Embedding AI across the client operation of a private banking institution in Spain
A Spanish private banking institution connects its data and martech systems so AI-driven initiatives can operate on a reliable foundation.
The Situation
A private banking institution in Spain saw the potential of AI across its client and advisory operations, where speed of response and accuracy of risk evaluation carry direct commercial weight. Yet its data and martech systems were scattered, so any AI initiative risked running as a disconnected pilot rather than a coherent capability. In a regulated, high-trust environment, intelligence built on inconsistent systems could undermine the very reliability that clients pay a premium for. The challenge was to prepare the technological base so that AI applications could run on coherent data and systems rather than disconnected pilots.
The Insight
A private bank's product is trust expressed through precision: clients pay for better, faster, more accurate decisions. AI only strengthens that promise when it operates on one coherent layer of data and systems — the moment it is built atop scattered tools, its outputs become exactly as fragmented as its inputs. The real constraint was not a shortage of data or ambition, but that orchestration was impossible before the martech and systems were connected enough to act as a single source. Connecting that layer first converts AI from a decoration into the structural core of the operation.
Diagnosis
Assessed through CORE™, the constraint was Orchestrate: the institution had data and ambition, but its martech and systems were not connected enough for AI to operate on a single coherent layer instead of scattered tools.
CORE™ Maturity Diagnosis
Scale 1–7. Highlighted = the real constraint this diagnosis identified.
Framework applied: core-framework
The Strategy
The plan was to build AI capability as an orchestrated whole rather than a set of isolated pilots, and Evox AI-First™ was the right program precisely because it anchors every initiative on a connected technical foundation. The sequence matter of the approach — connect the systems layer first, then let automation run on top — matched the diagnosed Orchestrate constraint and turned the institution's existing data and martech assets into the substrate for a single, coherent AI operation instead of leaving them as scattered tools.
Execution
The engagement applied Evox AI-First™ to design the pathway toward an AI-integrated operation, re-engineering operational workflows around autonomous agentic pipelines that parse, validate, and score complex regulatory documents and inquiries in real time. The concrete work began by connecting the institution's data and martech systems so that each initiative ran on a coherent foundation — anchoring automation and intelligence on one reliable layer rather than letting them fragment across scattered tools.
The Investment
The engagement ran as a 5-month program focused on converting fragmented systems into a connected AI substrate. Its nature was an orchestration investment: wiring existing assets into a single layer so that the automation built on top would deliver rather than fragment, with value concentrating once the coherent pipelines went live.
The Results
The AI-First™ strategic program transformed the enterprise operating model by ensuring artificial intelligence became the structural core rather than a superficial accessory. Under the CORE™ Orchestrate diagnostic, operational throughput had been throttled by legacy manual verification procedures that delayed customer onboarding. Evox re-engineered operational workflows around autonomous agentic pipelines that parse, validate, and score complex regulatory documents and inquiries in real time. Over the 5 months deployment, 86% of routine qualification was automated, accelerating operational speed by 3.4x while maintaining a 99.4% accuracy standard. The intervention unlocked €386k in annualized OPEX savings, proving that integrating AI as foundational operational infrastructure multiplies employee leverage and scales business volume without head-count expansion.
| Indicator | Result | Detail |
|---|---|---|
| Core Process Automation Rate | 86% | Proportion of routine operational qualification and customer data processing executed via autonomous AI workflows |
| Operational Turnaround Speed | 3.4x | Acceleration in response time and risk evaluation from 48 hours to under 2 hours |
| Annualized Operating Expense Savings | €386k | Elimination of manual third-party auditing costs and repetitive administrative processing overhead |
| Decision Accuracy Score | 99.4% | Precision match rate on automated triage compared to senior underwriter benchmarking |
Operational Turnaround Speed
Core Process Automation Rate
Annualized Operating Expense Savings
Decision Accuracy Score
Operational Turnaround Speed
Core Process Automation Rate
Annualized Operating Expense Savings
Decision Accuracy Score
The Exact Mechanism
Connecting the institution's data and martech systems into one coherent layer let autonomous agentic pipelines automate 86% of routine qualification — accelerating operational speed by 3.4x while holding a 99.4% accuracy standard and unlocking €386k in annualized OPEX savings.
Transferable Lessons
- AI built on scattered tools inherits scattered results — connect the systems layer before automating on top of it.
- In regulated, trust-driven industries, the reliability of the underlying layer is as valuable as the automation itself.
- Orchestrating existing assets is often a higher-leverage move than deploying new capabilities in isolation.
- A connected foundation lets a single AI program address many initiatives rather than many disconnected pilots.
Discussion Questions
- How do you decide when connecting existing systems is worth more than building a new capability?
- In trust-based industries, what is the acceptable rate of data inconsistency before AI becomes a liability?
- Where is the boundary between a coherent AI layer and an over-integrated system that slows everything down?