AI Strategy for FinTech: How a Scale-Up Built an Investor-Ready AI Roadmap

The process is the same as for larger companies: first understand, then evaluate, then prioritize. The difference lies in the pace. Startups and scale-ups don't need a strategy that takes six months to mature. They need concrete results in weeks, not quarters.

PLAN D starts with stakeholder interviews across the organization to capture operational bottlenecks, existing data, and initial AI ideas. In an AI Ideation Workshop, these ideas are collaboratively developed, evaluated along concrete dimensions, and translated into a prioritized roadmap.

The result is an AI roadmap — a strategic document that shows not only where AI creates value, but also where it doesn't.

The AI Ideation Workshop is a format developed by PLAN D that systematically unlocks AI potential within organizations. It combines pre-interviews with key stakeholders, a two-day workshop featuring an AI crash course and co-creation in cross-functional teams, followed by strategic synthesis.

The result is not an idea list, but a prioritized roadmap with business cases and make-or-buy recommendations.

Automation follows fixed rules: if A, then B. AI, on the other hand, recognizes patterns in data and makes decisions that cannot be fully captured in rules.

In practice, many supposed AI use cases turn out to be automation tasks — such as email routing or form validation. The distinction matters because automation is faster, cheaper, and easier to maintain.

AI pays off where patterns are complex, data is unstructured, or decisions are non-deterministic.

A viable AI use case meets four criteria: it creates measurable value for the business, the required data is available or obtainable, the technical implementation is feasible, and the use case aligns with the corporate strategy.

PLAN D additionally evaluates ideas along cost-effectiveness, organizational impact, and regulatory requirements. Not every good idea is a good first step. Prioritization determines success.

Make-or-buy describes the decision between developing an AI use case in-house or covering it with an existing product.

"Make" pays off when strategic relevance is high, data requirements are specific, and technical expertise is available. "Buy" makes sense when standard solutions meet the need — such as AI-powered chatbots or automated meeting summaries.

The decision depends not only on the technology, but also on maintenance effort, scalability, and time-to-value.

AI offers a wide range of applications in payment processing: from intelligent matching of incoming payments to automated document processing and anomaly detection in transaction data.

Additional use cases include churn prediction, personalized communication with payers, and data-driven liquidity forecasting.

The data foundation is decisive: companies in the payment sector often have large transaction datasets that are well suited for machine learning.

Investors don't expect AI visions — they expect concrete plans. A convincing AI strategy contains prioritized initiatives with business cases, a realistic roadmap with quick wins and long-term projects, and a clear make-or-buy logic.

Equally important: the strategy should define where AI is deliberately not deployed. That demonstrates strategic maturity.

PLAN D develops AI roadmaps with companies that meet exactly this standard: well-founded, actionable, and free from hype.