In the second part of this blog series, we looked at where AI, automation and low-code can create practical value for building societies, from mortgage application intake and broker servicing, through to complaints, colleague support and finance operations. But finding good use cases is only part of the challenge. The harder part is turning them into something that can be scaled safely, consistently and with confidence.
For many building societies, AI, automation and low-code are already starting to appear in different parts of their organisations. Some teams may be experimenting with Microsoft Copilot. Others may be building apps or workflows in Power Platform. Some may be looking at Copilot Studio or AI agents.
That is healthy progress but without a common approach,those early experiments quickly become disconnected pockets of activity.
The challenge is no longer starting, it is scaling
Most organisations are not short of ideas for AI or automation. The challenge is making sure those ideas can be delivered in a way that is controlled, measurable and trusted. That matters even more for building societies.
Building societies are regulated, member-led organisations handling sensitive financial data, mortgage journeys, savings products, complaints, support for vulnerable customers and financial crime processes. In that environment, the question is not just “can AI help?” but “can we evidence that it is being used safely, fairly and consistently?”
Good ideas need room to grow. But they also need clear ownership, data controls, support models and governance. This is where AI governance for building societies becomes critical.
AI changes the governance conversation
AI is moving beyond simple productivity support. Increasingly, it can summarise information, trigger workflows, answer colleague questions, support decisions and help teams move work forward faster. Microsoft’s Agentic Transformation Patterns Playbook describes this as the shift from AI that assists to AI that can increasingly execute work. This shift changes the risk profile.
When AI is helping someone draft a document or summarise a meeting, governance is still important. But when AI connects to systems, uses business data or supports regulated processes, the need for control increases significantly.
Building societies need to be clear on:
· What data AI can access
· What actions AI can take
· Who owns each app, automation or agent
· How outputs are checked
· How risks are monitored
· How value is measured
Without that clarity, confidence can disappear quickly.
Avoiding the two common traps
There are two common traps organisations fall into.
The first is too much control. Everything becomes slow. Business teams lose interest. Innovation stalls. People start looking for workarounds.
The second is too little control. Apps, flows and agents appear without clear ownership. Data and access risks increase. Support becomes unclear. Value becomes hard to track.
Neither model works. The role of governance should not be to slow everything down. It should give teams a clear, safe route from idea to live service.
At Robiquity, we describe this as finding the right balance between ambition and discipline. The ambition to innovate versus the disciplined foundations needed to scale safely. Robiquity’s Centre of Excellence approach is built around connecting governance, operating model, delivery and adoption across Power Platform, Copilot and AI agents.
One operating model, not three disconnected approaches
A common mistake is to treat Power Platform, Microsoft 365 Copilot, Copilot Studio and AI agents as separate initiatives. That creates duplication and confusion. Instead, building societies need one joined-up model covering:
· Power Platform governance - environments, DLP, app ownership, maker pathways and lifecycle management
· Copilot readiness - data access, oversharing, adoption, usage and colleague enablement
· Copilot Studio governance - knowledge sources, actions, publishing controls and auditability
· AI agent management - risk tiering, approvals, monitoring, ownership and support
· Security and compliance foundations -including Purview, Entra and Defender
· Operating model - front door, triage, design authority, support routes and reusable patterns
This is the basis of effective AI governance for building societies. It is not just about writing policies. It is about making the right way of working easy to follow.
The end state: an Agentic Centre of Excellence
For building societies, the end goal needs to be an Agentic Centre of Excellence.
Put simply, this is the operating model that helps an organisation decide which AI agents, apps and automations should be built, how they should be governed, who owns them, how they are monitored and how value is proven.
An Agentic Centre of Excellence is an evolution of a traditional automation or low-code Centre of Excellence. It brings together the governance, standards, people, processes and controls needed to scale AI-enabled work safely.
An Agentic Centre of Excellence helps building societies:
· Prioritise the right use cases
· Assess risk and complexity
· Define ownership
· Set build and deployment standards
· Manage data and access controls
· Support adoption and training
· Monitor usage, value and risk
· Create reusable templates and patterns
The aim is not to create bureaucracy. It is to give teams the confidence and guardrails to innovate safely.
A practical route to safe scale
Societies do not need to solve all of this in one move. With our customers, we find a phased approach usually works best:
Step 1: AI, automation and low-code maturity assessment - Understand current maturity, identify priority use cases and highlight blockers.
Step 2: Governance and guardrails review - Define the controls, ownership model and operating approach needed to scale safely.
Step 3: Use case delivery and adoption - Deliver priority use cases, prove value and build colleague confidence.
Step 4: Agentic Centre of Excellence - Move to a joined-up model that supports safe, measurable and repeatable innovation.
Final thoughts on this series
AI, automation and low-code creates real value for building societies. But that value only scales if trust, governance and adoption are built in from the start. The winners of the AI race in this sector will not be the building societies with the longest list of AI experiments, it will be the ones that connect those experiments to value, control and adoption.
At Robiquity, we help organisations move from isolated AI, automation and low-code activity to a governed model that can scale. For building societies, that means identifying the right use cases, putting the right guardrails in place and building towards an Agentic Centre of Excellence.
If your building society is looking at how to scale AI, automation or low-code safely, I’d love to talk. Robiquity can support you in assessing your maturity, identifying the right use cases, shaping the governance model and building a practical route towards safe, measurable transformation. Contact us today to arrange an initial, no obligations conversation.
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