Interview Series – Harry Folloder, Chief Digital & Technology Officer at Alorica
By Antoine Tardif, CEO & Founder of Unite.AI
Reposted from Unite.AI
Harry Folloder, Chief Digital & Technology Officer at Alorica, brings a blend of technical acumen and people-centric leadership to his role, having served in senior digital product and employee-experience positions before ascending to the CDTO role in January 2023. In his current capacity, he leads digital transformation efforts at Alorica’s innovation hub, Alorica IQ ®, driving initiatives that fuse AI, conversational intelligence, and human insight to elevate customer and employee experiences alike. Known for emphasizing clear communication, emotional intelligence, and a culture of continuous learning, Folloder underscores that technology adoption must be guided by empathy, stakeholder alignment and the human side of change management.
Your career journey—from technology leadership roles to guiding Alorica’s digital transformation—has emphasized empathy and communication as key to innovation. How have those principles shaped the way you integrate AI into customer experience today?
Technology without empathy is just expensive automation. At Alorica, we’ve built our entire AI strategy around a fundamental belief: the most powerful innovations augment human capability rather than replace it. My approach stems from a simple observation—every breakthrough in customer interaction happens when technology amplifies human intuition, not when it tries to mimic it. We’ve deployed AI across 65,000+ agents globally, and the results prove this philosophy works: 40% reduction in handle time while simultaneously increasing CSAT scores by 15-20 basis points.
I often say we’re building “super humans,” not replacing humans. Our AI systems are designed to handle the computational heavy lifting—analyzing sentiment across 120+ languages in real-time, predicting customer needs before they articulate them, surfacing knowledge instantly—so our agents can focus on what humans do best: connect, empathize, and solve complex problems creatively. When you design with empathy as your north star, AI becomes a bridge to deeper human connection.
Alorica IQ is at the center of your transformation strategy. Can you share how its AI tools, like real-time analytics and predictive insights, are redefining what customer service looks like for travel and hospitality clients?
Alorica IQ represents our commitment to making CX proactive rather than reactive. For our travel and hospitality clients, we’re not just solving problems—we’re preventing them from occurring.
Take our work with a major airline partner: Our evoAI conversational platform now handles 50% of routine inquiries autonomously while detecting flight disruptions in real-time. When we identify a delayed flight, the system proactively reaches out with rebooking options before the customer even knows there’s an issue. This has reduced complaint volumes by 35% and improved NPS by 12 points.
Our predictive analytics engine analyzes over 100 million interactions monthly, identifying patterns that human agents might miss. For instance, we discovered that customers who experience three or more IVR transfers have a 78% higher likelihood of churn. Now we automatically escalate these interactions to senior agents with full context, preventing frustration before it builds.
What makes Alorica IQ unique is our technology-agnostic approach. We integrate seamlessly with existing client ecosystems—whether they’re using Genesys Cloud, Amazon Connect, or proprietary platforms. We enhance what’s working, replace what isn’t, and ensure everything works together harmoniously.
The real transformation is in how we’ve redefined resolution. It’s no longer about fixing problems—it’s about anticipating needs and delivering solutions before customers realize they need them. That’s the difference between customer service and customer experience
You’ve spoken about creating “super agents” through AI-powered augmentation rather than replacement. How do you strike the balance between automation efficiency and maintaining a human touch in customer interactions?
The balance isn’t about drawing a line between human and AI tasks—it’s about creating seamless collaboration where each amplifies the other’s strengths. We follow what I call the ‘human-in-command’ model.
Here’s how it works in practice: Our AI handles the cognitive load—instantly analyzing customer history, sentiment, and context across multiple systems. It can process 10,000 data points in milliseconds. But the human agent maintains control of the emotional intelligence—reading between the lines, picking up on subtle cues, making judgment calls that require empathy and creativity.
For example, Alorica ReVoLT, our real-time voice translation system, doesn’t just translate words—it preserves emotional nuance across 75 languages and 200 dialects. The agent can focus on building rapport while AI ensures nothing gets lost in translation. Similarly, Knowledge IQ® surfaces answers from thousands of sources instantly, but agents decide how to contextualize that information for each unique customer situation.
We measure success not by how many interactions AI handles alone, but by how much more effective our agents become. Since implementing our augmentation strategy, agent satisfaction scores have increased by 22%, and our top performers are handling 40% more complex cases while maintaining higher quality scores. That’s the power of augmentation—everyone wins.
Alorica ReVoLT recently won an AI Breakthrough Award for real-time voice translation. What impact has this had on global customer support, and how do you see language AI evolving in the next few years?
Alorica ReVoLT has fundamentally democratized global customer support. With 97% semantic accuracy across 75 languages and 200 dialects, we’ve eliminated the traditional trade-off between scale and localization.
Our clients can now serve any market without establishing local operations. A travel company we partner with expanded into 12 new markets in six months—something that would have taken years and millions in infrastructure investment previously. Customer satisfaction in non-English markets increased by 28%, and we’ve reduced the need for third-party interpreters by 90%.
But here’s what excites me about the future: We’re moving from translation to true comprehension. The next generation of language AI will understand cultural context, regional idioms, and emotional undertones. We’re already testing systems that detect sarcasm, frustration, and even humor across languages—adjusting responses accordingly.
By 2027, I envision AI that doesn’t just translate languages but translates experiences—understanding that a complaint in Japanese culture might be expressed as subtle disappointment, while the same issue in New York might come across as direct frustration. That level of cultural intelligence will make global CX truly local.
Knowledge IQ is another impressive innovation—using large language models to surface information instantly for agents. How do you ensure that these AI systems remain accurate, secure, and aligned with client needs?
Knowledge IQ operates on three pillars: verification, governance, and continuous learning. We use retrieval-augmented generation (RAG) to ensure every response is grounded in verified source material, not AI hallucinations.
Our approach is deliberately conservative—we’d rather say ‘I don’t know’ than provide incorrect information. Every response includes source attribution, and we maintain a human-in-the-loop validation system for critical information. Our accuracy rate currently stands at 99.3% for verified responses.
Security is non-negotiable. We maintain SOC 2, ISO 27001, HITRUST, and PCI compliance across all systems. Data is encrypted at rest and in transit, with role-based access controls and full audit trails. For regulated industries like healthcare and financial services, we’ve implemented additional guardrails that ensure HIPAA and GDPR compliance.
But the real innovation is in alignment—Knowledge IQ learns from every interaction. Agent feedback loops help the system understand not just what information to surface, but when and how to present it. This continuous refinement means the system gets smarter every day, reducing average handle time by 32% while improving first-call resolution by 18%.
We’ve built this capability through strategic partnerships with leaders like OpenAI, Microsoft, Google, and Amazon, while maintaining our technology-agnostic approach. Many of our clients come to us with existing investments in platforms like Genesys Cloud or CallMiner—we enhance and extend these systems rather than replace them. It’s about meeting clients where they are on their journey, not forcing them onto ours.
Many companies still struggle with large-scale digital transformation. Based on your experience, what are the biggest communication or change-management pitfalls that derail AI initiatives before they succeed?
The biggest pitfall is skipping the journey map. Too many organizations buy technology before defining the experience they want to create. Transformation fails when it starts with procurement instead of purpose. Our mantra is simple: design the journey, then choose the tech. The correct solution emerges from empathy, timing, and applicability. Real change happens when teams understand why they’re transforming and see themselves reflected in the process.
The biggest failure I see is companies buying AI before designing the experience. They start with the technology catalog instead of the customer journey. That’s backwards.
Successful transformation follows this sequence: First, map the ideal customer journey. Second, identify friction points. Third, determine where AI can remove that friction. Only then do you select technology. Too many organizations skip straight to step three and wonder why their million-dollar AI investment isn’t moving the needle.
Another critical pitfall is underestimating the human element. Your agents aren’t obstacles to AI adoption—they’re your secret weapon. We invest heavily in change management, spending 40% of our transformation budget on training and enablement. We show agents how AI makes their jobs easier. When agents see AI as their copilot rather than their replacement, adoption accelerates dramatically.
Finally, companies often fail to establish clear success metrics upfront. You need to define what “better” looks like before you start. Is it faster resolution? Higher CSAT? Lower cost per interaction? Without clear KPIs, you’re just implementing technology for technology’s sake.
Not every company needs to start with AI-powered everything. Some of our most successful engagements begin with simple process optimization before adding intelligent automation. We meet clients wherever they are on their digital journey—whether they’re taking their first steps into automation or ready for advanced AI implementation. The key is having a partner who can guide you through the entire evolution, not just sell you the latest technology.
You’ve led a fully remote, global digital team. How do you maintain empathy, connection, and collaboration across time zones while pushing the limits of technical innovation?
Leading a 24-time-zone digital team has taught me that technology can transmit communication, but only empathy sustains culture. My role is to remove friction so creativity can flourish. True innovation doesn’t come from perfection—it comes from psychological safety. People need room to fail, learn, and iterate without fear of blame. When teams feel supported and trusted, they take smarter risks and build systems that mirror that same humanity and resilience for the customers we serve.
Leading teams across 24 time zones has taught me that proximity isn’t presence. Connection happens through intention, not geography.
We’ve built a culture of “asynchronous empathy”—using tools like Stream for video updates that teammates can watch in their own time zone, maintaining Teams channels for ongoing collaboration, and rotating meeting times so no single region always gets the 3 a.m. slot. But tools are just enablers—culture is what matters.
I am a big believer of radical transparency. Every week, I share unfiltered updates about challenges we’re facing, wins we’re celebrating, and decisions we’re making. This vulnerability creates psychological safety. My team knows it’s okay to fail fast, learn faster, and iterate without fear. Innovation requires risk, and people only take smart risks when they feel safe.
We also celebrate differently. Digital badges for transformation achievements, virtual coffee chats that are actually about coffee (not work), and “failure parties” where we celebrate bold attempts that didn’t work out. When people feel seen and valued, distance disappears. My globally distributed team ships innovations 30% faster than traditional co-located teams, with 40% lower turnover. Empathy scales when you make it systematic.
Customer churn remains a major issue across industries, especially in travel. How is Alorica using predictive AI models to identify at-risk customers and intervene before loyalty is lost?
Our AI analyzes over 150 behavioral signals—interaction frequency, sentiment trajectory, channel switching patterns, resolution velocity—to identify churn risk before customers make the decision to leave.
For travel clients, the model is remarkably accurate. We can predict with 87% accuracy which customers will churn within 30 days. But prediction without action is just fortune telling. The magic is in the intervention strategy.
When we identify at-risk customers, we trigger personalized retention campaigns: proactive outreach with relevant offers, priority routing to specialized retention agents, or automated loyalty rewards. For one hotel chain, this approach reduced churn by 23% and increased lifetime value by $430 per retained customer.
Churn rarely happens suddenly; it’s a gradual erosion of trust. Our AI detects these micro-frustrations and addresses them before they compound. We call it “loyalty preservation”—maintaining the relationship rather than trying to rescue it after it’s broken.
You often emphasize measurable impact—NPS, CSAT, and FCR scores. Which performance metrics best capture the value of AI-driven transformation for clients today?
Traditional metrics tell you what happened. Modern metrics need to tell you what’s possible.
While CSAT and NPS remain important, I focus on three transformational metrics:
- Customer Effort Score (CES): This is the strongest predictor of future loyalty. Our AI implementations have reduced customer effort by 45% on average. That’s the difference between a frustrated customer and a loyal advocate.
- Revenue per Resolved Interaction (RRI): This connects service to business outcomes. By using AI to identify upsell opportunities during service interactions, we’ve increased RRI by 28% for retail clients without feeling pushy or sales-focused.
- Agent Empowerment Index (AEI): This composite metric measures how effectively AI augments human performance—combining productivity gains, quality improvements, and agent satisfaction. High AEI correlates directly with customer satisfaction and employee retention.
The ultimate metric? Business impact. One client saw $47 million in cost savings while improving CSAT by 12 points. Another increased revenue by $73 million through AI-powered upselling during service interactions. Those numbers tell the real story of transformation.
Looking five years ahead, how do you envision the relationship between AI, agents, and customers evolving—and what role will Alorica play in shaping that future?
We’re entering what I call the “Ambient Experience Era”—where AI becomes invisible infrastructure that makes every interaction feel effortless and deeply personal.
In five years, customers won’t “contact support”—they’ll simply express needs, and the right solution will materialize through the optimal channel. AI will orchestrate seamless handoffs between automated and human interactions based on context, emotion, and preference. Imagine starting a conversation with AI chat, seamlessly transitioning to a video call with a human expert when complexity increases, then receiving automated follow-up—all feeling like one continuous conversation.
Agents will evolve into “Experience Architects”—using AI-powered insights to design personalized customer journeys in real-time. They’ll spend zero time on repetitive tasks and 100% of their energy on creative problem-solving and relationship building. We’re already piloting programs where agents manage AI assistants rather than phone queues—orchestrating multiple AI interactions simultaneously while focusing human attention where it matters most.
Alorica’s role is to be the bridge between human potential and AI capability. We’re not building technology companies—we’re building human companies powered by technology.
Our investments in conversational AI, predictive analytics, and real-time translation are all aimed at one goal: creating experiences where technology is so perfectly integrated that customers only notice the humanity.
Whether we’re helping a startup implement their first chatbot or guiding a Fortune 500 company through complete digital transformation, our mission remains the same: meet clients where they are and help them get where they need to be. Some clients come to us ready for cutting-edge AI; others need to start with basic automation. Both journeys are valid, and both deserve a partner who understands the destination matters more than the starting point.
The future belongs to organizations that view AI not as a cost-cutting tool but as a creativity multiplier. At Alorica IQ, we’re building that future—one augmented human at a time, one client journey at a time.