SquadStack's AI Agents Have Passed the Turing Test for Contact Centers

What is the Turing test and Why it Matters?

Alan Turing created the Turing test as a method for determining whether a machine can exhibit intelligent behavior indistinguishable from that of a human. The original test included a human judge that engages in a text-based conversation with two unseen participants: one human and one a machine. If the judge cannot reliably tell which is which, the machine is said to have "passed" the Turing test.

Contact centers have been called out as the ultimate proving ground for AI applications for many years. AI agents passing the Turing test for contact center applications is especially critical since enterprises heavily rely on contact centers for managing sales, customer support and customer experience.

Alan Turing’s idea wasn’t just an academic thought exercise - it was a line in the sand. Before a machine reaches “indistinguishable from human,” it’s treated as a tool. The moment it crosses that line, it becomes a replacement or upgrade to humans.

In every industry where AI or automation has hit human (or superhuman) performance, adoption hasn’t been gradual - it has exploded. There’s a tipping point:

• Before the tipping point: AI is treated like a copilot, assistant, or junior intern. It’s helpful, but it still needs oversight. This is where most industries sit for a while - people experiment, augment workflows, and create hybrid setups.
• After the tipping point: Once AI matches or beats humans at the core task, the conversation shifts from augmentation to automation. Budgets get reallocated. Entire workflows get redesigned. Adoption jumps exponentially instead of linearly.

Use Cases for Voice AI Agents in Contact Centers

Contact centers are an integral part of how enterprises engage with their customers and play a key role in enterprise workflows like:

• Financial Onboarding: Guiding customers through opening a brokerage account, signing up for a credit card, or onboarding to a banking product.
• Insurance & Lending Sales: Explaining policies, collecting KYC details, handling objections, and driving application completion.
• Marketplace Transactions: Collecting information for large marketplace platforms to qualify leads, verify intent, and route them faster to sellers.
• Education Counselling & Sales: Course discovery, eligibility screening, counselling calls, nudging application completion and fee payment.
• Blue Collar Hiring & Management: Hiring, onboarding and assisting field workers to improve productivity. 
• Customer Collections & Renewals: Following up on missed payments, renewals, or subscription upgrades in a compliant, empathetic manner.
• Customer support: Managing inbound customer queries, product support, payment discrepancies, return and refund claims. 

Each of these use cases demands nuance, persuasion, and trust-building. 

Defining the Turing Test for Contact Centers

There can be two methods of performing the Turing test for contact centers. 

Method 1: Blind Listening Test

The traditional method - like the original test by Turing - a blind listening test with a mixed set of human and AI recordings. However, this method only evaluates the naturalness of the AI agent without any concrete judgment on real business outcomes.

Results: Blind Listening Test

• Setup: Blind testing with a mixed set of AI and human agent call recordings for the same use case.
• Participants: 753 listeners across 3 months, including real consumers and people working in the industry.
• Result: 87% of listeners picked the AI recording as a human conversation.
• Interpretation: This is a positive signal on naturalness, but it only tests perception. Responses were inconsistent across raters and contexts, so as a standalone measure it trends toward a coin-toss. It determines little about real business outcomes or efficiencies demanded by contact centers. 

‍Method 2: Functional Turing Test for Contact Centers

For really passing the Turing test for contact center applications, indistinguishability needs to be achieved across the following pragmatic factors:

(1) Naturalness

Naturalness is about how close an AI agent is to conversing like a human contact center agent. This depends on the quality of voice, prosody, turn-taking, pronunciation, diction, and latency. However, these are subjective indicators that can be tested through blind listening. 

A really practical way to measure naturalness is the Abruptly Disconnected Rate (ADR). It tracks how many calls get cut off right after the system identifies that it’s a contact-center interaction. And it’s obvious why that matters — the moment a caller senses they’re talking to a machine instead of a human, they tend to hang up. So a lower ADR means the voice sounds more natural and keeps people engaged, which is critical if you want to pass the Turing Test in real-world conversations.

We’ve always benchmarked ADR for AI agents against human agents. Earlier automation like IVR menus and first-gen voice bots often had 70%+ ADR. As the stack improved, especially with our generative voice AI agents, ADR has fallen sharply to around 5-10% which is standard range for human-based campaigns as well.

(2) Performance‍

In a commercial application like contact centers, performance metrics are highly critical. An AI agent is only viable when it brings equal or better outcomes vs human contact center agents. Even if the AI agent is highly natural, it needs to deliver business outcomes to have truly “passed” the Turing test for contact centers. Performance can be measured objectively through metrics like qualification or conversion rate, containment rate, CSAT, NPS, etc.

(3) Efficiency

Contact centers are often treated as big cost centers. A contact center AI agent is only viable when it brings significant cost efficiencies. An AI agent with higher costs per outcome than a human agent isn’t viable and can’t be said to have passed the Turing test for this application. Cost per outcome for AI agents needs to be better than that of human contact center agents.

Results: Functional Turing Test for Contact Centers

As established before, the real bar for indistinguishability in contact centers is Naturalness, Performance, and Efficiency together. This is a more accurate test of “passing the Turing test” here than randomized blind testing alone.

• Campaigns evaluated (AI vs human benchmarks in the same campaign):
  
  • Campaign 1: Buyer query responses for the largest B2B marketplace in India
  • Campaign 2: Demat account opening for a top bank-led brokerage
  • Campaign 3: Delivery rider hiring for a leading third-party logistics provider
  • Campaign 4: Customer support for a regional entertainment app‍
• Controls: Each campaign had pre-existing, stable human benchmarks, the same lead sources, scripts, and compliance guardrails.

‍Result: Our AI agents have matched or beaten human metrics across naturalness, performance and efficiency in all 4 campaigns evaluated. 

During live campaigns, we also consistently notice lower Average Handle Time (AHT) with AI agents. AHT is the standard measure of how long an agent and customer are on the call; lower is better for cost. Across use cases, AI agents run at ~ 2x lower AHT than human agents while holding comparable outcomes. This reduction flows directly to overall cost pushing efficiency toward ~5-6x with AI agents vs human agents.

SquadStack AI Stack Powering Indistinguishable AI Agents

Our AI and tech strategy has focused on quality and outcomes from day 1. We use a mix of in-house and third-party components optimizing for the results in each use case. 

Critically, we’ve built the core voice infrastructure in-house - Speech-to-Text (STT) and Text-to-Speech (TTS) - since these are decisive for contact-center performance. Owning them gives us tight control and lets us solve complex, high-context problems.

At a glance:

• Hyper-realistic voices tuned for persuasion, with Indic dialect coverage so agents “sound local.”
• Engineered for real telephony: resilient to packet loss, crosstalk, and barge-ins.
• Ultra-low latency and natural turn-taking, with hundreds of micro-optimizations for conversational flow.

What also sets us apart is our deep application layer - built specifically for contact-center workflows rather than being a generic AI platform. We’ve invested heavily in: 

• Lead management, omnichannel journey builder and intelligent personalization
• Quality systems and feedback loops
• ROI tracking, experimentation frameworks, and A/B testing infrastructure

All of this is powered by our proprietary Buyer Graph™ and Outcome Graph™, which learns from every interaction and improves the system continuously. The result is hyper-personalized conversations that adapt to intent, history, and context in real time. 

Marketing has gone fully personalized over the past decade - but contact-center interactions have remained one-size-fits-all and static. Our stack fixes that gap by making every touchpoint intelligent, dynamic, and data-informed.

‍This focus has led to us crossing the  inflection point - same outcomes, 4x lower cost, and exponential scale.

Looking Ahead

We believe 80%+ of contact center traffic will be AI-led within the next 24 months. 

Passing this functional Turing test in contact centers is a starting line, not a finish. From here, the work is about cracking even harder use cases like insurance,  education, automobile, real estate, consumer durables and other high value sales processes with the same bar of  naturalness, performance, and efficiency. 

We’re already building our next generation of AI agents with a focus on dynamic rebuttals, sentiment & affect awareness, calibrated assertiveness and even better tone & prosody control.

The goal is simple: AI agents that don’t just sound human - they sell and support customers like top agents, with extremely high efficiency.