Today’s episode of the Punk CX podcast features a series of interviews that I conducted at Qualtrics’ recent X4 event in Seattle and features conversations with Qualtrics executives at the event:

• Brad Anderson, President of Products, UX, Engineering & Security;
• Mark Hammond, SVP Core AI – starting at 14:59;
• Assaf Keren, SVP and Chief Security Officer – starting at 36:44; and
• Ali Henriques, Executive Director of Market Research – starting at 59:19.

We discuss the highlights and themes of the event, the experience gap, the future of AI, and what organisations should be considering, alongside topics such as the role of security and trust in customer experience and synthetic panels, also known as customer simulation models. There’s a lot in there, so do check it out.

This interview follows on from my recent interview – The enduring and evolving ‘craft’ of customer support – Interview with Nick Francis – and is number 579 in the series of interviews with authors and business leaders who are doing great things, providing valuable insights, helping businesses innovate and delivering great service and experience to both their customers and their employees.

Here are the highlights of my chat with Brad:

• Prioritise Full-Spectrum Listening: The foundation of effective experience management requires CX leaders to move beyond relying solely on surveys, which capture less than 10% of customer data. You must architect a strategy to listen across all channels, including social reviews, calls, and chats, to gain the full texture of the customer journey.
• Bridge the Action Gap with AI: The industry-wide “experience gap” has shifted from the difficulty of understanding feedback (largely solved by AI) to the persistent difficulty of taking action. The current gap exists between understanding insights and actually closing the loop in the moment.
• Scale Empathy to Overcome Capacity Limits: Human capacity is the biggest barrier to action, limiting customer follow-up to just 1-2% of incoming CX feedback. Leverage AI, such as “experience agents,” to scale human empathy and judgment, enabling the organisation to theoretically close the loop on 100% of interactions.
• Ensure AI Trust for Regulated Environments: In the world of AI-driven analytics, only use providers that you trust. This means demonstrating that the AI platform has proven trustworthiness and is relied upon by sophisticated, regulated organisations in financial services, government, and healthcare.
• Shift to Proven ROI for Mainstream Adoption: To appeal to the “early majority” of customers—the mainstream market—the CX organisation must deliver predictability, standardisation, and proven ROI (return on investment), moving beyond rough experiments valued by early adopters.
• Lead with Business Outcomes: To secure organisational buy-in, consciously pivot all CX discussions away from product features and technical details to demonstrable customer stories and numbers-driven outcomes that show clear value to the business.
• Align CX Goals to Corporate Objectives: The single most critical focus must be to understand the overriding business outcomes your company is focused on (e.g., increasing renewal rate from 60% to 80%) and build a plan that clearly shows how experience management helps achieve and exceed that goal.
• Target CFO-Level Advocacy: The ultimate measure of a CX leader’s success is achieving the credibility to get your company’s CFO on stage to champion and talk about the quantifiable outcomes being driven by the experience management program.

Here are the highlights of my chat with Mark:

• Adopt a New AI Operating Model: Recognise that AI represents a new technology wave requiring a fundamental shift in your operating model, not just a “bolt-on” addition to existing processes. The biggest challenge is transforming the organisation to understand this new mental model and new “operating world”.
• Prioritise Value Over Technology: Treat AI and data as inputs or fuel; the ultimate objective is the measurable value you provide to customers and employees. To establish the new frame, CX strategy should be driven by “sci-fi” reimagining of the ideal customer experience, then working backward to technology implementation.
• Harness Context as the New Gold: View rich context as the “new gold” and the major differentiating asset in the AI-first era, as it determines which capabilities can be offered. Your systems must be specifically trained with your company’s deep mental model and business data, as external LLMs are inherently ignorant of your operations.
• Architect for Cross-Organizational Data: Complex CX challenges rarely exist in a single silo; building rich context requires pulling data and capabilities from multiple sources, often across half a dozen places outside your organization. You must build tooling and adopt collaboration standards to make this possible.
• Define Experiences by Four Themes: Future customer experiences will be defined by Dynamism (reacting to novel situations without rigid scripts), Context (rich, personal understanding), In the Moment (acting on feedback instantly, as the event occurs), and Predictive intelligence (proactively choosing the most successful remediation path).
• Prepare Services for AI Agent Interaction: Assume customers will increasingly interact with your company via their personal AI agents rather than directly via websites or apps. Redesign how you expose services so that AI systems can seamlessly consume and orchestrate them on the customer’s behalf.
• Manage the Legacy-to-New Transition: The greatest operational challenge is navigating the migration of existing, critical systems onto new platforms. This requires a “twin track” transformation strategy to overcome the legacy barrier that upstart competitors do not face.
• Temper Near-Term Expectations: Be realistic about the pace of change; the world will not be “upended in six months,” as transformation is a collective activity at the industry and society level. Understand the pattern that people consistently overestimate short-term changes and drastically underestimate long-term, multi-year possibilities in a technology wave.

Here are the highlights of my chat with Assaf:

• Acknowledge Trust as the Primary CX & Tech Adoption Barrier: Recognise that trust is a foundational challenge, acting as the biggest barrier to new technology adoption at the organisational level and a block to customer willingness to share data for personalised experiences.
• Build Trust Through Holistic CX and Support: A well-executed CX program is a core trust-building mechanism. Trust is not solely a security function, but a combination of security, availability, reliability, and resiliency, which requires using CX insights to know where customers are “hurting” and doing the right thing to support them.
• Insist on Integrity in B2B Security Engagements: In B2B contexts, rely on transparency, certification, and integrity. To build long-term rapport, be willing to refuse a deal or a one-off request that you believe is counterproductive to the client’s own security, demonstrating you know what you stand for.
• Transform Security into a Customer Trust Function: Use AI to automate low-value security tasks, such as filling out customer questionnaires. Repurpose that team to focus on “customer trust,” helping CX practitioners within client organisations navigate and get rapid security approvals from their own security teams.
• Address the Personalisation Paradox with Ethics: While customers desire hyper-personalised experiences, they are intensely nervous about data security and “creepy” intrusive tactics. Maintain professional conduct and constantly gauge actions by asking: Would this be acceptable if published on the Wall Street Journal’s front page?
• Treat Trust Loss Incidents as Building Opportunities: When incidents inevitably occur, be genuinely transparent and communicative. Focus on fixing the mistake and clearly explaining how you are “making it right” for customers. If you have been striving to do the fundamentals well, the world will be more forgiving.
• Recognise Customer Data as the New Competitive Moat: While AI models are rapidly becoming commoditized like new chipsets, the real differentiating asset is your proprietary customer data. This requires solving complex challenges related to data sovereignty, ownership, and the proper use of data for training.
• Establish a Shared Fate Ecosystem with Partners and Customers: Since using third-party vendors is unavoidable, treat them as transparent partners, ensuring they share your ethics, as you are operating in a “shared fate, shared accountability” supply chain. With customers, be explicit about the value you are driving for them to earn their trust to use anonymised and aggregated data for better outcomes.

Here are the highlights of my chat with Ali:

• Embrace AI-Driven Simulation for Predictive CX: Adopt synthetic panels (which Ali prefers to call “simulations”) as a representation of human behavior, trained on 95% real, anonymised, aggregated human survey data across 20+ industries. This predictive model offers a trusted, scientific foundation distinct from purely synthetic data.
• Prioritize Future-Focused “What If” Testing: Apply this new modeling approach to future-focused, counterfactual scenarios, specifically in the realm of likelihood to purchase, appeal of a new product, or the attractiveness of a service package (Likert scale, future tense questions). It is not currently recommended for assessing immediate, temporarily relevant, past-lived experiences (e.g., feedback on the speediness last night’s dinner service).
• Achieve Unprecedented Scale in Concept Screening: Leverage AI simulations to rapidly screen a high volume of ideas (e.g., testing 25 different offers or messages at once) that are impractical or absurd to test with human panels. This allows the CX team to quickly narrow down to the most viable top 10 concepts for further advancement.
• Use Simulations for Risk-Free Pre-Testing: Implement simulations as a mandatory, low-investment step for pre-testing designs and concepts before committing significant budget and time to full human studies. This provides a cost-effective mechanism to identify missing elements or surprises early in the design process.
• Demand Continuous Model Freshness (Hydration): A key operational requirement is constant “hydration”—monthly at a minimum—of the model with net new batches of aggregated human insight data. This ensures the model remains vital and relevant by reflecting a moving context window and prevents its accuracy from collapsing on its own generated output.
• Accelerate Trust and Adoption by Customizing AI: Plan to deploy “walled garden” extensions or adapters on top of generalised models, integrating your proprietary client research data. This specialisation makes the model more accurate and trustworthy to your specific business, facilitating the democratisation of insight access across internal stakeholders.
• Redefine the Researcher Role as Strategic Orchestrator: Support the shift of internal market researchers from manual, low-value work (like building charts) toward high-value strategic consulting. Their new, critical role is to orchestrate the use of multiple data sources—synthetic, human, operational, and CX data—for the optimal delivery of business insights.
• Prepare for Quantitative-Backed Qualitative Insights: Recognise that once the model proves quantitative accuracy, it can be trusted to deliver qualitative outputs, such as synthetic personas, within the same defined use cases. This offers a scalable way to acquire rich qualitative context without the expense and difficulty of traditional methods.

About Brad

Brad Anderson is President of Products, UX and Engineering, responsible for defining, crafting and supporting the Qualtrics experience management solutions. He leads a team of engineers, product managers, experience designers, program managers, IT professionals and security teams across Qualtrics’ global development centers. He is responsible for ensuring the Qualtrics SaaS service continues to scale and meet all SLAs, privacy and security requirements.

Prior to Qualtrics, he spent more than 17 years as a Corporate Vice President at Microsoft, where he led engineering teams that built several multi-billion dollar businesses serving more than 300 million monthly active users and devices.

Feel free to connect with Brad on LinkedIn here.

About Mark

Mark Hammond joined Qualtrics in November 2025 as SVP, Core AI, and has significant expertise and leadership built on decades of experience spanning AI, applied neuroscience, machine learning, autonomous systems, and human-centered design. As SVP, Core AI at Qualtrics, Mark leads the company’s AI infrastructure, model development, and applied science efforts. His work focuses on creating purpose-built AI capabilities that enable organizations to understand and respond to customer and employee needs in real-time, going beyond traditional surveys to deliver intelligent, contextual engagement at scale.

Prior to Qualtrics he founded Bonsai, an AI company pioneering machine teaching and reinforcement learning for industrial applications acquired by Microsoft. At Microsoft, he served as a GM, VP, and Corporate VP spanning AI for autonomous systems, infrastructure for bridging physical and virtual assets, and the incubation framework used to launch new innovations.

Feel free to connect with Mark on LinkedIn here.

About Assaf

Assaf Keren joined Qualtrics in 2024 as the Chief Security Officer. His team at Qualtrics continues to evolve and strengthen product security, maintain compliance with relevant regulations globally, and partner with security teams across Qualtrics’ more-than 20,000 customers.

Assaf’s career in cybersecurity is marked by significant achievements and leadership roles, most notably as the Chief Information Security Officer at PayPal where he reinforced the company’s standing as the most secure and trusted fintech platform. His extensive background encompasses pivotal experiences within the Military, Government, Defense Industry, and TechStartups, culminating in strategic leadership positions leveraging his expertise in innovation, leadership, and cybersecurity.

Feel free to connect with Assaf on LinkedIn here.

About Ali

Ali Henriques is the Executive Director of Market Research at Qualtrics. A market researcher by trade, but is now a global, cross-functional leader of Qualtrics’ innovative research services division. She enables and supports clients to deepen their understanding of their audiences, enhance or cultivate new products/ services or benchmark against competition.

Feel free to connect with Ali on LinkedIn here.

Image credit: Photo by Brett Jordan on Unsplash