By Dr. Gleb Tsipursky

In the evolving landscape of customer experience, leaders across industries are navigating how to integrate generative AI into their operations without compromising the human touch. For Craig Crisler, CEO of SupportNinja, this isn’t just a technical challenge. It’s a strategic imperative. His company, which partners with fast-scaling tech companies to manage customer support, has taken a deeply consultative approach to aligning Gen AI with client CX goals. The message is clear: AI must serve the brand, not dilute it, as my interview with him shows.

Strategy Before Technology

Crisler brings a unique lens to AI adoption. Having worked on language models even before founding SupportNinja, his view of Gen AI is grounded not in hype, but in how it genuinely enhances business outcomes. At SupportNinja, the emphasis is not on flashy deployments for internal gain. The real focus is on how AI can optimize client operations. This client-first mindset drives everything, starting with a strategic diagnostic phase that examines client systems, processes, and data readiness well before any tooling is introduced.

“It’s about taking five or six steps back before implementing anything,” Crisler explains. “We start with understanding the customer journey, the data structure, the integration landscape, and then move forward with proof-of-value deployments.” That depth of preparation helps defuse the common fears around AI, especially concerns about security and brand misalignment, by proving that any tool introduced has been selected and tested with rigor and precision.

The Maturity Mismatch

SupportNinja’s clients span a wide range of AI maturity levels. Some are just beginning to explore Gen AI, while others are actively piloting their own tools. But regardless of where they are on the spectrum, few have reached optimal deployment. According to Crisler, “Most are still struggling with implementation. Even among the 40 percent of CX executives we surveyed who say they’re actively using AI, few are doing it effectively.”

One reason is a misalignment between the technology deployed and the customer experience expected. Crisler points to a high-end luxury brand client who installed a chatbot to handle online inquiries. While the intention was good — automate repetitive queries — the result was a generic, impersonal experience that clashed with the brand’s high-touch identity. “Their average transaction is over $10,000,” he notes. “You can’t have a basic bot representing that level of luxury.”

The solution was to replace the bot with behind-the-scenes tooling that empowered human agents with faster access to customer data. This allowed for quick responses without sacrificing nuance or brand voice. The experience stayed personal, and the brand integrity remained intact.

Data, Design, and Deployment

SupportNinja’s approach hinges on a thorough current-state analysis using proprietary tools like their internal Ninja AI. By analyzing sentiment, language, and customer-agent interactions, they uncover how well a client’s CX actually aligns with its brand promises. “From there,” Crisler says, “we map the customer journey, identify where data can be integrated, and propose specific tools that fit those touchpoints.”

This design process is deeply human. SupportNinja conducts interviews with client-side teams, from frontline agents to engineers, to understand the full picture. “It’s funny,” he reflects. “AI is supposed to be about automation, but deploying it the right way takes a lot of conversation. At the end of the day, these are still human-to-human interactions.”

That insight underscores why Gen AI in CX is not simply a software problem. It is a change management challenge. Clients worry about the risks—data security, brand misrepresentation, job loss. Crisler and his team respond by embedding transparency and education into every step. “The opposite of fear is knowledge,” he says. Every SupportNinja agent—referred to internally as “ninjas”—receives training in AI fundamentals and prompt engineering. They learn not just what the tools do, but how they make their jobs better.

A Human-First Future

Despite growing fears about agentic AI taking over large swaths of customer service work, Crisler remains optimistic. He sees a future where AI handles repetitive Tier 1 and Tier 2 tasks such as password resets and basic refunds, which allows humans to focus on more complex, Tier 3 empathetic interactions. “The idea isn’t job loss,” he insists. “It’s job evolution. The work becomes more proactive, more human. You’re not stuck in repetition. You’re solving problems.”

Still, Crisler acknowledges that the path to fully autonomous agents isn’t short. Building agentic systems requires sophisticated data aggregation across multiple platforms, something most organizations aren’t yet ready for. “Data wins in AI,” he says. “And right now, most internal IT teams aren’t comfortable centralizing data to the degree that agentic agents need.”

What this means for the near future is clear. AI will become increasingly present in CX, but humans remain central to its success. The companies that thrive will be those that treat AI as a partner in the customer journey, not a replacement for the human element that defines great service.

In the end, aligning Gen AI with customer experience is not about chasing trends. It is about understanding your brand, your customers, and your people, and making thoughtful, strategic choices that elevate all three. As Crisler’s work at SupportNinja shows, the future of CX may be digital, but it remains deeply, and necessarily, human.

About the Author

Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with hybrid work and Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles in prominent venues such as Harvard Business Review, Fortune, and Fast Company. His expertise comes from over 20 years of consulting for Fortune 500 companies from Aflac to Xerox and over 15 years in academia as a behavioral scientist at UNC-Chapel Hill and Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

By Dr. Gleb Tsipursky

In the rapidly evolving fintech landscape, generative AI (Gen AI) is proving to be a quiet revolution. It’s not replacing the old guard with a bang, but transforming it with surgical precision, redefining how financial services are delivered in emerging markets. According to Monica Brand Engel, Co-founder and Managing Partner at Quona Capital, in her interview with me, the disruption is not about building flashy Gen AI-first companies—it’s about embedding this technology into practical applications that address deep, systemic inefficiencies. And in doing so, it’s giving fintech firms a sharp edge over their traditional finance counterparts.

Practical Intelligence, Not Hype

Quona Capital, with $800 million in assets under management and 70 portfolio companies across Latin America, South and Southeast Asia, Africa, and MENA, focuses on “fintech for inclusion.” Engel stresses that most of Quona’s portfolio companies are not built around Gen AI, but rather, they use it to supercharge their existing models. She outlines three core use cases that have already delivered substantial business and social impact.

The first is workflow automation: not a novel concept, but one transformed by Gen AI’s ability to process complex, unstructured data. Take Sunday, an insurtech firm in Southeast Asia, which uses Gen AI to automate claims adjudication by interpreting dense documentation with language models. This allows them to fast-track straightforward claims and free up human adjusters for complex cases.

The second use case is hyper-personalized customer engagement. Clark, a challenger bank in Mexico, has used Gen AI to analyze customers’ savings behaviors and nudge them toward better financial decisions, such as switching to interest-bearing accounts. This personalized approach not only enhances user satisfaction but has also demonstrably improved retention—even when the advice runs counter to the bank’s short-term profit.

Finally, there’s risk management through data augmentation. Vertel, a cross-border payments company, uses Gen AI for Anti-Money Laundering (AML) monitoring by parsing through thousands of documents to detect fraud signals. In jurisdictions regulated by the UK’s FCA as well as local financial authorities, this isn’t a nice-to-have, it’s an operational imperative.

From Skepticism to Trust

Introducing Gen AI into markets that have historically been paper-based and people-intensive is no small feat. Engel is quick to note that adoption curves depend heavily on digital readiness—not just among users, but employees as well. Many customers in emerging markets start with low expectations, given the often poor quality of legacy financial services. In this context, Engel says, Gen AI isn’t replacing something beloved; it’s offering something radically better.

For example, users may initially feel wary of receiving financial recommendations from a bot. But when that bot speaks in their dialect and provides useful, timely suggestions—often outside traditional business hours — the value becomes clear. As Engel puts it, “Who doesn’t want personalized services?” Once the convenience and utility are proven, user resistance quickly gives way to enthusiastic adoption.

Still, trust must be earned. The companies in Quona’s portfolio succeed by serving those whom traditional finance excludes. This mission-driven model helps build credibility. Gen AI tools are embedded in products customers already trust, reinforcing rather than replacing those relationships. The technology serves a purpose beyond profit — it enhances services for underserved populations in a way that aligns with the companies’ existing brand promise.

Employees: From Fear to Empowerment

Internally, the introduction of Gen AI raises its own set of questions. Engel emphasizes that these companies are not laying off workers. Instead, they are reallocating talent from low-value tasks — such as manually reviewing insurance claims — to more meaningful work. Many of these employees are drawn to mission-driven startups precisely because of their impact. When they see that Gen AI enables them to serve more customers more effectively, buy-in increases.

This internal adoption is carefully managed through pilots, not sweeping overhauls. Companies roll out Gen AI in targeted segments, evaluate the results, and refine before scaling. Education is key, both to mitigate fear and to ensure successful implementation. Digital literacy varies widely across roles and age groups, so companies must tailor their training strategies accordingly.

And the payoffs are real: greater efficiency, more engaging work for employees, and improved bottom lines. Most Quona portfolio companies offer equity or performance-based compensation, so employees have a tangible stake in the success of these tech integrations.

Navigating Bumps in the Road

Even as the benefits stack up, Engel is candid about the growing pains. Data integrity is the first hurdle—bad input leads to flawed decisions. From handwritten applications to miskeyed mobile entries, fintech in emerging markets must deal with messy, inconsistent data. This demands robust error-handling mechanisms and continuous model refinement.

Another challenge is bias and hallucination, particularly in high-stakes domains like credit underwriting or insurance. Engel acknowledges the risk but underscores that none of the portfolio companies rely solely on Gen AI. Human oversight remains essential, and applications are built to complement—not replace—expert judgment.

Privacy and governance are also top of mind. All companies using Gen AI must implement clear guidelines on what data can be shared and how it’s used. Engel describes a layered system of internal controls, from multifactor authentication to regular backtesting of outputs. The companies manage their own databases, and while Quona maintains reporting and transparency standards, it never demands access to raw, confidential customer data.

Sharing to Scale

One of the less visible but deeply impactful aspects of Quona’s approach is its platform function—a deliberate effort to ensure cross-pollination of insights across its portfolio. From town halls and guest speakers to direct introductions with domain experts, Quona fosters an ecosystem where lessons learned in Jakarta can benefit founders in Mexico City or Lagos.

This collaborative model accelerates innovation. Engel recalls how Clara, another portfolio company, benchmarked its Gen AI-driven customer service costs against those of NuBank—a company fifty times its size—and found them to be equivalent. That kind of exponential efficiency only becomes possible when knowledge is actively shared and applied.

What’s Next?

Looking ahead, Engel sees two major fronts for expansion. First, as companies deepen their understanding of the customer data they already collect, more will leverage Gen AI for product innovation. This includes everything from better segmentation to creating entirely new financial offerings.

Second, operational efficiency will continue to scale, making small fintechs increasingly competitive with incumbents, even in cost-heavy areas like compliance and service.

But the real frontier? Engel hints at a future where Gen AI isn’t just a feature—it’s the product. While most current applications are add-ons to existing platforms, the next wave may well include startups built around Gen AI as their core infrastructure. That, she says, is where the next generation of transformative business models will emerge.

In a world where traditional finance still struggles with decades-old systems and cumbersome workflows, fintechs powered by Gen AI are not just catching up—they’re leapfrogging. And with leaders like Engel at the helm, the future of inclusive, intelligent finance looks not just possible, but inevitable.

About the Author

Dr. Gleb Tsipursky was named “Office Whisperer” by The New York Times for helping leaders overcome frustrations with hybrid work and Generative AI. He serves as the CEO of the future-of-work consultancy Disaster Avoidance Experts. Dr. Gleb wrote seven best-selling books, and his two most recent ones are Returning to the Office and Leading Hybrid and Remote Teams and ChatGPT for Leaders and Content Creators: Unlocking the Potential of Generative AI. His cutting-edge thought leadership was featured in over 650 articles in prominent venues such as Harvard Business Review, Fortune, and Fast Company. His expertise comes from over 20 years of consulting for Fortune 500 companies from Aflac to Xerox and over 15 years in academia as a behavioral scientist at UNC-Chapel Hill and Ohio State. A proud Ukrainian American, Dr. Gleb lives in Columbus, Ohio.

By Dr. Gleb Tsipursky

In a rapidly changing world of work, organizations are increasingly turning to Gen AI to optimize operations, slash costs, and redefine the role of human resources. In an engaging conversation with Scott Cawood, CEO of WorldatWork, a nonprofit that specializes in total rewards optimization across 162 countries, the opportunities and challenges of Gen AI in the compensation and benefits space came into sharp focus.

Riding The Wave Of Explosive Gen AI Adoption

WorldatWork’s member platform, Engage, has become a fascinating bellwether for the pace of Gen AI adoption. According to Cawood, the conversations among compensation and benefits professionals have been “explosive,” moving swiftly from tentative questions to active knowledge-sharing and real-world test cases. What began with basic automation of routine tasks is evolving into a more sophisticated use of Gen AI to tackle massive data sets that no human could process in the same time frame.

Cawood emphasized that the conversation is no longer just about building new AI tools. Instead, companies are focusing on rethinking their existing data through the AI lens, leveraging what they already have to uncover insights faster and more comprehensively. This capability is critical in compensation analysis, where identifying pay gaps, predicting disengagement, and tailoring retention strategies can make or break an organization’s ability to compete for talent.

The sheer scale of activity shows that organizations recognize the necessity of Gen AI, not just for innovation but for survival. As Cawood put it, “If you’ve not spent time on AI as of now, you’re already behind.”

Balancing Innovation With Anxiety

Despite the exciting progress, anxiety about Gen AI’s impact on job security is a very real undercurrent. Cawood noted that about 55 percent of WorldatWork’s member companies are already providing AI training for their HR and total rewards teams. Yet, even with this proactive approach, concerns remain widespread, especially among those whose jobs involve more routine tasks.

Importantly, Cawood did not sugarcoat the risk. Job displacement will happen, particularly for roles that fail to evolve with digital skill demands. However, he remains optimistic that AI will ultimately create more jobs than it destroys, particularly for professionals who can master the critical skill of deciding when to deploy humans versus AI for a given task.

Supervising AI systems, coaching them, and refining their outputs will be essential future competencies. As Cawood pointed out, AI will not operate in a vacuum; it must be managed with the same care and nuance required to lead human teams. The future will belong to those who can navigate this hybrid landscape, blending human judgment with machine efficiency.

Unlocking The Power Of Predictive Analytics

Perhaps the most exciting frontier, according to Cawood, lies in using Gen AI to understand employees at a deeper level than ever before. Historically, employers have known far less about their employees than companies like Target or Walmart know about their customers. Gen AI offers the promise of reversing that dynamic.

By analyzing vast troves of compensation, performance, and engagement data, organizations can predict employee turnover, diagnose disengagement early, and even tailor benefits and incentive plans to individual needs. The potential savings are staggering—not only in reduced turnover costs but also in the productivity gains from a more engaged and aligned workforce.

However, Cawood issued a strong caution about rushing headlong into this data-driven future. With sensitive information like salaries, social security numbers, and health benefits involved, the risk of privacy breaches looms large. Many organizations are rapidly updating their AI usage policies to guard against unintended data exposure, but the margin for error remains thin. The mantra is clear: start small, with known data sets, and build expertise gradually rather than risking catastrophic mistakes.

Charting The Future Of Compensation With AI

Looking ahead, Cawood sees the future of compensation and benefits professionals as secure—but only for those willing to adapt. While routine tasks will increasingly be automated, core technical skills in rewards design, benefits structuring, and labor market analysis will remain in high demand. The new premium will be on digital literacy, strategic judgment, and the ability to integrate AI tools seamlessly into decision-making processes.

WorldatWork’s emphasis on hands-on experimentation is a telling sign of this shift. At their 2024 conference in San Diego, they introduced an AI playground that allowed attendees to experiment with real datasets. The experience was so successful that it will be expanded at the 2025 conference in Orlando, where the organization received an impressive 60 AI-related session proposals—up sharply from the previous year.

Predictive analytics, Cawood believes, will be the next major breakthrough. From forecasting turnover to identifying the true drivers of engagement, Gen AI will empower compensation professionals to move from reactive management to proactive strategy. But as he warned, AI should never be adopted for its own sake. Every deployment must have a clearly defined benefit, whether it is cost savings, risk mitigation, or enhanced employee experience.

Ultimately, slashing costs through Gen AI is not about replacing people with machines. It is about empowering organizations to make smarter, faster decisions that unlock the full potential of both their data and their people. In the hands of skilled professionals who understand both the promise and the perils of the technology, Gen AI will not just cut costs—it will build a stronger, more resilient future of work.

About the Author