Short answer. Score every candidate against four measures: return on investment, time to value, data readiness, and regulatory risk. Fund the two or three that rank highest across all four, ship one to production, then reinvest the result. Ableneo’s 2025 portfolio shows 4 of 5 selected AI projects reach production, while most banks that spread effort across a dozen unranked pilots watch the majority stall. Prioritization, not model choice, decides which banks show results.
Prioritizing AI use cases is a scoring exercise, not a brainstorm. Most banks already have a long list of ideas from business units, vendors, and the board. The scarce resource is the engineering, data, and governance capacity to finish a few of them properly, not the ideas themselves. Prioritization is how a bank turns that list into a ranked, fundable shortlist.
The scale of spend makes the discipline urgent. Banks under ECB supervision recorded 572 digitalisation projects in 2024, accounting for 4.1 billion euro of investment, with 42.5% going to foundational capabilities and 35.8% to retail banking. The constraint is choosing what to finish, not finding the money.
Ableneo shipped 34 production AI projects in 2025 across 20 clients and 7 industries, and 94% of them use large language models. The pattern behind that record is selection: winning use cases share three traits.
In a bank, the cost of a use case is not only build cost. It is the compliance obligation that comes with it. From 2 August 2026, the main EU AI Act obligations for high-risk systems begin to apply, and a bank must first identify where AI is used and whether each system is high-risk. Credit scoring and creditworthiness assessment sit in Annex III, so an AI use case there carries conformity assessment, technical documentation, human oversight, and logging duties that a back-office summarisation tool does not.
This changes the ranking. A use case with strong ROI but high-risk classification carries a governance bill that must be scored in from day one, and AI governance has to be integrated with existing DORA operational resilience programmes rather than bolted on later. A well-run prioritization step prices that in before funding, which is why compliance-native banks pick differently from banks that rank on ROI alone.
Score every AI use case on four measures: ROI, time to value, data readiness, and regulatory risk.
Use four measures and score each candidate 1 to 5 on every one. Return on investment: the size and certainty of the business result. Time to value: how many weeks until a measurable outcome, where anything past two quarters loses board attention. Data readiness: whether the data exists, is accessible, and is clean, which is the single most common reason a promising pilot stalls. Regulatory risk: whether the use case is high-risk under the EU AI Act and what oversight that demands.
Multiply or weight the four into a single rank rather than averaging them, because a zero on data readiness or an unmanaged high-risk classification should sink a use case regardless of its ROI. The output is a ranked table the board can read in one page, not a slide of adjectives.
The use cases that score highest early are the ones with clean internal data and a hard number attached. Fraud detection and anti-money-laundering rank first for many banks because the outcome is a measurable loss rate and the data is already structured. Document-heavy processes come next: intelligent document processing for onboarding, claims, and loan operations turns unstructured paper into structured decisions in days.
ECB supervisory data shows a strong increase in AI use for credit scoring and fraud detection between 2023 and 2024, while generative AI in regulated institutions is still concentrated in summarisation, translation, information retrieval, and code generation. That is the practical order: start where the data and the measurement already exist, then move to generative use cases as governance matures.
The Act moves compliance cost from an afterthought to a ranking input. A summarisation assistant for internal staff carries transparency duties but stays outside the high-risk regime. An AI credit-scoring model is high-risk under Annex III and must meet the full obligation set. Two use cases with identical ROI can therefore have very different total costs once governance is priced in.
This does not mean banks should avoid high-risk use cases. It means they should sequence them. Many banks fund a lower-risk operational win first to build the governance muscle, the logging, the human-oversight process, and the documentation habit, then apply that same operating model to a high-risk use case. Prioritization that ignores the Act produces a shortlist the second-line risk function will later reject.
Fewer than the list suggests. The failure mode is a dozen simultaneous pilots that each get partial attention and none of which reach production. Portfolio discipline means funding two or three use cases to completion, proving one in production, and only then widening the portfolio. Ableneo clients run an average of 1.7 projects at a time, a deliberately narrow front that keeps delivery focused.
The metric that matters is not how many pilots a bank starts but what share of them reach production. A pipeline where 4 of 5 selected projects ship is worth more than one with 20 pilots and a 15% production rate, because production is where the return actually appears.
Build the scored shortlist first, in one week. Take the existing idea list, score every item against the four criteria, and cut it to the top three. Pick the single use case with the highest score and the shortest time to value, and commit to putting it in production, not to another proof of concept. One shipped result with a real number does more for board confidence than five demos. The output of the first quarter should be one production use case and a governance pattern that the next use cases reuse.
Ableneo works with banks and insurers including ČSOB, Erste, UNIQA, and Česká spořitelna, and the record behind the advice is a production one: 34 AI projects shipped in 2025 with roughly 4 of 5 reaching production, delivered by a team of 80+ specialists who understand both legacy core banking platforms and EU AI Act obligations. That combination is what makes prioritization credible, because the scoring reflects what actually ships in a regulated environment rather than what demos well. Our full set of decision guides for regulated AI sits in the AI transformation FAQ.
Key takeaways
Planning AI in a regulated business? Ableneo takes systems from classification to governed production.