Meet the Innovators – A Q&A with ICBA ThinkTech Alumni

Community banks thrive when innovation is built for them, not around them. That’s the purpose of the ICBA ThinkTECH Accelerator—pairing early‑stage fintechs with community bankers to refine solutions that make banking smarter, simpler, and more efficient. In this new Q&A series, we’re highlighting program alumni and the practical, forward‑looking ideas reshaping how community banks serve their customers.

We’re kicking things off with Armen Sargsyan, Chief Marketing Officer of DeepSee.ai, to explore how AI‑driven automation is evolving inside community banks.

1. How would you describe DeepSee.ai and the problem it was created to solve?

DeepSee.ai is exclusively focused on financial services. We’ve invested deeply in the foundational market-building work. We took the time to truly understand the pain points bankers face, identifying the highest-impact areas to focus on, and delivering early wins that build confidence. We don’t just automate task-level, mundane work. We serve as a strategic partner on the broader transformation journey, helping banks move from manual processes to intelligent, AI-driven orchestration.

3. What sets DeepSee’s approach to AI‑driven automation apart from other solutions?

It’s never been just about automation for us. We understand how much work needs to happen behind the scenes for AI projects to succeed in production. From selecting the right first use case, to preparing the team to absorb the change, to navigating bank-specific requirements and integrations. That end-to-end orchestration is what we specialize in. We deliver outcomes, not just technology. That’s what sets DeepSee apart.

4. How has DeepSee evolved since participating in the ICBA ThinkTECH Accelerator?

The ICBA ThinkTECH Accelerator was transformative for us. It gave us direct access to bankers. We had hours of deep conversations that helped us understand how banks truly operate, where the biggest pain points are, and why previous AI initiatives have fallen short. Those insights shaped our entire approach and have been invaluable in building a product and a go-to-market strategy that resonates with this market.

AI & Community Banking

5. From your perspective, what are the biggest opportunities for community banks to use AI responsibly?

It’s critical not to boil the ocean. Don’t start with a problem so complex that you risk the project never reaching production. There are abundant “low-hanging fruit” use cases inside every bank, especially in the back office, where manual, repetitive work can be meaningfully augmented with AI. Our recommendation is always the same: start with an operational audit to identify those high-impact, low-risk opportunities, then build momentum from there.

6. What banking challenges do you see most often that AI can meaningfully improve?

The immediate answer is operational efficiency. There’s significant value in automating repetitive, labor-intensive work. But the longer-term strategic challenge is workforce transformation. Nearly every bank we’ve spoken with faces the same reality: an aging workforce approaching retirement, and an urgent need to capture institutional knowledge before it walks out the door. Banks are asking how to recruit world-class talent and retain their best people. We believe the next generation of bankers will be drawn to organizations that embrace innovation, and that’s the unlock. AI adoption isn’t just about efficiency; it’s a talent strategy.

7. How can AI reduce manual workload without disrupting the relationship‑driven model community banks value?

It all comes back to relationships. Nothing is more important than establishing a genuine partnership with the bankers we serve. The ICBA ThinkTECH Accelerator helped us build those relationships early, and we’ve gotten to know these institutions on every level. One of the biggest reasons AI projects fail is that organizations don’t bring their people along on the journey. We recognized this very early on. Our onboarding process is designed to address that head-on, and it’s become one of our core differentiators. We don’t drop users into a UI and say “figure it out.” AI adoption is a marathon, and we’re a committed partner for bankers every step of the way.

Risk, Compliance & Governance

8. How can AI strengthen risk management and compliance processes?

AI explainability is absolutely crucial in a regulated environment, and it needs to be part of every AI conversation from day one. For any process being reimagined with AI, it’s critical to have guardrails in place that surface real-time intelligence around risk and compliance metrics. Compliance teams need full visibility into how decisions are being made and flagged. That transparency is non-negotiable.

9. How does DeepSee ensure transparency and auditability in AI‑supported workflows?

We like to say that in banking, being 1% wrong can be 100% wrong. Determinism is critical in a regulated environment. Human judgment is irreplaceable. That’s why human-in-the-loop oversight remains central to how our clients operate, and we’ve factored that into our delivery model. From a broader perspective, it’s crucial to include full auditability, so compliance teams can trace decisions, validate outputs, and maintain the level of control regulators expect.

10. What misconceptions about AI and risk do you encounter most often in banking?

The biggest misconception is that building a proof of concept is the hard part. In reality, many banks launch AI projects that never make it to production because the organization wasn’t ready to absorb the change. I can’t tell you how many times we’ve seen projects stall before ever getting off the ground. This is exactly why the relationship-building and onboarding phases are just as important as the technology itself. You have to bring everyone along on the journey, including stakeholders, end users, and compliance teams. That has been our emphasis from the very beginning.

Practical Implementation

11. For a community bank just beginning to explore AI, where do you recommend, they start?

Start with a use case that’s “low risk” and where success is easy to measure. For example, if you have employees spending hours manually reconciling document checks, and AI can reduce that to minutes while also lowering error rates, that’s a clear-cut ROI. Those early wins build organizational confidence and create momentum for the broader AI journey.

12. What does a typical implementation look like, and how long does it take to see value?

We begin with process discovery, mapping the workflow, identifying pain points, and understanding all of the orchestration layers before moving to configuration and stakeholder alignment. We provide hands-on training and support throughout the entire process. Our deployment model is designed for simplicity and speed. Banks can expect to see measurable results within 90 days.

13. How do you help banks integrate AI tools without overwhelming their teams or existing systems?

Integration is a core part of our implementation methodology. For well-known banking systems, we have established integration pathways and can absorb that work with minimal disruption to the bank’s operations. For less common platforms, we conduct a deeper technical assessment to scope the integration effort and ensure a smooth deployment. Either way, we tackle that complexity, so the bank doesn’t have to.

Looking Ahead

14. What trends in AI or automation should bank directors be watching over the next few years?

One of the most significant shifts we’re closely aligned with is the move from traditional software to “service as a software,” which is the ability to measure real outcomes tied to labor-intensive work, such as hours saved and error reduction. Historically, this type of work has been difficult to quantify because pricing models were usage-based and disconnected from business impact. We’re entering a phase in AI where the operating model for measuring outcomes is going to change dramatically, and banks that get ahead of this will have a significant advantage.

15. What excites you most about DeepSee.ai’s future work with community banks?

Through the ICBA ThinkTECH Accelerator, we’ve built strong relationships and developed a deep understanding of the community banking landscape. What excites us most is the convergence we’re seeing. AI is leveling the playing field between large institutions and smaller community banks in ways that weren’t possible before. The challenges are remarkably similar across this market, and the technology is now accessible enough to deliver real impact regardless of institution size. We’re energized to help community banks do more with less and drive measurable outcomes in an industry that is ready for transformational change.

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