Turning a banking AI vision into real roles

A specialist AI consultancy was bidding to build an AI-enabled customer intelligence platform for a regional bank. The ambition was big and diffuse: credit risk, fraud detection, personalization, and marketing effectiveness were all in scope.

On paper, the draft staffing sheet looked impressive. In reality, it was a blur of overlapping titles: data scientists, ML engineers, data engineers, BI specialists, governance leads, change managers. The bank’s evaluation team kept asking the same questions:

• Who owns which use cases?

• How will this team work with our internal risk, marketing and data functions?

• Where does responsibility begin and end for each consultant?

The consultancy needed to turn a visionary AI proposal into a staffing model that a bank could actually believe in. That’s where Calimala came in.

Working with the bid team, Calimala translated the mixed list of roles into a clear operating structure for an AI-enabled banking programme. Instead of a flat list of “smart people,” the model defined who would own applied AI, who would own data engineering and analytics, who would enforce data governance, and who would steer the overall programme.

From vision to a structured AI team

Calimala’s first step was to group the chaos into a few clear tracks:

• Applied AI (solution architects, data scientists, ML engineers)

• Data engineering (data engineers, platform engineers)

• Analytics & BI (analysts, reporting and insight roles)

• Data governance & risk (data quality, model risk, controls)

• Programme leadership (engagement lead, delivery, change)

For each track, we defined responsibilities, seniority, and reporting lines so the bank could see how the team would actually work day to day: who owned credit models, who led fraud analytics, who ensured data pipelines were production-ready, who translated bank requirements into technical work, and who was accountable for outcomes.

We also set realistic expectations on location: which roles could credibly be remote, which needed partial on-site presence for stakeholder work, and which should be close to the bank’s teams for regulatory, risk and change reasons. Together with the consultancy, we agreed where named candidates were required for credibility at bid stage, and where indicative profiles were enough to show bench depth.

By the end of this phase, the consultancy wasn’t just selling “AI in banking.” It was presenting a concrete team blueprint the bank could plug into its existing structure.

Building a bid-ready bench of applied AI talent

With the structure in place, Calimala moved to sourcing. The focus was on applied AI roles that could carry a banking use case all the way from idea to production: solution architects, data scientists, ML engineers and data engineers with real experience in financial services.

We sourced across the region and internationally, prioritising candidates who had shipped models and decision systems into production, not just contributed to research or POCs. The strongest profiles had:

• Evidence of deployed work in banking domains such as credit risk, fraud detection or marketing analytics.

• Experience collaborating with engineers on data pipelines, APIs and deployment into existing bank systems.

• Comfort operating in regulated environments, where documentation, governance and controls matter as much as model performance.

Calimala then produced anonymised packs for each role type—demonstrating that the consultancy had real access to applied AI talent, not just theoretical headcount. Several candidates agreed to be named if the bid progressed to contract, giving the bank extra confidence in execution.

What this changed for the client

Armed with a clear team structure and real candidate profiles, the consultancy could walk into steering meetings with a credible, costed staffing model. Instead of generic promises about “data scientists” and “ML engineers,” they showed exactly how applied AI, data engineering, analytics, governance and programme leadership would work together inside the bank’s environment.

The impact was measurable:

• 18 key roles mapped, with sample candidates for each role type across the AI banking programme.

• A clear split between what the consultancy would staff and what the bank’s internal teams would own.

• Higher confidence from the bank that delivery would not stall due to hiring gaps or unrealistic role expectations.

• A ready-to-activate hiring plan that could move from bid to implementation without starting recruitment from scratch.

By combining a boutique, practitioner-led approach with an AI-native evaluation engine, Calimala helped turn a regional bank’s AI vision into a realistic, staffed model for customer intelligence—grounded in real people, clear roles, and a delivery plan that regulators and risk teams could live with.