From AI Ideas to Structured Finance
Putting ESG and AI at the core of real banking
AI becomes valuable in banking only when it is connected to reliable data, clear processes and concrete business decisions.
I have worked in IT, data analytics and AI since the early 2000s, initially driven by academic breakthroughs, algorithms and the promise of what well-designed technology can achieve. In industry, however, the reality is more complex.
Large software and data projects are often delayed, overcomplicated or poorly executed. Not because the technology itself cannot work, but because organization, culture, politics, legacy systems and changing priorities shape the outcome just as much as the architecture. In practice, enterprise innovation rarely happens in laboratory conditions.
When we started working more deeply with banks, the pattern felt familiar. ESG is now on every agenda, but Energy Performance Certificates are still stored as PDFs, Excel remains central to reporting processes, reporting cycles can take months and legacy systems continue to dominate critical workflows. At the same time, regulatory pressure is increasing, cost efficiency matters more than ever and access to cheaper ESG-linked funding has become a strategic priority.
This raises a practical question: what if data and AI could become an operational enabler rather than another experiment or bottleneck? What if asset data could be made transparent, structured and ESG-ready so it can support covered bonds, green bonds, securitisation risk monitoring and sustainable lending strategies?
That is why we founded EcoAsset.ai. We knew the domain would be demanding. Covered bonds and structured finance are not known for fast adoption cycles or loose requirements. Trust, auditability and domain understanding matter. The work requires listening, learning, adjusting and earning confidence step by step.
Recently, Eric Schmidt, former CEO of Google, argued that AI is underhyped. That may sound surprising in a market where the term appears everywhere. But in enterprise environments, the point is valid: truly production-grade AI is still rare.
The challenge is no longer to show that AI can produce impressive outputs. The challenge is to make it reliable enough for regulated workflows, connected enough to enterprise data and useful enough for teams that carry real operational responsibility.
That is the mission behind EcoAsset.ai. ESG and AI should not sit at the edge of modern European banking as innovation theatre. They should become part of the data infrastructure that supports funding, risk management and regulatory reporting.
Enterprise culture still sets the pace for AI adoption. Our job is to build technology that respects that reality and still moves it forward.
