Credit unions can’t get real value from AI without flexible, secure digital infrastructure. Here’s how to build an AI-ready platform that actually serves members.
Most credit union leaders underestimate how much their future depends on invisible pipes: networks, cloud platforms, data flows, security controls. Yet those “plumbing” decisions quietly decide whether you’ll deliver AI-powered, member-centric banking…or fight fires for the next decade.
Chris Sachse, CEO and Co-founder of Think|Stack, put it bluntly in his CUInsight Network conversation:
“Technology, in general, is really a board and CEO conversation.”
He’s right. If you want AI-driven fraud detection, smarter loan decisioning, and always-on digital member service, you don’t start with chatbots or models. You start with digital infrastructure and security that can actually support them.
This article connects Sachse’s perspective on digital infrastructure and cybersecurity with where credit unions are heading next: AI-powered, member-centric banking.
We’ll talk about why boards need to treat infrastructure as strategy, how to use cloud and web-scale technology safely, and what it really takes to build a flexible, secure platform for AI.
Why Digital Infrastructure Is Now a Board-Level Issue
The core point: infrastructure choices are now business model choices. For credit unions, this matters more in 2025 than it ever has.
Here’s what’s changed:
- Members expect 24/7, mobile-first, AI-assisted service
- Fraud tactics evolve weekly, not yearly
- Data is exploding across cores, LOS, CRM, cards, and fintech partners
- Regulators are sharpening expectations around cybersecurity and third-party risk
If your infrastructure can’t support AI tools, real-time decisioning, and secure integrations, you’ll feel it everywhere:
- Slower loan approvals while competitors give near-instant decisions
- Higher fraud losses because models can’t run on stale, batch data
- Frustrated members who can’t complete simple tasks without calling
- Talent burnout as staff “swivel-chair” between outdated systems
Chris’s stance matches what I’ve seen: boards and CEOs can’t outsource this to IT.
What boards actually need to understand
They don’t need to read firewall configs. They do need clarity on:
- Where critical systems live (on-prem, hosted, cloud, hybrid)
- How quickly the organization can change (new product in weeks or in 18 months?)
- How data moves between systems (real-time APIs vs flat-file overnight jobs)
- How risk is managed (MFA, zero trust, backups, incident response)
The simplest board-level question:
“Can our current infrastructure safely support the AI and digital experiences we want over the next 3–5 years?”
If the answer is “I’m not sure,” that’s your signal: infrastructure strategy is overdue.
Cloud, Web-Scale Tech, and Credit Union Reality
Chris talks about something many credit unions still underuse: standing on the shoulders of larger web service companies like Amazon instead of trying to build everything from scratch.
The reality? Cloud is the foundation for serious AI in credit unions.
AI-driven fraud detection, loan decisioning, and personalization all share three needs:
- Access to clean, well-governed data
- Scalable compute to train and run models
- Secure, reliable connectivity between systems
On-prem environments can support some of this, but it’s like trying to run a modern logistics operation with clipboards. Possible, but painful.
Practical cloud patterns that work for credit unions
Most successful credit union strategies I’ve seen look like this:
- Hybrid approach: Core may stay on-prem or with a traditional provider, while analytics, AI services, and member-facing apps move to secure cloud environments.
- API-first mindset: Every new system or vendor must expose APIs so data can feed into fraud engines, AI models, and digital experiences.
- Standardized security controls: Identity, logging, and encryption are managed centrally instead of bolted on individually.
This structure matters for AI because your fraud model, member service assistant, and loan decision engine will all want to:
- Pull in real-time transaction data
- Reference historical member behavior
- Update results and alerts across channels (app, online, contact center)
If your infrastructure requires manual file transfers and custom one-off integrations, you’re capping how far you can go with AI before you even start.
Building a Flexible, Nimble, and Secure Platform
Chris highlights three core traits of a dependable digital platform: flexible, nimble, and secure. Those same traits are exactly what you need to support AI for member-centric banking.
Here’s what they look like in practice.
1. Flexibility: design for change, not for permanence
AI projects have a high iteration rate. Models get retrained. Vendors change. Member expectations move.
A flexible infrastructure for credit unions usually includes:
- Microservices or modular architecture instead of monolithic apps
- Integration layers (API gateways, ESBs, event streams) that keep systems loosely coupled
- Data models designed for analytics and AI, not just core processing
Real example:
A CU wants to roll out an AI-powered financial wellness coach. If they’ve already built a member data platform in the cloud, with normalized data from core, cards, and digital banking, they can:
- Stand up a proof of concept in weeks
- Test with a small member segment
- Swap vendors or models without re-wiring everything
Without that flexibility, they’re looking at a 12–18 month project and huge sunk costs.
2. Agility: speed of change as a competitive advantage
Chris saw this during the pandemic: credit unions that could pivot quickly – stand up PPP workflows, enable remote work, adjust digital channels – were the ones that kept serving members effectively.
For AI adoption in 2025, agility means:
- Short, iterative pilots instead of multi-year, big-bang projects
- DevOps practices (CI/CD, automated testing) that make change safer
- Strong vendor ecosystems so you’re not trapped by a single provider
If you’re planning 9-month cycles to approve, build, and release something like AI-based call summarization or chatbot enhancements, you’ll always be behind fintech competitors.
3. Security: non-negotiable foundation for AI
Here’s the uncomfortable truth: AI amplifies whatever security posture you already have.
- Strong identity controls? Great, your AI tools inherit them.
- Weak access management? Now you’ve created more doors for attackers.
Chris emphasizes budgeting and planning for cybersecurity. For AI-ready infrastructure, I’d prioritize:
- Zero trust principles: never assume internal traffic is safe
- Multi-factor authentication everywhere, especially for admins
- Role-based access control (RBAC) for data used by AI tools
- Centralized logging and monitoring so AI activity is auditable
- Regular incident response exercises that include cloud and AI vendors
Members will not separate “our AI” from “our CU” in a breach. If an AI-powered experience mishandles data, it’s your reputation on the line.
Actionable Steps to Improve Cybersecurity and AI Readiness
Chris talks about making cybersecurity concrete: budgeting for it and developing a plan. For credit unions specifically pushing into AI and data-driven services, that plan should be tightly connected to your digital infrastructure roadmap.
Here’s a practical sequence that actually works.
Step 1: Assess where you are – honestly
Run a focused assessment on:
- Infrastructure inventory: Where do core, LOS, mobile, online banking, CRM, and analytics live?
- Data flows: How does data move between them today? Files, APIs, manual?
- Security controls: Identity, encryption, backups, monitoring, vendor risk
- AI usage: Any existing tools in use (fraud models, chatbots, scoring)? Shadow IT?
You don’t need a 100-page report, but you do need a clear “red/yellow/green” picture that the board can understand in one session.
Step 2: Align AI ambitions with infrastructure reality
Most credit unions in this “AI for member-centric banking” journey care about some combination of:
- Fraud detection and prevention
- Smarter loan decisioning and pricing
- Member service automation (chat, voice, email)
- Financial wellness and personalization
For each priority, ask:
- Do we have the data and can we access it in (near) real time?
- Do we have infrastructure that can connect these systems safely?
- Do we have security controls that satisfy regulators and risk appetite?
Where the answer is “not yet,” that’s where you invest in infrastructure before tooling.
Step 3: Budget for infrastructure and security as enabling investments
Too many CUs treat infrastructure and cybersecurity as sunk costs. They’re not. They’re the platform that allows AI and digital projects to generate ROI.
When building your budget:
- Tie infrastructure spend directly to specific AI or digital outcomes (e.g., “We need this data platform so we can reduce fraud losses by X% with real-time models”).
- Allocate a fixed percentage of IT/AI project budgets to security by design, not as an afterthought.
- Include training and change management as first-class line items – your people are part of the infrastructure.
Step 4: Build a 24-month roadmap, not a wish list
Chris talks about agility and adaptability. That starts with a realistic, time-bound roadmap that might look like:
Next 6 months:
- Complete infrastructure and security assessment
- Consolidate logging and monitoring
- Implement MFA and modern identity for staff
- Pilot one AI use case on a low-risk dataset (e.g., internal process automation)
6–12 months:
- Stand up a secure cloud environment for analytics and AI
- Build or buy a member data platform
- Launch AI-assisted fraud monitoring for card or ACH activity
12–24 months:
- Integrate AI into member-facing channels (chat, voice)
- Expand AI-driven decisioning in lending and collections
- Continuously refine security controls based on usage patterns
You’re not trying to “get to the cloud” or “implement AI.” You’re building a digital infrastructure that can support an evolving portfolio of AI-powered, member-centric services.
Culture, Communication, and Collaboration: The Human Layer
At the end of his conversation, Chris emphasizes communication and empowering people to work collaboratively. That’s not a soft add-on — it’s how infrastructure and AI strategies survive contact with reality.
Here’s what actually makes a difference inside credit unions:
- IT and business co-own outcomes. Fraud, lending, and member service leaders sit with IT and cybersecurity to define AI use cases and guardrails.
- Boards hear human stories, not just technical ones. Frame AI and infrastructure through the lens of member impact, like shortening loan decisions from days to minutes.
- Vendors are treated as partners, not magicians. External experts (like Think|Stack) bring patterns from other institutions, but your team still owns decisions and accountability.
I’ve found that the most successful AI and infrastructure efforts in credit unions are the ones where:
- The CEO can explain, in plain language, how digital infrastructure supports strategy
- The board regularly reviews cybersecurity posture with context, not fear
- Frontline and back-office staff are involved early in shaping AI use cases
Infrastructure and AI are technical, but the real constraint is usually culture.
Where AI-Ready Infrastructure Fits in the Series
This article sits in the “AI for Credit Unions: Member-Centric Banking” series for a reason: every fraud model, every AI chatbot, every personalized offer depends on infrastructure and security that you control.
The central idea is straightforward:
If you want member-centric AI, you need member-centric infrastructure.
That means digital platforms that are:
- Flexible, so you can adapt to new AI tools and member needs
- Nimble, so pilots become production in months, not years
- Secure, so members can trust every new digital interaction
The next step for most credit unions isn’t “buy more AI.” The next step is asking, at the board and CEO level:
- What kind of digital infrastructure do we need to serve members the way we say we want to serve them?
- Where are we strong today, and where are the gaps blocking AI adoption?
- Which partners and internal leaders will help us build this foundation over the next 24 months?
Credit unions don’t have to match big banks dollar-for-dollar. But they do need intentional infrastructure, clear security planning, and a willingness to treat technology as a strategic conversation — just like Chris Sachse argues.
If you get that foundation right, AI stops being a buzzword and starts becoming what members actually feel: safer accounts, smarter decisions, and faster, more human service in every channel.