Agentic AI in Banking - 5 Readiness Gaps
- Oliver Dlugosch
- 1 day ago
- 3 min read
Insights from banking executives on how Agentic AI outpaces their AI transformation - and the way to bring their bank forward.
2026 is the year when 81% of banks implement Agentic AI according to the nCino AI in Banking Benchmark. But it won't be an easy path.
As head of Beam AI x Banking, I discussed with dozens of AI leaders over the last months how to get agentic. They tell me about pain points and blockers, but also their best ways to get started. Receiving so many expert insights, I thought about sharing my experiences in the blog.
First, it was interesting for me that most of my contacts were clear about the ultimate relevance of a successful AI transformation:
"Banks that don't reinvent now won't be there in ten years".
We can already see digital leaders standing out with excellence in operations and faster innovation cycles. Those players will consolidate the markets.
But getting Agentic is a huge step on the AI ladder. It fosters the real value of AI, but also unveils the organizational challenges of AI. Implementing enterprise AI with platforms like Beam AI, banks shift autonomy from human to AI - in process management and decision making. These are the use cases with the most impact for a company, but also require some maturity of an AI organization.
In my discussions, many bankers told me about 5 fundamental points that are hindering them on their path to agentic AI.
1️⃣ AI Strategy
The strategy on the future business impact of AI will be the north star. It is the baseline for a value-ranked roadmap on AI use cases from initial learnings to ROI-driving automations.
2️⃣ AI organization
AI transformation needs two organizational decisions - the vision of how their operational model of an AI-led company looks, and the right structure to accelerate AI in the firm.
3️⃣ AI governance & guardrails ← decisice
The EU AI Act and further regulations put several bank processes under high-risk rules. Banks need to extend their compliance setup and select AI technology to keep AI processes and decisions transparent, explainable and under human oversight.
4️⃣ Data management & system integration ← decisive
Fragmented data and legacy cores often throttle access to data and limit AI roadmaps to scale fast. Enterprise-wide AI requires a company-wide perspective on data management, like data lakes and semantic data layers.
5️⃣ Data sovereignty & platform hosting
With a score of 76%, banking is the sector with the highest demand for AI sovereignty (Accenture, 2025). Where the models run and where data lives is one of the first board-level decisions on AI.
My discussions with domain expert partners came to the conclusion that banks would best start with an AI readiness assessment.
It brings clarity on the overall state of AI transformation and a strategic focus on the blocking points.
It allows them to align the first AI automation goals with their AI maturity level.
AI automation platforms are the ultimate next step for banking, but successful projects depend on a path that matches the bank's AI capabilities.
Advanced banks will proceed with agentic AI platforms for high-impact use cases or first learning pilots. Less mature companies should test their AI organization with first meaningful AI workflows, but in a lab environment of digital twins. Others speed up with external consulting on their biggest pain points to get AI ready.
Where do you see the pain points and path of banks to agentic AI?
