AI-First Banking: Top Priorities for Tech Leaders
How CIOs and CTOs Are Reshaping Financial Services with Intelligent Automation
Banking has always been at the front of the pack when it comes to adopting new technology. But fast adoption doesn’t necessarily mean smooth, effective operation. With technological advancements moving at a more rapid, fluid pace, tech leaders in banking need to reevaluate their priorities, and this article aims to serve as a guide.
Tech’s Potential in Banking
The potential for tech-powered growth is sky-high for the banking industry.
A Citigroup report estimates that AI could propel global banking industry profits to an incredible $2 trillion by 2028—a 9% increase over the next five years. Further, AI-assisted coding could save the US banking industry $2–16 billion annually.
Meanwhile, Gartner reports that 28% of the banking and financial services IT spending distribution has gone to application development. And yet, anecdotal research reveals a lack of ROI for banking software engineering.
Where Banking Needs to Step Up
Deloitte interviewed tech leaders in banking and finance, many of whom reported issues including:
Inefficient processes leading to project delays and wasted spending
Outdated legacy hardware and software leading to integration and runtime issues
Tech talent gaps further hindering already inefficient development
Based on my experience leading a tech recruiting firm, here are some courses of action I’ve seen work, in banking and across industries:
Embracing an Agile framework for faster development cycles
Upgrading talent acquisition and management and third-party vendor relationships
Updating outdated infrastructure with AI-powered digital transformation
More on some of these recommendations later.
AI Banking Use Cases
Let's explore some current use cases for AI in banking and finance:
24/7 customer service chatbots with personalized assistance based on customer information and behavior
Personalized nudges to help customers with investing and financial planning
Tools that pinpoint which loans might go bad, enabling the bank to take steps to intervene and support the client.
Boosting the productivity and efficiency of software developers
Adopting an AI-First Approach to Banking Tech
As a leader in the tech talent space, I encourage my teams and clients to embrace an AI-first approach.
This isn’t to be confused with an AI-only approach; we still need human judgment to ensure technology is working for us. An AI-first approach means starting every project with the mindset of, "Can AI do this task? And if so, how much of it?” Only when it’s confirmed that more talent is needed should headcounts increase.
Duolingo is an example of a big enterprise who has adopted an AI-first approach to its product. In a Slack message to employees, their Chief Engineering Officer Natalie Glance wrote, “Start with AI for every task. No matter how small, try using an AI tool first. It won’t always be faster or better at first—but that’s how you build skill. Don’t give up if the first result is wrong.”
In finance, we have Lettuce making headlines as an AI-powered tool for managing taxes and accounting as a business of one. Lettuce uses AI to automatically maximize tax savings—making personalized recommendations to save on quarterly tax payments.
Here are some practices I’ve seen work for banking and finance organizations during AI-first digital transformation:
AI vision: Start with a clear, intentional vision designed specifically for banking AI, across all departments within the organization—measuring the ROI of AI investments.
Use more than genAI: Combine genAI with analytical AI, predictive AI, and assistive AI; harness the power of diverse capabilities while preventing unnecessary energy consumption that genAI is responsible for.
Use multiagent systems: Rather than employing one huge AI model that theoretically does everything, employ several smaller models designed for specific expertise.
Make AI reusable: Maximize the reusability of AI projects to make the most of resources while enhancing consistency across projects.
Limitations and Challenges
Announcing one’s organization as AI-first both signals to investors that they will not be lagging when it comes to new tech and offers a heads up to employees that they too will need to adapt to these developments.
But that doesn’t mean workers aren’t still hesitant.
Cybersecurity Challenges in AI-First Organizations
I’ve seen cybersecurity teams, for example, express hesitancy to try AI because of stories they read about the hacking of AI systems, not to mention AI’s ability to supercharge cybercrime.
That’s why AI adoption should be synonymous with implementing cybersecurity at every level, in every department. While AI can enable security breaches, it can also help prevent them. Make sure to hire cybersecurity pros who are ready for the age of AI.
Banking Developer FOBO
McKinsey reported on a regional bank that used genAI to increase the efficiency of their software projects and achieved a 40% increase in productivity, with more than 80% of developers saying genAI improved their coding experience.
Studies like these are critical amid potential AI-induced FOBO (Fear of Becoming Obsolete) among developers. And while we aren’t sure if the bank in this study identified as an AI-first organization, we can still use these results as a potential indicator that AI-first organizations still need developers, and these developers will likely appreciate AI’s assistance.
AI Regulation, Transparency, and Trust
Regardless, tech leaders will need to communicate their intentions with AI usage not just to developers but to all employees and stakeholders to maintain clarity and trust.
The Power of Nearshore Development in Banking and Finance
My organization PTP has been leading the charge in nearshore staffing solutions for years, matching organizations with nearby tech talent for their digital transformation initiatives.
While offshoring tech talent is convenient and cost-effective, it does not lend itself to fast, collaborative development (Agile sprints) the way nearshoring does. By sourcing talent from nearby countries and regions, organizations enjoy the same benefits, but with better alignment in terms of time zones, language, and cultural affinity. The result: better real-time collaboration for faster development cycles with fewer bottlenecks.
Further, PTP is now building AI agents for every step of the recruiting process. From outreach to assessment to onboarding, each critical step of our process is being assigned an AI agent to optimize the experience for recruiters and candidates. We're excited to stay on the cutting edge of tech recruitment with this project. More to come soon.
Conclusion
For the banking and finance sectors to reach their full potential, tech leaders need to step up—creating more efficient processes, updating legacy systems, and closing tech talent gaps. By embracing AI, Agile frameworks, and partnering with the right third parties, leaders can propel their business to the front of the pack—not just in terms of adoption but in terms of tangible, long-term results.