Digital progression gives credit unions a practical AI roadmap—across automation, member experience, data, and decisioning—to deliver faster, smarter member-centric banking.
Digital progression: why credit unions can’t sit still on AI
Two things are true at the same time right now:
- Members expect instant, personalized, mobile-first banking.
- Most credit unions are still juggling legacy cores, manual workflows, and siloed data.
That gap is exactly where digital progression lives.
On a recent episode of The CUInsight Network, Wes Zauner, VP of Product at MeridianLink, framed it well: their goal is to help credit unions advance in their digital technology journey. Not a one-time “digital transformation” project that drains budgets and patience, but a continuous progression guided by a clear digital blueprint.
For this post in our AI for Credit Unions: Member-Centric Banking series, we’ll build on that idea and translate it into something practical: a roadmap any credit union can use to embed AI into lending, deposits, member service, and collections—without losing the human, member-first culture that makes credit unions different.
From “digital transformation” to digital progression
The most successful credit unions don’t treat technology as a destination project; they treat it as an ongoing operating discipline.
That’s the core difference between digital transformation and digital progression:
- Transformation says: “We’ll rip and replace, go live next year, then we’re done.”
- Progression says: “We’ll keep improving, month after month, guided by a clear blueprint and measurable outcomes.”
For AI in particular, this mindset shift matters.
AI models, member expectations, and risk dynamics change quickly. If your strategy is a one-time “AI initiative,” you’ll ship a chatbot or automated loan decisioning, then stall. If you commit to digital progression, you:
- Start with targeted, winnable use cases
- Learn from real member behavior
- Expand AI’s role as data quality, trust, and results improve
The credit unions that win with AI aren’t the ones with the fanciest models. They’re the ones that build a repeatable system for finding, testing, and scaling AI-powered improvements.
MeridianLink describes this system as a digital blueprint. Let’s unpack the five elements and what they look like in a member-centric AI strategy.
1. Process automation: where AI quietly fixes the plumbing
The fastest ROI on AI in credit unions usually comes from process automation.
You don’t start with flashy member-facing tools. You start with the boring, manual work that slows everything else down.
High-impact automation targets
Here are core operations where AI-driven automation makes an immediate difference:
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Loan applications and underwriting
- Automate data collection and document classification
- Use AI to pre-fill fields from uploaded docs
- Trigger rules-based and AI-assisted underwriting workflows
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Collections workflows
- Prioritize accounts based on risk signals and behavior patterns
- Suggest outreach channels and messaging strategies
- Automate routine payment reminders and follow-ups
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Back-office requests
- Member info updates, simple disputes, fee reversal eligibility checks
- Routing and categorizing support tickets using natural language processing
In practice, this doesn’t mean replacing staff. It means:
- Cutting manual touchpoints per loan or account change
- Reducing error rates from re-keyed or inconsistent data
- Freeing your best people to focus on complex member situations
If you’re just getting started with AI, I’d argue your first 12 months should be 60–70% focused on this type of automation. It creates capacity, cleans data, and builds confidence—all of which you’ll need for more advanced member-facing AI.
2. Member experience: AI that feels human, not robotic
AI should make the member experience feel more human, not less.
MeridianLink talks about digital progression as a way to enhance the member experience, not replace it. The reality is that smart AI orchestration can give members:
- Faster answers
- Fewer forms
- More relevant offers
- Better timing
Where AI improves member-centric banking
Here are specific ways credit unions are using AI to make interactions smoother and more personal:
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24/7 intelligent member service
- AI assistants that handle routine questions (balances, card freezes, basic troubleshooting)
- Smooth handoffs to live agents with full context, not “start over” frustration
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Guided digital applications
- Smart prompts that clarify confusing fields in loan or account applications
- Real-time eligibility insights: “Here’s why you’re seeing this option”
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Proactive financial wellness
- AI-powered insights like: “You’re paying more in interest than you need on this balance”
- Nudges to build savings, consolidate debt, or adjust budgets before problems escalate
The key: member-centric AI doesn’t just answer questions; it anticipates needs.
Done well, your digital channels start to feel like a skilled branch employee who:
- Knows the member’s history
- Understands their preferences
- Offers the right options at the right time
If your AI tools feel generic or scripted, they’re probably missing the credit union’s biggest advantage: deep, relationship-oriented member data.
3. Share of wallet growth: AI as your quiet growth engine
AI is extremely good at one thing credit unions often struggle with: matching the right product to the right member at the right moment.
Share of wallet growth isn’t about blasting more offers. It’s about relevance.
Practical AI plays for growing deposits and lending
Here’s how a digital blueprint can support growth using AI-driven insights:
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Next-best product recommendations
- Identify members likely to benefit from HELOCs, credit card upgrades, or refinancing
- Trigger highly targeted, contextual offers in-app or via email/SMS
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Deposit growth through behavior modeling
- Segment members by saving patterns and cash flow volatility
- Offer tailored savings plans, bonus structures, or CD options that match real behavior
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Loan portfolio optimization
- Use AI to spot early refinance risk and retention opportunities
- Adjust pricing, terms, or bundles dynamically within policy guardrails
When done right, AI-powered cross-sell doesn’t feel like selling. It feels like advice.
Members aren’t looking for more offers; they’re looking for help making good financial decisions. AI can give your staff superpowers by surfacing the one or two most relevant recommendations per member instead of a generic product grid.
4. Data centricity: the non-negotiable foundation for AI
Here’s the thing about AI for credit unions: if your data is scattered, stale, or inconsistent, everything else will disappoint.
Data centricity means building a unified, trusted view of each member, then powering analytics and AI from that foundation. MeridianLink’s blueprint highlights this because without it, you’re just layering new tools on top of old problems.
What data-centric credit unions actually do
Credit unions that succeed with AI usually:
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Create a single source of truth
- Consolidate key member data across core, LOS, CRM, contact center, and digital channels
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Invest in data quality
- Standardize formats, resolve duplicates, and define clear data ownership
- Establish data governance: who can use what, for which purpose
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Build analytics and AI on top of that layer
- Risk scoring, churn prediction, marketing segmentation, member profitability models
If you skip this work, your AI outputs will feel random or unfair. That’s where trust erodes—internally and with members.
For a member-centric strategy, data centricity isn’t optional. It’s how you:
- Ensure AI decisions are explainable
- Monitor for bias and fairness
- Give regulators clear audit trails
The payoff: each new AI initiative becomes easier because the data plumbing is already sorted.
5. Instant decisioning: speed without losing control
Members don’t compare your lending speed to other credit unions; they compare it to the fastest digital experiences they’ve seen anywhere.
That’s why instant decisioning is such a big part of digital progression.
Where instant decisioning fits in the AI roadmap
Instant decisions aren’t just about approvals and declines. They’re about:
- Shortening time-to-yes (or clear alternative)
- Reducing manual reviews to the cases that truly need them
- Giving members real-time clarity during stressful financial moments
AI can support instant decisioning in several ways:
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Pre-qualification & pre-approval
- Use internal and credit data to present pre-qualified offers before a member starts an application
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Automated underwriting tiers
- Clear-cut approvals and declines handled automatically within policy
- Gray-area applications escalated to human underwriters with rich context
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Real-time risk adjustments
- Adjust pricing or terms based on updated data while staying inside risk frameworks
The safeguard: policy-first design. You don’t hand the keys to an opaque model. You:
- Define acceptable ranges, thresholds, and exceptions
- Use AI to recommend or classify, while humans own the final rules and oversight
This is how you get the benefits of instant decisioning—speed, consistency, better member experience—without sacrificing your risk posture or compliance obligations.
Turning the digital blueprint into an execution plan
A concept like “digital progression” is only useful if it translates into clear next steps. Here’s a practical way to begin.
Step 1: Define your member-centric outcomes
Start with 3–5 outcomes, not tools. For example:
- Reduce time from application to decision on consumer loans by 40%
- Increase digital self-service resolution rate to 70% of inquiries
- Grow average products per member from 2.1 to 2.6 over 24 months
- Cut manual touches per loan in half while maintaining risk controls
These outcomes anchor your digital blueprint.
Step 2: Map use cases to the five blueprint pillars
For each outcome, identify AI and automation opportunities across:
- Process automation
- Member experience
- Share of wallet growth
- Data centricity
- Instant decisioning
Then prioritize:
- Low effort, high impact: start here
- Medium effort, high impact: design now, implement next
- High effort, high impact: plan as multi-phase programs
Step 3: Start small, measure hard
Pick 1–2 use cases and treat them as pilots, for example:
- AI-guided digital loan application with automated document handling
- Intelligent member service assistant in digital channels plus agent assist
Measure:
- Cycle time
- Member satisfaction (NPS/CSAT)
- Staff workload and error rates
- Conversion and adoption
Successful pilots give you internal credibility and a template you can reuse across other lines of business.
Step 4: Build a repeatable digital progression cadence
Digital progression sticks when you treat it as a rhythm, not a project. Many high-performing credit unions:
- Run quarterly reviews of AI use cases, performance, and new opportunities
- Maintain a shared roadmap across IT, lending, operations, and member experience
- Standardize how they evaluate risk, compliance, and ROI for each new initiative
That cadence is what turns one-off wins into a durable competitive advantage.
Where this fits in your AI for Credit Unions journey
Across this AI for Credit Unions: Member-Centric Banking series, a pattern keeps showing up:
- The most member-centric AI strategies are also the most disciplined.
- The strongest results come from progression, not big-bang projects.
Digital progression, as Wes Zauner describes it, gives credit unions a practical frame: focus on process automation, member experience, share of wallet, data centricity, and instant decisioning—then iterate.
If you’re leading a credit union right now, the question isn’t whether you need AI. It’s whether you have a clear, member-focused blueprint for how AI will:
- Reduce friction in daily banking
- Support smarter lending and collections
- Strengthen relationships instead of commoditizing them
The credit unions that answer that question with intent—and treat digital progression as an ongoing journey—will be the ones members trust with more of their financial lives over the next decade.