Thank you so much for doing this with us! To set the stage, tell us briefly about your childhood and background.

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Justin: I grew up in a dusty, working-class part of New Mexico that, by luck, sat next to several of Intel’s early factories. I was the kid teaching myself to code on a TI-99/4A and saving programs to a cassette recorder, with one of the world’s most advanced fabs practically in my backyard. That combination of curiosity and proximity changed everything.

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I joined Intel right out of high school as a manufacturing tech in a bunny suit. Overnight, my world shifted from friends talking weekend plans to engineers and scientists solving complex problems. It was energizing and humbling. From there, I moved into engineering and later into technical marketing.

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My next chapter at SAS brought a new audience entirely. I went from enabling developers to helping executives understand and invest in analytics solutions. It reinforced a simple truth: you can’t fool smart people. Whether it’s engineers, analysts, or C-suite buyers, they can tell instantly if something’s built for their success or yours, and even a small imbalance erodes confidence. That’s when I began to see marketing as a confidence business: not persuasion, but providing the clarity and context people need to make confident decisions.

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Returning to Intel years later gave me the chance to apply that idea at scale, leading global web and content strategy. That focus — confidence as the driver of loyalty and growth — shaped our work from strategy to measurement. We stopped chasing vanity metrics and started asking a better question: “Did people leave more certain than when they arrived?”

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We’ve relocated cross-country a few times for work and growth, but landed in the Portland, Oregon area, an ideal home base for camping, photography, and time with my wife and our Silken Windhound, Bo.

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Jason: I moved to Canada when I was seven years old. I grew up in Toronto, Canada, as a son of immigrants. As a teenager, I worked behind the meat counter at a carnival-themed grocery. This is where I got my first taste of what makes for a great customer experience. This job sparked a lifelong curiosity about what drives people’s decisions and how businesses can build meaningful connections with their customers.

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I received my undergraduate degree in Political Science from the University of Toronto and my master’s from York University specializing in Quantitative Methods. I was fascinated by voting behaviour and data-driven decision-making. During my master’s, I co-ran a small computer technology company that built and installed computers and networks. This early entrepreneurial experience taught me how to run a business and gave me the confidence to start my own.

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In 2001, I founded ONR, a customer experience consulting firm dedicated to helping leading companies like Intel, Deloitte, and Coca-Cola strengthen their customer relationships. Throughout the years, and my experience, I’ve learned that the most successful brands thrive by transforming how they understand and act on customer needs. I call this process Customer Experience Transformation (CX Transformation). I built the Collect, Share, and Act framework to help organizations gather better data, share customer insights across teams, and take aligned, customer-focused action. I have applied this approach to help brands to not only improve loyalty and satisfaction but also drive sustainable growth and operational excellence. In 2021, I published my first book, called Unbreakable which demystifies the art of deepening customer relationships.

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Right now, I spend time in Toronto and Orlando, continuing my passion, while spending quality time with my sons.

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What were the early challenges you faced in your career, and how did they shape your approach to leadership?

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Justin: Early on, I struggled with confidence in my own voice. I’ve always been someone who listens first, and early in my career that sometimes meant staying quiet when I actually had something useful to add. I’d leave a meeting realizing the idea I kept to myself might’ve helped the team. I still catch myself doing that sometimes, but now I’m more aware of it and intentional about creating spaces where quieter voices can contribute before they’re completely sure.

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At the same time, the factory culture I started in seemed to manufacture challenges just to test our determination. It was a place that expected everyone to take responsibility for improvement, regardless of title. If something could be better, you worked on it. You owned it until it was resolved or handed off responsibly. That experience taught me that real leadership comes from participation and care: rolling up your sleeves, contributing, and helping things move forward. Those two principles, use your words and take initiative, are still at the core of how I work today.

 

 

Jason: The challenge has always been this: how can I influence better decisions, faster? For me, that meant figuring out how to help the people I work with move beyond relying solely on “gut feel” and start incorporating more “science” into the decision-making process. That question is what sparked my passion for data.

 

Data has the power to cut through noise and bias to reveal what truly matters. It acts as a dependable north star, helping you focus on the signals that drive meaningful impact.

 

At university, while analyzing public opinion research, I observed how voters often took cognitive shortcuts when making decisions about complex issues. These shortcuts — by definition — reflect what people care most about. I later applied this same lens to market and customer research. When you can decode both the conscious and unconscious influencers of behavior, you uncover the strategic insight brands need to build deeper, more enduring customer relationships.

 

 

We often learn the most from our mistakes. Can you share one mistake that turned out to be one of the most valuable lessons you’ve learned?

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Justin: On the factory floor, data was everything. It told us whether a process was stable, a part was good, or a tool was failing. In that world, facts really were facts, the answer we were all working toward. But early in my career I took that too literally. I treated data as the end of the story, when really it’s just the beginning.

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The lesson was realizing that accuracy isn’t the same as understanding. The real value of data comes from how you use it, to guide discussion, test assumptions, and move work forward. Facts set the starting line, not the finish line.

That shift, from seeing data as proof to seeing it as propulsion, changed how I collaborate, lead, and make decisions. It’s a mindset that turns information into momentum.

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Jason: I look at the importance of data similarly. Early in my career, I was overly focused on what the data said. My mindset was simple: the data is the decision because that’s what the data says. But over time, I realized that knowing what percentage of customers are satisfied or what drives that satisfaction isn’t the decision itself. It’s simply an input or a starting point for making better, faster decisions.

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I came to understand that presenting research findings is not the end. In many situations, my role evolved into ensuring the data was being properly interpreted and thoughtfully applied to real decisions. When someone at the decision-making table truly understands what the data is and what it is not, it creates the conditions for innovation to thrive.

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That’s when data becomes most powerful: not as a static number on a dashboard, but as a dynamic input that informs smarter choices. This, to me, is the most valuable lesson about data. Its impact comes not from the numbers themselves, but from how well they are understood and used in context.

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A.I. is a big leap for many businesses. When and what first sparked your interest in incorporating it into your operations?

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Justin: Our teams were already used to working with advanced systems and solving efficiency problems, so AI initially felt like a natural extension. We started with practical wins, finding internal knowledge faster, transcribing SME interviews, and it worked beautifully.

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Those types of efficiency gains are the no-brainer places to begin. But the real shift, I believe, comes from training chat agents on your own content and data, opening up new ways for employees, customers, and target audiences to quickly get what they need to feel confident taking action, whether that’s downloading a resource, scheduling a meeting, or making a purchase.

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That clicked for me when we built a prototype where visitors could simply ask, “Does this apply to me?” and get a clear, personalized answer spoken back to them. That turned a passive reading experience into one that built confidence. I see that as the direction forward: AI helping people move faster, with greater certainty.

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Jason: From a data perspective, the true appeal of AI lies in its processing speed as it became a key focus 2 years ago. In today’s world, research and customer data in particular, are no longer separate functions. It has become a core part of every brand activity aimed at creating more personalized and impactful interactions.

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The computational power of AI allows us to analyze data, extract insights, and take action in seconds rather than days, weeks, or months. For example, we use AI to address specific digital experience challenges through our Voice AI solution. This solution tracks a customer’s website journey to identify the key topics they are interested in and cross-references that with the information the brand wants to emphasize during the decision-making process.

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All of this analysis happens before our Voice AI even responds to the customer’s question, allowing it to frame the conversation in ways that lead to more positive outcomes for both the customer and the brand.

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AI can be a game-changer for individuals and their responsibilities. Can you share how you personally use AI and what are your go-to resources or tools?

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Justin: AI is part of my daily process. I use it to organize ideas, stress-test assumptions, and clarify messaging before I share it. It’s basically my 24/7 sounding board.

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The biggest benefit has been speed. I used to wait for perfect conditions — more data, more certainty — before speaking up. Now I can explore an idea, get feedback, and iterate quickly. That responsiveness cuts down overthinking and the self-critique that comes from slow follow-through.

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The shift lifted the whole team. As everyone began using AI to shape thoughts earlier, discussions got livelier and decisions came faster. It lowered the barrier to contribute. People became more curious, collaborative, and comfortable sharing rough ideas we could refine together.

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And yes, slop and hallucinations are a thing. Some of those rough ideas probably shouldn’t be shared or tried. AI gets things wrong. But if you squint, that’s part feature, part bug. It forces critical thinking, double-checking sources, trusting your logic, sharpening judgment. In that way, it’s both a tool and a training partner.

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Jason: Leadership has given companies a clear mandate: integrate AI and show its value. However, AI is not a product; it’s an ingredient. It works best when it has a clear purpose. Too often, technology providers approach AI as if it can solve every problem, the latest hammer looking for a nail. The real value comes from taking a more strategic approach to adoption, ensuring AI is applied where it can make the greatest impact.

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We use AI to help brands analyze data they already collect but rarely transform into insight. A perfect example is customer and agent phone conversations, a goldmine of information that has long been overlooked because it’s unstructured and difficult to analyze. With AI, we can now evaluate every interaction and assess agents across key dimensions such as professionalism, empathy, and knowledge. These insights reveal improvement opportunities and enable brands to extract far greater value from their existing data investments.

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On the flip side, what challenges or setbacks have you encountered while implementing A.I. into your company?

 

 

Justin: I’ve had to reduce using em dashes in my writing so people wouldn’t assume it was AI. But that’s part of the challenge, isn’t it? Separating the hype from the help.

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The hard part isn’t the tech; it’s purpose. Everyone wants to “AI everything,” but few can say why. Internal models and retrieval systems are powerful, but their value comes from focus. The best results start with a human problem like confusion, inefficiency, or lack of confidence, and work backward from there.

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Take online commerce. Product expertise has shifted from store associates to customers clicking through endless filters. That’s a lot to ask of someone just trying to make a good choice. Used thoughtfully, AI can bring back that sense of guidance and trust, helping people find what fits faster and with more confidence.

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In the end, AI doesn’t replace people. It amplifies what we do best: helping others make better decisions.

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Jason: The key issue today is that we often expect too much from AI. To use it effectively, it’s important to understand its foundation and why things sometimes go wrong.

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What most people call “AI” is actually a large language model (LLM). Simply put, you can think of it as a highly advanced text prediction system. It has been trained on massive amounts of words and phrases, and based on the input you provide, it predicts the most likely sequence of words that answer your question. It doesn’t actually know what’s right or wrong, it only knows what is most likely based on the data it has seen.

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When an AI model is trained on too much unfiltered data, its “net” becomes too wide, and it can pull in the wrong information. This is what’s known as an AI hallucination. The danger is that the response might sound perfectly logical or factual, but in reality, it’s inaccurate and users have no easy way of knowing.

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AI is far less prone to hallucinations when it’s designed for a specific purpose and trained on focused, high-quality data relevant to that use case. With a smaller, more precise pool of information, the AI is more likely to deliver accurate, trustworthy results instead of misleading ones.

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Let’s dig into this further. Can you share the top 5 A.I. tools or different ways you’re integrating AI into your business? What specific functions do they serve and what kind of result have you seen so far? If you can, please share a story or example for each.

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1. Voice AI for Multi-Modal Conversational Research

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We use Voice AI to collect customer insights through natural, multi-modal conversations, including voice, text, and sentiment cues. This approach generates richer, more authentic data than traditional qualitative or quantitative methods, combining the best of both in a single interaction.

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By analyzing how respondents say their answers, such as tone, emotion, and hesitation, alongside what they say, we uncover deeper insight into their true feelings and motivations. Voice AI also improves efficiency and scalability since multiple interviews can be conducted simultaneously, reducing project timelines from months to weeks while significantly lowering costs.

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2. AI for Data Organization

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AI plays a critical role in how we manage and connect data. Our in-house AI algorithms clean, link, analyze, and report on large, complex datasets rapidly and accurately.

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The first step to actionable insights is ensuring data quality. Many organizations face challenges because different teams collect different types of data, often stored in disconnected systems and formats. We help unify this information by cleaning and stitching unstructured datasets together, creating a single, coherent view of the customer. This alignment allows teams to collaborate more effectively and make decisions based on complete, connected intelligence.

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3. Accurate Forecasting Using AI

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Once data is cleaned and connected, the next step is to make sense of it. Our AI-powered dashboards simplify complex information, helping clients identify trends, anomalies, and opportunities in real time. This accelerates decision-making and improves forecasting accuracy.

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For example, we helped a major financial institution improve its cash forecasting capabilities. Using our predictive models, the organization was able to anticipate cash flow needs with precision, enhancing customer satisfaction while reducing the operational costs associated with service delivery.

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4. Website-Embedded AI Assistants

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Our on-site chat and voice assistants guide users seamlessly through their digital journey. These assistants can answer questions, summarize information from web pages, and help troubleshoot issues, saving users time and reducing frustration.

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By analyzing the content on each page, the assistant provides accurate, contextual answers and directs users to customer support only when necessary. This reduces the workload on live agents, decreases support ticket volume, and frees agents to focus on higher-value, more complex interactions.

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5. Voice AI Contact Center IVR

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We developed a Voice AI IVR system designed to reduce call center load and improve customer experience. The system intelligently triages incoming calls, resolving simple inquiries automatically and routing more complex issues to the appropriate agents.

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When a call is transferred, the agent receives the full context and information gathered earlier in the interaction. This eliminates the need for customers to repeat themselves, reducing frustration, shortening handle times, and improving overall satisfaction.

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There’s concern about A.I. taking over jobs. How do you balance A.I. tools with your human workforce and have you already replaced any positions using technology?

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Justin: I share that concern. I’ve read too many stories of AI being used as a blunt cost-cutting tool instead of what it should be, a lever for amplification and efficiency.

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I think about it less as replacing work and more as rebalancing it. AI handles the repetitive time -consuming parts like summarizing calls, drafting outlines, pulling insights, so people can focus on judgment, creativity, and relationships. It frees up mental space for the kind of thinking only humans do well.

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The result isn’t fewer jobs, but better ones. We’ve been able to spend more time on high-value discussions, faster iteration, and more meaningful collaboration. My rule of thumb: automate where it reduces friction, but always keep a human where trust matters. The goal isn’t speed; it’s clarity and confidence. That’s where AI creates real value.

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Jason: Companies are always looking for ways to cut costs. For example, leaders are instructing their managers to find ways to use AI to replace marketing and customer service roles. The calculation is straightforward; headcount reduction will save the company X dollars. However, there are unintended consequences that are often overlooked because they are more difficult to measure such as the decline in customer service that leads to lower levels of customer loyalty. It will take time to understand the impact of AI on customer relationships. I believe companies that take a more measured and purpose-driven approach to the implementation of AI will be more successful long term.

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Looking ahead, what’s on the horizon in the world of AI that people should know about? What do you see happening in the next 3–5 years? I would love to hear your best prediction.

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Justin: For marketers, I hope the next few years are less about chasing every new AI feature and more about rethinking how we build and measure confidence in our audiences.

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The first big shift will be personal AI assistants that understand context. Private agents grounded in a person’s own data and intent. Instead of just publishing content, we’ll need to equip these assistants with clear, trustworthy brand language so they can represent us accurately in conversation.

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We’ll also start measuring confidence alongside engagement and conversion. The question won’t just be “Did they act?” but “Did they feel sure enough to act?” That shift will change how we define success and design experiences that build trust.

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Finally, journey design will zoom back out. Rather than optimizing single pages and CTA’s, marketers will focus on end-to-end flows that reduce friction and build certainty at every step. The brands that help people move forward with clarity not just speed, will be the ones that last.

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Jason: I think what we’re seeing in the U.S. right now is a high level of confidence in AI. Many companies are betting on it as a way to reduce costs and boost profitability. However, only a few will realize meaningful ROI. Most will likely over-invest early, then pull back once they see the unintended consequences, such as declining customer relationships.

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For the market research, customer experience, and journey analytics industries, this moment represents a major inflection point. The organizations that successfully use AI to uncover insights from data sources that have long been ignored or underutilized and apply those insights to drive faster, smarter decision-making will be the ones that thrive. Those that fail to adapt quickly enough risk fading into irrelevance.

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If you had to pick just one AI tool that you feel is essential, one that you haven’t mentioned yet, which would it be and why?

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Justin: If I had to pick one, it would be a private, retrieval-augmented assistant — an LLM connected to your company’s own content and data. Generic tools are impressive, but grounding them in real documentation, messaging, and customer conversations is what makes them trustworthy and actionable.

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For marketers, it’s a way to surface clearer insights, spot friction points, and ensure customers find confident answers faster. Start small, connect a few key sources, test with real users, refine… and you’ll quickly see how much smarter your ecosystem becomes.

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Jason: Tools that make it easier for companies to connect their many sources of data into a unified view of brand performance are on the horizon. Leadership teams are increasingly seeking ways to integrate data from marketing, sales, R&D, and support functions to tell a single, cohesive story about how the business is performing.

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AI will make this possible by connecting these diverse data sources into one compelling narrative that can directly inform both strategy and execution. While we’re not quite there yet, this type of solution is coming and it will fundamentally reshape how organizations are structured. The result will be smaller, more integrated companies that can collaborate more effectively and pivot much faster.

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For the uninitiated, what advice would you give someone looking to integrate AI into their business and doesn’t know where to start?

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Start with why. Too many teams rush to “AI everything” without a clear purpose, and that’s where projects stall or add cost without impact. Define the specific business problem you want to solve — confusion, inefficiency, slow decision-making — and decide how you’ll measure improvement.

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Don’t try to overhaul everything at once. Pick one high-value area, run a pilot, and measure what changes: time saved, confidence gained, customer clarity. Let results guide where to scale next.

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Most importantly, stay grounded in the human side. The best AI use cases make employees and customers feel more capable, not replaced.

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Where can our readers follow you to learn more about leveraging A.I. in the business world?

 

Websites:

bespokecsi.com

 

LinkedIn Profiles:

Justin: 

www.linkedin.com/in/huntsman

Jason: 

www.linkedin.com/in/jtenpow

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