From pilots to progress: what’s really driving AI success in banking
Insights by Sergio Segura
VP of Partner Ecosystems, Sales Excellence and Growth for EMEA, nCino
03 August 2026
Executive summary
Every bank has an AI strategy. Most have several. And yet the gap between institutions running pilots and those delivering results at scale is widening every quarter, not because the technology is failing, but because too many organisations are treating AI as a technology project when it is fundamentally an operational transformation.
In this guest article, Sergio Segura, VP of Partner Ecosystems for EMEA at nCino, sets out why the winning AI bet in banking is not the model, and what it actually takes to build the foundations that make AI scale.
_____________________
Every bank I speak to has an AI initiative. Most have several. And yet McKinsey found in late 2024 that most banks have still not delivered revenue growth or efficiency gains at scale from AI. In banking, one of the most data-rich industries on the planet, that should be alarming.
Where is the issue then? The models are ready. The computer is available. But most institutions are treating AI as a technology project when it is fundamentally an operational transformation. Pilots fail to scale not because the technology underperforms, but because they were never connected to a real workflow, a real decision, or a real outcome from the start — and because explainability, governance and audit trails were an afterthought rather than a foundation.
Deloitte's 2026 Banking & Capital Markets Outlook identifies the barriers plainly: fragile data foundations, legacy infrastructure, compliance pressure and internal resistance to change.
That is the divide I see widening every quarter. Not between banks that have AI and banks that do not. Between banks that have embedded AI into how they actually work and those still running pilots.
AI has moved into the decisions that matter
For a while, AI in banking meant basic chatbots and simple customer service copilots. Useful at the margins, invisible in the core. That is changing rapidly.
In onboarding, lending, underwriting and risk management, AI is now inside the decisions that matter — not summarising them after the fact, but shaping them in real time.
In onboarding, lending, underwriting and risk management, AI is now inside the decisions that matter — not summarising them after the fact, but shaping them in real time.
But this shift exposes a critical distinction that too many institutions are missing. Large language models are exceptional at interpreting and summarising vast amounts of text. They are not, on their own, appropriate for regulated credit decisions. Those decisions require predictive models that are explainable, repeatable, auditable and completely consistent — and the quality of those models depends entirely on the data behind them. Models informed by patterns across many lenders, not just a single institution's own book, will make fundamentally better decisions, which is why data depth, not model choice, is becoming the real differentiator, and the gap is widening.
The explainability imperative is not optional
Speed without trust is worthless in banking. And trust, in this context, is not just about customer confidence — it is about regulatory compliance.
If a bank cannot explain how a system made a credit decision, what data it used and why a specific outcome happened, that model will never reach production. And the regulatory bar is about to become explicit. Under the EU AI Act, credit scoring and creditworthiness assessment are classified as high-risk, which means they are subject to strict requirements on transparency, data governance and human oversight. At the time of writing, the high-risk obligations for credit decisioning are expected to move from August 2026 to December 2027, pending final publication in the Official Journal, and many institutions are quietly treating that as breathing room.
That is precisely the wrong response. Explainability cannot be retrofitted into a model already running in production. The institutions that will clear the bar comfortably are the ones designing for it now — treating governance as a foundation, not a feature to bolt on before an audit. They have stopped thinking of compliance as a constraint on AI and started treating it as a design principle. They do not choose between speed and control — they architect for both simultaneously. Responsible governance is not the brake on AI adoption. It is the gating factor that separates pilots from enterprise.
Sergio Segura of nCino discusses responsible AI governance in our Forecasting Fintech webinar with Mambu and Google Cloud. To watch the full webinar, click here.
The real value of AI remains human
The industry debate about AI replacing jobs is a distraction. It mistakes the symptom for the problem.
The right question is not whether AI will replace bankers. It is whether your bankers are spending their time on work that actually requires a banker. Right now, in most institutions, the answer is no. Highly trained credit analysts spend the majority of their day gathering data, testing covenants and building reports. Relationship managers spend more time on administration than on relationships. Loan processors spend their hours chasing documents rather than closing deals.
Agentic AI changes this reality fundamentally. These are systems that do not just answer questions but act, orchestrating complex multi-step processes autonomously. When an AI operates continuously, monitoring risk exposure at 3 a.m., testing covenants before the banker arrives, alerting on early warning signals in real time, the banker's role does not disappear. It is elevated. They start the day with analysis done, recommendations ready and their attention freed for judgment, strategy and the client.
The real decisions AI surfaces are not only technical. They are human too: how your people spend their time, what they are trusted to do, and why the best of them will choose to stay.
The institutions getting this right treat AI as a colleague — one that works continuously, embedded within existing workflows, handling the parts of the job that don't require a banker's specific expertise. Their workflows become smarter and more efficient, unconstrained by the hours people work.
AI alone will not replace your bankers. But bankers who can leverage AI will replace bankers who cannot. Institutions that build AI-augmented workflows today are not just improving efficiency. They are building the environment that the next generation of banking talent will choose to work in. In a sector where the war for skilled underwriters, analysts and relationship managers is intensifying, that is a strategic advantage that goes well beyond productivity metrics.
The orchestration layer: where the advantage compounds
All of this — better data, explainable models, AI woven into the work — delivers far more when the pieces are connected than when they stand alone. Which leads to the decision that matters most over the next 18 months. It is not which AI model to deploy, or which workflow to automate first. It is the orchestration layer: what governs how AI-driven decisions flow across the institution.
Think of it as the difference between a bank that has ten capable AI tools and a bank where intelligence moves between them. In the first, a credit insight sits in the underwriting system; a risk signal sits in monitoring; a cross-sell prompt sits in the CRM — each useful, none aware of the others. In the second, the orchestration layer connects them, so a covenant breach flagged at 3 a.m. reaches the right banker with the right context already assembled. That is what makes intelligence compound rather than accumulate.
It is also why the gap widens every quarter. The banks that get this right will not just deploy AI faster — they will compound its value across every function, while their competitors are still wiring up one use case at a time.
Practical steps to scale your capabilities
Knowing what to build for is the first step. Knowing how to build it is the second. The execution comes down to six choices — not technology choices, but operational ones. If you are planning your next move, keep the approach simple and focused on measurable outcomes:
1. Start where it matters
Prioritise workflows where AI has an immediate, measurable impact: onboarding, lending, underwriting, portfolio monitoring. Do not start with the most visible use case. Start with the most impactful one.
2. Get your data in shape
Ensure your data is clean, connected and accessible to your analytical models. AI amplifies what it operates on. If you amplify well-structured, high-quality data embedded in well-designed processes, you get extraordinary results. If you amplify fragmented data and broken workflows, you get chaos at machine speed. Data readiness is not a technical prerequisite. It is a strategic decision.
3. Build trust from day one
Explainability, as I argued earlier, is not a feature to add later. Some decisions must be fully explainable and auditable; others benefit from AI's ability to interpret, summarise and synthesise. Knowing which is which — and deploying the right type of model for each — is one of the most consequential design choices you will make. Confusing the two is one of the most common and costly mistakes in banking AI implementations.
4. Set yourself up to scale
The Bet
The winning bet, then, is not the model. It is the orchestration around it, the data depth beneath it, the workflows that carry it and the governance model that holds it accountable.
The technology is ready. The question is not whether to move. It is how fast, and whether you are building on the foundation that will actually scale.
The time to move is now.
Fixing the foundations with Mambu Intelligent Core
Scaling these initiatives is exactly where most digital transformation strategies fail. To function effectively, advanced analytical models need clean, connected and highly accessible data. They require seamless workflows that do not rely on manual workarounds or slow batch processing.
Legacy core banking systems make achieving this nearly impossible. Monolithic architectures create rigid data silos, slow down daily operations and make scaling across new regions incredibly difficult. Banks are now taking a much more practical approach to modernisation. Instead of pursuing risky rip-and-replace projects, they modernise where it matters most by adopting composable architecture.
This is exactly where Mambu Intelligent Core changes the landscape as it provides an artificial intelligence-ready foundation designed to empower financial institutions to operate at the speed of modern technology. By leveraging a cloud-native, API-first architecture, and our MCP solution, Mambu Intelligent Core ensures that every single component feeds accurate, up-to-the-second data to your operational models.
Legacy systems that rely on end-of-day batch processing simply cannot support proactive banking. Mambu processes millions of transactions in real time, guaranteeing that decision engines have the freshest information available for fraud detection, dynamic pricing and instant loan approvals. This composable approach gives banks the ultimate flexibility to upgrade specific modules, test new features and deploy updates without disrupting the entire system.
Why having the right partner combination matters
Intelligence does not deliver value in isolation. It needs the right environment to thrive, which requires clean data, flexible infrastructure and intelligent workflows. No single vendor can provide every tool needed for a modern financial institution. This is where ecosystem approaches become a definitive advantage.
Mambu and nCino have partnered to deliver exactly this optimised environment. Together, we enable end-to-end lending from initial onboarding to servicing and final account closure.
Mambu Intelligent Core provides a robust, highly scalable foundation. It makes data easier to access, financial products simpler to configure and disparate systems faster to connect. Our API-led integration offers developers total freedom to build sophisticated workflows quickly and scale them with absolute control.nCino brings deep domain expertise and targeted operational intelligence. Purpose-built for the banking sector, nCino embeds advanced analytics directly into lending and decisioning workflows. More than 14 years of industry data from over 1,600 financial institutions actively shape their sophisticated models. This combination of Mambu's flexible core and nCino's intelligent workflows creates a highly effective ecosystem for institutions ready to modernise.
Watch our comprehensive on-demand webinar with nCino and Google Cloud today to learn how to fix your foundations, build explainable models and empower your teams to drive measurable progress across your entire institution.
Sergio is the VP of Partner Ecosystems, Sales Excellence and Growth for EMEA, working with financial institutions and strategic partners across Europe, the Middle East and Africa on their digital transformation journey. More recently, he has taken on broader commercial leadership for the region.
At nCino, his focus is helping financial institutions modernise their core operations through cloud-based technology and AI solutions to streamline onboarding, lending and portfolio monitoring, enhance relationship management, and enable faster, better-informed decisions.
Curious how Mambu’s Intelligent Core can support your business goals?
Get in touch with one of our experts for a conversation
© Mambu 2026