The shocking truth about why AI call solutions aren’t delivering the voice automation and call deflection results you were promised
In boardrooms across the globe, executives are asking the same question: “We invested millions in AI for our contact center, so why are our containment rates still so low?”
The answer may be more disturbing than you think.
While tech giants and consultancies have been aggressively pushing Large Language Models (LLMs) from providers like OpenAI, Anthropic, and Google (Gemini) as the silver bullet for customer service automation, a disturbing reality is emerging from the trenches: simply wrapping an LLM in a basic interface is creating an illusion of intelligence that crumbles when faced with the harsh realities of voice interactions and ai phone calls.
The Containment Crisis: What Vendors Hide
The numbers don’t lie. Despite massive investments in AI technology, contact centers are facing what industry insiders who are on alert related to the low numbers of containment. Customer calls continue to flood human agents, automation rates remain stubbornly low, and the promise of end-to-end resolution without human intervention remains elusive, particularly in voice channels where call ai solutions are failing to meet expectations.
“The problem with only measuring Containment Rate is that deflecting a customer doesn’t mean they’ve had their issue resolved,” said Per Ottosson, CEO at Teneo.ai.
This distinction between call deflection and resolution represents a fundamental flaw in how organizations are approaching automation, a flaw that generic LLM implementations are spectacularly failing to address.
The Voice Challenge: Silicon Valley’s Secret
For voice interactions specifically, the technical hurdles create a perfect storm of speech recognition errors, natural language disfluencies, background noise, and conversation complexities combine to create scenarios where even the most advanced general-purpose voice LLM systems fail catastrophically.
The result? Frustrated customers, wasted investments, and a growing crisis of confidence in AI’s ability to deliver on its promises in ai call channels.
The Specialized Solution: What You’re Missing
While most vendors are pushing one-size-fits-all LLM solutions, a radically different approach has been quietly delivering breakthrough results. Rather than simply wrapping existing models, Teneo.ai platform has developed purpose-built technologies that address the specific technical and accuracy challenges of voice-based customer service.
The results are nothing short of revolutionary: over 99% end-to-end accuracy in LLM and conversational AI systems, dramatically outperforming standard LLM implementations and delivering the call deflection rates that other vendors can only dream about.
In this article, we’ll reveal:
- The technical challenges that make end-to-end containment so difficult to achieve
- Why LLM-wrappers are failing spectacularly in voice interactions
- How Teneo’s specialized technologies are delivering the results that other vendors promised but couldn’t deliver
Whether you’re a Head of CX, an IT leader evaluating AI solutions, an AI specialist implementing conversational technologies, or a business executive trying to separate AI fact from fiction, this investigation will arm you with the knowledge to avoid the costly mistakes that are derailing AI implementations across the industry.
EXCLUSIVE DOWNLOAD: Get immediate access to our Voice Automation Accuracy Benchmark and discover what the AI vendors aren’t telling you about maximizing containment →
The Metric Deception: Your Dashboard Lies
A critical paradigm shift is currently exposed when automation is measured by deflection or containment success, it’s not uncommon for contact centers to see only marginal improvements toward these pain points. But when automation resolves customer requests at scale, substantial improvements can be made.
This transition from deflection-focused metrics to resolution-based evaluation represents a fundamental change in how organizations should approach automation. Rather than simply reducing the number of calls that reach human agents (call deflection), the goal has become fully resolving customer issues through automated systems (resolution).
The financial implications are staggering: When automation resolves requests end to end, the cost to complete each call goes down from 5-6 USD to 0.40 cents, leading to enormous cost savings.
The Hidden Cost: Budget Drain
Despite the industry’s growing focus on resolution, many organizations still rely heavily on containment rate as their primary success metric. This problem is particularly acute in voice interactions, where the technical challenges of speech recognition and natural language understanding (NLU) create additional barriers to true resolution. A conversational AI system might successfully keep a customer from reaching an agent, but if it fails to accurately understand and address their needs, the result is frustration rather than satisfaction.
The AI Gold Rush: Left Behind
The introduction of Large Language Models (LLMs) like GPT-4o, Anthropic Claude, and others has triggered what industry insiders are calling “The AI Gold Rush” in the contact center industry. These models demonstrate remarkable capabilities in understanding and generating human-like text, leading many organizations to implement them as the foundation for their automation strategies.
The potential benefits are compelling: improved understanding of customer intent, more natural conversational flow, and the ability to handle a wider range of queries without human intervention.
However, as we’ll expose in subsequent sections, simply implementing an LLM, even a state-of-the-art one, is creating a dangerous illusion of intelligence that crumbles when faced with the unique challenges of voice-based customer service. The gap between LLM capabilities in controlled text environments and their performance in real-world ai phone call scenarios remain substantial, creating a critical need for specialized technologies designed specifically for voice interactions.
EXCLUSIVE ANALYSIS: Request your personalized call deflection assessment and discover the hidden flaws in your current automation strategy →
The Technical Nightmare: Voice LLM Failures
The hidden technical barriers that are sabotaging your AI investment and costing you millions in lost containment
For IT and AI teams implementing LLMs, a disturbing reality is emerging: the technical challenges of voice automation are far more complex than vendors are willing to admit. These challenges represent massive barriers that generic LLM implementations simply cannot overcome, no matter how impressive their demos might seem.
The Speech Recognition Crisis: The Unspoken Truth
Speech-to-Text (STT) technology is the backbone of any voice-driven system, responsible for converting spoken language into accurate text. While many vendors claim over 90% accuracy in controlled settings, these numbers often don’t hold up in real-world applications. In practical deployments, the minimum acceptable accuracy is typically around 95%, a threshold that many solutions fail to meet. In contrast, platforms like Teneo consistently deliver over 99% accuracy, setting a new benchmark for performance in real-world voice interactions.
THE ACCENT APOCALYPSE: Metrics Destroyer
Contact centers typically serve diverse customer populations with wide-ranging accents, dialects, and speech patterns. Human perception of speech is heavily influenced by speaker characteristics, including accent variations that can devastate ASR accuracy. Standard ASR systems, even those powered by advanced neural networks, often fail catastrophically with non-standard accents or regional dialects. This creates a fundamental barrier to understanding, if the customer’s speech isn’t accurately transcribed, even the most sophisticated LLM will be working with incorrect input, leading to a cascade of failures throughout the ai call interaction.
THE NOISE FACTOR: Voice AI Killer
Real-world calls rarely take place in the pristine acoustic environments of vendor demos. Background noise, poor call quality, and signal distortion are common challenges that dramatically reduce ASR accuracy. Environmental factors significantly influence speech perception, creating conditions that standard ASR systems aren’t equipped to handle.
These environmental challenges are particularly problematic in mobile contexts, where customers may be calling from busy streets, public transportation, or other noisy settings. The resulting transcription errors create a substantial barrier to accurate understanding and appropriate response generation in call ai systems.
THE HUMAN SPEECH PARADOX: Vendor Blind Spot
Human conversation is messy. We hesitate, restart sentences, use filler words, and speak in incomplete phrases. Natural conversation between agents and customers can be especially confusing due to disfluencies (stuttering or the stopping and starting of sentences), repetition of information, or non-grammatical utterances. These natural speech patterns pose significant challenges for ASR systems, which typically perform best with clear, well-structured utterances. The resulting transcription errors create a substantial gap between what the customer actually said and what the system “hears,” undermining the entire interaction.
The Intent Recognition Failure: Million-Dollar Mistake
Even with perfect speech recognition, LLM systems face significant challenges in accurately determining customer intent, understanding not just what words were spoken, but what the customer is trying to accomplish.
THE COMPLEXITY CONUNDRUM: The Untold Challenge
Customers rarely express their needs in simple, straightforward terms. They often combine multiple requests, provide unnecessary context, or express their needs indirectly. For example, rather than saying “I want to check my balance,” a customer might say, “I just made a payment yesterday and I’m wondering if it went through and what my current balance is now.”
These complex, compound requests are usually the weakness point for intent recognition systems, which typically perform best when matching utterances to predefined intent categories. The nuanced, multi-faceted nature of real customer queries creates a significant barrier to accurate understanding and appropriate response in ai phone call systems.
THE CONTEXT COLLAPSE: AI’s Breaking Point
Understanding customer intent often requires considering the broader context of the conversation, including previous interactions, customer history, and the specific journey that led to the current contact. Standard LLM implementations typically lack this contextual awareness, treating each utterance as an isolated input rather than part of an ongoing dialogue. This limitation is particularly problematic in scenarios where the customer’s intent evolves throughout the conversation or where critical information is distributed across multiple turns. Without robust contextual understanding, even advanced language models struggle to maintain coherence and relevance throughout a complex interaction.
THE JARGON JUNGLE: AI’s Confusion Zone
Every industry has its own specialized vocabulary, product names, and terminology. Healthcare, finance, telecommunications, and other sectors each present unique linguistic challenges that generic language models aren’t equipped to handle.
The out-of-box language models are trained on clean text corpuses, such as articles from Wikipedia or other stories that have been through a rigorous editing and proofreading process. This training doesn’t prepare them for the domain-specific language encountered in specialized contact center environments, creating a significant gap in understanding and response accuracy for voice LLM implementations.
The Integration Impossibility: Success Blocker
True end-to-end containment requires more than just understanding customer requests, it demands the ability to take action on those requests by integrating with backend systems and databases.
THE DATA DILEMMA: Unsolved Problem
Resolving customer issues often requires accessing up-to-date information from multiple systems: account details, transaction history, product information and more. This relevant data access presents significant technical challenges, requiring secure, reliable connections to various backend systems with minimal latency.
Standard LLM implementations typically lack these integration capabilities, functioning as standalone conversational interfaces rather than connected components in a broader enterprise architecture. This limitation severely restricts their ability to provide the relevant, personalized responses that customers expect in ai call scenarios.
THE SECURITY WARNING: Board-Level Concern
Many customer interactions require secure authentication before sensitive information can be accessed or transactions processed. Implementing robust authentication in voice channels presents unique challenges, balancing security requirements with usability considerations.
Generic LLM implementations often lack the specialized capabilities needed to handle secure authentication in voice interactions, creating a critical barrier to end-to-end containment for many high-value customer journeys.
THE TRANSACTION TRAP: ROI Killer
Beyond information retrieval, many customer interactions involve processing transactions: making payments, changing account settings, scheduling appointments, and more. These transactions often require complex workflows, validation rules and error handling procedures that standard LLM implementations aren’t designed to manage.
The inability to securely and reliably process transactions represents a fundamental limitation for generic AI solutions, forcing many interactions to be escalated to human agents despite the system’s language understanding capabilities.
EXCLUSIVE INSIGHT: Discover how Teneo’s approach overcomes these technical barriers to deliver true end-to-end containment →
The LLM Illusion: Voice Self-Service Failures
As organizations rush to implement Large Language Models in their contact centers, a disturbing pattern is emerging: systems that perform brilliantly in controlled demos are failing spectacularly when deployed in real-world voice environments. When wrapping an LLM in a basic interface creates an illusion of intelligence that shatters when confronted with the harsh realities of voice interactions.
The Hallucination Hazard: Business Threat
Perhaps the most concerning limitation of generic LLM implementations is their tendency to “hallucinate”, to generate plausible-sounding but factually incorrect or fabricated information. This tendency creates significant risks in contact center applications:
THE LEGAL LIABILITY: Can’t Be Ignored
The consequences of LLM hallucinations in customer service can be severe. As noted in a Forbes article, in February 2024, for example, Canada’s Civil Resolution Tribunal ruled that Air Canada must fulfill a reimbursement to a customer erroneously promised a refund by the airline’s AI chatbot. The airline argued that it can’t be held responsible for any incorrect information that AI provides on its own. However, the Tribunal determined that it’s incumbent upon companies ‘to take reasonable care to ensure their representations are accurate and not misleading.’ This case highlights a critical reality: organizations are legally responsible for the information provided by their AI systems, regardless of whether that information was explicitly programmed or emerged from the model’s training. Without robust guardrails and specialized mechanisms to prevent hallucinations, generic LLM implementations create significant legal and reputational risks.
THE COMPLIANCE CATASTROPHE: Waiting to Happen
In regulated industries like Healthcare, finance and more, providing incorrect information can have serious compliance implications beyond customer dissatisfaction. Financial advice, healthcare guidance, insurance coverage details, and other sensitive topics require absolute accuracy, a standard that generic LLMs struggle to consistently meet.
The liability concerns are substantial. As the Air Canada case demonstrates, organizations can be held legally responsible for commitments or information provided by their AI systems, even when those systems operate autonomously. This creates a significant barrier to deploying generic LLM implementations in high-stakes customer service scenarios.
THE AI GUARDRAIL GAP: Vendor Secret
Generic LLM implementations typically lack the robust guardrails needed to prevent hallucinations in enterprise contexts. While basic prompt engineering can reduce the risk, truly reliable performance requires specialized architectures with explicit verification mechanisms, domain-specific knowledge bases, and controlled response generation.
Organizations that rush to resolve without first implementing the proper guardrails put their business at risk. This risk is particularly acute in voice interactions, where the real-time nature of the conversation and the challenges of speech recognition create additional opportunities for error in call ai systems. People do not need to be advanced programmers or hackers to achieve this either. Just by asking the correct questions in the right order is usually enough to make your AI Agent say something controversial that is enough to be covered in headlines around the country.
EXCLUSIVE DEMO: See how Teneo’s Conversational IVR solution overcomes these limitations to deliver true end-to-end containment →
The Teneo Breakthrough: Voice Containment Solution
While most vendors push generic LLM solutions that fail in real-world voice environments, Teneo.ai has developed a radically different approach that’s delivering unprecedented results to achieve true end-to-end containment in voice AI systems.
The Linguistic Revolution: Call Deflection Transformer
At the core of Teneo’s AI capabilities is the Teneo Linguistic Modeling Language (TLML™), a proprietary technology that’s transforming how AI systems understand and process natural language in voice interactions.
THE 99% ACCURACY: Competitive Advantage
Unlike generic LLMs that rely solely on statistical patterns learned from massive text corpora, TLML™ combines deterministic linguistic rules with native machine learning to achieve what many industry experts considered impossible: 99% end-to-end accuracy in understanding customer intent. This hybrid approach is particularly effective in handling the noisy, error-filled transcriptions typical of real-world voice interactions.
“Teneo’s NLU engine is enhanced by the Teneo Linguistic Modeling Language or TLML™, a unique proprietary that significantly improves intent detection accuracy.”
This enhancement enables the system to maintain high performance even in challenging acoustic environments or with diverse speaker characteristics.
The result is remarkable: over 99% end-to-end accuracy in intent recognition, far surpassing the capabilities of generic LLM implementations. This accuracy forms the foundation for true end-to-end containment, ensuring that customer requests are correctly understood from the outset in ai phone call scenarios.
THE DETERMINISTIC ADVANTAGE: Vendor Blind Spot
TLML™’s deterministic components provide critical advantages over pure machine learning approaches in enterprise contexts:
- Predictability: Unlike black-box neural networks, TLML™’s deterministic rules produce consistent, predictable results that can be audited and verified.
- Explainability: The system’s decisions can be traced to specific linguistic patterns and rules, including making use of part of speech (POS) tags, named entity recognition (NER) and entity detection, providing transparency that’s essential for governance and compliance.
- Efficiency: TLML™ requires significantly less training data than pure machine learning approaches, enabling faster deployment and easier adaptation to new domains.
- Robustness: The deterministic components provide guardrails that prevent the system from making catastrophic errors or hallucinations, even when encountering novel inputs.
These advantages are particularly valuable in regulated industries where explainability and predictability are not just operational benefits but compliance requirements.
THE PERFORMANCE GAP: Hidden Truth
The performance gap between TLML™ and traditional approaches is substantial and measurable. Cyara highlights comparative NLU accuracy metrics across various vendors:
- Teneo: >99% end-to-end accuracy
- Sprinklr: 90.6%
- Ultimate AI: 86%
- IBM Watson: 81%
- Cognigy: 80%
- Google Dialogflow: 76%
- Microsoft CLU: 74%
This dramatic performance advantage translates directly to improved call deflection rates, higher customer satisfaction, and significant cost savings. A +10% increase in NLU Engine accuracy for a call center receiving 1 million calls monthly can translate to savings of up to $500,000, in addition to much happier customers.
The Accuracy Revolution: Competitors Miss Out
Building on the foundation of TLML™, Teneo’s NLU Accuracy Booster™ addresses the specific challenges of speech recognition errors and variations in voice interactions.
THE MULTI-LAYERED DEFENSE: CX Protection
The NLU Accuracy Booster™ employs a sophisticated multi-layered architecture designed to detect and compensate for common speech recognition errors. Rather than treating STT output as definitive, the system maintains multiple hypotheses about what the customer might have said, evaluating each against linguistic patterns, conversation context and domain knowledge.
This approach enables the system to recover from transcription errors that would derail generic LLM implementations. For example, if a customer says “I need to check my balance” but the ASR transcribes it as “I need to check my talents,” the NLU Accuracy Booster™ can recognize the error based on conversation context and domain knowledge, correctly interpreting the customer’s intent despite the transcription error.
THE ERROR-CORRECTION: Unmatched Capability
The NLU Accuracy Booster™ employs several specialized mechanisms to address STT errors and speech variations:
- Error pattern recognition: The system learns common STT error patterns specific to different accents, dialects, and acoustic environments, enabling it to detect and correct these errors automatically.
- Contextual disambiguation: By maintaining awareness of the conversation context, the system can disambiguate unclear utterances based on the logical flow of the interaction.
- Phonetic matching: For critical terms like product names or commands, the system can match phonetic patterns rather than exact text, accommodating pronunciation variations and ASR errors.
- Confidence scoring: Each interpretation is assigned a confidence score based on multiple factors, allowing the system to take appropriate actions based on its certainty level, requesting clarification when needed rather than proceeding with low-confidence interpretations.
These mechanisms work together to create a robust understanding pipeline that maintains high accuracy even in challenging conditions that would cause generic voice LLM implementations to fail.
The Enterprise Integration: Competitive Advantage
Beyond language understanding, Teneo’s approach includes robust capabilities for enterprise integration and governance, essential components for achieving true end-to-end containment in production environments.
THE SECURE CONNECTION: Data Protection
Teneo’s platform includes native integrations and secure integration frameworks for connecting AI systems with enterprise backends. These components enable:
- Secure API access to customer data, account information, and transaction history
- Secure processing of payments, account changes, and other transactions
- Integration with knowledge management systems for accurate, up-to-date responses
- Connection to any frontend or backend system, including CRMs for personalized service based on customer history
This seamless integration capability ensures that the conversational AI system can not only understand customer requests but take action on them, this is a critical requirement for true end-to-end containment that generic LLM implementations typically lack.
THE COMPLIANCE SHIELD: Business Safeguard
For regulated industries, Teneo’s platform includes specialized compliance and audit capabilities:
- Comprehensive logging of all interactions for regulatory compliance
- Sensitive data redaction to protect customer privacy
- Configurable compliance rules for different regulatory frameworks
- Audit trails for all system decisions and actions
These capabilities address the governance challenges that often prevent organizations from fully deploying AI in customer-facing roles, particularly in highly regulated industries like healthcare, finance, and insurance.
THE HUMAN-AI PARTNERSHIP: Results Maximizer
Recognizing that even the most advanced AI systems benefit from human oversight, Teneo’s platform includes sophisticated human-in-the-loop configurations:
- Access to relevant data that can be presented on dashboards for contact center supervisors
- Configurable escalation thresholds based on confidence scores and interaction patterns
- Seamless handoff to human agents when needed, with full context transfer on what each customer has said.
- Continuous learning from human agent interactions to improve system performance through native optimization loop.
These human-in-the-loop capabilities create a virtuous cycle of improvement while providing essential safety nets for complex or sensitive interactions.
The Final Verdict: Beyond Generic LLM-Wrappers
As we’ve exposed throughout this investigation, achieving true end-to-end containment in voice AI systems requires more than simply wrapping a Large Language Model in a basic interface. The technical challenges of speech recognition accuracy, intent understanding, and backend integration, combined with the specific limitations of generic LLMs in voice contexts create substantial barriers that must be addressed with specialized technologies and approaches.
The shocking truth is that organizations investing in generic LLM wrappers for call deflection are setting themselves up for failure. The gap between demo performance and real-world results is not just disappointing, it’s potentially catastrophic for customer experience and operational efficiency.
Teneo’s specialized approach, combining the TLML™ linguistic engine, NLU Accuracy Booster™, and robust enterprise integration capabilities, delivers the performance that generic LLM implementations simply cannot match. With over 99% end-to-end accuracy and proven success in the most challenging voice environments, Teneo is transforming what’s possible in conversational AI for contact centers.
Don’t settle for the limitations of generic LLM-wrappers, pretending to deliver value for your entperise. Discover how Teneo’s specialized approach can help you achieve true end-to-end containment, delivering better customer experiences and significant operational savings.