From Knowledge Worker to Value Creator

Peter Drucker, who coined the term "the knowledge worker" in 1959, warned that knowledge only yields value when it's applied toward results. Today, nearly anyone (or any machine) can access expertise instantly. Knowledge alone can no longer serve as the primary source of competitive advantage.

For years, centers of excellence in business analysis, experience design, software architecture, development, cyber-security, and quality engineering each ran their own workflows, collaborating toward a shared goal but rarely moving as one. That fragmentation assumed iterative learning and long project cycles, a pace that no longer matches what the business expects.

The value creator is a knowledge worker who understands the end goal and is willing to make decisions beyond their trained skill set, to deliver results on the timescale that actually matters to the business. We're building the tools that a value creator needs to thrive in this knowledge-abundant era, and it starts with culture and architecture.

A few signals worth watching this month: India's banking regulator is considering a mandatory "kill-switch" for every AI model used in a banking process. An MIT-licensed open-weight model is now matching frontier models on complex cybersecurity and bug-finding benchmarks. Kunal Shah, founder of CRED, has been appointed global CEO of WhatsApp, a move that could reshape WhatsApp into a financial product distribution engine in its own right, the way WeChat did in China. And ONDC is moving to digitize B2B procurement for kirana stores through DigiDukaan.

Responding to shifts like these takes a whole-of-body approach. No single function, business, technology, or cyber-security alone, can do it. Regulatory and cyber-security events can't wait to be retrofitted into architecture after the fact. Modularity, zero-trust, and model optionality from day one are the bare minimum architectural decisions that make rapid response possible. The technology architecture has to design in an ecosystem a bank can plug into, or out of, at will, so that windows of opportunity like the ones opening at WhatsApp and ONDC can actually be captured in time.

Design of the Ecosystem, Both Within and Without, as the Real Product

If there was ever a time to move from a small number of narrow, skill-based partnerships toward a rich and diverse ecosystem, it's now. Inside a bank, that means business, product, engineering, infrastructure, QA, cyber-security, risk, and compliance all working from the same picture. Outside the bank, it means DSAs, digital lead providers, legal, technical and data API partners, all playing a role.

The right software is the one that enables frictionless collaboration between all of these players, inside and outside the institution. That means designing for goal-seeking behavior in ambiguous environments, multi-disciplinary teams that think quickly and deeply (this compounds over time), a recognition that relationships are real moats, tools for automation and continuous improvement, and a clear measure of ROI for every element in the ecosystem.

We're past the era of single-point, static software. We're heading into a world of complex relationships, real-time visibility, and decision support for the right outcome, at every level. A product owner of the future will be judged by their situational awareness, decision-making, timing, and the depth of their relationships, as much as their technical expertise. This culture has to transcend any single organization or role. It's you, me, our people, our partners, and our customers, not bounded by silos.

Freedom to Dream and Innovate: The Future of Software and Services Economics

Early on, we noticed customers didn't have the freedom to dream and innovate. Every conversation about a new feature came loaded with dread, because someone would have to negotiate a large bill with the business afterward. Product owners who came to us with genuinely valuable ideas would go quiet after the initial excitement, because they couldn't get the budget approved.

That's a classic two-speed problem. The business evolves and people dream every day, but budgets get decided once a year, and large organizations build backlogs that eventually bite them. So we built an architecture, and a pricing model, that let us stay profitable while remaining reliable and agile at a fixed price: unlimited change requests per year, for a fixed fee.

Years of working directly with customers told us this was a solvable problem, and AI only made it easier. We believe there are many more problems like this one waiting to be solved the same way, which is part of why we announced Ontoz, our domain-agnostic, AI-enabled workflow platform.

Emerging Opportunities

Recent advances in AI, the emergence of sovereign AI infrastructure, and the wide availability of APIs are opening up real opportunities. Our stack is built to care about how well an AI model performs, not which specific model it is, because model capabilities are shifting fast (and regulators are watching closely) as the race toward AI-as-utility plays out. We see real potential in partnerships with sovereign infrastructure providers, both cloud and on-prem, in the near future.

A bundled offering, hardware, software, and a sovereign or open AI model together, is something our customers find genuinely appealing. We're working to make our platform as self-served as possible, so that a state-of-the-art lending cloud running on sovereign infrastructure becomes reachable even for the smallest NBFCs and banks.

We see ourselves, at core, as a repository: of industry knowledge, products, API integrations, skills, agents, automations, and data models, all shareable across compatible systems. For an industry as fragmented as lending, still searching for a coherent way to organize its own knowledge, we think that could be genuinely significant.

A Word of Gratitude for the Trust of Our Customers

I want to personally thank the customers who have tested our delivery and our software architecture over the years. A recent example is InCred Finance, which migrated its complex education-loan business onto the Ontoz platform. It's a credit to our combined teams that previously unanticipated edge cases got built and tested within 24 to 48 hours of being discovered, almost every time. When a customer is waiting, nobody has time to check the requirements document, assign blame, or file a change request with a budget attached. That kind of intense focus on outcomes simply isn't possible under the old economic model, or the old architecture.

As of today, Ontoz is live in five countries, with active implementations underway in four more. InCred Finance believed in our vision early on, and has been an investor in Lending Labs as well.

Industry Dilemma

As a co-founder of both Kuliza and Lending Labs, I had a front-row seat to a brief period of so-called hyper-growth in the lending space. Looking closely at how that growth was often financed tells a familiar story across the industry: projects priced below cost on the expectation of future change-request fees, contracts driven by logo-acquisition incentives rather than long-term relationships, and delivery teams spending more time negotiating fees than focused on outcomes. When the commercial relationship is adversarial by design, trust is usually the first casualty, and that's a dilemma the whole industry is grappling with today, not any one competitor.

We chose to walk away from both the culture and the software architecture that enabled that pattern. We believe AI will only accelerate this shift across the industry. I feel fortunate to be part of a team that chose the harder, less-travelled path years ago.

Always grateful for your trust and partnership, Anand Co-founder and CEO, Lending Labs