Digital Bank Employees Used to be the Stuff of Science Fiction. Not Anymore
By Ben Udell, Contributor at The Financial Brand
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If you ask most bankers how they’re using AI today, the answers are starting to sound consistent: Professionals are using it to write emails faster, summarize meetings, clean up reports, and brainstorm ideas.
But on the horizon is another AI revolution: AI agents acting as digital employees. The foundation to transform the industry is accelerating even faster than what we’ve seen in recent years and we can look at the hype of OpenClaw and its influence on coworking developments to gain important insights.
The industry is building a responsible foundation centered on “copilot” models, tools like Microsoft Copilot, ChatGPT, and Google Gemini, or AI features embedded into existing vendor software. These solutions work with and alongside bankers. This represents real progress, but it also highlights how our industry is trailing the true wave of innovation happening in the broader AI landscape. It’s critical to balance responsible application with the risk of falling behind.
Recently, the developer community was taken by storm by an open-source AI agent framework called Open Claw. This sparked intense excitement and served as a unique proof of concept that ignited debate about the speed of AI autonomy. OpenClaw, which evolved rapidly through the names Clawdbot and Moltbot, has ignited a new wave of development focused on AI agents. For bankers, the implications for the future of work are both obvious and mind-bending.
Reality check: OpenClaw is not a solution that financial institutions will deploy. It lacks the security, governance, and controls required for enterprise environments. But it has had an outsized impact on how we think about AI agents and digital employees and their role in professional settings, including banks and credit unions. Enterprise solutions are being built knowing their customers, like banks and credit unions, need controls and defined environments that make them viable for real-world use.
John Kowal, Chief Technology Officer for Peapack Private, a community bank based in New Jersey, is deeply engaged in the next wave of AI innovation. “An AI agent isn’t just answering questions. It understands your objective, creates a plan, executes it, and if needed, adapts and tries again. That’s what makes it feel much more like an employee than a tool. That’s exactly how we’re thinking about it internally. We’re starting to see AI as digital employees, systems that can handle tasks, double-check work, attend meetings, and actively support how departments operate.”
What Do We Mean by “Digital Employees”?
To understand this shift to AI agents and digital employees, we need to define some key concepts that help show this progression.
In practical terms, this means moving from “AI helping me complete a task” to “AI owning part or all of the work.” That could be campaign performance analysis, onboarding follow-ups, reporting workflows, or preparing daily priorities. The shift is subtle, but significant. You’re no longer just using a tool. You’re beginning to structure work in a way that can be supported, and in some cases carried, by a digital employee.
Large language models (LLM): By now, most are familiar with ChatGPT, Microsoft Copilot (which is powered by ChatGPT, Google Gemini, and Anthropic’s Claude. LLMs are AI-powered conversation partners that generate text, code, or images. These are transformative tools for thinking and drafting, but they are fundamentally reactive: they wait for your prompt, perform a single task, and stop.
AI agents: While an LLM provides the “brain,” an agent provides the “hands.” An agent begins to feel like a digital employee and uses an LLM to work autonomously, often in the background, completing multi-step goals without needing a human to prompt every move. If ChatGPT is a world-class cookbook, an AI agent is the chef who finds the ingredients, plans the meal, turns on the stove, and serves the dish.
OpenClaw: This is an open-source framework designed to let these agents operate on your local hardware rather than a corporate cloud, a critical distinction for privacy-conscious industries. It allows agents to interact with each other and your local files without a human in the loop. In our cooking analogy, OpenClaw doesn’t just cook the meal; it autonomously manages the entire kitchen, deciding the menu for the week, sourcing ingredients, and optimizing the schedule, and maybe deciding to open its own restaurant, and create a website to market its new business, all while you sleep.
Digital employees: This represents the next evolution in the workforce, where AI agents and “AI Coworker” capabilities converge to create a persistent, intelligent partner. While a standard AI agent might handle a single transaction (like booking a flight or writing a marketing campaign), coworking, or the creation of digital employees, is about persistence and partnership. OpenClaw is a giant step forward that is influencing coworking and digital employees in Claude, Microsoft Copilot, and other solutions, but embedded with acceptable guardrails. In this instance, you’ve explicitly limited your chef to only working in your kitchen and not focusing on franchising its ideas globally.
When you begin thinking of AI as a digital employee, the true scale of this disruption, and the reason for angst, becomes clear. Framed as a digital employee, it becomes more apparent that a group of AI agents can easily represent adding or subtracting headcount in organizations, at scale. While the banking industry is unlikely to replace human professionals with AI agents overnight, the shift toward a hybrid workforce has already begun. Work is fundamentally changing; for the modern banker, understanding the why and what’s next is vitally important.
AI Coworker Tools: Why They Blew Up, and Why It Matters
The concept of an AI coworker tool seemed to come out of nowhere, drawing significant media attention and scrutiny. It represented something people hadn’t really seen in a practical, visible way before. AI taking action, appearing to operate on its own. This was a triggering event that accelerated development with AI agents.
At its core, AI Coworker tools allow AI agents to operate more independently. Up until now, most people’s experience with AI has been request-based. You ask a question, you get an answer. You refine it, maybe ask a follow-up, and then you move on. AI Coworker solutions flipped that model. It introduced the idea that AI could take an objective and work toward it, handling multiple steps along the way without constant input while engaging multiple files, folders, and applications.
That’s where things got interesting, and where the future of this technology became clearer. Kowal noted, “We’ve seen this pattern before. Early AI tools were experimental and sometimes unreliable, but they helped us understand where the technology was going. The organizations that paid attention early were the ones ready when enterprise-ready solutions arrived.”
Bankers understand this tension better than most. When there are no guardrails, outcomes can be both impressive and unpredictable, and even unacceptable in our regulated industry. The concept of AI Coworker solutions showed how quickly AI capabilities are evolving beyond simple interaction into execution.
Why this matters: This is how the concept of digital employees starts to take shape. For bankers who understand this distinction, the implications become much more tangible. You can begin to see how your own work might evolve with one or more AI agents supporting you directly. When AI agents move from completing tasks to consistently supporting defined responsibilities, they begin to function like digital employees.
And it raises a simple question: what professional wouldn’t want a team of digital employees helping them get their work done? Framing it this way makes it easier to understand how this model can scale across teams and entire organizations. The potential to transform productivity and creativity at scale is significant, and on the horizon.
What This Means for Bankers Right Now
It’s easy to read about digital employees and AI agents and feel like this is still a step removed from day-to-day work. The shift is already showing up, just in smaller, more controlled ways.
The simplest way to think about it is this: today you’re using GenAI to complete tasks. What’s coming next is AI helping you manage and execute workflows. That starts with basic Copilot Agents, ChatGPT GPTs, and other smaller scale agents. Then it evolves into richer, more dynamic coworking solutions, with AI acting as a true digital employee. If you don’t deploy AI agents, your vendors certainly will.
Practical Examples Bankers Can Apply Today
Start by thinking less about “what can AI write for me” and more about “where do I repeat the same thinking over and over” or “what would make my life easier”. Kowal shared, “The future of AI isn’t a tool we pose questions to. It’s something that does work for us, proactively identifying problems, taking action, and supporting employees in real time.”
There are practical examples you can create today with basic AI agents by using a Microsoft Copilot Agent or ChatGPT GPTs:
Campaign performance reviews: Instead of manually reviewing results and building summaries, use AI to standardize how performance is analyzed. Have it identify trends, call out outliers, and suggest next steps. Over time, this becomes a repeatable workflow, not a one-off task. You can do this with Microsoft Copilot Agents or ChatGPT GPTs, with stronger coworking tools on the horizon. It helps to give the agent a template or example so it can model its work on successful outcomes.
Content iteration: Most marketing teams, and any department in a bank or credit union, don’t struggle to create ideas. They struggle to create five good versions that relate to specific internal and external audiences. AI can help you expand variations quickly, test different tones, and refine messaging without starting from scratch each time. Simple agents can recreate content for specific audiences in minutes and replace persistent prompting.
Campaign monitoring: Create an AI agent with structured prompts that give you consistent results and a repeatable workflow. Your agent can be preloaded with an approach to reviewing your data and an output format that aligns with your brand or expectations.
If you’re unsure of where to start, use your preferred GenAI tool to help guide you.
Prompt: “I need to create an agent that does [copy and paste and example above, or use your own]. I’m unsure of how to do that. Walk me through the process to build and test.”
These agents are easy to build and can be more powerful than simple prompting; they’re a gateway to more powerful AI agents running behind the scenes. Kowal is deploying more powerful agents now, “We’ve already seen this in practice. In one case, we replaced a manual spreadsheet-driven process with a simple, automated web-based tool and reduced the time required by at least 50%. That’s the type of efficiency this shift can unlock.”
Coworking solutions with AI will supercharge your work and begin to create the concept of a true digital employee. The banking industry will need time to understand and digest how to manage risk and think through vendor management with these examples. Banks or credit unions that are focused on innovation can begin to apply solutions like these today. For banks and credit unions with a more conservative risk posture, these are solutions on the horizon and provide examples of why you want to increase your AI focus today.
Personal digital assistants: Many bankers are using Copilot to write emails. In a coworking environment with AI agents, bankers can start their day with a list of email drafts ready to send based on AI reviewing to-do lists, understanding workflows, and time frames, all while having access to the bankers entire file and email system. Jumpstart huge productivity gains with a digital assistant.
Campaign creation: A marketer today might be developing a Q3 marketing plan for checking acquisition. That process includes pulling data, reviewing trends, writing a plan, mapping out scenarios, budgeting, and more. GenAI can support each of these topics one step at a time. In a coworking model with AI agents acting as a digital employee, that preparation is assembled for them automatically in one step, pulling from internal systems, recent activity, and external insights before they even ask.
Campaign monitoring: Once the campaign is active, traditionally a marketer would manually pull campaign results, analyze performance, and then ask GenAI for ideas to assess performance or modify the campaign. With AI agents this is done for you before you even arrive at work. Plus, it can flag underperforming segments, suggest messaging adjustments, and prepare the next round of content before the team starts from scratch again. Your agents can begin to write all new content.
These are examples bankers can apply today or in the near future while continuing to be the human in the loop. In these examples, bankers are less engaged in each step, while more engaged in reviewing, editing, and improving the final output.
How to Stay Relevant as AI Evolves
This is where a lot of professionals get stuck. They either feel behind or assume they need to jump straight to the most advanced tools. Neither is true. Start with a focus on real work an employee, team, or bank or credit union is doing everyday.
“If every employee had access to a capable assistant that could take work off their plate, no one would turn that down. That’s the direction this is heading.” Kowal believes there’s a future that involves humans working closely with AI, responsibly, and that future is closer than most think.
1. Build consistency and a foundation with what you already have. If you’re not regularly using tools like Microsoft Copilot, ChatGPT, or Google Gemini in your daily work, start there. If these tools are not transforming how you work today, it will be harder to appreciate the next wave of innovation.
2. Understand your own workflows. Where do you spend time? What steps do you repeat? Where does work slow down? Those are the areas where AI will have the most impact as it evolves. Professionals often struggle with thinking of ways AI agents can support their work. Then, throughout the week when they have greater awareness, they begin to see workflows, outputs, and activities that are busy work, disconnected from real value added work, or just take too much time. Start building easy to create AI agents and develop complexity over time.
3. Start thinking beyond prompts. Instead of asking “what can I use AI for,” begin asking “where could AI take this off my plate or move this forward without me starting it every time.” That mindset shift is what prepares you for digital assistants, even before you have access to more robust solutions.
4. Stay close to the space without chasing it. You don’t need to install OpenClaw or experiment with every new tool that shows up. But you do need to understand what they represent. The people who benefit most from these shifts are not the ones who jump first, but the ones who understand what’s coming and are ready when it becomes practical.
Bottom line: If your bank or credit union is unwilling to deploy GenAI, AI agents, or other AI tools today, professionals who learn on their own time with personal examples will be in a strong position when your organization catches up. AI is in the early stages of transforming banking. Professionals and organizations who apply GenAI today, and learn how to apply the next iterations early, will have a competitive advantage as AI transforms the banking industry.
