Mike Clifton, Co-CEO at Alorica – Interview Series

Mike Clifton is Co-CEO of Alorica, a global leader in digitally-powered customer experiences (CX). In this role, Mike oversees the company’s digital transformation strategy—including its award-winning AI products—to deliver optimal CX across channels (voice, chat, web, etc.)  and industries on behalf of FORTUNE 500 brands. With strong expertise and experience in digital innovation, AI and enterprise technology, Mike has a proven track record of driving profitable growth by integrating scalable tech solutions to meet evolving market demands.

Alorica is a global leader in customer experience and business process outsourcing, providing tech-enabled, human-centered solutions for industries like banking, healthcare, retail, and telecommunications. With over 100,000 employees across 17+ countries, the company manages billions of interactions annually in more than 75 languages, delivering services such as contact centers, analytics, AI solutions, content moderation, and back-office operations—all focused on driving measurable outcomes for clients.

The industry is moving towards augmentation over automation—how does Alorica’s strategy reflect this hybrid model?

Alorica’s strategy reflects the hybrid model of augmentation over automation by focusing on enhancing human agent performance with AI tools, rather than replacing them. This approach ensures that humans remain at the core of customer interactions, supported by advanced technologies to improve efficiency and effectiveness.

For example, Alorica has launched several advanced solutions such as evoAI, Knowledge IQ, Digital Trust & Safety Model, and CX2GO®. These tools are designed to amplify human agent performance by providing real-time, context-aware interactions that improve knowledge management and ensure digital trust and safety.

By integrating AI tools that offer emotionally intelligent and context-aware interactions across multiple languages with sub-second response times, Alorica enables agents to provide personalized and efficient support to customers. This real-time responsiveness translates into improved customer outcomes.

Overall, Alorica’s strategy emphasizes the importance of human agents while leveraging AI to enhance their capabilities, reflecting the industry’s shift towards augmentation over automation.

Could you share specific examples where AI has amplified human agent performance rather than replaced it?

There are many examples of amplification that we’ve leveraged in delivering our services. One is the ability for agents to interact with a knowledge engine that listens to real-time speech and translates it into an auto-response engine that prompts for assistance; this is a powerful, preemptive tool that we’ve used across many solutions. Another example is the use of conversational AI engines to enhance our ability to train agents on the most difficult client scenarios. By running AI-driven simulations of real-time interactions, we reduce stress, and the models continuously learn—updating agents on sentiment and empathy as they gain more experience.

How are you tracking the performance impact of these AI tools—for instance, in First Contact Resolution, handle time, or agent efficiency?

Tracking of AI tools in augmented usage falls squarely on the metrics assigned to the agent as if no tools existed. The difference is in the ability to take on more calls at a higher satisfaction yield and the confidence to predict better workforce strategies when you have solid data from the models.

You’ve launched several advanced solutions this year—evoAI, Knowledge IQ, Digital Trust & Safety Model, and CX2GO®. Which one do you see as having the most immediate “superpower” effect for agents, and why?

Our in-house use of evoAI gives agents the ability to leverage mock calls to train with a higher degree of situational awareness, delivering the greatest impact. This is followed by Knowledge IQ, which augments an agent’s ability to find the right answer. These two have been game changers for our employees, completely changing how quickly and accurately our agents can address customers’ needs.

From a machine learning perspective, how are your models trained to maintain accuracy and adaptability as customer needs, language, and market conditions evolve?

To maintain accuracy and adaptability in the face of evolving customer needs, language, and market conditions, our machine learning models undergo continuous training and refinement.

Here are some key strategies we employ:

• Continuous Learning: Our models are designed to learn from new data continuously. This involves regularly updating the training datasets with recent interactions, feedback, and market trends. By incorporating the latest information, our models can adapt to changing customer preferences and emerging market conditions.
• Diverse Data Sources: We use a wide range of data sources to train our models, including customer interactions, social media, market reports, and more. This diversity ensures that our models are exposed to various scenarios and linguistic nuances, enhancing their ability to understand and respond accurately.
• Feedback Loops: We implement robust feedback loops where customer interactions and agent inputs are used to fine-tune the models. This real-time feedback helps identify and correct inaccuracies so the models remain relevant and effective.
• Multilingual Capabilities: Our models are trained on multilingual datasets to handle interactions in multiple languages. This is crucial for providing accurate, localized, and context-aware responses to a global customer base.
• Regular Audits and Evaluations: We conduct regular audits and evaluations of our models to assess their performance. This includes testing the models against benchmark datasets and real-world scenarios to ensure they meet accuracy and adaptability standards.
• Human-in-the-Loop: We maintain a human-in-the-loop approach where human agents collaborate with AI to manage complex queries. This hybrid model ensures that the technology learns from human expertise and improves its performance over time.
• Leveraging Smaller Language Models: Training vertically oriented smaller models (via a hybrid or ensemble approach) alongside commercially available LLMs allows for efficiencies in compute, search, and response time while shortening bias and fairness testing cycles.

These strategies enable our machine learning models to remain accurate, adaptable, and capable of delivering high-quality customer experiences in dynamic environments.

evoAI offers emotionally intelligent, context-aware interaction across 120+ languages with sub-second response times. How does this real-time responsiveness translate into agent support and customer outcomes?

evoAI provides better agent support and improved customer outcomes in several ways:

• Performance: context-aware interactions help find and sort vast amounts of information quickly for agent queries.
• Personalization: offers multilingual adaptability, giving the freedom to select the input and output languages in real time for any prompt. For example, a customer asking in English for a response in French so that an older parent listening can understand.
• Efficiency: reduces response times and often eliminates the need for a human to respond.
• Emotional Intelligence: enables agents to adjust options for callers based on situational awareness (tone, mood, and word choice), allowing for de-escalation faster.

With agentic AI gaining traction, how do you manage risks like hallucinations, bias, or loss of control while ensuring agents remain the decision-makers?

At Alorica, we believe the right architecture behind the tech makes all the difference. That’s why managing the risks of agentic AI requires a multi-layered governance framework that we’ve built into every level of our AI operations.

Here’s how we address each critical risk:

• Hallucination Mitigation:We employ a three-tier verification system to minimize hallucinations. First, our models use retrieval-augmented generation (RAG) that grounds responses in verified knowledge bases and real-time data sources, reducing the likelihood of fabricated information by 85%. Second, we implement confidence scoring on all AI-generated suggestions, where responses below an 80% confidence threshold trigger automatic human review. Third, our models are constrained to operate within defined parameter spaces specific to each client’s business rules and factual domains—the AI cannot generate information about products, policies, or procedures that aren’t explicitly documented in the training data.
• Bias Detection and Prevention:Our bias management strategy operates across the entire AI lifecycle. During model training, we use adversarial debiasing techniques and fairness-aware learning algorithms that actively counteract historical biases in training data. We maintain demographic parity metrics across protected categories and conduct monthly audits using tools like fairness indicators and disparate impact assessments. Our models undergo testing with synthetic data designed to reveal biases across different demographic groups, languages, and cultural contexts. When bias is detected, we employ targeted retraining on balanced datasets and adjust model weights to ensure equitable outcomes. Importantly, we maintain transparency reports that track bias metrics over time, allowing clients to see exactly how our models perform across different populations.
• Maintaining Human Control:Human agents remain the ultimate decision-makers through our “AI as Advisor” architecture. The AI system provides recommendations with explainability features—agents can see why the AI suggested a particular action, what factors it considered, and what alternatives exist. We’ve implemented hard stops where AI cannot autonomously execute certain actions: financial transactions, contract modifications, legal commitments, or health-related advice always require human authorization. Our escalation protocols automatically route complex or high-risk scenarios to senior agents or supervisors when the AI detects situations outside its competency bounds.
• Continuous Monitoring and Kill Switches:Every AI interaction is logged and monitored through our Model Performance Observatory, which tracks deviation from expected behaviors in real-time. We maintain instant rollback capabilities and “kill switches” at multiple levels—individual model components, entire models, or system-wide AI features can be disabled within seconds if anomalous behavior is detected. Our drift detection algorithms continuously compare model outputs against human expert decisions, flagging divergences for immediate review.
• Human-in-the-Loop Validation:We’ve designed feedback loops where agents rate AI suggestions after each interaction, creating a continuous learning system that adapts to human expertise. Our top-performing agents participate in weekly calibration sessions where they review edge cases and help refine the AI’s decision boundaries. This creates a collaborative intelligence model where human judgment continuously shapes and constrains AI behavior.
• Accountability and Audit Trails:Every AI-influenced decision maintains a complete audit trail showing the AI’s recommendation, confidence level, data sources used, and the human agent’s final decision. This ensures accountability and allows us to continuously improve our models based on outcomes. Regular third-party audits validate our risk management practices against industry standards and regulatory requirements.

By implementing these comprehensive safeguards, we ensure that our agentic AI systems augment human capabilities while maintaining human agency, ethical standards, and operational control.

How do you approach model retraining and continuous learning to ensure your AI systems remain aligned with both compliance requirements and the nuances of customer sentiment?

Alorica’s approach to model retraining and continuous learning at Alorica IQ is built on a robust MLOps framework that balances regulatory compliance with customer experience optimization.

We’ve implemented a multi-layered retraining architecture that operates on different cadences. Our compliance-critical models undergo daily drift detection and weekly performance audits, with automated triggers for immediate retraining when regulatory changes occur. For customer sentiment models, we leverage real-time feedback loops that capture agent corrections and customer satisfaction scores, feeding these into our training pipeline every 72 hours.

Our proprietary Compliance Intelligence Layer acts as a guardrail system, automatically validating model outputs against regulatory frameworks specific to each geography—from GDPR in Europe to CCPA in California. This layer is continuously updated through our partnership with legal technology providers and regulatory feeds, ensuring our AI systems remain compliant without manual intervention.

For sentiment nuance, we’ve developed what we call “cultural context embeddings” within Alorica IQ, the company’s innovation incubator. These are fine-tuned regional models that understand not just language but cultural communication patterns. For instance, our models recognize that directness levels vary significantly between German and Japanese customer interactions, and adjust their sentiment scoring accordingly.

We maintain versioned model registries with full rollback capabilities, allowing us to instantly revert to previous versions if new training introduces unexpected behaviors. Our A/B testing framework runs continuously, comparing new model versions against production baselines across thousands of interactions before full deployment.

Most importantly, we’ve established a Human Feedback Integration Protocol where our top-performing agents regularly review edge cases and provide corrective feedback, creating a virtuous cycle where human expertise continuously enhances our AI capabilities. This approach has reduced compliance violations by 94% while improving sentiment detection accuracy to 92% across all supported languages.

With rapid international expansion—especially in markets like India, Egypt, and EMEA—how do you tailor your AI-human approach to diverse linguistic and cultural needs?

We believe localization isn’t just about speaking the language—it’s about reflecting the culture.

Our AI platforms like evoAI and ReVoLT are tuned to capture tone, nuance, and context across hundreds of languages and dialects, so interactions feel familiar and authentic. But we don’t stop at technology. We hire talent from within each region, train teams around cultural expectations, and adapt our service design to reflect local norms. This hybrid model ensures every interaction feels like it was built for that market.

In India, where we support 75 official languages plus numerous dialects, we’ve deployed our Linguistic Mesh Architecture that doesn’t just translate but maintains context across code-switching scenarios—where customers naturally blend Hindi, English, and regional languages in the same conversation. Our models are trained on actual conversation patterns from tier-2 and tier-3 cities, not just metropolitan areas, ensuring we capture the full spectrum of communication styles.

For our Egypt operations serving the broader MENA region, we’ve developed Arabic dialect-specific models that distinguish between Egyptian Arabic, Gulf Arabic, and Levantine Arabic, with specialized handling for formal (Fusha) versus colloquial (Ammiya) registers. Our AI understands when a customer switches from formal to informal Arabic as an emotional cue, triggering appropriate agent coaching in real-time.

In EMEA markets, we’ve implemented what we call “Regulatory-First AI Design.” Each country’s deployment includes pre-configured compliance modules—from Germany’s strict data localization requirements to France’s language protection laws requiring French-first interfaces. Our models are trained not just on language but on local business etiquette; for example, our German deployment emphasizes precision and detailed documentation, while our Italian model allows for more conversational flexibility.

The technical backbone is our Federated Learning Framework within Alorica IQ, where local models learn from regional data without that data leaving the country, ensuring data sovereignty while still benefiting from global model improvements. We maintain regional GPU clusters to ensure sub-100ms latency for real-time agent assistance.

Our Cultural Intelligence Team, comprising linguistic experts and behavioral scientists from each region, continuously validates our AI outputs. They’ve helped us identify over 3,000 culture-specific scenarios that require special handling—from religious observances affecting service availability to local payment preferences that impact conversation flows.

This approach has yielded impressive results: our India operations show 40% higher CSAT scores when using culturally-adapted AI versus generic models, and our EMEA deployments have achieved 98% first-contact resolution rates for language-specific queries.

How does evoAI’s ability to recognize and adapt to regional dialects and emotional cues help drive adoption in new markets?

Adoption accelerates when people feel the technology “gets” them. evoAI goes beyond word-for-word translation by understanding slang, accent, and even emotional tone in real time.

evoAI’s sophisticated dialect and emotion recognition capabilities have become our primary competitive differentiator in new market penetration, directly addressing the trust gap that often inhibits AI adoption in emerging markets.

From a technical standpoint, evoAI employs our proprietary Acoustic-Linguistic Fusion Model, which simultaneously processes phonetic patterns, prosodic features, and semantic content. This tri-modal approach allows us to detect subtle emotional states that are expressed differently across cultures. For instance, in Japanese markets, we could detect “honne” versus “tatemae” (true feelings versus public facade) through micro-variations in pitch and speaking pace, while in Middle Eastern markets, we would recognize honor-shame dynamics through specific phrase constructions and emphasis patterns.

Our dialect recognition goes beyond simple accent detection. evoAI maintains dynamic dialect maps that understand socioeconomic indicators embedded in speech patterns. In India, for example, the system recognizes not just whether someone speaks Tamil or Telugu, but can identify educational background and urban versus rural origins, allowing agents to calibrate their communication style appropriately. This granular understanding has been shown to increase customer trust scores by 67% in pilot programs.

The emotional intelligence layer uses our Contextual Emotion Graph technology, which maps emotional trajectories throughout conversations rather than just point-in-time sentiment. This allows evoAI to predict emotional escalation 30 seconds before it occurs with 89% accuracy, giving agents crucial time to intervene with de-escalation techniques specific to that culture’s conflict resolution preferences.

For new market adoption, our info action lab has a “Progressive Localization” strategy through Alorica IQ. We begin with a base model trained on the target market’s media content, social media, and public discourse. Within the first 30 days of deployment, evoAI would adapt to local customer patterns through our Active Learning Pipeline, which would prioritize learning from conversations with the highest emotional variance. By day 90, our models should achieve 95% accuracy in dialect recognition and 88% in emotional state detection.

The business impact would model out to be substantial. Our studies show that an Egyptian deployment, with evoAI’s ability to recognize and respond to Cairene versus Alexandrian dialects, combined with appropriate cultural courtesy patterns, would reduce the typical 6-month market penetration timeline to just 8 weeks. Customer acquisition costs could drop by as much as 45% as word-of-mouth recommendations increased due to the natural, culturally-aware interactions.

evoAI’s emotional adaptation capabilities would have the ability to open entirely new service categories. For example, we’ve hypothesized that a mental health support service powered by evoAI could help recognize early markers of depression and anxiety based on natural expression patterns, enabling timely intervention and escalation to our health and wellness team—ensuring agent well-being is always prioritized.

This technological advantage translates directly to market adoption: regions using evoAI’s full dialect and emotion capabilities show 3.2x faster adoption rates compared to standard AI deployments, with agent satisfaction scores improving by 78% as they feel more confident handling culturally complex interactions.

Looking past 2025, what do you envision as the next frontier for human-centric AI in CX? 

The next frontier is the convergence of conversational AI, agentic AI, and neural networks to orchestrate a higher level of outcomes not previously contemplated. This will redesign how we do business. The orchestration is no longer human-to-machine; it’s machine-to-machines or machine-to-thousands of machines simultaneously.

Imagine you’re planning a business trip: visiting a website to select an airline, then booking a hotel, arranging transportation, scheduling dinner, and planning the return. This is a simple example of prompting once and letting integrated bots—powered by a neural network—process all available options and build a multi-choice response for you to select from. In this model, the orchestration is neural, the agentic AI powers the bots, and the conversation is the response.

Thank you for the great interview, readers who wish to learn more should visit Alorica.