AI in collections 

• A fully bilingual AI workforce capable of handling large volumes without sacrificing compliance or customer respect

This isn’t a lab demo. It’s a live, regulated bank, in a challenging language environment, using AI workers as a core part of its collections strategy.

3. An anonymized European bank: service and support as an AI workload

Our second case involves a European bank that prefers to stay anonymous, but whose patterns are increasingly common.

The starting point

The bank had:

• High inbound volumes for routine service queries—balances, transactions, card limits, simple disputes

• Classic IVR and FAQ bots that deflected some calls, but rarely resolved issues end-to-end

• Growing frustration from both customers (long waits, repetitive questions) and agents (repetitive tasks, burnout)

They weren’t looking for a showpiece chatbot. They wanted to see whether AI agents for customer service could:

• Take entire categories of calls off human queues

• Deliver “zero wait time” for those journeys

• Maintain or improve CX and compliance

What we deployed

We worked together to build a voice-first AI workforce for service:

• Service AI workers that could authenticate customers, access accounts, answer questions, update details, and reschedule appointments

• Clear goals for each journey (e.g., “confirm balance and next due date,” “reschedule appointment,” “update address”)

• Integrations into core banking, CRM, and scheduling systems so AI could do things, not just talk

We also adopted a “jobs, not channels” mindset that Harvard Business Review would recognize as part of building an “intelligent experience engine”: define the jobs AI should own, then make them available wherever customers show up.

The outcomes

Across the journeys we targeted, what were the results?:

• Many customers experienced 0 seconds of waiting time—the AI picked up immediately for those specific use cases

• Automation rates in targeted workflows reached levels like 30–80%, depending on complexity

• Banks saw meaningful cost reductions on those journeys while maintaining service quality

Humans didn’t disappear; they shifted to higher-value interactions and complex cases. The AI workforce acted as a 24/7, always-patient front line for routine service—exactly the pattern McKinsey describes when AI-enabled customer service is embedded into operations with proper governance.

4. An anonymized LatAm deployment: outbound and collections economics

The third case we often share—especially with leaders in Mexico, Brazil, and across LatAm—is an anonymized deployment where we ran controlled comparisons between human-only programs and AI-led ones.

The institution (a large consumer lender) wanted to know if they could:

• Make outbound programs economically viable at scale

• Improve collections and sales metrics without building a huge outbound team

• Maintain customer experience and compliance standards

Outbound and cross-sell

For sales and reactivation:

• AI workers handled the initial outreach across tens of thousands of customers

• They qualified interest, checked eligibility, and only then handed off to human advisors or booked appointments

• Journeys were designed around specific goals: “qualified and booked” or “qualified and transferred”

The result?

• In comparable outbound campaigns, cost per lead/contact dropped by up to 50%

• Human advisors spent more time with warm, high-intent customers instead of cold calls

These outcomes are consistent with broader research on AI-powered “next best experience” engines, which can boost customer satisfaction and revenue while reducing cost-to-serve. 

Early-stage collections in LatAm

On the collections side, AI workers:

• Took the lead on early-stage reminder and follow-up calls

• Worked from the same compliance playbooks as human collectors

• Negotiated within predefined guardrails and confirmed outcomes directly into systems

Across multiple programs, we’ve seen patterns similar to TBC’s, though each institution’s numbers are different:

• 7–10% improvements in recovery in targeted early-stage segments

• Lower marginal cost for each additional dollar collected

• More consistent adherence to policy and scripts than large human teams under pressure

Because these engagements involve sensitive commercial and market information, we keep the institutions anonymous. But the core story—AI agents for collections delivering real, measured lift—is one we’ve now repeated in multiple markets.

5. What these three stories have in common

Although these case studies span different regions, languages, and regulatory environments, they all share a set of design choices that matter more than any specific model or feature.

Voice-first, not voice-bolted-on

In every case, the most valuable and risky conversations were happening on the phone:

• TBC’s bilingual collections in Uzbek and Russian

• The European bank’s high-volume service queues

• LatAm projects where voice remains the preferred channel for financial conversations

A generic chatbot with a telephony add-on wasn’t enough. All of these deployments use a voice-first AI platform, built with streaming ASR, low-latency TTS, and robust handling for interruptions, accents, and noisy audio.

That’s aligned with what McKinsey calls out: AI-enabled customer service is most powerful when it covers the channels customers actually use for high-stakes interactions. 

Goal-oriented AI agents

In all three, AI workers were designed as goal-oriented AI agents, not open-ended talkers:

• In TBC’s case: secure a compliant promise or payment in early-stage delinquency

• In the European bank: resolve specific service tasks end-to-end

• In LatAm: qualify and book, or qualify and escalate

That meant:

• Clear definitions of “success” for each worker

• Tight mapping between conversational flows and business outcomes

• Measurement on recovery, resolution, and conversion—not just “automation rate”

This is also why these stories resonated during our LatAm tour: leaders could map the same logic directly onto their own collections, CX, and sales journeys.

Enterprise-grade governance and integration

Finally, all three programs treated AI as part of the enterprise stack, not a side experiment:

• Deep integrations into core systems (banking, CRM, ticketing, dialers)

• Configurable guardrails and escalation rules tuned by risk and compliance

• Full observability over what AI workers say and do, with searchable transcripts and analytics

That governance posture matches how regulators and boards are increasingly thinking about AI in financial services: not as a toy, but as part of the operating model.

6. How we presented this to LatAm leaders

During our workshops with LatAm banks, fintechs, and telcos, we didn’t start with models or architectures. We started with these real stories:

• A named case (TBC Uzbekistan) that proves a voice-first AI workforce can succeed even in low-resource languages

• An anonymized European bank showing how to turn service from a cost center into an AI-friendly workload

• An anonymized LatAm deployment demonstrating measurable lift in outbound and collections economics

We then worked backward with participants to ask:

• Where do you see similar patterns in your own portfolio?

• Which journeys have clear goals, clear rules, and repeatable volume?

• What would an AI worker look like in your environment—and how would you measure it?

For many leaders, this was the moment AI stopped being a vague innovation topic and became a practical workforce and P&L question.

7. What you can take from these case studies

If you’re leading CX, collections, or service in a bank, fintech, or telco, here are the main takeaways from these real deployments:

1. You don’t need perfect conditions to start.

   • TBC built a bilingual AI workforce in Uzbek and Russian—languages with limited open training data—by leaning on internal calls and bespoke speech tech. (Aiphoria)

2. Voice is where the highest-value wins often are.

   • Every case above targeted phone-heavy journeys first, then extended logic to digital channels.

3. Define jobs, not “the bot.”

   • Start with specific roles: AI collector, AI service agent, AI sales qualifier. Give each a clear job description and KPIs.

4. Use the same rigor you use for human teams.

   • Scorecards, QA, coaching loops, and governance all matter just as much for AI workers as for people.

5. Treat your call recordings as strategic data.

   • In all three stories, internal audio and outcomes made more difference than generic benchmarks.

8. Where to go from here

If you want to move from “we’re curious about AI” to “we have real numbers from real deployments,” a useful next step is:

• Identify 2–3 journeys in your operation that look like the ones above

• Define what success would mean (recovery lift, reduced cost-to-serve, improved CX)

• Map the rules, data, and systems those journeys depend on

• Pressure-test whether an AI worker could take the lead, with humans in a supervisory and escalation role

That’s the same exercise we ran with LatAm leaders when we shared these case studies. The details differ by market and institution, but the core pattern is the same: a voice-first, goal-oriented AI workforce that you can measure and manage like a team, not a science project.

To experience how voice-first AI can revolutionize your enterprise’s CX challenges, enjoy a demo.

Further resources:

• This TBC Uzbekistan case study on AI applications in banking and autonomous collections

• An exploration of AI in banking customer service and collections

• A realistic appraisal of the impact of AI on human jobs

• Why banks are “breaking up” with legacy customer-facing tech