Fintech founders': Here's how you are using AI wrong

I have sat in enough product reviews to recognize a pattern. The pilot went well. The demo impressed the investors. The engineering team is three sprints into the build. And somewhere around that point, someone quietly moves the compliance review to a later phase. The data pipeline question gets deferred. The architecture decisions that would have accounted for the production environment get locked in before anyone has verified that the production environment can support them.

This is not negligence. It is pressure. Investor timelines, competitive positioning, internal delivery targets, they all create a gravitational pull toward speed. Production readiness work is abstract until it is not, and by the time it becomes concrete, the cost of addressing it has multiplied several times over.

A McKinsey report cited in a recent GeekyAnts post put a number on the broader failure: 94% of organizations report no significant value from their AI investments, even as deployment has grown across business functions. That statistic deserves more attention than it typically gets, because it is not describing bad ideas. It is describing ideas that reached production before the infrastructure required to support them was in place.

Why Fintech Is Particularly Unforgiving

Most industries can absorb a delayed launch or a remediation cycle with some reputational discomfort and a hit to the quarter. Fintech cannot absorb it the same way.

Regulatory timelines are fixed. Compliance obligations do not bend because your engineering sprint ran long. When the SEC obtained $8.2 billion in financial remedies in fiscal year 2024, the highest in its history, a meaningful share of those actions targeted governance gaps in production systems across financial services. That is not a theoretical risk for a founder building a credit decisioning tool or a fraud detection layer. That is the actual environment their product will operate in.

The GeekyAnts analysis of production readiness makes a point I found worth sitting with: the decisions that cause production failures are almost always made at the scoping stage, not during deployment. Data availability gets assumed before anyone checks what the production environment actually holds. Compliance requirements get scheduled for a later phase when incorporating them will cost three times as much. The team defines the business problem in terms broad enough that success has no measurable standard before the build begins.

What a Readiness-First Approach Actually Looks Like

The argument is not that speed is bad. It is that the sequence matters. A proof of concept should answer whether the approach is technically feasible. A pilot should confirm whether the system performs under conditions that resemble real use. Full deployment should follow only when both stages have produced evidence that the system is ready for the environment it will operate in.

That structure is not slower. It is cheaper. The expensive outcome is the remediation cycle that follows a production deployment that was not ready. Fixing compliance architecture after the rest of the build depends on it is a structural rebuild, not a patch.

The Legacy Problem That Predates AI

The second issue these blogs surface is one that founders building on top of or adjacent to traditional banking infrastructure run into constantly: the core systems they are integrating with were designed in a different era for a different world.

Ninety percent of US banking core software is classified as legacy. These systems were built between the 1960s and 1980s, run on COBOL, and were designed for batch processing. As of 2025, only 49% of US banks offered real-time payments, while fintechs had made it a baseline feature years earlier.

The cost of maintaining this infrastructure is not a footnote. Banks spend 78% of their IT budgets keeping legacy systems operational. COBOL programmers command between $200 and $250 per hour compared to around $90 per hour for developers on modern stacks, and that gap is widening as the existing pool of engineers retires.

Why Full Replacement Is Not the Answer

TSB Bank's 2018 core migration locked out 1.9 million customers for days and cost over $416 million in total losses. That case has become the canonical example of what happens when a financial institution attempts to replace its core all at once. The risk is not theoretical. It is quantifiable and, for most institutions, not worth taking.

The approaches gaining traction are incremental. API wrapping introduces a modern interface layer in front of the legacy core, allowing new products and integrations to communicate through standardized APIs without touching the underlying system. The strangler fig pattern gradually replaces legacy components with new services while a proxy layer routes requests between the old and new architecture as confidence builds. A sidecar strategy limits modernization to a specific product line or customer segment, containing risk while operational experience with new infrastructure accumulates.

Each of these approaches shares a design philosophy: reduce the blast radius of any single decision. That philosophy is worth internalizing beyond core banking.

What This Means If You Are Building Right Now

The two failure modes these posts describe are related. Rushing AI products to production without the architecture to support them and attempting legacy modernization in a single large-scale effort both reflect the same underlying error: treating engineering decisions as reversible when the cost of reversing them is actually very high.

For a founder building an AI-driven fintech product today, the practical takeaway is this. The market window you are optimizing for will not be closed by the time a readiness-first build reaches it. The companies that will be in the strongest position two years from now are the ones building the operational foundation now, not the ones absorbing remediation costs while trying to keep pace.

The firms that have worked through legacy banking modernization -- fintech modernization partners with genuine track records in this space -- consistently report that incremental, well-sequenced delivery outperforms aggressive timelines when total cost and time-to-value are measured honestly.

That is not a comfortable conclusion for anyone staring at a competitive landscape that looks like it rewards speed above all else. But the data from production environments suggests otherwise.