Product leaders in fintech are being pushed in two directions at once: ship faster as AI accelerates execution, and make better calls as trust, regulation, and risk raise the cost of mistakes.
In this interview, Pradhan Anubhav, Director of Product at BUSINESSNEXT, shares how he builds products for credit unions and community banks — and the decisions behind a platform that helped BUSINESSNEXT earn Leader status in the Forrester Wave™ 2025 for Financial Services CRM, ahead of Salesforce and Microsoft.
Read the full conversation to see how he thinks about judgment-driven product leadership, AI in regulated environments, and what “speed to value” actually means in financial services.
You joined BusinessNext as one of the very first team members in the U.S. and essentially built the American arm from the ground up. What did those early days look like, and what helped you gain the trust of your first clients in a brand-new market?
I joined as the fourth or fifth employee in the U.S. — it was a period of hard work and energizing growth. The core challenge we faced was this: BusinessNext had strong platform capabilities and intellectual property, but we needed to figure out how to package that into solutions that would actually work for the credit union market. I also led the brand transition from CRMNEXT to BusinessNext in the U.S., which was part of aligning our market perception with the platform’s expanded capabilities.
One of my first major projects was building a digital account opening journey, which gave me deep exposure to how deposit accounts are opened, the friction points, and what institutions needed to drive adoption. Encouragingly, our first U.S. customer was doing around 100 to 110 digital account applications monthly, and within the second month of deploying our solution, that grew nearly tenfold — into the thousands.
At the same time, we were working with four or five other credit unions in parallel, and the challenge quickly became: how do we move from custom deals sold on platform potential to a repeatable solution we can deploy consistently? From a product perspective, I focused on understanding unmet needs, identifying standardizable capabilities, communicating value clearly, and building repeatability in successful deployments.
On the sales side, we faced real challenges in a market dominated by established players with deep credibility, so we had to quickly build understanding and trust by demonstrating our knowledge of credit unions and their operational needs.
What were the strategic objectives behind the rebrand, and how did it reshape the company’s positioning among financial institutions?
The rebrand was driven by a real strategic shift in what the company had become. The name “CRMNEXT” had become limiting — customers and prospects would immediately bracket us as just a CRM player. But the product had evolved well beyond that. We’d become a true enterprise-grade solution with multiple capabilities: CRM and workflow automation, journey builders for things like account opening and loan origination, data capabilities — including mining and warehousing — and deep ecosystem integration.
So there was a real mismatch between how the market perceived us and what we’d actually built, and the rebrand was about closing that gap. It gave us room to tell a more complete story — not just CRM, but the full cycle: data identification and intelligence, automated workflow triggers and orchestration, conversion tracking, and revenue metrics. We were solving for enterprise growth end to end, not just customer relationship management.
Could you name some of the complexities that this transition introduced, particularly regarding packaging, pricing, and market education?
As we expanded our story, we faced difficult decisions about packaging and pricing — determining which capabilities should be grouped together, what the pricing model should entail, and how to effectively communicate value without overwhelming institutions with too many options.
And then there’s market education. We needed financial institutions to recognize that we’d evolved — through events, direct engagement, customer stories, and education — because simply changing a name doesn’t mean the market immediately understands what’s different. It took sustained effort, but it was the right move, because the platform had outgrown the old label.
What product decisions and methodologies drove your Forrester Wave Leader outcome?
In Forrester’s write-up, a few themes come through very clearly. They call out a clear product thesis — “autonomous journeys” and “do, not just view” — meaning the platform is designed to execute action-oriented workflows, with a human in the loop when judgment is needed. They also emphasize that the platform is comprehensive and composable, with financial-services editions that shorten the path to value and deliver real depth, not a horizontal CRM retrofitted for banking.
Forrester also highlights that AI is treated as a core design choice, not a bolt-on. The point wasn’t to ship a single copilot feature — it was to blend predictive, generative, and agentic AI so the platform can produce context-driven output and take actions, like generating offers and orchestrating outreach based on profiling, goals, and life events. They even call out domain-trained models, including “empathy-trained” models that can detect emotional nuance and help teams respond more effectively.
Another theme they come back to is usability. Customers can model, deploy, and optimize workflows and AI solutions without significant effort, and they praise the ease of customization. On top of that, Forrester credits a strong roadmap tied to concrete bets — financial-services AI agents, an AI-enabled app builder, and AI analytics — and they explicitly mention customer feedback and support responsiveness as part of why the platform scores well.
They also draw clear contrasts with the market. Compared to Salesforce, the feedback is that capabilities are deep, but pricing and packaging are complex, and many institutions don’t fully exploit what they’ve bought. In contrast to Microsoft, they call out broad investment and a mature ecosystem, but a lack of a distinct vision and roadmap that’s purpose-built for financial services.
If you translate what Forrester rewarded into how the product was built, it points to a few consistent habits: shipping out-of-the-box financial-services workflows as the default path to value, investing in configuration so customers can adapt without heavy custom code, productizing AI as a full stack — not a demo — and maintaining a tight feedback loop where customer input reliably turns into roadmap execution.
From a team and leadership perspective, how do you cultivate a strong product mindset within your organization? What principles have helped you develop product managers capable of driving strategic, high-impact initiatives?
The most effective way to build a strong product mindset is to shape an environment where product thinking becomes the norm. The fastest way to kill it is to hand teams a roadmap and ask them to execute. Instead, I have teams’ own problems with clear outcomes: What customer behavior are we trying to change? Why does this problem matter now? How will we know if we made things better? When PMs are accountable for outcomes rather than output, their mindset shifts — from shipping features to learning what actually works.
Most PMs struggle because strategy is implied, not stated. So I’m explicit about what we will not do, which customers we’re prioritizing, and which metrics matter. Strong PMs emerge when they can trace their decisions back to a few strategic bets. If everything is important, nothing is strategic.
I hire and grow for judgment, not frameworks. Frameworks are easy to learn; judgment is hard to build. I focus on how people reason through ambiguity, whether they can say, “I don’t know, but here’s how I’d find out.” The best PMs aren’t the ones with perfect answers; they’re the ones asking better questions.
How do you encourage your PMs to embrace this decision-making responsibility in a way that fosters confidence and alignment with stakeholders?
It’s one of the biggest mindset shifts I push. PMs synthesize input rather than average it, take responsibility for saying no, and build conviction through narrative rather than authority. When PMs explain why a bet is worth making and what they’d do if they’re wrong, they level up quickly.
I optimize for learning velocity over perfection. High-impact PMs operate in loops: hypothesis, test, learn, adjust. That means fast feedback from customers, lightweight experiments over big launches, and clear post-mortems focused on learning rather than blame. A culture that punishes being wrong creates cautious PMs; one that punishes not learning creates strong ones.
Product leadership is as much about what you don’t tolerate. Teams learn from what leaders let slide — shipping without clarity on success metrics, hiding behind dependencies, and confusing effort with impact. Calling these out consistently, calmly, and constructively is more effective than any training program.
Let’s dive deeper into the evolving role of a product leader in fintech. Which skills — whether technical, strategic, or cross-disciplinary — have become essential for those building products for the financial institutions of tomorrow?
The role of a product leader in fintech is evolving not so much because the products themselves have changed, but rather because the areas where human judgment is required have shifted. Generative AI hasn’t simplified product leadership; instead, it has raised the bar significantly.
Much of what used to consume product managers’ time — prototyping, synthesizing interviews, exploring use cases, pattern detection in data, and drafting specifications — has become more accessible and less time-intensive. The real question is: what will product leaders do with the time they’ve gained? Average PMs may simply increase output, but strong PMs will leverage this time to enhance their thinking. Today, product leaders are judged primarily on the quality of their judgment rather than sheer output.
In an era where information and analysis are abundant, decisions have become the scarce resource. Modern fintech product leaders face increasingly complex questions: Which bets are worth making now versus deferring? Where does automation enhance services, and where might it erode trust? What complexities should be abstracted, and what must remain transparent? How do we balance speed with regulatory, reputational, and systemic risks? While AI can surface options, it cannot determine the right trade-offs.
As execution accelerates, the differentiator becomes fundamentally human: understanding incentives across customers, regulators, partners, and internal teams; anticipating second- and third-order effects; designing systems that earn trust rather than simply optimize conversion; and knowing when automation is inappropriate. In fintech, especially, the cost of being wrong can be significant. Product leaders must think in terms of potential failure modes, not just success paths.
Technical fluency is now a baseline requirement, not a competitive edge. Product leaders don’t need to be engineers, but they must grasp system boundaries, understand data flows, model behavior, ask critical questions about explainability and reliability, and identify where AI fits within the architecture — and where it shouldn’t. The goal isn’t to build models but to make informed architectural and product decisions that can withstand scale, scrutiny, and regulation.
Strategy is more crucial than ever because capacity is no longer the constraint. In a landscape where teams can achieve more with fewer resources, strategy transforms into a matter of restraint. Focus becomes a leadership decision rather than a mere resource limitation. Saying no is both harder and more important now. Product leaders must clearly define which problems are worth solving, identify which customer behaviors truly matter, and establish the long-term positions they aim to build towards. Without this clarity, AI merely accelerates efforts in potentially misguided directions.
With open banking and open finance gaining momentum, the rise of the API economy, and Big Tech increasing its presence in financial services, the competitive landscape is shifting rapidly. Do you expect the fintech market to become more fragmented, or are we moving toward consolidation around a few dominant ecosystems?
The infrastructure layer — which includes data access rails, APIs, consent, and authentication — tends toward consolidation. Achieving consistent standards is both hard and expensive; regulated entities standardize to mitigate risk, and the market penalizes “unique” plumbing when security and uptime are non-negotiable. The CFPB’s Section 1033 timeline is still under development, but the trend is clearly toward greater standardization.
Conversely, the distribution and workflow layer — where users actively interact — is where fragmentation is likely to increase. Open banking reduces costs for vertical SaaS companies to embed finance, allows fintechs to specialize in specific workflows, and enables banks to compose best-of-breed capabilities. Embedded finance integrates services into applications that users already utilize, creating more entry points and increasing the number of market participants.
Big Tech does not need to “be a bank” to succeed. Their advantages lie in distribution, identity, and device trust, data, and developer ecosystems. The real risk is not that Big Tech will replace banks; rather, it is that Big Tech could become the primary interface, relegating financial institutions to the role of balance-sheet utilities. In Europe, there is an ongoing debate about limiting Big Tech’s access to open finance frameworks to prevent this shift.
