How Mid-Market Banks Can Leverage AI for Growth ft. Aman Mahapatra

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TL;DR:

• AI adoption is growing, but only a small percentage of companies achieve enterprise-wide success.
• AI should solve business problems, not just be a shiny tech demo—process and leadership are key.
• Mid-market financial institutions can benefit from AI by leveraging partner ecosystems for faster, more impactful results.
• AI’s real power comes from integrating across different workflows, not siloing it in isolated departments.
• Governance, compliance, and security are no longer just cost centers—they are vital drivers of AI’s success.

Before we dive into the key takeaways from this episode, be sure to catch the full episode here:

Meet Aman - Chief Strategy Officer at Tribeca Softech

Aman Mahapatra is the Chief Strategy Officer at Tribeca Softech, where he leads efforts to help high-growth companies scale through AI and automation.

With over a decade of experience in digital transformation, venture partnerships, and financial services, Aman has become a strategic advisor to banks, fintechs, and insurance companies navigating the AI landscape.

He helped Tribeca scale its $30 million GenAI and cyber risk vertical within BFSI and has been at the forefront of integrating AI into risk management and compliance systems. Aman’s focus is on building AI systems that align with real business outcomes and ensuring that technology serves both innovation and risk mitigation needs.

Aman advocates for scaling AI through collaborative partnerships, clear governance frameworks, and a focus on measurable results that drive long-term value.

The Role of AI in Financial Services: Moving Beyond Demos

Aman explains that the financial industry is moving beyond proof-of-concept demos into real-world, impactful AI applications.

“The real challenge is not the technology; it’s aligning it with business objectives,” he says. While 88% of companies are experimenting with AI, only 5-6% see enterprise-wide success.

“You have to move beyond the sandbox and integrate AI into core workflows,” — Aman Mahapatra.

This transition requires strong leadership and a focus on outcomes. Financial institutions must take lessons from early adopters and build on the pilots that have been successful.

“The learning curve from these early experiments should inform faster, more practical AI adoption,” Aman adds, emphasizing the need to turn AI from a buzzword to a business driver.

AI and the Mid-Market: Leveraging Partnerships for Growth

In his experience, Aman sees mid-market financial institutions as the most agile adopters of AI.

“They don’t have the same legacy systems as the large institutions, so they can move faster,” he explains.

By partnering with fintechs and smaller AI vendors, mid-market players can avoid the inertia that large banks face. “The speed advantage they have is in partnering with the right startups, which allows them to be more nimble and innovative.”

These institutions can tap into AI’s benefits without the massive overhead or complexity of larger organizations. “They may not have full engineering teams, but they can still implement AI with the right partners,” Aman advises, pointing out the power of collaboration and flexibility for growth.

Scaling AI in Financial Institutions: Governance and Leadership

For AI to succeed in financial institutions, governance and leadership are key, according to Aman. “AI isn’t just a tech problem; it’s a business transformation problem,” he says.

AI projects often fail when leadership doesn’t align on objectives. “You need the right process, strategy, and ownership from the beginning,” Aman emphasizes.

He also stresses that data governance, compliance, and risk management can no longer be seen as cost centers. “Compliance is a product feature now,” Aman notes, highlighting the importance of embedding governance directly into AI systems from day one.

This ensures that institutions can scale AI securely while meeting regulatory requirements, avoiding future headaches when scaling.

Why AI Should Solve Business Problems, Not Just Technology Ones

Aman firmly believes that AI projects should always address business challenges, not just technological ones. “AI can’t just be a shiny toy,” he explains.

Financial institutions need to first define their goals and desired outcomes, whether it’s improving customer experience, reducing operational costs, or enhancing fraud detection.

“AI will only work if it’s solving the real problems businesses face every day,” Aman says. This approach avoids the trap of launching AI tools without clear use cases.

“Too many companies use AI as a buzzword and never move beyond pilot projects because they didn’t tie the technology to a specific problem,” he adds.

The Future of AI in Banking: A Collaborative, Responsible Approach

Looking toward 2026, Aman emphasizes the importance of collaboration in AI adoption.

“The future of AI in banking will be shaped by partnerships between banks, fintechs, and AI providers.” — Aman Mahapatra

These collaborations must be founded on trust, security, and shared goals. “Banks and fintechs need to collaborate to bring the right AI tools into the fold while ensuring the security, privacy, and transparency that regulators demand,” Aman notes.

For him, AI’s role in banking is to augment rather than replace, supporting employees and improving business processes. “Banks will need to make AI a part of how work gets done, not just a tool to be plugged in at the back end,” he adds.

Would you like to learn more about AI use in banking? Check out this episode on the future of AI in small business banking ft Jeremy Hodges.