Well, you’re in luck. There’s a quick way to make your life easier. No more continuous re-prompting to get what you want.

Let’s talk about creating custom GPTs for credit unions.

What Is a Custom GPT?

Custom GPTs are essentially a built-in prompt. When you open a new chat with a custom GPT, it automatically retrieves the instructions already built into it. It’s sort of like saving a long prompt right into ChatGPT itself.

The biggest benefit isn’t that you don’t have to re-paste in the prompt. No, the best part is that you can include entire documents as context. For example, if you want a custom GPT to craft a job description, you can provide a document with one (or several) of your recent job descriptions into the custom GPT. The output will always resemble exactly what you put in.

How Do I Make a Custom GPT?

The process of creating your own GPT is very simple. You’ll be able to do it in 5 minutes or less.

1. When you open ChatGPT, click your profile icon or look for My GPTs in the left sidebar.
2. In the upper right corner, select Create GPT.
3. A new section will open on the left side where you can fill out custom GPT instructions.

You can fill in as much or as little information as you want in that custom GPT instruction section. One thing to note is that, at least historically, there’s a practical limit to how many words ChatGPT can handle in the instructions. Going beyond that reduces the accuracy of the prompt.

Be careful not to make the instructions too long or complicated. Also try to make sure the GPT is tasked with only one thing. If you need to accomplish multiple tasks, that’s best done by multiple GPTs.

Give the GPT a name, add in any context documents you want it to reference, and give it a spin.

Note: context documents could be anything from desired output examples to a strict set of rules/regulations to follow for the task.

Hot Tips for Better Custom GPTs

If you’re not sure where to start, you can begin by asking ChatGPT. Tell if you want to make a custom GPT to do… whatever it is you want it to do. Then, ask it to create a brief prompt to put in the prompt/context window.

Prompts can get “overbaked.” If your instructions are too exacting or very complex/wordy, it may actually reduce the quality of the final output. Aim for brevity when you can—a tall order if ChatGPT is crafting the prompt for you!

Keep it focused. Don’t ask one custom GPT to do too much. Think of it as a way to solve one problem effectively, efficiently, and repeatedly. If your workflow requires multiple steps, you may be better served by multiple GPTs.

Potential GPTs You Might Create

You’re limited only by need and imagination. Here are a few custom GPTs we’ve helped create with and for credit unions recently:

1. Job posting
2. President’s report to the board
3. FP&A report
4. Minute-taking for NCUA files

We’ll spare you the longer list. The takeaway is that anything you could use ChatGPT for more than one time would be better served by a custom GPT.

Want to Dig Deeper?

CU 2.0 works with credit union executive teams at institutions of all sizes to accelerate AI adoption. It doesn’t matter if you’ve barely touched ChatGPT or Copilot—we can help.

We’ll guide you through every step of mastery, starting from wherever you are now. Whether that means custom GPT creating or using Claude Code and n8n to automate entire workflows in a custom dashboard, we’re ready if you are.

Book your first free call here: https://cu-2.com/ai-coaching-for-credit-unions/

The post Building Custom GPTs for Credit Unions appeared first on CU 2.0.

But here’s perhaps our spiciest take, and it’s not even on the list:

AI represents a larger cultural shift than a technological one.

The technology is here. It works. The question isn’t whether AI can deliver value to credit unions, but whether credit unions are willing to adopt it… and if their teams have the expertise to implement it effectively.

With that in mind, let’s look at a few not-so-bold predictions for 2026.

1. Most Midsize Credit Unions Will Have AI in Their Tech Stack

This one’s almost guaranteed. Even credit unions that aren’t actively pursuing AI will end up with it through vendor upgrades, core system enhancements, or fraud detection tools. The AI adoption question will shift from “should we” to “where do we already have it?”

For midsize credit unions, AI will show up whether leadership makes a conscious decision or not. Core providers are building it into their roadmaps. Fraud detection vendors are upgrading their systems. Cybersecurity tools are incorporating AI-powered threat detection.

By the end of 2026, the credit unions without any AI in their stack will be the outliers.

2. A Lot of AI Use Won’t Be Noticed at First

AI adoption won’t always happen at the institutional level. Much of it will start small and personal. Executives will use AI to summarize meeting notes. Loan officers will use AI to draft emails. Marketing teams will use AI to generate social media content.

These individual use cases won’t show up in strategic plans or board reports, but they’ll become table stakes for efficiency. The credit unions that resist this shift—either through policy or culture—will find themselves working slower than their peers.

By the end of the year, AI-powered meeting summarization and similar executive-level tools will be as common as shared calendars.

3. AI Implementations Will Start to Reveal Data Quality Problems

This is the uncomfortable truth about AI adoption. Believe us, we’ve experienced this firsthand as we build AI agents internally. AI and bad data mix like oil and water. Or like water and pure potassium.

Credit unions think they’re ready because they have data. Then they try to implement an AI tool and discover their data is inconsistent, incomplete, or formatted in ways that don’t work with modern systems.

Credit unions will find duplicate records, missing fields, inconsistent naming conventions, and data that’s technically accurate but contextually useless. It’ll be a lot of work.

The good news is that cleaning up data quality issues makes credit unions stronger even if the AI project gets delayed.

4. Members Will Start Expecting AI-Level Service Speed

Think about it. We’re already Googling information differently, relying more on AI summaries (or even asking LLMs instead of the internet).

Once a few credit unions start offering instant loan decisions or real-time fraud alerts, member expectations shift across the industry. This is how every service improvement works—early adopters set the new baseline, and everyone else has to catch up or explain why they’re slower.

Members won’t care that your credit union is still evaluating vendors or that your core system doesn’t support real-time decisioning yet. They’ll compare you to the credit union down the street that approved their neighbor’s loan in two minutes.

AI-level speed will become the expectation, not the exception.

5. Staff Resistance Will Become a Bigger Impediment Than Technical Implementation

The technical challenges of AI adoption are solvable. Vendors can integrate systems, consultants can clean up data, and IT teams can manage infrastructure. The harder problem is getting staff to trust and use AI tools.

Employees worry that AI will replace them, make their jobs less meaningful, or produce results they’ll be blamed for when things go wrong. These concerns are understandable, and they won’t disappear just because leadership says AI is safe.

Credit unions that invest in change management, training, and transparent communication about AI’s role will see faster adoption and better results than those that treat it as purely a technical implementation.

Bonus: Agentic AI Will Drive the Biggest Efficiency Gains

Credit unions that embrace agentic and automated AI—tools that can act independently within defined parameters—will see the most dramatic improvements in staff efficiency.

We’re not just talking chatbots that answer questions. We’re talking whole systems that complete tasks, process applications, route requests, and handle routine decisions without human intervention.

(Shout outs to GoAbacus, a fintech that gives credit unions dozens of ready-to-use agents on a scalable platform.)

The credit unions that figure out how to deploy agentic AI safely and effectively will create significant competitive advantages. The ones that limit AI to advisory roles will see incremental improvements at best.

Where Will You Start Your AI Journey in 2026?

This is where we’re supposed to sell you something, right?

That seems like a lot of work. Instead, let’s just make sure you have everything you need to succeed, no matter how far along you are on your AI path. These are all free:

1. Credit union AI readiness assessment
2. Credit union AI policy
3. Job description for a credit union AI leader

If that’s not enough for you and you’d like to do more, consider joining our Fintech Call Program. Once per quarter, we’ll call you to discuss a handful of fintechs we think you’ll like.

(Spoiler: most of them use AI somehow.)

It’s free, takes only 30 minutes, and it’s a dead-simple tech scouting resource for your team. You can sign up here to schedule your first call.

The post AI Predictions for Credit Unions in 2026 appeared first on CU 2.0.

Please select the option that best describes your current situation for each question. You may see where you’re doing well and where there might be room for improvement.

If you want to score yourself, use the following rubric:

A = 0 points
B = 1 point
C = 2 points
D = 3 points

At the conclusion, you should have between 0 and 48 points total. We’ll tell you what that means then.

Key Resources for Each Stage:

1. Credit union AI policy
2. Join our Fintech Call Program to learn about new AI solutions
3. Get an AI planning session
4. Join the CUSO AI Task Force (by approval only): email us at info@cu-2.com

Download the assessment to share with your leadership team here:

Section 1: Total Basics

1. Your credit union AI policy

Do you have something you can show your board, an examiner, or an employee how you use AI?

1. No. Why would we need one?
2. Not explicitly, but we’ve provided some general guidance.
3. Yes, but it could use work (or an update).
4. Our AI policy is a living document, constantly changing to reflect new technologies, best practices, organizational needs, and regulatory guidance.

2. AI-friendly culture

Do you encourage, incentivize, or even mandate understanding of and/or familiarity with AI?

1. No, we’re not ready or comfortable with that.
2. Not really, but we’ve let staff know they can explore AI use; we consider AI when searching for new tech solutions.
3. Yes, and we’ve given our staff some training and/or ideas on how they can adopt AI responsibly; we look for AI solutions when searching for new solutions.
4. Yes, and we host regular training sessions to accelerate AI usage; we prioritize AI when searching for new solutions.

3. Personal and professional AI use

Do you use AI for personal or non-institutional use?

1. No, I’m not interested and/or I’m waiting for it to be more useful.
2. I’ve played around with tools like ChatGPT and MidJourney, but I haven’t gotten too deep. (e.g. writing emails, etc.)
3. I’m very fluent with a broad selection of AI tools, and I use them to help me with more than a few personal- or work-related tasks.
4. I’ve built AI agents and automated them so that they can tackle complex workflows either with the click of a button or at predetermined, recurring intervals.

Section 2: Organizational Readiness

4. Strategic alignment and leadership support

Does your leadership and board prioritize and integrate AI into your strategic plan?

1. We don’t currently include or prioritize AI in our strategic plans.
2. We’re kicking the tires on AI, but it’s low on the priority list for next year.
3. Our strategic plans include at least one high-priority AI initiative.
4. We’re staying ahead of the curve, committing resources toward multiple AI initiatives.

5. Organizational readiness

How prepared are you to manage the cultural and operational changes required for AI?

1. We haven’t prepared or planned for any changes associated with AI.
2. We understand changes need to be made, and we’ve started planning for them.
3. We’ve already begun making operational changes to accommodate AI, and we plan to make more.
4. We’ve established a culture that embraces AI and continuous innovation.

6. Policy readiness, including data use and ethics

Do your policies cover AI—including how you use data and handle ethical concerns like fairness and transparency?

1. Not really. Our policies don’t mention AI, data use, or ethics in any meaningful way.
2. We’re working on it—reviewing and updating policies to address AI and ethical concerns.
3. Yes. Our policies support AI initiatives and include guidelines for data use and ethics.
4. Yes, and they’re robust: comprehensive, enforced, and designed to ensure responsible, ethical, compliant AI use.

7. Staff readiness and engagement

How prepared and engaged is your team when it comes to AI?

1. Not very. Most staff aren’t involved in AI initiatives and wouldn’t know where to start.
2. Somewhat. A few people have basic awareness (maybe using AI privately or for small tasks) but engagement is limited.
3. Pretty good. Most staff have some AI training and are participating in AI-related projects.
4. Very. Staff are highly engaged, with broad understanding and active contributions to AI efforts across the organization.

8. Readiness for AI and Automation

Are your workflows ready for AI-driven decision-making and automation?

1. No. Most of our processes are manual, with little room for AI.
2. Getting there. We’re modifying some processes to incorporate AI.
3. Yes. Many processes are optimized for AI, and we’re already seeing efficiency gains.
4. Fully. Our workflows are designed for seamless AI integration across the board.

Section 3: Technology Readiness

9. Core systems and infrastructure

Can your core handle AI, including the scalability and security AI demands?

1. Probably not. Our core systems weren’t built with AI in mind, and our infrastructure isn’t ready for the load or the risk.
2. Maybe with some work. Our core might need updates or workarounds; we have some scalability and basic security, but it’s not ideal.
3. Yes. Our core supports AI, and our infrastructure is scalable and secure enough to handle it.
4. Absolutely. Our core is optimized for AI, and our infrastructure is built for expansion and data protection.

10. Data management and analytics

Can your data systems support AI-driven analytics?

1. Probably not. Our core systems weren’t built with AI in mind, and our infrastructure isn’t ready for the load or the risk.
2. Maybe with some work. Our core might need updates or workarounds; we have some scalability and basic security, but it’s not ideal.
3. Yes. Our core supports AI, and our infrastructure is scalable and secure enough to handle it.
4. Absolutely. Our core is optimized for AI, and our infrastructure is built for expansion and data protection.

11. Digital platforms integration

Are your mobile and online platforms AI ready? Are your systems well connected?

1. No. Our platforms don’t have AI features and aren’t set up to integrate with AI tools.
2. Partially. We have some AI features, but they’re limited—and our integrations aren’t rock solid.
3. Mostly. Our platforms use some AI and are ready for more; systems are well-integrated.
4. Fully. AI is embedded where it makes sense, and our systems are seamlessly connected across the organization.

12. AI in fraud detection and risk management

How are you using AI to detect fraud and manage risk?

1. We’re not—AI isn’t part of our fraud or risk strategy.
2. Lightly. We use AI for some basic fraud detection or risk monitoring.
3. Effectively. AI helps us reduce fraud and improve risk management.
4. Extensively. We use advanced AI for real-time fraud detection and proactive risk solutions.

13. Alignment of AI strategy with IT investments and roadmap

Does your AI strategy align with your IT investments and future technology roadmap?

1. What AI strategy? If we have one, it’s not connected to IT planning.
2. Loosely. Our AI strategy partially aligns with IT plans, but it needs work.
3. Yes. AI strategy is well-aligned with IT investments and supports our roadmap.
4. AI leads the way. It drives IT investments and shapes our future technology direction.

Section 4: Fintech Strategy Readiness

14. Fintech strategy and roadmap

Do you have a clear fintech strategy for AI-driven partnerships over the next 3-5 years?

1. No. We either don’t have a fintech strategy or we’re working with vague ideas at best.
2. Sort of. We have a basic strategy with some initial plans for AI partnerships.
3. Yes. Our strategy is well-defined, and we’re actively pursuing AI partnerships.
4. Absolutely. Our fintech strategy is highly developed, with multiple AI partnerships already driving innovation.

15. Integration and usage of fintech AI solutions

How prepared are you to integrate AI solutions from fintech partners?

1. Not very. We’re minimally prepared… or not at all.
2. Somewhat. We have some partnerships in place or nearing deployment.
3. Actively doing it. We’re integrating fintech AI solutions into operations now.
4. Fully prepared. We’re already using fintech AI solutions to meaningfully enhance operations and member services.

16. Collaboration with fintechs, especially for scale

How well do you collaborate with fintechs on AI—particularly when it comes to scaling?

1. We don’t. Fintech collaboration on AI isn’t happening.
2. Loosely. We collaborate with fintechs, but not much on AI specifically.
3. Effectively. We’re partnering on AI and seeing scalable results.
4. Deeply. We’re co-developing scalable AI products and operational improvements with fintech partners.

17. Risk management and compliance in fintech partnerships

How well do you manage risk and compliance in fintech partnerships that involve AI?

1. We don’t! There’s no formal risk or compliance process for these partnerships.
2. Inconsistently. We do basic risk management, but compliance checks aren’t reliable.
3. Well. We have effective risk management and ensure compliance.
4. Thoroughly. We have comprehensive risk management with strict compliance controls baked into every partnership.

5. Data policies around fintech AI

Do your data policies support fintech AI integration while staying compliant?

1. No. Our data policies don’t account for fintech AI.
2. Partially. Our policies offer some support, but they need significant updates.
3. Yes. Our policies are solid—secure integration, compliant use.
4. Fully. Our policies are optimized for seamless integration, full compliance, and continued innovation.

Scoring Rubric

Congratulations! You finished. Now, tally up your scores (if you did that). Here’s what your total might indicate:

0–12: Beginning

Your credit union is in the early stages of AI readiness. There are many fundamental elements still necessary before you can safely and efficiently adopt AI at scale.

Your next steps should focus on building awareness among leadership, staff, and the board. Have conversations about what you can do with AI and where it might fit into your short- and long-term goals. Build (or steal) basic policies and infrastructure to support AI.

Key resource: Credit union AI policy

13–24: Developing

Your credit union is well on its way to being AI ready, though perhaps not fully ready… yet. You have things in place, but you may need further preparation in a few key areas.

Your next steps should be to get a better sense of what’s out there in the world of AI solutions in the industry. See what’s been successful—and what kind of good ideas are just catching on.

Continue what you’re doing, and make investments in both your CU’s culture and technology to support ongoing advancements with AI.

Key resource: Join our Fintech Call Program to learn about new AI solutions

25–36: Advanced

Your credit union is well on its way to being AI ready. Arguably, you are AI ready—you might just not be fully AI-enabled. Either way, that’s impressive.

At this stage, your next steps are twofold: fine tune and scale. You’re likely ready to get your hands dirty with some AI development of your own. Moving from “Hey ChatGPT, please summarize these meeting notes,” and “Hey Gemini, please rewrite this email,” into building autonomous agents is a big step.

If you want some insight about how to get the most out of your move into AI, book a speaking or AI planning session with us!

Key resource: Get an AI planning session (it says strategic planning, but we’re more than happy to go AI-only)

37–48: Leading

If you scored in this range, congratulations! Your credit union isn’t just keeping up—you’re forging ahead. (I know, it sounds like AI wrote that, but it didn’t.) You can adopt new AI technologies with ease.

If you’re in the leading stage, your next step is to lead not just in your own credit union, but in the industry. It’s not just about presenting at conferences, on calls, in niche forums, etc. to help the rest of the industry catch up to where you are.

It’s also about creating the solutions that will keep credit unions competitive.

Key resource: Share your expertise with others

Our task force is geared toward developing and implementing custom AI solutions. If you’re interested in pursuing true AI development, please email us at info@cu-2.com.

49+: Liar

Okay, maybe you’re not lying, but… we have our eyes on you.

However, if you honestly scored over 49, please let us know. We have some stages for you.

Key resources: See above.

Download the full assessment here to distribute to your leadership team:

The post Credit Union AI Readiness Assessment appeared first on CU 2.0.

Maybe not tomorrow. Maybe not this year. But the role is coming.

AI is no longer a side project you can hand off to a junior analyst in their spare time. It’s shaping how credit unions work, how members are served, and how data is turned into clear business decisions. Someone will need to lead that work—and not as a hobby squeezed between board packets and budget meetings.

They’ll need to own it.

There’s both a strategic and a tactical component to this need. It ranges from defining strategy and setting culture, to buying software and building agents.

Here’s why you need an AI leader.

Why Credit Unions Need an AI Leader

AI can already write, summarize, predict, and automate. Any fintech that works with AI would be happy to tell you what they’ve done with it. And if you need more ideas about its possibilities, look here.

The trick isn’t what AI can do, but what your credit union is ready to do with it. That’s where a dedicated AI leader comes in. This person connects your long-term goals, the available technology, and the people on your team. They help turn new tools into real, everyday improvements.

This person is a strategic necessity for two big reasons:

Results and safety.

First, an AI leader makes sure you actually get a return on your investment. They spot the difference between a shiny new product and a tool that can save hundreds of employee hours.

Most of all, they make AI practical. Instead of looking for shiny, interesting tools, they seek to solve problems. Can they save time on manual work, surface better data for managers, or help your staff serve members faster?

They understand that installing a new chatbot isn’t the transformation. But freeing up your best employees to focus on complex member needs is.

Second, they take ownership of risk and compliance. Credit unions must follow strict rules. Using AI in lending, marketing, or fraud detection has major legal consequences if it’s unfair or discriminatory.

Your AI leader will be the gatekeeper, spotting risks before regulators do and making sure the technology is always used responsibly and ethically.

What This Role Might Look Like

The job could take a few forms, depending heavily on your credit union’s size and goals. In a very large organization, it may be an executive-level role.

Are you ready for a Chief AI Officer or VP of AI Strategy?

This person is focused on long-term vision, vendor partnerships, and cultural change. They ensure every new AI initiative lines up perfectly with compliance, risk, and member value, reporting directly to the top.

In smaller credit unions, it might look like a hands-on builder. Think Director of AI Innovation. This person is a doer who enjoys testing tools, making workflows more efficient, and helping coworkers get comfortable with new tech. Picture a mix of someone with sharp technical skills, an understanding of your operations, and a whole lot of curiosity.

Regardless of the title, the mission is the same: to drive results with AI.

This leader will begin by reviewing and testing tools to support lending, marketing, and member service. They will help departments use AI to save time on routine tasks and start building internal automations or AI assistants for your staff.

Crucially, they’ll also develop clear, written policies for responsible AI use and lead the training programs so everyone feels confident, not cautious, about the change.

Over time, that person becomes the bridge between “we could do this” and “we did it.” They’ll guide pilot projects, gather real results, and scale up what works across the entire organization.

AI Culture Comes First

Tools are important, but trust comes first. Credit unions thrive on relationships, and that doesn’t change just because we are using digital tools.

For AI to truly succeed, your employees need to feel curious, safe, and supported. Your members need to know AI makes their experience better, faster, and more personal, not colder and more distant.

An AI leader helps create that essential balance, shaping a culture where technology lifts people up instead of replacing them. This kind of cultural fluency—the comfort and confidence to use the new tools—will matter more than any single piece of software you buy.

Better service and operations are the goal. AI is the tool that helps you get there.

How to Prepare for an AI Leader Now

Even if you aren’t ready to hire a full-time AI executive, it’s time to start thinking about it.

Who in your organization could take that first step?

Who’s already experimenting, training others, or spotting new efficiencies?

To make it easier, we’ve created two pre-made job descriptions you can use or adapt: a hands-on AI Director, and a strategic Chief AI Officer.

Each outlines how an AI leader could turn curiosity into capability… and capability into results.

Sooner or later, every credit union will need someone in charge of AI. If you want to remain competitive, sooner is better than later.

The post The Next Credit Union Hire: Your AI Leader appeared first on CU 2.0.

We’ve spoken with countless credit unions about AI. We’ve also worked closely with several to uplevel their AI abilities.

That’s why we feel so confident saying:

Credit unions are way, way behind. In 2026, You need to do more.

Where Credit Unions Are Falling Short

When we speak with credit unions about AI use, we’re… not impressed. There’s a lot of room for improvement.

For example, we hear about people using AI to:

• Write custom GPTs to reliably tackle specific tasks
• Summarize board meeting notes
• Write emails and memos
• Create graphics for social media

We could go on, but you get the idea. This is basic generative AI use. The extent to which most credit unions use AI is limited to opening the LLM or tool of choice, entering a prompt, and accepting or tweaking the outcome.

Ultimately, it may shave time off of a single task. But it’s not doing much beyond that. And if that’s the extent of your use of AI, you’re not learning quickly enough to keep up.

What People Are Doing with AI Now

In the past couple of months, we’ve spoken with people who are pushing the boundaries of what AI can do. Even when LLMs are used, the user never even touches them.

People are using AI to:

• Prototype products (or product features) with code
• Conduct deep research and analysis, and develop a full report and presentation
• Create bespoke, brand-aligned landing pages
• Manage their entire email inbox

Okay. Wait. That’s not even that impressive.

That’s all a step beyond basic generative AI use, but it’s still a simple query with context. It’s almost there, but not quite.

Here’s what’s really impressive. People are connecting multiple AI agents into repeatable, automated workflows that:

• Fully automate social posting and engagement, with algorithms that optimize over time according to what performs best;
• Scan spreadsheets or databases for new information, analyzes it, writes a report, and delivers it to your inbox;
• Turn natural language prompts into fully classified and managed CRM inputs with the click of a button.

Orchestrating AI agents and automating workflows is the next big step. We know people doing this already. (Some of them keep appearing in the mirror.)

Credit unions need to expand what they think is possible with AI.

A Disclaimer About AI in Credit Unions

Naturally, a highly regulated industry with mountains of sensitive data must approach AI differently.

You can’t just go rogue. You can’t “move fast and break things,” like the Silicon Valley folks used to say.

When you break things, people’s livelihoods are at stake.

It’s up to you to exercise due caution when exploring AI use.

However, don’t let reasonable safety and caution stop you from exploring. There’s a difference between moving forward carefully and standing still.

Just don’t stand still!

What Can You Do to Close the Gap?

There are so many ways to get started with AI that it would be impossible to enumerate them here. But everyone has to start somewhere.

Here’s what we recommend:

1. Get comfortable with a few tools. Whether ChatGPT, Claude, or Gemini, just get fluent with what you can and can’t do. Try image generation with any of the above, or seek out models by Stable Diffusion (such as Flux), Ideogram, or Alibaba (Qwen). Explore what Retrieval Augmented Generation (RAG) tools like NotebookLM can do.
2. Learn about how that data gets used and stored. You’ll want to know this if you use any of these tools professionally. You’d be surprised by how many tools don’t store any data at all, let alone sensitive data.

If you’ve already started with the first two, here’s what we suggest next:

• Discover new AI fintechs in the credit union space. Our Fintech Call Program is a low-lift, low-stakes way to learn about new solutions that won’t give you regulatory headaches. (Form below.)
• Learn with us. We’ve worked with credit unions to accelerate AI adoption. We’ve taught people how to create custom GPTs. Led vibe-coding sessions. Prototyped landing pages and new services. Just shoot us an email at info@cu-2.com

The post AI Needs to Be a Priority in 2026 appeared first on CU 2.0.

In the next 5 years, will you use AI more or less at work?

More, right?

Which means everyone needs to know how to use AI responsibly. This is especially true for any task or role that uses generative AI.

At the end, we’ll share one recommendation for how to use AI more responsibly at work. (Spoiler alert: be transparent about when and how you use AI.)

Let’s dive in.

Generative AI Is Highly Useful

This blog isn’t about doom and gloom. Nor is it about making fantastical claims about the future. It’s just solid, level-headed advice.

Generative AI can summarize your meetings. It can organize your email inbox and draft replies. It can write that response/post/thing you need.

AI can generate images, video, and voice. It can create presentations and slides. It can handle basic business intelligence.

AI can code, build, create, automate, and more.

If you’re not using it now, you’ll be a few steps behind when you inevitably do use it in the future.

Shortcomings of Generative AI

Remember: All LLMs say something like, “This model can make mistakes. Double-check it.”

Very roughly, generative AI often tests in a high percentile humans for any given task or knowledge set.

However, ranking in the top percentile isn’t that hard. Models are compared to the average human, not expert humans. If AI can do Task X better than 98% of humans, the other 2% are probably somewhere around expert level.

Anyone with expert-level knowledge of a subject can instantly spot generative AI’s shortcomings… in their field of knowledge.

But we have blind spots everywhere else. We see highly competent output on a broad range of subjects and think, “Well, that’s probably right.”

Even though an expert could point out where it falls short.

Generative AI is almost never an expert. Not yet. Right now, it’s just highly competent.

When Generative AI Becomes a Problem

So, we’ve established that generative AI is useful. You should use it.

We’ve also noted that it’s fallible. You shouldn’t trust it to be 100% accurate.

AI becomes a problem only when users don’t use it responsibly.

• Understand its weaknesses
• Don’t trust it blindly
• Check its work

It’s good guidance. But a quick scan will always “look good.” It appears credible. A deeper, more considered review might surface issues that a quick scan wouldn’t.

Generative AI becomes a problem when the output is trusted too much… or when it enters a collaborative workflow.

Yep. AI-generated material may also confuse someone in your team.

An AI-generated project plan may include unnecessary or irrelevant steps. AI-generated code may include vulnerabilities or inconsistencies. And so on.

What this means is that you’re introducing non-expert-level material to a workflow. You may know that, but someone else might not. Plus, that material may lack all the context that lives in your brain.

This can cause problems. Colleagues may follow an overcomplicated process, wasting time. They may have to troubleshoot code. They may ask you questions about the details of what you sent them, and you might be unable to answer… or even contradict what the AI-generated material said.

How to Use Generative AI Responsibly at Work

So, what can you do? How can you use AI if you can’t trust it completely? How can you save time on tasks or build something that may have significant issues?

How can you use it responsibly so that you don’t introduce vulnerabilities, unclear expectations, or complications into your work?

Easy.

First, remember that AI isn’t perfect. It can do good, but it can’t do amazing. At least, not yet.

Second, give AI some context. It can’t read your mind. It will guess at what you want, but it might guess wrong. The more context you give it, the more accurate your output will be.

Third, don’t ship anything generated with AI without reviewing it first. Just review it and change what needs to be changed.

Fourth, be extra careful when other people rely on what you generate. Anything inaccurate or confusing could derail their workflow and end up back on your plate.

These are four best practices for working with AI at your job. But there’s one more that we’d like to suggest. It may be controversial, but…

Transparency and Using AI Responsibly at Work

There’s one thing everyone can do to improve your outcomes when using AI. If you do this one thing, it will even allow you to fudge a bit on the previous four steps.

Be transparent about your use of AI.

That’s it. That’s the trick. Just let people know when AI generated the thing you gave them.

Here are three ways you can do it:

Add an AI-Assisted tag. Add this disclaimer however you want to: “This is AI-assisted.”

Use this tag when AI generates at least a portion of something, and you’ve fully reviewed and edited the output.

Again, this lets people know they may need to review for accuracy, etc. It also lets them know that you trust the output and it should be accurate.

Is Adding an “AI Transparency Tag” Necessary?

Yes? No? You be the judge.

You don’t need to do this for everything AI. Low-importance things don’t need disclaimers. If you’d be just as happy trusting a non-expert with the output, skip the tag.

Examples: If you pass off AI meeting notes to someone, they don’t need an explicit disclaimer. If you generate a social image, you can skip the tag.

However, if you’re working on something that requires expert-level competence or knowledge, add that tag. Same if you’re working with multiple people.

So if you pass over a project plan? Or a detailed report that someone will use for their role? Or code?

That’s when the AI tags come in handy.

Sometimes, using AI responsibly comes down to transparency. Think of it as good communication. By adding a tag, you’re telling someone that there may be elements, steps, or conclusions that come from a fallible tool with limited context.

People can do their best work only when they have good information and strong communication.

These tags just allow people to do their best work.

What Else Should Your Company Have to Use AI Well?

We believe credit unions can succeed by using AI efficiently, effectively, and responsibly. Some of what we’ve done:

• Delivered keynote speeches to credit unions about AI
• Published an addendum to our credit union book on AI
• Developed AI readiness assessments and AI policies
• Led vibe-coding sessions with credit union leaders
• Offered individual, executive-level AI coaching sessions

If you’d like to learn more about vibe coding or AI coaching, send us a message at info@cu-2.com

Or just get an early look at new fintechs that are using AI by joining our Fintech Call Program. In 30-minute quarterly calls, we’ll introduce you to new tech solutions. No pressure.

Get started here:

The post Using AI Responsibly at Work appeared first on CU 2.0.

It’s time to revisit which roles are changing…and what you can do to adapt.

Who Will AI Affect… Really?

Most US adults believe that AI will “eliminate more jobs than it creates,” but very few are worried about losing their own jobs. Clearly, people understand the theoretical capabilities of AI… but is there a disconnect when it comes to how it will affect them?

Yes. But will it change your job, or take it?

Whether you’re a bank teller, a member service or call center representative, a credit union CEO, finance or accounting person, or an IT professional, the reality is that automation and AI are 100% going to impact your job.

How AI Will Affect Your Job

Now, just because your job will be “impacted” doesn’t necessarily mean that it will be eliminated. Most people tend to think that job replacement means a robot shows up one day does your job for you. Yet, most automation is actually a progression of changes.

Think back to 1992 when tellers had calculators, debit and credit batch headers, and cash drawers. Everything was manual. Within 10–15 years, the debit and credit batch headers were gone, and everything was done through PC applications.

A few years later, we added cash dispensers and saw the cash draw diminish. Now almost nobody has a calculator, Check 21 scans our checks, and ITMs replace entire branch teams. The progression took 20–30 years and shifted over time.

AI adds automation tools that replace specific manual tasks. Instead of management tools and platforms that help employees to behave like robots, we leave the repetitive stuff to automation and AI.

In general, we’re not good at predicting where job losses come from—and they don’t really come from this automation progression. Instead, they stem from innovation around work processes. For example, spreadsheets ousted armies of data entry staff. We didn’t replace the work, but we changed the nature of it.

The Jobs AI Will Affect First

Although all jobs will eventually be changed by AI, some will experience progressive changes before others. Here are 3 of our predictions, plus a killer application idea that could completely eliminate a traditional job.

1.     Call center representatives

Call Center Representatives remain most impacted, though now 90% of routine queries are handled by AI chatbots or voice bots. Live agents step in only for complex or sensitive conversations.

When live reps are needed, AI-powered voice biometrics and similar technologies will reduce the average handling time by eliminating the need for ID verification.

Additionally, each call center rep will have a context app that shows them how to deliver the information more efficiently to the member according to the member’s sentiment to help create an engaging experience. Up to 75% of routine transactions will be switched to self-service chat bots etc. Finally, language-specific skills won’t be a barrier because AI will automatically translate for the employee and member.

The killer change: An app that automates consumer lifestyle banking. Imagine each consumer sets financial goals, and AI moves money, deposits checks, pays bills, etc. according to their identity and bank account.

Something like Cache might fit the bill here (click to learn more!).

2.     Accounting positions

Accounting Positions are increasingly automated with AI‑enabled reconciliation and anomaly detection. CFOs and accountants now spend less time running reports and more time interpreting AI‑generated insights.

Accounts Payable and reconciliations will be automated. Fraud detection uses AI and machine learning (ML) to detect anomalies quickly and accurately. AI improves financial analysis improves by using increasing amounts of data to make better predictions and trends. Check holds, fee analysis, and monthly reporting will all be automated.

The killer change: DeFi- or blockchain-based identities with a real-time general ledger that enables 100% transparency and tracking of data into whatever form both parties want. Essentially, most of accounting creates individual versions of truth, whereas distributed ledger technology allows us to share a truth.

3.     Lending positions

Lending fintechs like Scienaptic have proven that AI models make faster, better underwriting decisions than humans. Expect universal adoption on these for 80% of loan decisions. Fully digital lending systems will replace the multitude of patchwork solutions for applications, underwriting, funding, electronic signatures, and paperwork.

Lending Roles continue to evolve. AI underwrites up to 85% of standard loans—humans now focus on exceptions, relationship lending, and higher‑risk cases.

The killer change: A syndicated network of lenders that enables credit unions to deploy capital efficiently without actually making individual loan decisions, all in real time.

Want to learn about real-time loan participations? Start here:

Of course, we have no idea which (if any) of these will happen. Predicting the impact of technology, especially when AI is concerned, is difficult at best. But we can assure you each position mentioned above will change somehow.

How to Prepare for an AI Future

If the reality is that AI will affect all our jobs—and eliminate some of them—then it’s natural to ask:

What should we do?

To avoid becoming obsolete, we can focus on developing skills where AI can’t (yet) do better. Here are three ways to remain professionally relevant in the age of AI:

1. Focus on surprise: It turns out that automation and AI are not so good when unexpected things happen. So, focus on grey areas and learn to problem solve in poorly defined or unstructured situations. Humans are actually pretty good when it comes to the unexpected.
2. Develop your social skills: Focus on your emotional intelligence. Skills like empathy, listening, and building relationships make you invaluable and hard to replace. A beloved member service rep who makes a few mistakes is better than one who is 100% accurate but creeps out members. These skills help people build culture, lead teams, and help others succeed.
3. Understand the scare areas: AI is great with large amounts of data. But there are tons of things that happen only rarely in a credit union, which makes them hard to automate or solve with AI. Add value by finding those things, developing expertise around them, and handling uncommon and unique problems.
4. Get comfortable with AI. Take time to learn how your AI tools work. Tinker with prompts, test their limits, and ask “why” when their answers surprise you. The goal isn’t to replace humans—it’s to elevate what humans do best: connect, strategize, and decide.
   

Additional Resources

The key to developing a healthy, long-term culture that includes AI and values humans is good (and frequent) strategic planning. See our Strategic Planning Facilitators Guide for suggestions on where to look.

Also, sign up for our Fintech Call Program and get a personalized, 30-minute call each quarter. We’ll discuss the latest technologies and solutions, make key introductions, and offer early access to events, giveaways, and more!

If you want high-level AI coaching for you and your executive team, reach out to us here:

The post Which Credit Union Jobs Are in Danger of Being Automated by AI? appeared first on CU 2.0.

So, let’s cut through the buzz and map it out. Where are credit unions on the AI adoption bell curve?

Innovators: The Crazy Ones

Innovators make up only about 2.5% of the credit union population. They’re the ones pushing boundaries, helping others see what’s possible. I love them.

These are the CUs doing weird, brilliant stuff: 

• Custom GPTs for underwriting policies 
• AI-powered member service bots with sentiment analysis 
• Actual data scientists on payroll (not just “a guy who’s good at Excel”) 

They’re not asking, “should we use AI?” They’re building it, testing it, and breaking it before breakfast. 

Is this you? CUs with CUSOs, internal dev teams, and execs who read Wired and CU Times. 

Early Adopters: Brave and Budget-Conscious

Early Adopters make up 13.5% of credit union AI users. They’re willing to try things, but most of the things they’re willing to try have already been tested by the Innovators.

These people have bought into the vision, they’ve run some pilots, and they’re automating the boring stuff (like call center FAQs or marketing content). 

They’ve got internal Slack channels titled #gpt-playground and they’re starting to see AI as a strategic edge, not just a tech toy. 

Is this you? Prompt libraries, chatbot fine-tuning, AI-powered BI dashboards. 

Early Majority: On the Bench, but Ready to Sub In 

Around 34% of credit unions are “AI-curious.” They’re watching webinars, grilling vendors, and saying things like, “We’ll wait for compliance to weigh in.” 

They’re dabbling—but mostly through vendors doing it for them (think: fraud detection, LOS tools, or chatbot plug-ins). 

Is this you? These folks have their eye on a few solutions, but they’re looking for successful use cases and proven ROI from Innovators and Early Adopters. 

Late Majority: “We’re a Relationship Business” 

Another 34% of credit unions fall into the Late Majority group. They’re keeping an eye on new developments, but they’re comfortable doing the same things they’ve been doing for the last ten+ years. If it ain’t broke…

This group will join the party once the new technology has been safe and sanitized. They need to see more than a successful pilot before they jump in. Not until the rest of the market is saturated will they take their first real step.

They won’t be first, but they won’t get left behind either—as long as their core provider or digital banking vendor builds AI in by default. 

They’re more likely to say, “AI makes me nervous,” than, “AI helps my team move faster.” 

Is this you? Theseare the, “Let’s form a committee to explore it in Q3… next year,” crowd. 

Laggards: Still Printing Emails 

This is the final 16% of credit unions. They still have fax machines. They think AI means “robots replacing humans.” They use words like “gimmick.” Their response to innovation is usually, “But what if it breaks?” 

They’ll adopt AI eventually—after it’s essentially been forced on them. If they’re proactive about it? It’s after three peer groups, one merger, and a painfully obvious ROI case…

And even then, they’ll try to turn it off on Fridays. 

Is this you? Probably not, if you’ve read this far.

So Where Are We Really? 

We’re at the tipping point between Early Adopters and Early Majority. 

The visionaries have proven it works. The cautious ones are circling. The smart ones are building strategic muscle around AI before it becomes table stakes. 

And make no mistake, it will become table stakes.

If you’re a fintech: figure out how to embed AI in your product—and make it simple.

If you’re a credit union: pick a use case, start small, and build the habit now. 

Because in 12–18 months, if you’re not using AI to improve member experience, reduce cost of delivery, or make better decisions, you’re falling behind. 

Ready to Get Started with AI?

Since 2020, we’ve been helping credit unions get started with AI. Everything from AI education, to strategy, to tech scouting.

You can take your first step with the book here: https://a.co/d/co2drmM

Or get in touch for an AI speaking or training session at your credit union now:

Or learn more about our AI coaching here: https://cu-2.com/cu-executive-ai-coaching/

The post Where Are Credit Unions on the AI Adoption Curve?  appeared first on CU 2.0.

Flashy. Sounds super secret. A little bit of Bond meets Mission Impossible, but inside The Matrix.

But what is an AI agent, really? What can it do for credit unions? And… are they worth paying attention to (yet)?

Let’s dig in.

What Is an AI Agent?

An AI agent is a software program that can perform tasks or make decisions on its own. It usually works toward a goal, and it uses data, logic, and sometimes trial-and-error to get there. It can analyze a situation, decide what to do next, and take steps to complete a task.

AI agents like automated assistants. I could just pull up an account balance, sure. Or it might recognize when a member is likely to ask about a suspicious charge and proactively offer support options. Or it might detect that a home equity line of credit is open but unused and offer that member some information about how they might put it to use.

AI agents are built on newer AI models—often large language models like GPT—but they can do quite a bit more. They act. And increasingly, they can do so without supervision.

How Do AI Agents Work?

Most AI agents start with a goal, a list of possible actions, a memory or way to track past steps, and access to tools or data. Give them a goal, and they’ll try to achieve it by choosing the best available actions. That could mean anything from pulling transaction data to filling out a form to sending an alert.

In a credit union setting, this might mean listening to a conversation in the call center, identifying the member’s intent, and triggering a series of back-office processes. One agent might verify identity. Another might pull up relevant documents. A third might update the CRM. Some agents are specialized, and others are generalists that can call on specialized tools when needed.

They’re still early tech, so most of the time you’ll see AI agents doing simple, high-volume tasks like pre-qual checks, balance transfers, or FAQ-style member support.

But that’s changing. Many can string together multiple steps to handle something end-to-end. We’re really staring down the future, where we’ll all be working alongside AI.

What Are AI Agents Used for Today?

Right now, AI agents are being used in a few ways across financial services. The most common place you’ll find them is in call centers and “chatbots,” though “chatbot” might not be the best term anymore.

In fact, most chatbot vendors have resisted the term for years. Makes sense, since so many of the chatbots have evolved—they can finally do more than say, “I don’t understand that.”

Modern chat agents can (usually) fully understand the user… and they can actually complete tasks. Think: processing payments, updating member records, or initiating card disputes.

Some are even handling internal workflows. Need to update a batch of loan records? There are agents that can do that automatically. Others help with training by answering questions from new employees, guiding them through systems, or flagging when they’ve missed something important. They can be good at repetition, and they don’t forget steps.

In a few cases, credit unions are using AI agents to help spot fraud. They monitor transaction patterns, member behavior, and other signals, and they alert human staff only when something looks suspicious.

How Will Credit Unions Use AI Agents in the Future?

Over the next two to three years, you’ll likely see AI agents get more involved in member support, operations, and even marketing. You might see agents that listen to every call, summarize the outcome, and recommend next steps. Or agents that automatically generate personalized offers based on a member’s transaction history and life stage.

One of the more interesting developments is how agents are being trained to work across systems. Right now, a big barrier is integration. But AI agents are getting better at understanding screens, forms, and data, even in legacy systems. That opens the door for agents that can do real work without major tech overhauls.

As long as they have access, they won’t need perfect APIs.

We’ll also start seeing more coordination between agents. One agent handles the member chat. Another handles the backend updates. A third flags the interaction for a follow-up.

This kind of agent teamwork will make it feel less like you’re interacting with a tool and more like you’re dealing with a responsive, intelligent system.

Should Credit Unions Look for AI Agents Now?

That depends on what you want to solve. If you have a high volume of calls with repetitive questions, or if your staff spends too much time jumping between systems to complete simple tasks, then yes, an AI agent could help today.

However, if you feel like you’re well staffed, and aren’t leaving members hanging—or you’re handling training and internal knowledge and tedious workflows with ease… then maybe you can wait.

It’s also fair to be cautious. AI still has question marks around capability, explainability, regulations, ethics and environment, and probably a few more things.

Also, not every AI agent on the market is equal. Some are little more than chatbots with fancy names. Others require a lot of setup or only work well in narrow situations. Before jumping in, it helps to define a single problem you want to solve. Something small, repeatable, and measurable.

From there, it’s easier to evaluate what’s available. You don’t need to build your own, but you can. In fact, if you have complete, accurate content on your website, you can build a good one in a single day. Learn more here.

Next Steps

Even if you want to build your own AI agent, you don’t need a full AI team. In most cases, you’ll just need an internal champion… and maybe partner who understands the credit union ecosystem who can help connect the right tools to the right tasks.

The technology is moving fast, but you don’t need to rush. You just need to get clear on what would actually make life easier for your staff and members.

If you’d like to learn more about how to use AI in your credit union, let’s talk! We’ve been helping a lot of execs tackle everything from AI policies and ChatGPT prompts to best-fit fintech solutions and strategic planning.

Get started here:

The post A Primer on AI Agents for Credit Unions appeared first on CU 2.0.

But all these tools are doing is reacting.

A member applies, the system pulls data, and a decision is made. That’s the model.

Modelshop, on the other hand, is trying something different. Read on to learn how.

Modelshop’s Proactive Credit Offers

With traditional lending solutions, the member asks for a loan, and then the fintech does its magic. But Modelshop doesn’t wait for the members to apply.

Instead, Modelshop helps credit unions anticipate who needs what products—and why. It builds a rich picture of each member, using everything from traditional markers of creditworthiness to their broader financial situation. And that extra context changes everything.

Suddenly, you’re not just waiting around to see if someone qualifies. You’re spotting opportunity, understanding the risk, and making the first move. Before the member starts shopping for a loan, you’ve already prepared a personalized one.

That shift from reactive to proactive lending is subtle, but powerful.

By tapping into member data, cash flow modeling, and advanced analytics, Modelshop helps you understand your members instead of just scoring them. 

Members Are People, Not Walking Credit Scores

Credit scores are helpful, but they only tell part of the story. The same could be said about newer AI risk models that, despite improved accuracy, are essentially better credit scores.

Both leave out a lot of critical info, though:

• What about potential income growth?
• What about job stability?
• What about lifestyle spending?

Those things don’t appear in a traditional credit model, but they matter.

This is one of the things that caught our attention with Modelshop. Two people with similar credit scores might be in very different places. One might be running out of cash and options, coasting on past financial success. Another might be leveraging debt to reach a more stable financial picture.

A traditional model would lend to the first person, but not to the second. Yet using a cashflow model and deeper understanding of the two borrowers, you’d learn that the second borrower is much lower risk.

Modelshop helps credit unions tell the difference.

If you want to know your members and not just score them, this is where you start.

Making the Offer, Not Just the Decision

The other thing that stands out about Modelshop is that it doesn’t wait for members to ask for a loan. It constantly analyzes data to see which members might benefit from an offer. That could be a refi on an old loan, a consolidation option, or a new product entirely.

Then, the system prepares a member offer, ready to deliver it through the channel that works best for the member, whether that’s an outbound message, a notification when they log into their member portal, or instantly presented when the member explores credit options.

This is proactive, not reactive. Reactive lending is a bottleneck on opportunity. Reactive means you only get applications from members who already know what they want and think they’ll get approved. Everyone else—members with needs, potential, and good long-term outlooks—gets left behind because they didn’t ask at the right time or didn’t know what to ask for.

Modelshop is proactive, delivering best-fit loan and credit options to members before they even start shopping around.

Let’s Talk Modelshop Tech Stacks

Modelshop fits into existing systems, so it doesn’t need to replace your current stack. Instead, it works alongside decisioning engines, origination platforms, CRMs, and core systems, helping them all work a little smarter.

It doesn’t demand a full system overhaul. It just makes what you already have more useful.

Modelshop isn’t a narrow, single-use product. It’s not “loan decisioning software” in the traditional sense, though it can be that if that’s what you’re after. But overall, it’s more like an intelligence layer that helps your other systems understand members better and act on that understanding in real time.

Learn More About Modelshop

Our two big takeaways after looking into Modelshop are:

1. It helps credit unions understand their members instead of just scoring them, and
2. It makes the offer, rather than just making the decision.

On a less formal note, Modelshop also addresses two major technology initiatives that we’ve seen credit unions struggle to make work: AI and data.

Modelshop uses both, and it does so without getting all hands on deck for a major data or AI project. We’re excited by the possibilities that Modelshop poses for credit unions (and by what they’ve accomplished so far with a big one in California—not sure if we have the go-ahead to use their name, but you’ve heard of it).

Want to learn more about Modelshop? Start here:

Learn more about how this is impacting the credit unions. There is an interesting podcast here: https://www.modelshop.com/beyond-the-score-how-personalized-offers-can-help-modernize-cu-lending/

Modelshop is also offering a free trial here: https://www.modelshop.com/trial/

The post Reactive vs Proactive Credit Offers with Modelshop appeared first on CU 2.0.