Andreea Pleşea, PhD, Co-Founder and COO at Druid AI
AI is no longer seen as an add-on. It is expected as a standard in enterprise IT infrastructure
Digital transformation is often hailed as the answer to improve productivity, and yet, despite significant investment, the UK continues to lag behind similar markets such as the US, France and Germany in productivity growth.
The UK is recognised internationally for its financial and banking sector, and it sits at the heart of the UK economy. When banks operate efficiently, businesses move faster, but when banks are slowed by operational friction, the ripple effects are felt far and wide.
UK financial institutions operate a technology stack across core banking platforms, CRM systems, contact centre infrastructure, mobile apps, fraud systems, onboarding tools, compliance platforms and knowledge bases. Each was designed to solve a specific problem, but together they have created a fragmented set of solutions that require employees to constantly switch between applications to find the information they need to answer customer queries or understand how to make improvements to the business.
Andreea Pleşea, PhD, Co-Founder and COO at Druid AI, explains that this fragmentation has created an orchestration gap, and agentic AI is the technology that can bridge it – not by adding another tool, but by becoming an essential part of the IT infrastructure.
Scripted bots are out. Autonomous execution is in.
The first wave of banking automation focused on what is referred to as ‘deflection’. Essentially, chatbots and Interactive Voice Response (IVR) systems were rolled out with the goal to reduce call volumes and answer basic account questions. But 61% of customers still escalate to human agents because these systems fail to resolve issues. Regulated banks cannot allow public Large Language Models (LLMs) to access core systems without strict governance. They generate responses, not orchestrate workflows.
The introduction of Generative AI tools in recent years has allowed for more natural language capabilities, but improving language alone does not complete work. Many, if not all, financial institutions don’t want public LLMs accessing their core banking systems, enforcing business rules, or figuring out whether they can stand up to the test of being audited in a highly regulated industry. Quite simply, they generate responses, they do not orchestrate business processes.
The fundamental difference of Agentic AI
AI agents are built from the ground up to be decision-capable and goal-oriented. They are capable of executing multi-step workflows or processes across different core platforms while operating within the strict boundaries of financial governance.
If a customer asks the question “What is the balance of my current account?” an AI agent will authenticate the customer, retrieve the necessary account data from core banking systems and provide the answer. They can also help with queries such as a card replacement, updating contact details or guiding a customer through the process of a loan application, to completion. Irrespective of whether the customer chooses to engage across chat, SMS, voice or mobile banking, the AI agent won’t lose the context of the request even if they switch platforms.
Retail banking customers interact with their bank approximately 150 times per year, and when those touchpoints are fragmented across channels, cost-to-serve rises and trust declines. However, when they are resolved quickly and securely in digital channels, efficiency and retention improve.
Making the productivity case for UK banking
The productivity opportunity for UK banking lies in automating the high-volume, repeatable journeys – not through rigid, scripted chatbots, but through intelligent, governed workflow execution.
High-volume journeys such as account servicing, loan applications and fraud inquiries require secure verification, system checks and downstream actions. Yet customers are often forced to escalate to human agents to complete them.
By applying unified business rules across digital channels and legacy IVR systems, AI agents standardise this fragmented logic. A single workflow can be built once and deployed consistently across web, mobile, contact centre and messaging channels. This reduces repeat contacts, eliminates “start over” frustration and frees human advisors to focus on complex cases, cross-sell opportunities and relationship management.
In a market where 17- 22% of UK consumers are actively looking for a new bank or considering switching their main bank account, consistent, frictionless service is not a luxury – it’s a competitive defence.
Improve the infrastructure rather than replace it
The productivity impact extends beyond front-line customer enquiries and extends to how employees can navigate the maze of business applications to onboard suppliers, generate compliance reports, update policies or process internal IT requests. Agentic AI sits across these internal systems as well, automating repetitive processes and orchestrating tasks without forcing employees to switch between interfaces.
One of the biggest barriers to adopting this transformation from CIOs and IT leaders is a fear of “rip-and-replace” programmes. Core banking systems are deeply embedded with the organisation, CRM systems anchor case management and Contact Centre as a Service (CCaaS) platforms manage routing and workforce engagement.
Agentic AI does not require these embedded systems to be replaced, it securely integrates with them, creating an operational layer that improves productivity.
Conversational AI Platforms with autonomous agents act as an orchestration layer across existing stacks. They plug into core banking systems, CRM and CCaaS infrastructure, performing governed actions while maintaining audit trails and role-based access control. This highly customisable approach allows finance and banking institutions to modernise customer journeys without destabilising foundational systems.
The opportunity is clear
This is where the infrastructure argument becomes clear. UK finance and banking institutions don’t need more applications layered onto already complex, data-sensitive, highly secure enterprise IT environments – they need intelligent systems that unify what already exists.
The UK’s next productivity gains will not come from incremental feature upgrades. They will come from rethinking how repetitive tasks move across enterprise systems. Agentic AI represents a shift from tools that respond to requests to an infrastructure that completes complex tasks, at scale. For mid-to-large retail banks and credit unions, the opportunity is clear: resolve more interactions digitally, scale capacity without expanding headcount, protect margins and strengthen customer trust.