AI Skills Shortage Could Stall Innovation in Irish Financial Services

AI skills shortage could slow innovation in Irish financial services

Irish financial services firms are being warned: the AI skills gap is widening, and it's already slowing product velocity and competitive edge. Demand for AI expertise is outrunning supply. Teams that upskill now will ship better products, faster, with fewer risks.

What the new FSI-Accenture report signals

The report identifies three clear areas where generative AI moves the needle for product teams.

• Customer experience: Faster response times and more personalised interactions. Example: Irish Life's CARA (Claims AI Reasoning Assistant) supports assessors to deliver quicker outcomes at difficult moments.
• Productivity and efficiency: Automate manual work like data entry and document processing so people focus on higher-value problems. A large Irish insurer expects automation to streamline claims, underwriting, quotes, and product personalisation.
• Faster innovation: Shorter prototyping cycles and quicker rollout of new services. Revolut has integrated AI across product development, from real-time fraud detection to intelligent support.

Partnerships are rising too: 47% of surveyed firms in Ireland are considering or have set up AI ecosystem partnerships with providers such as Microsoft, Salesforce, and OpenAI.

Why product leaders should care

AI isn't a side project anymore. It touches discovery, delivery, and post-launch operations. If your team can't ship AI-enabled features safely and quickly, your backlog grows, your customer experience lags, and competitors widen the gap.

A practical plan for 2025-2026

• Map skills and set learning tracks: Define what PMs, designers, engineers, analysts, and risk/compliance need to know to build and ship AI features. Give people protected time for upskilling and form internal AI guilds for shared standards.
• Prioritise "now" use cases: Customer service assistants, claims triage, document automation, underwriting support, and broker quote tooling. Track impact with response time, claim cycle time, cost-to-serve, and CSAT/NPS.
• Tighten data foundations: Clean, well-catalogued data with access controls, lineage, and clear retention rules. No solid data, no reliable AI.
• Choose a model strategy: Start with partner platforms (e.g., Microsoft, Salesforce, OpenAI) where it makes sense, and reserve custom models for differentiators. Build an evaluation layer for quality, safety, and cost monitoring.
• Bake AI into the product workflow: Make AI spikes a standard part of discovery. Prototype with guardrails, run A/Bs in narrow segments, and ship behind feature flags.
• Governance that speeds, not stalls: Set human-in-the-loop checkpoints for high-impact decisions, keep audit logs, define escalation paths, and align to the EU AI Act.
• Right-size your platform: Standardise on a small stack that supports retrieval (for private knowledge), prompt management, evaluation, observability, and safe deployment workflows.
• Fill critical roles: AI-savvy product managers, ML platform engineers, data engineers, and risk partners who understand model risk in production contexts.
• Set partnership criteria: Security, data residency, model transparency, TCO, and exit options. Get legal and procurement ready for faster reviews.
• Push for a sandbox: Support calls for a Central Bank of Ireland AI sandbox so teams can trial products in line with regulation. Until then, structure internal sandboxes with strict data and safety gates.

Reusable patterns for product teams

• Claims co-pilot: Triage, summarise evidence, suggest next actions, and draft communications for assessor approval (CARA-style).
• Intelligent support: Retrieval-augmented chat for policy queries, identity checks, and fee explanations with handoff to agents when confidence drops.
• Fraud signals in-flow: Real-time risk scoring embedded directly into onboarding, payments, and account changes.
• Broker tooling: Automated quote generation with transparent rationale and editable terms, improving speed and relationships.

Risks to address early

• Incorrect outputs in customer communications: Use grounded retrieval, confidence thresholds, and agent review.
• Biased outcomes: Test across cohorts, track fairness metrics, and add override paths.
• Data leakage: Mask PII, keep strict access controls, and separate training from transient inference data.
• Vendor lock-in: Abstract model calls, keep prompts/versioning portable, and plan exit paths.
• Operational drift: Monitor quality and cost, run canaries, and keep an immediate rollback switch.

Where to upskill fast

If your roadmap depends on AI features landing in 2025-2026, upskilling is the shortest path to impact. You can explore role-based learning paths here: AI courses by job. For market scans and build-vs-buy decisions, this curated list can help: AI tools for finance.

Bottom line

The skills gap is real, but it's solvable. Product leaders who invest in people, data, and governance now will ship better experiences, cut cycle times, and keep pace with where the market is headed.