Removal of Siloes: In today’s data-driven economy, businesses are drowning in information yet still struggle to extract meaningful insights due to fragmented data silos. Customer interactions, behavioral insights and operational data often reside in separate systems across marketing, sales, customer service, and operations, creating blind spots that hinder decision-making. AI is transforming this landscape by integrating diverse data sources, reducing the inefficiencies caused by disconnected systems and providing a unified, real-time view of the customer journey. By embedding AI-powered journey analytics directly into engagement platforms, businesses can assess customer behaviors, predict needs, and deliver more personalized experiences — all without relying on slow, manual data extraction and analysis.
In today’s tech-driven world, artificial intelligence has become a key enabler of business success. But the question remains — how can businesses effectively harness AI to address their unique challenges while staying true to ethical principles? To explore this topic further, we are interviewing Glenn Nethercutt, Chief Technology Officer at Genesys.
Glenn Nethercutt is the Chief Technology Officer and a Technical Fellow at Genesys, where he oversees cloud architecture strategy: scalability, microservices, cloud-native design, fault tolerance, disaster recovery, service consistency, new technology evaluation, and continuous delivery mechanisms. He has a background in telecommunications, complex event stream processing, application performance management, OS development, data visualization, and continuous delivery principles. Glenn lives in Raleigh, North Carolina where he is an avid hiker and runner.
Thank you so much for joining us in this interview series. Before we dive into our discussion, our readers would love to “get to know you” a bit better. Can you share with us the backstory about what brought you to your specific career path in AI?
My journey into AI started as an evolution of my broader work in cloud architecture and complex event processing. Early in my career, I was focused on scalability and reliability in distributed systems, but I understood that intelligence — true insight and automation — was the missing aspect. At Genesys, AI was a natural extension of our experience orchestration vision, allowing us to move from reactive customer interactions to predictive and proactive experiences. We are fortunate to have access to the type of data needed to train and fine-tune these types of systems, and to be front-and-center for the perfect use-cases in the enterprise.
Can you share the most interesting story that happened to you since you started working with artificial intelligence?
One of the most mind-opening moments in my work with AI came when I first saw the true power of embeddings — how they encode meaning in ways that even humans might not fully grasp. Early on, expert systems and “AI” relied heavily on keyword matching and rigid rule-based approaches, but then we started working with vector embeddings to represent words, phrases, and even entire conversations in higher-dimensional space. I remember running a simple experiment: we plotted customer sentiment embeddings and noticed that complaints about ‘slow service’ and ‘unhelpful responses’ were clustering closely with completely different phrasing — things like ‘Why is this taking forever?’ or ‘I keep getting transferred.’ It was a striking moment because, unlike traditional keyword models, the AI understood that these were semantically related even though they had no overlapping words.
But what really amazed me was when researchers expanded beyond text and started embedding entire interactions — speech, sentiment shifts, pauses in conversation — and saw that the AI could map frustration or satisfaction without ever being explicitly trained on those emotions. It was learning a deeper structure of human communication. That was the moment I knew technologists weren’t just building ‘smart automation’ — we were encoding understanding in a way that could reshape how humans interact.
You are a successful leader in the AI space. Which three character traits do you think were most instrumental to your success? Can you please share a story or example for each?
- Relentless Curiosity — In technology, complacency is the enemy. I’m always asking, ‘What’s next?’ and pushing boundaries. When we first explored “big data,” systems were too expensive to operate, and insights were difficult to attain. When we first delved into the cloud, many doubted the adoption from on-premises data centers. When we first explored AI copilots, there was scepticism about their adoption. But my curiosity led me to experiment with real-world use cases, proving their value before many competitors even considered them.
- Data-Driven Decision Making — One of the most important traits in my career has been a relentless commitment to making data-driven decisions. In a world full of opinions, instincts, and gut feelings, I’ve always believed that the best way to make complex decisions — especially in technology — is to let the data speak. Being data-driven doesn’t mean ignoring intuition — it means validating it. The key is to ensure that every major decision, whether it’s about AI strategy, business investments, or technical architecture, is anchored in empirical evidence rather than assumptions.
- Resilience in Change — Technology shifts can be unpredictable. I had a background in carrier telecommunications, a world where purpose-built hardware systems ruled. Seeing the cloud as an opportunity was a great boon for me, as was applying principles learned from highly available real-time systems in a space where only b2c (e.g. Netflix) was taking those bets.
Let’s jump to the primary focus of our interview. Can you share a specific example of how you or your organization used AI to solve a major business challenge? What was the problem, and how did AI help address it?
One of the biggest challenges businesses face is fragmented, siloed data that’s difficult to access, incomplete, or not structured in a way that delivers meaningful insights. In customer service this lack of integration can lead to frustrating experiences for both consumers and employees. Genesys Cloud AI is native, purpose built and deeply embedded across Genesys Cloud, our experience orchestration platform. Working in unison, our end-to-end architecture of conversational, predictive, and generative AI capabilities continuously learn for smarter outcomes, better results, and clearer context. These contextual experiences show consumers that their wants and needs matter to a business. This means virtual and human agents already know a customer’s name and information regardless of their contact method. With Genesys Cloud AI, companies can better anticipate customers’ needs, understand their preferences, and resolve issues effectively at the first point of contact and on the channel of a customer’s choice.
What are some of the common misconceptions you’ve encountered about using AI in business? How do you address those misconceptions?
Some organizations assume that simply feeding all their data into an AI system will generate accurate, actionable insights. But unstructured or low-quality data can result in AI hallucinations and unreliable outputs. To get real value from AI, businesses must focus on data quality and relevance. AI performs best when it has access to well-structured, well-documented information and context, especially in environments where accuracy is critical. That’s why designing AI with a humans-in-the-loop approach is so important. This ensures subject matter experts are always reviewing AI-generated responses before they reach customers.
In your opinion, what is the most significant way AI can make a positive impact on businesses today?
AI is transforming the way businesses engage with customers by orchestrating the highly personalized experiences required in today’s competitive digital landscape. By leveraging data from multiple sources, AI can analyze preferences, behaviors, and history to deliver tailored interactions to consumers. From recommendations with precision focus to proactive support, AI empowers businesses to meet customers with the right solutions at the right time. This builds stronger relationships, increases satisfaction, and drives long-term loyalty, all of which can play a significant role in increasing businesses’ bottom line.
Ok, let’s dive deeper. Based on your experience and research, can you please share “5 Ways AI Can Solve Complex Business Problems”? These can be strategies, insights, or tools that companies can use to make the most of AI in addressing their challenges. If possible, please share examples or stories for each.
- Removal of Siloes: In today’s data-driven economy, businesses are drowning in information yet still struggle to extract meaningful insights due to fragmented data silos. Customer interactions, behavioral insights and operational data often reside in separate systems across marketing, sales, customer service, and operations, creating blind spots that hinder decision-making. AI is transforming this landscape by integrating diverse data sources, reducing the inefficiencies caused by disconnected systems and providing a unified, real-time view of the customer journey. By embedding AI-powered journey analytics directly into engagement platforms, businesses can assess customer behaviors, predict needs, and deliver more personalized experiences — all without relying on slow, manual data extraction and analysis.
- Visibility across the customer journey: AI-powered journey management gives businesses visibility across the customer journey, revealing opportunities to streamline interactions, eliminate roadblocks and enhance satisfaction while reducing service costs. By leveraging advanced analytics, organizations gain deeper insights into the holistic customer journey, unlocking new levels of control and personalization. This end-to-end approach doesn’t just work to improve engagement, it drives business growth by helping organizations deliver seamless, tailored experiences across every touchpoint.
- Hyper-personalization: Many businesses still operate reactively, addressing customer concerns only after they arise. But AI is shifting the game toward proactive, even “invisible” service, where organizations anticipate and resolve issues seamlessly often before customers even notice. This proactive personalization not only saves time and frustration but also helps ensure customers feel informed and supported without unnecessary effort. However, the future of personalization isn’t about replacing the human element. It should create harmony between AI-powered interactions and human expertise. Whether through a chatbot, voice assistant, or human agent, the goal is the same: to make every customer experience effortless, intelligent and deeply personalized.
- Workforce automation and augmentation: AI-powered workforce engagement management and products like advanced AI copilots are transforming the way employees work to boost efficiency and productivity. Instead of just automating repetitive tasks like summarizing conversations or checking orders, AI can provide real-time support so employees can work smarter and faster. By integrating AI into daily workflows, businesses can reduce workload, streamline operations and maximize ROI. The real value isn’t just in convenience. It’s in empowering employees to focus on higher-value tasks while AI handles the heavy lifting. The benefits of AI in workforce engagement extend beyond employees to impact customer experience. Engaged employees are more likely to deliver empathetic, high-quality service.
- Loyalty in an experience economy: All of the above comes together to help businesses build loyalty and drive results. Our research found that nearly 60% of CX leaders surveyed expect the adoption of AI in the customer experience to increase customer loyalty and lifetime value.
How can smaller businesses or startups, with limited budgets, begin to integrate AI into their operations effectively?
Smaller businesses can start with pilot projects to test AI solutions on a smaller scale. This allows time to see what works, identify challenges early and build a strategy and approach before going all in. Pilots can be used to gather feedback, measure results and adjust along their AI journey. Think of it as an AI test drive to ensure a smooth and successful full-scale rollout.
Additionally, AI pricing should be flexible and cost-efficient. At Genesys, we’ve adopted a consumption-based model powered by Genesys AI Experience tokens. Tokenization in AI is a way to track AI engagement in real time by allocating fixed units of measurement to usage costs. This can help businesses of all sizes allocate resources dynamically and efficiently. By paying only for the AI functionalities you actually use, tokenization offers a scalable, cost-efficient way to integrate AI into your operations.
What advice would you give to business leaders who are hesitant to adopt AI because of fear, misconceptions, or lack of understanding?
Start with developing a comprehensive AI strategy that outlines the roadmap for integration. This should include timelines, resource allocation, and a detailed plan for each phase of implementation. Ensure that the strategy aligns with the overall business goals. A well-chosen AI solution can scale with the business by supporting growth and adapting to changing needs. To unlock AI’s full potential, business leaders need a secure, unified platform that’s easy to deploy and allows them to innovate and tailor features for their customers’ needs.
In your opinion, how will AI continue to shape the business world over the next 5–10 years? Are there any trends or emerging innovations you’re particularly excited about?
We’re only at the tip of the iceberg with AI experimentation, and some of the behind-the-scenes developments are making waves. Take the Golden Gate Model. Instead of fully retraining an AI, researchers adjusted certain weights in its neural network and nudged it to naturally steer conversations toward San Francisco’s Golden Gate Bridge. It’s similar to how people with a strong interest in a topic always find ways to bring it up in conversation.
This kind of experimentation is the predecessor to highly specialized AI personas. These could be AI financial advisors fluent in investment jargon or customer service bots that perfectly match your brand’s voice. The possibilities are endless, from ultra-personalized experiences to industry-specific AI experts that feel surprisingly human.
But I think what’s coming next is even more transformative: agentic AI — autonomous, non-deterministic AI systems that don’t just respond to inputs but actively pursue goals, refine their own strategies, and adapt in real-time. Unlike today’s AI, which is largely passive and assistive, agentic AI will be capable of long-term planning, complex decision-making, and proactive problem-solving without explicit human intervention. Imagine an AI-powered enterprise system that doesn’t just surface insights but autonomously optimizes workflows, mitigates risks before they become problems, or even negotiates vendor contracts on behalf of a business, all while learning and improving over time.
This shift toward AI as an autonomous agent rather than a reactive tool will fundamentally reshape industries. In customer experience, for example, we’ll move from static chatbots to AI-powered service orchestrators that independently resolve issues, manage multi-step processes across different platforms, and anticipate customer needs before they arise. In engineering, we’ll see self-healing systems that identify and correct software bugs autonomously. The implications for finance, healthcare, and operations are equally massive.
The leap from assistive AI to true agentic AI won’t just change how businesses use technology — it will redefine the relationship between humans and machines. The next five to ten years will be about moving from automation to autonomy, and I think businesses that embrace this shift early will have a massive competitive advantage.
How do you think the use of AI to solve business problems influences relationships with customers, employees, and the broader community?
AI is playing a significant role in the evolution of how organizations are empowering their employees and serving their customers across industries. In the customer experience industry, its forging new opportunities for organizations to engage with consumers with increased speed, effectiveness and empathy. Organizations can offer instant, round-the-clock support fine-tuned to each person’s needs and resolve common issues with minimal human intervention. When issues require the human touch, it can pass context seamlessly to a human agent. Solutions like AI-driven analytics enable companies to analyze vast amounts of customer data, uncovering insights into their preferences, behaviors, and pain points. Similarly, AI copilots that surface knowledge and automate tasks allow human agents to resolve complex and high-value customer inquiries more efficiently and quickly.
One example where we can see how organizations use of AI is directly affecting the broader community is transforming crisis response by providing immediate, 24/7 support when people need it most. During emergencies like medical challenges, unemployment, or utility outages, long hold times and repetitive inquiries only add to the stress. AI streamlines access to information, enables self-service for those who can navigate solutions independently and swiftly connects others to the right human agents. By enhancing efficiency and reducing frustration, AI helps organizations deliver critical relief and support when it matters most.
You are a person of great influence. If you could start a movement that would bring the most amount of good to the most amount of people through AI, what would that be? You never know what your idea can trigger. 🙂
If I could start a movement, it would be to create an AI-powered educational system inspired by Neal Stephenson’s novel “The Diamond Age” — a personalized, adaptive tutor that transforms learning into a lifelong, interactive journey. Imagine a world where every child, regardless of background, has access to an AI mentor that dynamically adjusts to their curiosity, learning style, and pace. This wouldn’t be just another static curriculum or video-based learning platform. It would be an AI-driven, immersive experience — one that evolves alongside the learner, guiding them through complex concepts with real-time feedback, interactive simulations, and even narrative-driven lessons where the student becomes the protagonist in their own education.
Such a system could democratize access to high-quality learning in ways traditional institutions never could. Whether it’s a young girl in a remote village learning advanced mathematics through AI-generated puzzles or an aspiring engineer exploring quantum mechanics via interactive storytelling, the Primer would make education engaging, individualized, and limitless. It would be the bridge between raw information and true understanding — instilling not just knowledge but the ability to think critically, solve problems creatively, and cultivate lifelong intellectual curiosity. In a world of rapid technological advancement, the greatest gift AI can give us isn’t just automation — it’s empowering the next generation to dream bigger, learn faster, and push the boundaries of human potential.
This was great. Thank you so much for the time you spent sharing with us.
About the Interviewer: Chad Silverstein is a seasoned entrepreneur with 25+ years of experience as a Founder and CEO. While attending Ohio State University, he launched his first company, Choice Recovery, Inc., a nationally recognized healthcare collection agency — twice ranked the #1 workplace in Ohio. In 2013, he founded [re]start, helping thousands of people find meaningful career opportunities. After selling both companies, Chad shifted his focus to his true passion — leadership. Today, he coaches founders and CEOs at Built to Lead, advises Authority Magazine’s Thought Leader Incubator.
Glenn Nethercutt of Genesys On How Artificial Intelligence Can Solve Business Problems was originally published in Authority Magazine on Medium, where people are continuing the conversation by highlighting and responding to this story.